Feng Chen

EAAI Journal 2026 Journal Article

An explainable machine learning framework for long-term spatiotemporal incident modeling in expanding urban rail networks

• Pengcheng Li
• Linmu Zou
• Zijia Wang
• Yadi Zhu
• Lu Zhao
• Feng Chen

AAAI Conference 2026 Conference Paper

Dynamic Deep Graph Learning for Incomplete Multi-View Clustering with Masked Graph Reconstruction Loss

• Zhenghao Zhang
• Jun Xie
• Xingchen Chen
• Tao Yu
• Hongzhu Yi
• Kaixin Xu
• Yuanxiang Wang
• Tianyu Zong

The prevalence of real-world multi-view data makes incomplete multi-view clustering (IMVC) a crucial research. The rapid development of Graph Neural Networks (GNNs) has established them as one of the mainstream approaches for multi-view clustering. Despite significant progress in GNNs-based IMVC, some challenges remain: (1) Most methods rely on the K-Nearest Neighbors (KNN) algorithm to construct static graphs from raw data, which introduces noise and diminishes the robustness of the graph topology. (2) Existing methods typically utilize the Mean Squared Error (MSE) loss between the reconstructed graph and the sparse adjacency graph directly as the graph reconstruction loss, leading to substantial gradient noise during optimization. To address these issues, we propose a novel Dynamic Deep Graph Learning for Incomplete Multi-View Clustering with Masked Graph Reconstruction Loss (DGIMVCM). Firstly, we construct a missing-robust global graph from the raw data. A graph convolutional embedding layer is then designed to extract primary features and refined dynamic view-specific graph structures, leveraging the global graph for imputation of missing views. This process is complemented by graph structure contrastive learning, which identifies consistency among view-specific graph structures. Secondly, a graph self-attention encoder is introduced to extract high-level representations based on the imputed primary features and view-specific graphs, and is optimized with a masked graph reconstruction loss to mitigate gradient noise during optimization. Finally, a clustering module is constructed and optimized through a pseudo-label self-supervised training mechanism. Extensive experiments on multiple datasets validate the effectiveness and superiority of DGIMVCM.

AAAI Conference 2026 Conference Paper

Evaluating, Synthesizing, and Enhancing for Customer Support Conversation

• Jie Zhu
• Huaixia Dou
• Junhui Li
• Lifan Guo
• Feng Chen
• Chi Zhang
• Fang Kong

Effective customer support requires not only accurate problem-solving but also structured and empathetic communication aligned with professional standards. However, existing dialogue datasets often lack strategic guidance, and real-world service data is difficult to access and annotate. To address this, we introduce the task of Customer Support Conversation (CSC), aimed at training customer service supporters to respond using well-defined support strategies. We propose a structured CSC framework grounded in COPC guidelines, defining five conversational stages and twelve strategies to guide high-quality interactions. Based on this, we construct CSConv, an evaluation dataset of 1,855 real-world customer–agent conversations rewritten using LLMs to reflect deliberate strategy use, and annotated accordingly. Additionally, we develop a role-playing approach that simulates strategy-rich conversations using LLM-powered roles aligned with the CSC framework, resulting in the training dataset RoleCS. Experiments show that fine-tuning strong LLMs on RoleCS significantly improves their ability to generate high-quality, strategy-aligned responses on CSConv. Human evaluations further confirm gains in problem resolution.

AAAI Conference 2026 Conference Paper

OmniSparse: Training-Aware Fine-Grained Sparse Attention for Long-Video MLLMs

• Feng Chen
• Yefei He
• Shaoxuan He
• Yuanyu He
• Jing Liu
• Lequan Lin
• Akide Liu
• Zhaoyang Li

Existing sparse attention methods primarily target inference-time acceleration by selecting critical tokens under predefined sparsity patterns. However, they often fail to bridge the training–inference gap and lack the capacity for fine-grained token selection across multiple dimensions—such as queries, key-values (KV), and heads—leading to suboptimal performance and acceleration gains. In this paper, we introduce OmniSparse, a training-aware fine-grained sparse attention of long-video MLLMs, which is applied in both training and inference with dynamic token budget allocation. Specifically, OmniSparse contains three adaptive and complementary mechanisms: (1) query selection as lazy-active classification, aiming to retain active queries that capture broader semantic similarity, while discarding most of lazy ones that focus on limited local context and exhibit high functional redundancy with their neighbors, (2) KV selection with head-level dynamic budget allocation, where a shared budget is determined based on the flattest head and applied uniformly across all heads to ensure attention recall after selection, and (3) KV cache slimming to alleviate head-level redundancy, which selectively fetches visual KV cache according to the head-level decoding query pattern. Experimental results demonstrate that OmniSparse can achieve comparable performance with full attention, achieving 2.7x speedup during prefill and 2.4x memory reduction for decoding.

TMLR Journal 2026 Journal Article

Reconciling In-Context and In-Weight Learning via Dual Representation Space Encoding

• Guanyu Chen
• Ruichen Wang
• Tianren Zhang
• Feng Chen

In-context learning (ICL) is a valuable capability exhibited by Transformers pretrained on diverse sequence tasks. However, previous studies have observed that ICL often conflicts with the model’s inherent in-weight learning (IWL) ability. By examining the representation space learned by a toy model in synthetic experiments, we identify the shared encoding space for context and samples in Transformers as a potential source of this conflict. To address this, we modify the model architecture to separately encode the context and samples into two distinct spaces: a \textit{task representation space} and a \textit{sample representation space}. We model these two spaces under a simple yet principled framework, assuming a linear representational structure and treating them as a pair of dual spaces. Both theoretical analysis and empirical results demonstrate the effectiveness of our proposed architecture, CoQE, in the single-value answer setting. It not only enhances ICL performance through improved representation learning, but also successfully reconciles ICL and IWL capabilities across synthetic few-shot classification and a newly designed pseudo-arithmetic task. The code is available at: \url{https://github.com/McGuinnessChen/dual-representation-space-encoding}.

NeurIPS Conference 2025 Conference Paper

ACT as Human: Multimodal Large Language Model Data Annotation with Critical Thinking

• Lequan Lin
• Dai Shi
• Andi Han
• Feng Chen
• Qiuzheng Chen
• Jiawen Li
• Zhaoyang Li
• Jiyuan Zhang

Supervised learning relies on high-quality labeled data, but obtaining such data through human annotation is both expensive and time-consuming. Recent work explores using large language models (LLMs) for annotation, but LLM-generated labels still fall short of human-level quality. To address this problem, we propose the Annotation with Critical Thinking (ACT) data pipeline, where LLMs serve not only as annotators but also as judges to critically identify potential errors. Human effort is then directed towards reviewing only the most "suspicious" cases, significantly improving the human annotation efficiency. Our major contributions are as follows: (1) ACT is applicable to a wide range of domains, including natural language processing (NLP), computer vision (CV), and multimodal understanding, by leveraging multimodal-LLMs (MLLMs). (2) Through empirical studies, we derive 7 insights on how to enhance annotation quality while efficiently reducing the human cost, and then translate these findings into user-friendly guidelines. (3) We theoretically analyze how to modify the loss function so that models trained on ACT data achieve similar performance to those trained on fully human-annotated data. Our experiments show that the performance gap can be reduced to less than 2% on most benchmark datasets while saving up to 90% of human costs.

NeurIPS Conference 2025 Conference Paper

Adaptive Fission: Post-training Encoding for Low-latency Spike Neural Networks

• Yizhou Jiang
• Feng Chen
• Yihan Li
• Yuqian Liu
• Haichuan Gao
• Tianren Zhang
• Ying Fang

Spiking Neural Networks (SNNs) often rely on rate coding, where high-precision inference depends on long time-steps, leading to significant latency and energy cost—especially for ANN-to-SNN conversions. To address this, we propose Adaptive Fission, a post-training encoding technique that selectively splits high-sensitivity neurons into groups with varying scales and weights. This enables neuron-specific, on-demand precision and threshold allocation while introducing minimal spatial overhead. As a generalized form of population coding, it seamlessly applies to a wide range of pretrained SNN architectures without requiring additional training or fine-tuning. Experiments on neuromorphic hardware demonstrate up to 80\% reductions in latency and power consumption without degrading accuracy.

NeurIPS Conference 2025 Conference Paper

Alternating Gradient Flows: A Theory of Feature Learning in Two-layer Neural Networks

• Daniel Kunin
• Giovanni Luca Marchetti
• Feng Chen
• Dhruva Karkada
• James Simon
• Michael Deweese
• Surya Ganguli
• Nina Miolane

What features neural networks learn, and how, remains an open question. In this paper, we introduce Alternating Gradient Flows (AGF), an algorithmic framework that describes the dynamics of feature learning in two-layer networks trained from small initialization. Prior works have shown that gradient flow in this regime exhibits a staircase-like loss curve, alternating between plateaus where neurons slowly align to useful directions and sharp drops where neurons rapidly grow in norm. AGF approximates this behavior as an alternating two-step process: maximizing a utility function over dormant neurons and minimizing a cost function over active ones. AGF begins with all neurons dormant, corresponding to an initialization at the origin. At each iteration, a dormant neuron activates, triggering the acquisition of a feature and a drop in the loss. AGF quantifies the order, timing, and magnitude of these drops, matching experiments across several commonly studied architectures. We show that AGF unifies and extends existing saddle-to-saddle analyses in fully connected linear networks and attention-only linear transformers, where the learned features are singular modes and principal components, respectively. In diagonal linear networks, we prove AGF converges to gradient flow in the limit of vanishing initialization. Applying AGF to quadratic networks trained to perform modular addition, we give the first complete characterization of the training dynamics, revealing that networks learn Fourier features in decreasing order of coefficient magnitude. Altogether, AGF offers a promising step towards understanding feature learning in neural networks.

TMLR Journal 2025 Journal Article

Efficient Multi-Agent Cooperation Learning through Teammate Lookahead

• Feng Chen
• Xinwei Chen
• Rong-Jun Qin
• Cong Guan
• Lei Yuan
• Zongzhang Zhang
• Yang Yu

Cooperative Multi-Agent Reinforcement Learning (MARL) is a rapidly growing research field that has achieved outstanding results across a variety of challenging cooperation tasks. However, existing MARL algorithms typically overlook the concurrent updates of teammate agents. An agent always learns from the data that it cooperates with one set of (current) teammates, but then practices with another set of (updated) teammates. This phenomenon, termed as ``teammate delay'', leads to a discrepancy between the agent's learning objective and the actual evaluation scenario, which can degrade learning stability and efficiency. In this paper, we tackle this challenge by introducing a lookahead strategy that enables agents to learn to cooperate with predicted future teammates, allowing the explicit awareness of concurrent teammate updates. This lookahead strategy is designed to seamlessly integrate with existing policy-gradient-based MARL methods, enhancing their performance without significant modifications to their underlying structures. The extensive experiments demonstrate the effectiveness of this approach, showing that the lookahead strategy can enhance the cooperation learning efficiency and achieve superior performance over the state-of-the-art MARL algorithms.

NeurIPS Conference 2025 Conference Paper

EvaLearn: Quantifying the Learning Capability and Efficiency of LLMs via Sequential Problem Solving

• Shihan Dou
• Ming Zhang
• Chenhao Huang
• Jiayi Chen
• Feng Chen
• Shichun Liu
• Yan Liu
• Chenxiao Liu

We introduce EvaLearn, a pioneering benchmark designed to evaluate large language models (LLMs) on their learning capability and efficiency in challenging tasks, a critical, yet underexplored aspect of model potential. EvaLearn contains 648 challenging problems across six task types, grouped into 182 sequences, each sequence dedicated to one task type. Diverging from most existing benchmarks that evaluate models in parallel, EvaLearn requires models to solve problems sequentially, allowing them to leverage the experience gained from previous solutions. EvaLearn provides five comprehensive automated metrics to evaluate models and quantify their learning capability and efficiency. We extensively benchmark nine frontier models and observe varied performance profiles: some models, such as Claude-3. 7-sonnet, start with moderate initial performance but exhibit strong learning ability, while some models struggle to benefit from experience and may even show negative transfer. Moreover, we investigate model performance under two learning settings and find that instance-level rubrics and teacher-model feedback further facilitate model learning. Importantly, we observe that current LLMs with stronger static abilities do not show a clear advantage in learning capability across all tasks, highlighting that EvaLearn evaluates a new dimension of model performance. We hope EvaLearn provides a novel evaluation perspective for assessing LLM potential and understanding the gap between models and human capabilities, promoting the development of deeper and more dynamic evaluation approaches. All datasets, the automatic evaluation framework, and the results studied in this paper are available in the supplementary materials.

AAAI Conference 2025 Conference Paper

GRICP: Granular-Ball Iterative Closest Point with Multikernel Correntropy for Point Cloud Fine Registration

• Yihao
• Limei Hu
• Feng Chen
• Sen Zhao
• Shukai Duan

The Iterative Closest Point (ICP) algorithm suffers from sensitivity to outliers and tendency to local optima in point cloud fine registration. In this paper, we introduce a global and robust ICP framework called Granular-Ball Iterative Closest Point with MultiKernel Correntropy (GRICP). This approach transforms the point cloud into a granular ball cloud and employs MultiKernel Correntropy (MKC) as the loss function, which is designed to smooth out the effects of noise points and provide global information for registration. Specifically, we propose a coarse-grained representation of the point cloud using the granular ball model, which adaptively captures the coarse-grained features of the data and converts the point cloud into a multi-granularity ball cloud. The normal points within each granular ball help mitigate the influence of noise points. To ensure that ICP finds the globally optimal transformation, MKC is introduced to measure the distribution of registration errors, thereby offering global insights for ICP to achieve the optimal solution. The transformations based on MKC and the granular ball cloud are then derived. Extensive experiments on both simulated and real-world datasets demonstrate that GRICP delivers superior registration performance, particularly in scenarios involving large rotation offsets, partial overlaps, and Gaussian noise.

NeurIPS Conference 2025 Conference Paper

Informed Correctors for Discrete Diffusion Models

• Yixiu Zhao
• Jiaxin Shi
• Feng Chen
• Shaul Druckmann
• Lester Mackey
• Scott Linderman

Discrete diffusion has emerged as a powerful framework for generative modeling in discrete domains, yet efficiently sampling from these models remains challenging. Existing sampling strategies often struggle to balance computation and sample quality when the number of sampling steps is reduced, even when the model has learned the data distribution well. To address these limitations, we propose a predictor-corrector sampling scheme where the corrector is informed by the diffusion model to more reliably counter the accumulating approximation errors. To further enhance the effectiveness of our informed corrector, we introduce complementary architectural modifications based on hollow transformers and a simple tailored training objective that leverages more training signal. We use a synthetic example to illustrate the failure modes of existing samplers and show how informed correctors alleviate these problems. On the Text8 dataset, the informed corrector improves sample quality by generating text with significantly fewer errors than the baselines. On tokenized ImageNet 256x256, this approach consistently produces superior samples with fewer steps, achieving improved FID scores for discrete diffusion models. These results underscore the potential of informed correctors for fast and high-fidelity generation using discrete diffusion.

JBHI Journal 2025 Journal Article

Prior Visual-Guided Self-Supervised Learning Enables Color Vignetting Correction for High-Throughput Microscopic Imaging

• Jianhang Wang
• Tianyu Ma
• Luhong Jin
• Yunqi Zhu
• Jiahui Yu
• Feng Chen
• Shujun Fu
• Yingke Xu

Vignetting constitutes a prevalent optical degradation that significantly compromises the quality of biomedical microscopic imaging. However, a robust and efficient vignetting correction methodology in multi-channel microscopic images remains absent at present. In this paper, we take advantage of a prior knowledge about the homogeneity of microscopic images and radial attenuation property of vignetting to develop a self-supervised deep learning algorithm that achieves complex vignetting removal in color microscopic images. Our proposed method, vignetting correction lookup table (VCLUT), is trainable on both single and multiple images, which employs adversarial learning to effectively transfer good imaging conditions from the user visually defined central region of its own light field to the entire image. To illustrate its effectiveness, we performed individual correction experiments on data from five distinct biological specimens. The results demonstrate that VCLUT exhibits enhanced performance compared to classical methods. We further examined its performance as a multi-image-based approach on a pathological dataset, revealing its advantage over other state-of-the-art approaches in both qualitative and quantitative measurements. Moreover, it uniquely possesses the capacity for generalization across various levels of vignetting intensity and an ultra-fast model computation capability, rendering it well-suited for integration into high-throughput imaging pipelines of digital microscopy.

NeurIPS Conference 2025 Conference Paper

Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting Confidence Improves Mathematical Reasoning

• Feng Chen
• Allan Raventós
• Nan Cheng
• Surya Ganguli
• Shaul Druckmann

Recent progress in large language models (LLMs) highlights the power of scaling test-time compute to achieve strong performance on complex tasks, such as mathematical reasoning and code generation. This raises a critical question: how should model training be modified to optimize performance under a subsequent test-time compute strategy and budget? To explore this, we focus on pass@N, a simple test-time strategy that searches for a correct answer in N independent samples. We show, surprisingly, that training with cross-entropy (CE) can be misaligned with pass@N in that pass@N accuracy decreases with longer CE training. We explain the origins of this misalignment in terms of model overconfidence induced by CE, and experimentally verify our prediction of overconfidence as an impediment to scaling test-time compute via pass@N. Furthermore we suggest a principled, modified training loss that is better aligned to pass@N by limiting model confidence and rescuing pass@N test performance. Our algorithm demonstrates improved mathematical reasoning on MATH and MiniF2F benchmarks under several scenarios: (1) providing answers to math questions both with and without Chain-of-Thought reasoning traces; and (2) proving theorems by searching over proof trees of varying shapes. Overall our work underscores the importance of co-designing two traditionally separate phases of LLM development: training-time protocols and test-time search and reasoning strategies.

JBHI Journal 2025 Journal Article

Semi-Supervised Instance Segmentation in Whole Slide Images via Dense Spatial Variability Enhancing

• Jiahui Yu
• Tianyu Ma
• Dong Hua
• Feng Chen
• Junfen Fu
• Yingke Xu

Current whole slide image (WSI) segmentation aims at extracting tumor regions from the background. Unlike this, segmenting distinct tumor areas (instances) within a WSI driven by limited annotated data remains under-explored. In this paper, we formally propose semi-supervised instance segmentation (Semi-IS) in WSIs. We address a key challenge: learning intra-class similarity and inter-class dissimilarity driven by unlabeled data. Specifically, we generally perceive the patch as composed of tokens (together), not the patch alone. We employ contrastive learning to develop a segmentation framework. In the Semi-IS, we find that the boundaries of segmented instances are usually disturbed by noise. We jointly eliminate and preserve noise features to address this problem. We conduct extensive experiments to evaluate the effectiveness and generalizability of Semi-IS, including histopathology and cellular pathology. The results show that in clinical multi instance segmentation tasks, Semi-IS achieves almost full-supervised state-of-the-art results with only 30% annotated data. Semi-IS can improve segmentation accuracy by about 2% on public cell pathology datasets.

NeurIPS Conference 2025 Conference Paper

SolverLLM: Leveraging Test-Time Scaling for Optimization Problem via LLM-Guided Search

• Dong Li
• Xujiang Zhao
• Linlin Yu
• Yanchi Liu
• Wei Cheng
• Zhengzhang Chen
• Zhong Chen
• Feng Chen

Large Language Models (LLMs) offer promising capabilities for tackling complex reasoning tasks, including optimization problems. However, existing methods either rely on prompt engineering, which leads to poor generalization across problem types, or require costly supervised training. We introduce SolverLLM, a training-free framework that leverages test-time scaling to solve diverse optimization problems. Rather than solving directly, SolverLLM generates mathematical formulations and translates them into solver-ready code, guided by a novel Monte Carlo Tree Search (MCTS) strategy. To enhance the search process, we modify classical MCTS with (1) dynamic expansion for adaptive formulation generation, (2) prompt backpropagation to guide exploration via outcome-driven feedback, and (3) uncertainty backpropagation to incorporate reward reliability into decision-making. Experiments on six standard benchmark datasets demonstrate that SolverLLM outperforms both prompt-based and learning-based baselines, achieving strong generalization without additional training.

YNIMG Journal 2025 Journal Article

The role of gut microbiota in modulating brain structure and psychiatric disorders: A Mendelian randomization study

• Zheng Ye
• Yingying Gao
• Jiaqi Yuan
• Feng Chen
• Peng Xu
• Wenbin Liu

ICML Conference 2025 Conference Paper

ZipAR: Parallel Autoregressive Image Generation through Spatial Locality

• Yefei He
• Feng Chen
• Yuanyu He
• Shaoxuan He
• Hong Zhou
• Kaipeng Zhang
• Bohan Zhuang

In this paper, we propose ZipAR, a training-free, plug-and-play parallel decoding framework for accelerating autoregressive (AR) visual generation. The motivation stems from the observation that images exhibit local structures, and spatially distant regions tend to have minimal interdependence. Given a partially decoded set of visual tokens, in addition to the original next-token prediction scheme in the row dimension, the tokens corresponding to spatially adjacent regions in the column dimension can be decoded in parallel. To ensure alignment with the contextual requirements of each token, we employ an adaptive local window assignment scheme with rejection sampling analogous to speculative decoding. By decoding multiple tokens in a single forward pass, the number of forward passes required to generate an image is significantly reduced, resulting in a substantial improvement in generation efficiency. Experiments demonstrate that ZipAR can reduce the number of model forward passes by up to 91% on the Emu3-Gen model without requiring any additional retraining.

EAAI Journal 2024 Journal Article

Detection and utilization of new-type encrypted network traffic in distributed scenarios

• Ping Zhang
• Feng Chen
• Hongyuan Yue

TMLR Journal 2024 Journal Article

From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling

• Aneesh Komanduri
• Xintao Wu
• Yongkai Wu
• Feng Chen

Deep generative models have shown tremendous capability in data density estimation and data generation from finite samples. While these models have shown impressive performance by learning correlations among features in the data, some fundamental shortcomings are their lack of explainability, tendency to induce spurious correlations, and poor out-of-distribution extrapolation. To remedy such challenges, recent work has proposed a shift toward causal generative models. Causal models offer several beneficial properties to deep generative models, such as distribution shift robustness, fairness, and interpretability. Structural causal models (SCMs) describe data-generating processes and model complex causal relationships and mechanisms among variables in a system. Thus, SCMs can naturally be combined with deep generative models. We provide a technical survey on causal generative modeling categorized into causal representation learning and controllable counterfactual generation methods. We focus on fundamental theory, methodology, drawbacks, datasets, and metrics. Then, we cover applications of causal generative models in fairness, privacy, out-of-distribution generalization, precision medicine, and biological sciences. Lastly, we discuss open problems and fruitful research directions for future work in the field.

NeurIPS Conference 2024 Conference Paper

Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning

• Daniel Kunin
• Allan Raventós
• Clémentine Dominé
• Feng Chen
• David Klindt
• Andrew Saxe
• Surya Ganguli

While the impressive performance of modern neural networks is often attributed to their capacity to efficiently extract task-relevant features from data, the mechanisms underlying this rich feature learning regime remain elusive, with much of our theoretical understanding stemming from the opposing lazy regime. In this work, we derive exact solutions to a minimal model that transitions between lazy and rich learning, precisely elucidating how unbalanced layer-specific initialization variances and learning rates determine the degree of feature learning. Our analysis reveals that they conspire to influence the learning regime through a set of conserved quantities that constrain and modify the geometry of learning trajectories in parameter and function space. We extend our analysis to more complex linear models with multiple neurons, outputs, and layers and to shallow nonlinear networks with piecewise linear activation functions. In linear networks, rapid feature learning only occurs from balanced initializations, where all layers learn at similar speeds. While in nonlinear networks, unbalanced initializations that promote faster learning in earlier layers can accelerate rich learning. Through a series of experiments, we provide evidence that this unbalanced rich regime drives feature learning in deep finite-width networks, promotes interpretability of early layers in CNNs, reduces the sample complexity of learning hierarchical data, and decreases the time to grokking in modular arithmetic. Our theory motivates further exploration of unbalanced initializations to enhance efficient feature learning.

AIIM Journal 2024 Journal Article

Integrated block-wise neural network with auto-learning search framework for finger gesture recognition using sEMG signals

• Shurun Wang
• Hao Tang
• Feng Chen
• Qi Tan
• Qi Jiang

IJCAI Conference 2024 Conference Paper

Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms

• Aneesh Komanduri
• Yongkai Wu
• Feng Chen
• Xintao Wu

Learning disentangled causal representations is a challenging problem that has gained significant attention recently due to its implications for extracting meaningful information for downstream tasks. In this work, we define a new notion of causal disentanglement from the perspective of independent causal mechanisms. We propose ICM-VAE, a framework for learning causally disentangled representations supervised by causally related observed labels. We model causal mechanisms using nonlinear learnable flow-based diffeomorphic functions to map noise variables to latent causal variables. Further, to promote the disentanglement of causal factors, we propose a causal disentanglement prior learned from auxiliary labels and the latent causal structure. We theoretically show the identifiability of causal factors and mechanisms up to permutation and elementwise reparameterization. We empirically demonstrate that our framework induces highly disentangled causal factors, improves interventional robustness, and is compatible with counterfactual generation.

TMLR Journal 2024 Journal Article

M$^3$PL: Identifying and Exploiting View Bias of Prompt Learning

• Chujie Zhao
• Tianren Zhang
• Guanyu Chen
• Yizhou Jiang
• Feng Chen

Prompt learning is an effective means of fine-tuning multi-modal foundation models such as CLIP. Despite existing success, the inner mechanism of multi-modal prompt learning has not been well understood. In this work, we identify an inductive bias of multi-modal prompt learning, which we refer to as view bias, that the learned prompts may extract only a partial subset of useful features (views) and ignore others. This bias can undermine the model's generalization ability, particularly under distribution shifts. We further observe that independently trained prompts have distinct view biases, contrary to the existing belief that they may converge to similar local optima due to having the same cross-modal representation matching objective. Based on our observations, we propose Multi-modal Matching Multi-Prompt Learning (M$^3$PL), which incorporates multiple paired prompts and a cross-modal contrastive regularizer that facilitates the prompt pairs to encapsulate a broader spectrum of views. Extensive experiments show that M$^3$PL effectively boosts the model's generalization capability, achieving state-of-the-art performance under various distribution shifts.

TMLR Journal 2024 Journal Article

One by One, Continual Coordinating with Humans via Hyper-Teammate Identification

• Cong Guan
• Feng Chen
• Ke Xue
• Chunpeng Fan
• Lichao Zhang
• Ziqian Zhang
• Pengyao Zhao
• Zongzhang Zhang

One of the primary objectives in modern artificial intelligence researches is to empower agents to effectively coordinate with diverse teammates, particularly human teammates. Previous studies focused on training agents either with a fixed population of pre-generated teammates or through the co-evolution of distinct populations of agents and teammates. However, it is challenging to enumerate all possible teammates in advance, and it is costly, or even impractical to maintain such a sufficiently diverse population and repeatedly interact with previously encountered teammates. Additional design considerations, such as prioritized sampling, are also required to ensure efficient training. To address these challenges and obtain an efficient human-AI coordination paradigm, we propose a novel approach called \textbf{Concord}. Considering that human participants tend to occur in a sequential manner, we model the training process with different teammates as a continual learning framework, akin to how humans learn and adapt in the real world. We propose a mechanism based on hyper-teammate identification to prevent catastrophic forgetting while promoting forward knowledge transfer. Concretely, we introduce a teammate recognition module that captures the identification of corresponding teammates. Leveraging the identification, a well-coordinated AI policy can be generated via the hyper-network. The entire framework is trained in a decomposed policy gradient manner, allowing for effective credit assignment among agents. This approach enables us to train agents with each generated teammate or humans one by one, ensuring that agents can coordinate effectively with concurrent teammates without forgetting previous knowledge. Our approach outperforms multiple baselines in various multi-agent benchmarks, either with generated human proxies or real human participants.

ICML Conference 2024 Conference Paper

RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation

• Yufei Wang 0007
• Zhou Xian
• Feng Chen
• Tsun-Hsuan Wang
• Yian Wang 0001
• Katerina Fragkiadaki
• Zackory Erickson
• David Held

We present RoboGen, a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation. RoboGen leverages the latest advancements in foundation and generative models. Instead of directly adapting these models to produce policies or low-level actions, we advocate for a generative scheme, which uses these models to automatically generate diversified tasks, scenes, and training supervisions, thereby scaling up robotic skill learning with minimal human supervision. Our approach equips a robotic agent with a self-guided propose-generate-learn cycle: the agent first proposes interesting tasks and skills to develop, and then generates simulation environments by populating pertinent assets with proper spatial configurations. Afterwards, the agent decomposes the proposed task into sub-tasks, selects the optimal learning approach (reinforcement learning, motion planning, or trajectory optimization), generates required training supervision, and then learns policies to acquire the proposed skill. Our fully generative pipeline can be queried repeatedly, producing an endless stream of skill demonstrations associated with diverse tasks and environments.

NeurIPS Conference 2023 Conference Paper

DiffVL: Scaling Up Soft Body Manipulation using Vision-Language Driven Differentiable Physics

• Zhiao Huang
• Feng Chen
• Yewen Pu
• Chunru Lin
• Hao Su
• Chuang Gan

Combining gradient-based trajectory optimization with differentiable physics simulation is an efficient technique for solving soft-body manipulation problems. Using a well-crafted optimization objective, the solver can quickly converge onto a valid trajectory. However, writing the appropriate objective functions requires expert knowledge, making it difficult to collect a large set of naturalistic problems from non-expert users. We introduce DiffVL, a method that enables non-expert users to communicate soft-body manipulation tasks -- a combination of vision and natural language, given in multiple stages -- that can be readily leveraged by a differential physics solver. We have developed GUI tools that enable non-expert users to specify 100 tasks inspired by real-life soft-body manipulations from online videos, which we'll make public. We leverage large language models to translate task descriptions into machine-interpretable optimization objectives. The optimization objectives can help differentiable physics solvers to solve these long-horizon multistage tasks that are challenging for previous baselines.

YNIMG Journal 2023 Journal Article

Evaluation of whole-brain oxygen metabolism in Alzheimer's disease using QSM and quantitative BOLD

• Aocai Yang
• Hangwei Zhuang
• Lei Du
• Bing Liu
• Kuan Lv
• Jixin Luan
• Pianpian Hu
• Feng Chen

AAAI Conference 2023 Conference Paper

Fast Counterfactual Inference for History-Based Reinforcement Learning

• Haichuan Gao
• Tianren Zhang
• Zhile Yang
• Yuqing Guo
• Jinsheng Ren
• Shangqi Guo
• Feng Chen

Incorporating sequence-to-sequence models into history-based Reinforcement Learning (RL) provides a general way to extend RL to partially-observable tasks. This method compresses history spaces according to the correlations between historical observations and the rewards. However, they do not adjust for the confounding correlations caused by data sampling and assign high beliefs to uninformative historical observations, leading to limited compression of history spaces. Counterfactual Inference (CI), which estimates causal effects by single-variable intervention, is a promising way to adjust for confounding. However, it is computationally infeasible to directly apply the single-variable intervention to a huge number of historical observations. This paper proposes to perform CI on observation sub-spaces instead of single observations and develop a coarse-to-fine CI algorithm, called Tree-based History Counterfactual Inference (T-HCI), to reduce the number of interventions exponentially. We show that T-HCI is computationally feasible in practice and brings significant sample efficiency gains in various challenging partially-observable tasks, including Maze, BabyAI, and robot manipulation tasks.

NeurIPS Conference 2023 Conference Paper

Improvements on Uncertainty Quantification for Node Classification via Distance Based Regularization

• Russell Hart
• Linlin Yu
• Yifei Lou
• Feng Chen

Deep neural networks have achieved significant success in the last decades, but they are not well-calibrated and often produce unreliable predictions. A large number of literature relies on uncertainty quantification to evaluate the reliability of a learning model, which is particularly important for applications of out-of-distribution (OOD) detection and misclassification detection. We are interested in uncertainty quantification for interdependent node-level classification. We start our analysis based on graph posterior networks (GPNs) that optimize the uncertainty cross-entropy (UCE)-based loss function. We describe the theoretical limitations of the widely-used UCE loss. To alleviate the identified drawbacks, we propose a distance-based regularization that encourages clustered OOD nodes to remain clustered in the latent space. We conduct extensive comparison experiments on eight standard datasets and demonstrate that the proposed regularization outperforms the state-of-the-art in both OOD detection and misclassification detection.

NeurIPS Conference 2023 Conference Paper

Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression

• Allan Raventós
• Mansheej Paul
• Feng Chen
• Surya Ganguli

Pretrained transformers exhibit the remarkable ability of in-context learning (ICL): they can learn tasks from just a few examples provided in the prompt without updating any weights. This raises a foundational question: can ICL solve fundamentally new tasks that are very different from those seen during pretraining? To probe this question, we examine ICL’s performance on linear regression while varying the diversity of tasks in the pretraining dataset. We empirically demonstrate a task diversity threshold for the emergence of ICL. Below this threshold, the pretrained transformer cannot solve unseen regression tasks, instead behaving like a Bayesian estimator with the non-diverse pretraining task distribution as the prior. Beyond this threshold, the transformer significantly outperforms this estimator; its behavior aligns with that of ridge regression, corresponding to a Gaussian prior over all tasks, including those not seen during pretraining. Thus, when pretrained on data with task diversity greater than the threshold, transformers can optimally solve fundamentally new tasks in-context. Importantly, this capability hinges on it deviating from the Bayes optimal estimator with the pretraining distribution as the prior. This study also explores the effect of regularization, model capacity and task structure and underscores, in a concrete example, the critical role of task diversity, alongside data and model scale, in the emergence of ICL.

AAAI Conference 2023 Conference Paper

Robust Multi-Agent Coordination via Evolutionary Generation of Auxiliary Adversarial Attackers

• Lei Yuan
• Ziqian Zhang
• Ke Xue
• Hao Yin
• Feng Chen
• Cong Guan
• Lihe Li
• Chao Qian

Cooperative Multi-agent Reinforcement Learning (CMARL) has shown to be promising for many real-world applications. Previous works mainly focus on improving coordination ability via solving MARL-specific challenges (e.g., non-stationarity, credit assignment, scalability), but ignore the policy perturbation issue when testing in a different environment. This issue hasn't been considered in problem formulation or efficient algorithm design. To address this issue, we firstly model the problem as a Limited Policy Adversary Dec-POMDP (LPA-Dec-POMDP), where some coordinators from a team might accidentally and unpredictably encounter a limited number of malicious action attacks, but the regular coordinators still strive for the intended goal. Then, we propose Robust Multi-Agent Coordination via Evolutionary Generation of Auxiliary Adversarial Attackers (ROMANCE), which enables the trained policy to encounter diversified and strong auxiliary adversarial attacks during training, thus achieving high robustness under various policy perturbations. Concretely, to avoid the ego-system overfitting to a specific attacker, we maintain a set of attackers, which is optimized to guarantee the attackers high attacking quality and behavior diversity. The goal of quality is to minimize the ego-system coordination effect, and a novel diversity regularizer based on sparse action is applied to diversify the behaviors among attackers. The ego-system is then paired with a population of attackers selected from the maintained attacker set, and alternately trained against the constantly evolving attackers. Extensive experiments on multiple scenarios from SMAC indicate our ROMANCE provides comparable or better robustness and generalization ability than other baselines.

EAAI Journal 2023 Journal Article

Sketch2Photo: Synthesizing photo-realistic images from sketches via global contexts

• Heng Liu
• Yao Xu
• Feng Chen

NeurIPS Conference 2023 Conference Paper

Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks

• Feng Chen
• Daniel Kunin
• Atsushi Yamamura
• Surya Ganguli

In this work, we reveal a strong implicit bias of stochastic gradient descent (SGD) that drives overly expressive networks to much simpler subnetworks, thereby dramatically reducing the number of independent parameters, and improving generalization. To reveal this bias, we identify invariant sets, or subsets of parameter space that remain unmodified by SGD. We focus on two classes of invariant sets that correspond to simpler (sparse or low-rank) subnetworks and commonly appear in modern architectures. Our analysis uncovers that SGD exhibits a property of stochastic attractivity towards these simpler invariant sets. We establish a sufficient condition for stochastic attractivity based on a competition between the loss landscape's curvature around the invariant set and the noise introduced by stochastic gradients. Remarkably, we find that an increased level of noise strengthens attractivity, leading to the emergence of attractive invariant sets associated with saddle-points or local maxima of the train loss. We observe empirically the existence of attractive invariant sets in trained deep neural networks, implying that SGD dynamics often collapses to simple subnetworks with either vanishing or redundant neurons. We further demonstrate how this simplifying process of stochastic collapse benefits generalization in a linear teacher-student framework. Finally, through this analysis, we mechanistically explain why early training with large learning rates for extended periods benefits subsequent generalization.

AAAI Conference 2023 Short Paper

Towards Deployment-Efficient and Collision-Free Multi-Agent Path Finding (Student Abstract)

• Feng Chen
• Chenghe Wang
• Fuxiang Zhang
• Hao Ding
• Qiaoyong Zhong
• Shiliang Pu
• Zongzhang Zhang

Multi-agent pathfinding (MAPF) is essential to large-scale robotic coordination tasks. Planning-based algorithms show their advantages in collision avoidance while avoiding exponential growth in the number of agents. Reinforcement-learning (RL)-based algorithms can be deployed efficiently but cannot prevent collisions entirely due to the lack of hard constraints. This paper combines the merits of planning-based and RL-based MAPF methods to propose a deployment-efficient and collision-free MAPF algorithm. The experiments show the effectiveness of our approach.

AAAI Conference 2022 Conference Paper

A Nested Bi-level Optimization Framework for Robust Few Shot Learning

• Krishnateja Killamsetty
• Changbin Li
• Chen Zhao
• Feng Chen
• Rishabh Iyer

Model-Agnostic Meta-Learning (MAML), a popular gradientbased meta-learning framework, assumes that the contribution of each task or instance to the meta-learner is equal. Hence, it fails to address the domain shift between base and novel classes in few-shot learning. In this work, we propose a novel robust meta-learning algorithm, NESTEDMAML, which learns to assign weights to training tasks or instances. We consider weights as hyper-parameters and iteratively optimize them using a small set of validation tasks set in a nested bi-level optimization approach (in contrast to the standard bi-level optimization in MAML). We then apply NESTED- MAML in the meta-training stage, which involves (1) several tasks sampled from a distribution different from the meta-test task distribution, or (2) some data samples with noisy labels. Extensive experiments on synthetic and real-world datasets demonstrate that NESTEDMAML efficiently mitigates the effects of ”unwanted” tasks or instances, leading to significant improvement over the state-of-the-art robust meta-learning methods.

AAAI Conference 2022 Conference Paper

Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs

• Chunpai Wang
• Daniel B. Neill
• Feng Chen

We propose a new approach, the calibrated nonparametric scan statistic (CNSS), for more accurate detection of anomalous patterns in large-scale, real-world graphs. Scan statistics identify connected subgraphs that are interesting or unexpected through maximization of a likelihood ratio statistic; in particular, nonparametric scan statistics (NPSSs) identify subgraphs with a higher than expected proportion of individually significant nodes. However, we show that recently proposed NPSS methods are miscalibrated, failing to account for the maximization of the statistic over the multiplicity of subgraphs. This results in both reduced detection power for subtle signals, and low precision of the detected subgraph even for stronger signals. Thus we develop a new statistical approach to recalibrate NPSSs, correctly adjusting for multiple hypothesis testing and taking the underlying graph structure into account. While the recalibration, based on randomization testing, is computationally expensive, we propose both an efficient (approximate) algorithm and new, closed-form lower bounds (on the expected maximum proportion of significant nodes for subgraphs of a given size, under the null hypothesis of no anomalous patterns). These advances, along with the integration of recent core-tree decomposition methods, enable CNSS to scale to large real-world graphs, with substantial improvement in the accuracy of detected subgraphs. Extensive experiments on both semi-synthetic and real-world datasets are demonstrated to validate the effectiveness of our proposed methods, in comparison with state-of-the-art counterparts.

NeurIPS Conference 2022 Conference Paper

Efficient Multi-agent Communication via Self-supervised Information Aggregation

• Cong Guan
• Feng Chen
• Lei Yuan
• Chenghe Wang
• Hao Yin
• Zongzhang Zhang
• Yang Yu

Utilizing messages from teammates can improve coordination in cooperative Multi-agent Reinforcement Learning (MARL). To obtain meaningful information for decision-making, previous works typically combine raw messages generated by teammates with local information as inputs for policy. However, neglecting the aggregation of multiple messages poses great inefficiency for policy learning. Motivated by recent advances in representation learning, we argue that efficient message aggregation is essential for good coordination in MARL. In this paper, we propose Multi-Agent communication via Self-supervised Information Aggregation (MASIA), with which agents can aggregate the received messages into compact representations with high relevance to augment the local policy. Specifically, we design a permutation invariant message encoder to generate common information aggregated representation from raw messages and optimize it via reconstructing and shooting future information in a self-supervised manner. Each agent would utilize the most relevant parts of the aggregated representation for decision-making by a novel message extraction mechanism. Empirical results demonstrate that our method significantly outperforms strong baselines on multiple cooperative MARL tasks for various task settings.

NeurIPS Conference 2022 Conference Paper

Learning to Branch with Tree MDPs

• Lara Scavuzzo
• Feng Chen
• Didier Chetelat
• Maxime Gasse
• Andrea Lodi
• Neil Yorke-Smith
• Karen Aardal

State-of-the-art Mixed Integer Linear Programming (MILP) solvers combine systematic tree search with a plethora of hard-coded heuristics, such as branching rules. While approaches to learn branching strategies have received increasing attention and have shown very promising results, most of the literature focuses on learning fast approximations of the \emph{strong branching} rule. Instead, we propose to learn branching rules from scratch with Reinforcement Learning (RL). We revisit the work of Etheve et al. (2020) and propose a generalization of Markov Decisions Processes (MDP), which we call \emph{tree MDP}, that provides a more suitable formulation of the branching problem. We derive a policy gradient theorem for tree MDPs that exhibits a better credit assignment compared to its temporal counterpart. We demonstrate through computational experiments that this new framework is suitable to tackle the learning-to-branch problem in MILP, and improves the learning convergence.

IJCAI Conference 2022 Conference Paper

Multi-Agent Concentrative Coordination with Decentralized Task Representation

• Lei Yuan
• Chenghe Wang
• Jianhao Wang
• Fuxiang Zhang
• Feng Chen
• Cong Guan
• Zongzhang Zhang
• Chongjie Zhang

Value-based multi-agent reinforcement learning (MARL) methods hold the promise of promoting coordination in cooperative settings. Popular MARL methods mainly focus on the scalability or the representational capacity of value functions. Such a learning paradigm can reduce agents' uncertainties and promote coordination. However, they fail to leverage the task structure decomposability, which generally exists in real-world multi-agent systems (MASs), leading to a significant amount of time exploring the optimal policy in complex scenarios. To address this limitation, we propose a novel framework Multi-Agent Concentrative Coordination (MACC) based on task decomposition, with which an agent can implicitly form local groups to reduce the learning space to facilitate coordination. In MACC, agents first learn representations for subtasks from their local information and then implement an attention mechanism to concentrate on the most relevant ones. Thus, agents can pay targeted attention to specific subtasks and improve coordination. Extensive experiments on various complex multi-agent benchmarks demonstrate that MACC achieves remarkable performance compared to existing methods.

YNICL Journal 2022 Journal Article

Predicting prognosis of primary pontine hemorrhage using CT image and deep learning

• Shuo Wang
• Feng Chen
• Mingyu Zhang
• Xiaolin Zhao
• Linghua Wen
• Wenyuan Wu
• Shina Wu
• Zhe Li

JBHI Journal 2022 Journal Article

Progressive Residual Learning With Memory Upgrade for Ultrasound Image Blind Super-Resolution

• Heng Liu
• Jianyong Liu
• Feng Chen
• Caifeng Shan

For clinical medical diagnosis and treatment, image super-resolution (SR) technology will be helpful to improve the ultrasonic imaging quality so as to enhance the accuracy of disease diagnosis. However, due to the differences of sensing devices or transmission media, the resolution degradation process of ultrasound imaging in real scenes is uncontrollable, especially when the blur kernel is usually unknown. This issue makes current end-to-end SR networks poor performance when applied to ultrasonic images. Aiming to achieve effective SR in real ultrasound medical scenes, in this work, we propose a blind deep SR method based on progressive residual learning and memory upgrade. Specifically, we estimate the accurate blur kernel from the spatial attention map block of low resolution (LR) ultrasound image through a multi-label classification network, then we construct three modules–up- sampling (US) module, residual learning (RL) model and memory upgrading (MU) model for ultrasound image blind SR. The US module is designed to upscale the input information and the up-sampled residual result will be used for SR reconstruction. The RL module is employed to approximate the original LR and continuously generate the updated residual and feed it to the next US module. The last MU module can store all progressively learned residuals, which offers increased interactions between the US and RL modules, augmenting the details recovery. Extensive experiments and evaluations on the benchmark CCA-US and US-CASE datasets demonstrate the proposed approach achieves better performance against the state-of-the-art methods.

AAAI Conference 2021 Short Paper

A Double Phases Generation Network for Yes or No Question Generation (Student Abstract)

• Jiayuan Xie
• Feng Chen
• Yi Cai
• Zehang Lin

This paper aims to solve the task of generating yes or no questions, which generates yes/no questions based on given passages. These questions can be used for evaluation automatically. We propose a double phases generation network that can identify specific phrases related to facts from the input passage and use them as auxiliary information for generation. Specifically, the 1st-phase prediction uses the extracted phrases as assistance to generate an initial question. Then, the 2nd-phase prediction utilizes an attention network to focus on the relevant phrases related to the initial question in the passage to generate questions that are more relevant to the specific facts contained in the initial question. Extensive experiments we performed on BoolQ dataset demonstrate the effectiveness of our framework.

AAAI Conference 2021 Conference Paper

Multidimensional Uncertainty-Aware Evidential Neural Networks

• Yibo Hu
• Yuzhe Ou
• Xujiang Zhao
• Jin-Hee Cho
• Feng Chen

Traditional deep neural networks (NNs) have significantly contributed to the state-of-the-art performance in the task of classification under various application domains. However, NNs have not considered inherent uncertainty in data associated with the class probabilities where misclassification under uncertainty may easily introduce high risk in decision making in real-world contexts (e. g. , misclassification of objects in roads leads to serious accidents). Unlike Bayesian NN that indirectly infer uncertainty through weight uncertainties, evidential NNs (ENNs) have been recently proposed to explicitly model the uncertainty of class probabilities and use them for classification tasks. An ENN offers the formulation of the predictions of NNs as subjective opinions and learns the function by collecting an amount of evidence that can form the subjective opinions by a deterministic NN from data. However, the ENN is trained as a black box without explicitly considering inherent uncertainty in data with their different root causes, such as vacuity (i. e. , uncertainty due to a lack of evidence) or dissonance (i. e. , uncertainty due to conflicting evidence). By considering the multidimensional uncertainty, we proposed a novel uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an out-of-distribution (OOD) detection problem. We took a hybrid approach that combines Wasserstein Generative Adversarial Network (WGAN) with ENNs to jointly train a model with prior knowledge of a certain class, which has high vacuity for OOD samples. Via extensive empirical experiments based on both synthetic and real-world datasets, we demonstrated that the estimation of uncertainty by WENN can significantly help distinguish OOD samples from boundary samples. WENN outperformed in OOD detection when compared with other competitive counterparts.

NeurIPS Conference 2021 Conference Paper

RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning

• Krishnateja Killamsetty
• Xujiang Zhao
• Feng Chen
• Rishabh Iyer

Semi-supervised learning (SSL) algorithms have had great success in recent years in limited labeled data regimes. However, the current state-of-the-art SSL algorithms are computationally expensive and entail significant compute time and energy requirements. This can prove to be a huge limitation for many smaller companies and academic groups. Our main insight is that training on a subset of unlabeled data instead of entire unlabeled data enables the current SSL algorithms to converge faster, significantly reducing computational costs. In this work, we propose RETRIEVE, a coreset selection framework for efficient and robust semi-supervised learning. RETRIEVE selects the coreset by solving a mixed discrete-continuous bi-level optimization problem such that the selected coreset minimizes the labeled set loss. We use a one-step gradient approximation and show that the discrete optimization problem is approximately submodular, enabling simple greedy algorithms to obtain the coreset. We empirically demonstrate on several real-world datasets that existing SSL algorithms like VAT, Mean-Teacher, FixMatch, when used with RETRIEVE, achieve a) faster training times, b) better performance when unlabeled data consists of Out-of-Distribution (OOD) data and imbalance. More specifically, we show that with minimal accuracy degradation, RETRIEVE achieves a speedup of around $3\times$ in the traditional SSL setting and achieves a speedup of $5\times$ compared to state-of-the-art (SOTA) robust SSL algorithms in the case of imbalance and OOD data. RETRIEVE is available as a part of the CORDS toolkit: https: //github. com/decile-team/cords.

TIST Journal 2020 Journal Article

CSL+

• Adil Alim
• Jin-Hee Cho
• Feng Chen

Using unreliable information sources generating conflicting evidence may lead to a large uncertainty, which significantly hurts the decision making process. Recently, many approaches have been taken to integrate conflicting data from multiple sources and/or fusing conflicting opinions from different entities. To explicitly deal with uncertainty, a belief model called Subjective Logic (SL), as a variant of Dumpster-Shafer Theory, has been proposed to represent subjective opinions and to merge multiple opinions by offering a rich volume of fusing operators, which have been used to solve many opinion inference problems in trust networks. However, the operators of SL are known to be lack of scalability in inferring unknown opinions from large network data as a result of the sequential procedures of merging multiple opinions. In addition, SL does not consider deriving opinions in the presence of conflicting evidence. In this work, we propose a hybrid inference method that combines SL and Probabilistic Soft Logic (PSL), namely, Collective Subjective Plus, CSL +, which is resistible to highly conflicting evidence or a lack of evidence. PSL can reason a belief in a collective manner to deal with large-scale network data, allowing high scalability based on relationships between opinions. However, PSL does not consider an uncertainty dimension in a subjective opinion. To take benefits from both SL and PSL, we proposed a hybrid approach called CSL + for achieving high scalability and high prediction accuracy for unknown opinions with uncertainty derived from a lack of evidence and/or conflicting evidence. Through the extensive experiments on four semi-synthetic and two real-world datasets, we showed that the CSL + outperforms the state-of-the-art belief model (i.e., SL), probabilistic inference models (i.e., PSL, CSL), and deep learning model (i.e., GCN-VAE-opinion) in terms of prediction accuracy, computational complexity, and real running time.

NeurIPS Conference 2020 Conference Paper

Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning

• Tianren Zhang
• Shangqi Guo
• Tian Tan
• Xiaolin Hu
• Feng Chen

Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training inefficiency as the action space of the high-level, i. e. , the goal space, is often large. Searching in a large goal space poses difficulties for both high-level subgoal generation and low-level policy learning. In this paper, we show that this problem can be effectively alleviated by restricting the high-level action space from the whole goal space to a k-step adjacent region of the current state using an adjacency constraint. We theoretically prove that the proposed adjacency constraint preserves the optimal hierarchical policy in deterministic MDPs, and show that this constraint can be practically implemented by training an adjacency network that can discriminate between adjacent and non-adjacent subgoals. Experimental results on discrete and continuous control tasks show that incorporating the adjacency constraint improves the performance of state-of-the-art HRL approaches in both deterministic and stochastic environments.

NeurIPS Conference 2020 Conference Paper

Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning

• Weishi Shi
• Xujiang Zhao
• Feng Chen
• Qi Yu

We present a novel multi-source uncertainty prediction approach that enables deep learning (DL) models to be actively trained with much less labeled data. By leveraging the second-order uncertainty representation provided by subjective logic (SL), we conduct evidence-based theoretical analysis and formally decompose the predicted entropy over multiple classes into two distinct sources of uncertainty: vacuity and dissonance, caused by lack of evidence and conflict of strong evidence, respectively. The evidence based entropy decomposition provides deeper insights on the nature of uncertainty, which can help effectively explore a large and high-dimensional unlabeled data space. We develop a novel loss function that augments DL based evidence prediction with uncertainty anchor sample identification. The accurately estimated multiple sources of uncertainty are systematically integrated and dynamically balanced using a data sampling function for label-efficient active deep learning (ADL). Experiments conducted over both synthetic and real data and comparison with competitive AL methods demonstrate the effectiveness of the proposed ADL model.

NeurIPS Conference 2020 Conference Paper

Uncertainty Aware Semi-Supervised Learning on Graph Data

• Xujiang Zhao
• Feng Chen
• Shu Hu
• Jin-Hee Cho

Thanks to graph neural networks (GNNs), semi-supervised node classification has shown the state-of-the-art performance in graph data. However, GNNs have not considered different types of uncertainties associated with class probabilities to minimize risk of increasing misclassification under uncertainty in real life. In this work, we propose a multi-source uncertainty framework using a GNN that reflects various types of predictive uncertainties in both deep learning and belief/evidence theory domains for node classification predictions. By collecting evidence from the given labels of training nodes, the Graph-based Kernel Dirichlet distribution Estimation (GKDE) method is designed for accurately predicting node-level Dirichlet distributions and detecting out-of-distribution (OOD) nodes. We validated the outperformance of our proposed model compared to the state-of-the-art counterparts in terms of misclassification detection and OOD detection based on six real network datasets. We found that dissonance-based detection yielded the best results on misclassification detection while vacuity-based detection was the best for OOD detection. To clarify the reasons behind the results, we provided the theoretical proof that explains the relationships between different types of uncertainties considered in this work.

NeurIPS Conference 2019 Conference Paper

Convolution with even-sized kernels and symmetric padding

• Shuang Wu
• Guanrui Wang
• Pei Tang
• Feng Chen
• Luping Shi

Compact convolutional neural networks gain efficiency mainly through depthwise convolutions, expanded channels and complex topologies, which contrarily aggravate the training process. Besides, 3x3 kernels dominate the spatial representation in these models, whereas even-sized kernels (2x2, 4x4) are rarely adopted. In this work, we quantify the shift problem occurs in even-sized kernel convolutions by an information erosion hypothesis, and eliminate it by proposing symmetric padding on four sides of the feature maps (C2sp, C4sp). Symmetric padding releases the generalization capabilities of even-sized kernels at little computational cost, making them outperform 3x3 kernels in image classification and generation tasks. Moreover, C2sp obtains comparable accuracy to emerging compact models with much less memory and time consumption during training. Symmetric padding coupled with even-sized convolutions can be neatly implemented into existing frameworks, providing effective elements for architecture designs, especially on online and continual learning occasions where training efforts are emphasized.

AAAI Conference 2019 Conference Paper

Uncovering Specific-Shape Graph Anomalies in Attributed Graphs

• Nannan Wu
• Wenjun Wang
• Feng Chen
• Jianxin Li
• Bo Li
• Jinpeng Huai

As networks are ubiquitous in the modern era, point anomalies have been changed to graph anomalies in terms of anomaly shapes. However, the specific-shape priors about anomalous subgraphs of interest are seldom considered by the traditional approaches when detecting the subgraphs in attributed graphs (e. g. , computer networks, Bitcoin networks, and etc.). This paper proposes a nonlinear approach to specific-shape graph anomaly detection. The nonlinear approach focuses on optimizing a broad class of nonlinear cost functions via specific-shape constraints in attributed graphs. Our approach can be used to many different graph anomaly settings. The traditional approaches can only support linear cost functions (e. g. , an aggregation function for the summation of node weights). However, our approach can employ more powerful nonlinear cost functions, and enjoys a rigorous theoretical guarantee on the near-optimal solution with the geometrical convergence rate.

ICLR Conference 2018 Conference Paper

Training and Inference with Integers in Deep Neural Networks

• Shuang Wu
• Guoqi Li
• Feng Chen
• Luping Shi

Researches on deep neural networks with discrete parameters and their deployment in embedded systems have been active and promising topics. Although previous works have successfully reduced precision in inference, transferring both training and inference processes to low-bitwidth integers has not been demonstrated simultaneously. In this work, we develop a new method termed as ``"WAGE" to discretize both training and inference, where weights (W), activations (A), gradients (G) and errors (E) among layers are shifted and linearly constrained to low-bitwidth integers. To perform pure discrete dataflow for fixed-point devices, we further replace batch normalization by a constant scaling layer and simplify other components that are arduous for integer implementation. Improved accuracies can be obtained on multiple datasets, which indicates that WAGE somehow acts as a type of regularization. Empirically, we demonstrate the potential to deploy training in hardware systems such as integer-based deep learning accelerators and neuromorphic chips with comparable accuracy and higher energy efficiency, which is crucial to future AI applications in variable scenarios with transfer and continual learning demands.

IJCAI Conference 2017 Conference Paper

Query-Driven Discovery of Anomalous Subgraphs in Attributed Graphs

• Nannan Wu
• Feng Chen
• Jianxin Li
• Jinpeng Huai
• Bo Li

For a detection problem, a user often has some prior knowledge about the structure-specific subgraphs of interest, but few traditional approaches are capable of employing this knowledge. The main technical challenge is that few approaches can efficiently model the space of connected subgraphs that are isomorphic to a query graph. We present a novel, efficient approach for optimizing a generic nonlinear cost function subject to a query-specific structural constraint. Our approach enjoys strong theoretical guarantees on the convergence of a nearly optimal solution and a low time complexity. For the case study, we specialize the nonlinear function to several well-known graph scan statistics for anomalous subgraph discovery. Empirical evidence demonstrates that our method is superior to state-of-the-art methods in several real-world anomaly detection tasks.

IJCAI Conference 2016 Conference Paper

A Generalized Matching Pursuit Approach for Graph-Structured Sparsity

• Feng Chen
• Baojian Zhou

Sparsity-constrained optimization is an important and challenging problem that has wide applicability in data mining, machine learning, and statistics. In this paper, we focus on sparsity-constrained optimization in cases where the cost function is a general nonlinear function and, in particular, the sparsity constraint is defined by a graph-structured sparsity model. Existing methods explore this problem in the context of sparse estimation in linear models. To the best of our knowledge, this is the first work to present an efficient approximation algorithm, namely, GRAPH-structured Matching Pursuit (GRAPH-MP), to optimize a general nonlinear function subject to graph-structured constraints. We prove that our algorithm enjoys the strong guarantees analogous to those designed for linear models in terms of convergence rate and approximation accuracy. As a case study, we specialize GRAPH-MP to optimize a number of well-known graph scan statistic models for the connected subgraph detection task, and empirical evidence demonstrates that our general algorithm performs superior over state-of-the-art methods that are designed specifically for the task of connected subgraph detection.

AAAI Conference 2016 Conference Paper

Efficient Nonparametric Subgraph Detection Using Tree Shaped Priors

• Nannan Wu
• Feng Chen
• Jianxin Li
• Baojian Zhou
• Naren Ramakrishnan

Non-parametric graph scan (NPGS) statistics are used to detect anomalous connected subgraphs on graphs, and have a wide variety of applications, such as disease outbreak detection, road traffic congestion detection, and event detection in social media. In contrast to traditional parametric scan statistics (e. g. , the Kulldorff statistic), NPGS statistics are free of distributional assumptions and can be applied to heterogeneous graph data. In this paper, we make a number of contributions to the computational study of NPGS statistics. First, we present a novel reformulation of the problem as a sequence of Budget Price-Collecting Steiner Tree (B- PCST) sub-problems. Second, we show that this reformulated problem is NP-hard for a large class of nonparametric statistic functions. Third, we further develop efficient exact and approximate algorithms for a special category of graphs in which the anomalous subgraphs can be reformulated in a fixed tree topology. Finally, using extensive experiments we demonstrate the performance of our proposed algorithms in two real-world application domains (water pollution detection in water sensor networks and spatial event detection in social media networks) and contrast against state-of-theart connected subgraph detection methods.

AAAI Conference 2016 Conference Paper

Topical Analysis of Interactions Between News and Social Media

• Ting Hua
• Yue Ning
• Feng Chen
• Chang-Tien Lu
• Naren Ramakrishnan

The analysis of interactions between social media and traditional news streams is becoming increasingly relevant for a variety of applications, including: understanding the underlying factors that drive the evolution of data sources, tracking the triggers behind events, and discovering emerging trends. Researchers have explored such interactions by examining volume changes or information diffusions, however, most of them ignore the semantical and topical relationships between news and social media data. Our work is the first attempt to study how news influences social media, or inversely, based on topical knowledge. We propose a hierarchical Bayesian model that jointly models the news and social media topics and their interactions. We show that our proposed model can capture distinct topics for individual datasets as well as discover the topic influences among multiple datasets. By applying our model to large sets of news and tweets, we demonstrate its significant improvement over baseline methods and explore its power in the discovery of interesting patterns for real world cases.

AAAI Conference 2013 Conference Paper

A Generalized Student-t Based Approach to Mixed-Type Anomaly Detection

• Yen-Cheng Lu
• Feng Chen
• Yang Chen
• Chang-Tien Lu

Anomaly detection for mixed-type data is an important problem that has not been well addressed in the machine learning field. There are two challenging issues for mixed-type datasets, namely modeling mutual correlations between mixed-type attributes and capturing large variations due to anomalies. This paper presents BuffDetect, a robust error buffering approach for anomaly detection in mixed-type datasets. A new variant of the generalized linear model is proposed to model the dependency between mixed-type attributes. The model incorporates an error buffering component based on Student-t distribution to absorb the variations caused by anomalies. However, because of the non- Gaussian design, the problem becomes analytically intractable. We propose a novel Bayesian inference approach, which integrates Laplace approximation and several computational optimizations, and is able to ef- ficiently approximate the posterior of high dimensional latent variables by iteratively updating the latent variables in groups. Extensive experimental evaluations based on 13 benchmark datasets demonstrate the effectiveness and efficiency of BuffDetect.

NeurIPS Conference 2013 Conference Paper

Variational Planning for Graph-based MDPs

• Qiang Cheng
• Qiang Liu
• Feng Chen
• Alexander Ihler

Markov Decision Processes (MDPs) are extremely useful for modeling and solving sequential decision making problems. Graph-based MDPs provide a compact representation for MDPs with large numbers of random variables. However, the complexity of exactly solving a graph-based MDP usually grows exponentially in the number of variables, which limits their application. We present a new variational framework to describe and solve the planning problem of MDPs, and derive both exact and approximate planning algorithms. In particular, by exploiting the graph structure of graph-based MDPs, we propose a factored variational value iteration algorithm in which the value function is first approximated by the multiplication of local-scope value functions, then solved by minimizing a Kullback-Leibler (KL) divergence. The KL divergence is optimized using the belief propagation algorithm, with complexity exponential in only the cluster size of the graph. Experimental comparison on different models shows that our algorithm outperforms existing approximation algorithms at finding good policies.

AAAI Conference 2012 Conference Paper

Approximating the Sum Operation for Marginal-MAP Inference

• Qiang Cheng
• Feng Chen
• Jianwu Dong
• Wenli Xu
• Alexander Ihler

We study the marginal-MAP problem on graphical models, and present a novel approximation method based on direct approximation of the sum operation. A primary difficulty of marginal-MAP problems lies in the non-commutativity of the sum and max operations, so that even in highly structured models, marginalization may produce a densely connected graph over the variables to be maximized, resulting in an intractable potential function with exponential size. We propose a chain decomposition approach for summing over the marginalized variables, in which we produce a structured approximation to the MAP component of the problem consisting of only pairwise potentials. We show that this approach is equivalent to the maximization of a specific variational free energy, and it provides an upper bound of the optimal probability. Finally, experimental results demonstrate that our method performs favorably compared to previous methods.