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Yuchen Li

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19 papers

2 author rows

AAAI Conference 2026 Conference Paper

AdaFuse: Accelerating Dynamic Adapter Inference via Token-Level Pre-Gating and Fused Kernel Optimization

Qiyang Li
Rui Kong
Yuchen Li
Hengyi Cai
Shuaiqiang Wang
Linghe Kong
Guihai Chen
Dawei Yin

The integration of dynamic, sparse structures like Mixture-of-Experts (MoE) with parameter-efficient adapters (e.g., LoRA) is a powerful technique for enhancing Large Language Models (LLMs). However, this architectural enhancement comes at a steep cost: despite minimal increases in computational load, the inference latency often skyrockets, leading to decoding speeds slowing by over 2.5 times. Through a fine-grained performance analysis, we pinpoint the primary bottleneck not in the computation itself, but in the severe overhead from fragmented, sequential CUDA kernel launches required for conventional dynamic routing. To address this challenge, we introduce AdaFuse, a framework built on a tight co-design between the algorithm and the underlying hardware system to enable efficient dynamic adapter execution. Departing from conventional layer-wise or block-wise routing, AdaFuse employs a token-level pre-gating strategy, which makes a single, global routing decision for all adapter layers before a token is processed. This ``decide-once, apply-everywhere'' approach effectively staticizes the execution path for each token, creating an opportunity for holistic optimization. We capitalize on this by developing a custom CUDA kernel that performs a fused switching operation, merging the parameters of all selected LoRA adapters into the backbone model in a single, efficient pass. Experimental results on popular open-source LLMs show that AdaFuse achieves accuracy on par with state-of-the-art dynamic adapters while drastically cutting decoding latency by a factor of over 2.4x, thereby bridging the gap between model capability and inference efficiency.

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JMLR Journal 2026 Journal Article

Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization

Yuchen Li
Laura Balzano
Deanna Needell
Hanbaek Lyu

Block majorization-minimization (BMM) is a simple iterative algorithm for nonconvex optimization that sequentially minimizes a majorizing surrogate of the objective function in each block coordinate while the other block coordinates are held fixed. We consider a family of BMM algorithms for minimizing nonsmooth nonconvex objectives, where each parameter block is constrained within a subset of a Riemannian manifold. We establish that this algorithm converges asymptotically to the set of stationary points, and attains an $\epsilon$-stationary point within $\widetilde{O}(\epsilon^{-2})$ iterations. In particular, the assumptions for our complexity results are completely Euclidean when the underlying manifold is a product of Euclidean or Stiefel manifolds, although our analysis makes explicit use of the Riemannian geometry. Our general analysis applies to a wide range of algorithms with Riemannian constraints: Riemannian MM, block projected gradient descent, Bures-JKO scheme for Wasserstein variational inference, optimistic likelihood estimation, geodesically constrained subspace tracking, robust PCA, and Riemannian CP-dictionary-learning. We experimentally validate that our algorithm converges faster than standard Euclidean algorithms applied to the Riemannian setting. [abs] [ pdf ][ bib ] &copy JMLR 2026. ( edit, beta )

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AAAI Conference 2026 Conference Paper

Efficient Thought Space Exploration Through Strategic Intervention

Ziheng Li
Hengyi Cai
Xiaochi Wei
Yuchen Li
Shuaiqiang Wang
Zhi-Hong Deng
Dawei Yin

While large language models (LLMs) demonstrate emerging reasoning capabilities, current inference-time expansion methods incur prohibitive computational costs through exhaustive sampling. Through analyzing decoding trajectories, we observe that most next-token predictions align well with the golden output, except for a few critical tokens that lead to deviations. Inspired by this phenomenon, we propose a novel Hint-Practice Reasoning (HPR) framework that operationalizes this insight through two synergistic components: 1) a hinter (powerful LLM) that provides probabilistic guidance at critical decision points, and 2) a practitioner (efficient smaller model) that executes major reasoning steps. The framework's core innovation lies in Distributional Inconsistency Reduction (DIR), a theoretically-grounded metric that dynamically identifies intervention points by quantifying the divergence between practitioner's reasoning trajectory and hinter's expected distribution in a tree-structured probabilistic space. Through iterative tree updates guided by DIR, HPR reweights promising reasoning paths while deprioritizing low-probability branches. Experiments across arithmetic and commonsense reasoning benchmarks demonstrate HPR's state-of-the-art efficiency-accuracy tradeoffs: it achieves comparable performance to self-consistency and MCTS baselines while decoding only 1/5 tokens, and outperforms existing methods by at most 5.1% absolute accuracy while maintaining similar or lower FLOPs.

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AAAI Conference 2026 Conference Paper

VPN: Visual Prompt Navigation

Shuo Feng
Zihan Wang
Yuchen Li
Rui Kong
Hengyi Cai
Shuaiqiang Wang
Gim Hee Lee
Piji Li

While natural language is commonly used to guide embodied agents, the inherent ambiguity and verbosity of language often hinder the effectiveness of language-guided navigation in complex environments. To this end, we propose Visual Prompt Navigation (VPN), a novel paradigm that guides agents to navigate using only user-provided visual prompts within 2D top-view maps. This visual prompt primarily focuses on marking the visual navigation trajectory on a top-down view of a scene, offering intuitive and spatially grounded guidance without relying on language instructions. It is more friendly for non-expert users and reduces interpretive ambiguity. We build VPN tasks in both discrete and continuous navigation settings, constructing two new datasets, R2R-VP and R2R-CE-VP, by extending existing R2R and R2R-CE episodes with corresponding visual prompts. Furthermore, we introduce VPNet, a dedicated baseline network to handle the VPN tasks, with two data augmentation strategies: view-level augmentation (altering initial headings and prompt orientations) and trajectory-level augmentation (incorporating diverse trajectories from large-scale 3D scenes), to enhance navigation performance. Extensive experiments evaluate how visual prompt forms, top-view map formats, and data augmentation strategies affect the performance of visual prompt navigation.

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JBHI Journal 2025 Journal Article

EDG-Net: Encryption and Decryption based Gan-attention Network for CT images in the Internet of Medical Things and Telemedicine

Kai Chen
Yuchen Li
Shipeng Xie
Zhan Wu
Yikun Zhang
Jean-Louis Coatrieux
Wei Yan
Yang Chen

CT images provide medical practitioners with a scientific and intuitive rationale for the diagnosis of clinical diseases. The Internet of Medical Things (IoMT) and telemedicine facilitate the preservation, transmission, and application of medical data and drive the sharing of medical data, especially medical images. Encryption and decryption of CT images distributed in the IoMT and telemedicine are becoming critical because they contain a large amount of private patient–ensitive information and are vulnerable to third-party attacks, resulting in information exposure and privacy leakage. In this paper, we propose an Encryption and Decryption based Gan-attention network (EDG-Net) for CT images in the IoMT and telemedicine. EDG-Net consists of a generator, two discriminators, a domain transfer of attention, and adaptive normalization. In addition, a double encryption and decryption strategy is introduced by EDG-Net to effectively improve the security of the ciphertext image and the fidelity of the decrypted plaintext image. Specifically, during the encryption or decryption phase, the generator transforms the CT images mutually in the plaintext and ciphertext domains. Two discriminators to identify and modify the differences between these two domain transformations, especially improve the accuracy of the reconstruction during decryption. The parameters of the trained encryption and decryption network are considered as the secret keys of encryption and decryption. Qualitative and quantitative analysis of public and private datasets demonstrates the superior performance of EDG-Net regarding encryption security and robustness as well as decryption accuracy.

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NeurIPS Conference 2025 Conference Paper

GS2E: Gaussian Splatting is an Effective Data Generator for Event Stream Generation

Yuchen Li
Chaoran Feng
Zhenyu Tang
Kaiyuan Deng
Wangbo Yu
Yonghong Tian
Li Yuan

We introduce GS2E (Gaussian Splatting to Event Generation), a large-scale synthetic event dataset designed for high-fidelity event vision tasks, captured from real-world sparse multi-view RGB images. Existing event datasets are often synthesized from dense RGB videos, which typically suffer from limited viewpoint diversity and geometric inconsistency, or rely on expensive, hard-to-scale hardware setups. GS2E addresses these limitations by first reconstructing photorealistic static scenes using 3D Gaussian Splatting, followed by a novel, physically-informed event simulation pipeline. This pipeline integrates adaptive trajectory interpolation with physically-consistent event contrast threshold modeling. As a result, it generates temporally dense and geometrically consistent event streams under diverse motion and lighting conditions, while maintaining strong alignment with the underlying scene structure. Experimental results on event-based 3D reconstruction highlight GS2E’s superior generalization capabilities and its practical value as a benchmark for advancing event vision research.

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ICML Conference 2025 Conference Paper

HPS: Hard Preference Sampling for Human Preference Alignment

Xiandong Zou
Wanyu Lin
Yuchen Li
Pan Zhou

Aligning Large Language Model (LLM) responses with human preferences is vital for building safe and controllable AI systems. While preference optimization methods based on Plackett-Luce (PL) and Bradley-Terry (BT) models have shown promise, they face challenges such as poor handling of harmful content, inefficient use of dispreferred responses, and, specifically for PL, high computational costs. To address these issues, we propose Hard Preference Sampling (HPS), a novel framework for robust and efficient human preference alignment. HPS introduces a training loss that prioritizes the most preferred response while rejecting all dispreferred and harmful ones. It emphasizes “hard” dispreferred responses — those closely resembling preferred ones — to enhance the model’s rejection capabilities. By leveraging a single-sample Monte Carlo sampling strategy, HPS reduces computational overhead while maintaining alignment quality. Theoretically, HPS improves sample efficiency over existing PL methods and maximizes the reward margin between preferred and dispreferred responses, ensuring clearer distinctions. Experiments on HH-RLHF and PKU-Safety datasets validate HPS’s effectiveness, achieving comparable BLEU and reward scores while greatly improving reward margins and thus reducing harmful content generation.

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AAAI Conference 2025 Conference Paper

MLC-NC: Long-Tailed Multi-Label Image Classification Through the Lens of Neural Collapse

Zijian Tao
Shao-Yuan Li
Wenhai Wan
Jinpeng Zheng
Jia-Yao Chen
Yuchen Li
Sheng-Jun Huang
Songcan Chen

Long-tailed (LT) data distribution is common in multi-label image classification (MLC) and can significantly impact the performance of classification models. One reason is the challenge of learning unbiased instance representations (i.e. features) for imbalanced datasets. Additionally, the co-occurrence of head/tail classes within the same instance, along with complex label dependencies, introduces further challenges. In this work, we delve into this problem through the lens of neural collapse (NC). NC refers to a phenomenon where the last-layer features and classifier of a deep neural network model exhibit a simplex Equiangular Tight Frame (ETF) structure during its terminal training phase. This structure creates an optimal linearly separable state. However, this phenomenon typically occurs in balanced datasets but rarely applies to the typical imbalanced problem. To induce NC properties under Long-tailed multi-label classification (LT-MLC) conditions, we propose an approach named MLC-NC, which aims to learn high-quality data representations and improve the model’s generalization ability. Specifically, MLC-NC accounts for the fact that different labels correspond to different feature parts located in images. MLC-NC extracts class-wise features from each instance through a cross-attention mechanism. To guide the features toward the ETF structure, we introduce visual-semantic feature alignment with a fixed ETF structured label embedding, which helps to learn evenly distributed class centers. To reduce within-class feature variation, we introduce collapse calibration within a lower-dimensional feature space. To mitigate classification bias, we concatenate features and feed them into a binarized fixed ETF classifier. As an orthogonal approach to existing methods, MLC-NC can be seamlessly integrated into various frameworks. Extensive experiments on widely-used benchmarks demonstrate the effectiveness of our method.

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ICML Conference 2025 Conference Paper

On the Query Complexity of Verifier-Assisted Language Generation

Edoardo Botta
Yuchen Li
Aashay Mehta
Jordan T. Ash
Cyril Zhang
Andrej Risteski

Recently, a plethora of works have proposed inference-time algorithms (e. g. best-of-n), which incorporate verifiers to assist the generation process. Their quality-efficiency trade-offs have been empirically benchmarked on a variety of constrained generation tasks, but the algorithmic design landscape is still largely poorly understood. In this paper, we develop a mathematical framework for reasoning about constrained generation using a pre-trained language model generator oracle and a process verifier—which can decide whether a prefix can be extended to a string which satisfies the constraints of choice. We show that even in very simple settings, access to a verifier can render an intractable problem (information-theoretically or computationally) to a tractable one. In fact, we show even simple algorithms, like tokenwise rejection sampling, can enjoy significant benefits from access to a verifier. Empirically, we show that a natural modification of tokenwise rejection sampling, in which the sampler is allowed to "backtrack" (i. e. , erase the final few generated tokens) has robust and substantive benefits over natural baselines (e. g. (blockwise) rejection sampling, nucleus sampling)—both in terms of computational efficiency, accuracy and diversity.

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AAAI Conference 2025 Conference Paper

SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learning

Tianhao Peng
Xuhong Li
Haitao Yuan
Yuchen Li
Haoyi Xiong

Graph contrastive learning has emerged as a powerful technique for learning graph representations that are robust and discriminative. However, traditional approaches often neglect the critical role of subgraph structures, particularly the intra-subgraph characteristics and inter-subgraph relationships, which are crucial for generating informative and diverse contrastive pairs. These subgraph features are crucial as they vary significantly across different graph types, such as social networks where they represent communities, and biochemical networks where they symbolize molecular interactions. To address this issue, our work proposes a novel subgraph-oriented learnable augmentation method for graph contrastive learning, termed SOLA-GCL, that centers around subgraphs, taking full advantage of the subgraph information for data augmentation. Specifically, SOLA-GCL initially partitions a graph into multiple densely connected subgraphs based on their intrinsic properties. To preserve and enhance the unique characteristics inherent to subgraphs, a graph view generator optimizes augmentation strategies for each subgraph, thereby generating tailored views for graph contrastive learning. This generator uses a combination of intra-subgraph and inter-subgraph augmentation strategies, including node dropping, feature masking, intra-edge perturbation, inter-edge perturbation, and subgraph swapping. Extensive experiments have been conducted on various graph learning applications, ranging from social networks to molecules, under semi-supervised learning, unsupervised learning, and transfer learning settings to demonstrate the superiority of our proposed approach.

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ICML Conference 2024 Conference Paper

Convergence and Complexity Guarantee for Inexact First-order Riemannian Optimization Algorithms

Yuchen Li
Laura Balzano
Deanna Needell
Hanbaek Lyu

We analyze inexact Riemannian gradient descent (RGD) where Riemannian gradients and retractions are inexactly (and cheaply) computed. Our focus is on understanding when inexact RGD converges and what is the complexity in the general nonconvex and constrained setting. We answer these questions in a general framework of tangential Block Majorization-Minimization (tBMM). We establish that tBMM converges to an $\epsilon$-stationary point within $O(\epsilon^{-2})$ iterations. Under a mild assumption, the results still hold when the subproblem is solved inexactly in each iteration provided the total optimality gap is bounded. Our general analysis applies to a wide range of classical algorithms with Riemannian constraints including inexact RGD and proximal gradient method on Stiefel manifolds. We numerically validate that tBMM shows improved performance over existing methods when applied to various problems, including nonnegative tensor decomposition with Riemannian constraints, regularized nonnegative matrix factorization, and low-rank matrix recovery problems.

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IJCAI Conference 2024 Conference Paper

GS2P: A Generative Pre-trained Learning to Rank Model with Over-parameterization for Web-Scale Search (Extended Abstract)

Yuchen Li
Haoyi Xiong
Linghe Kong
Jiang Bian
Shuaiqiang Wang
Guihai Chen
Dawei Yin

While Learning to Rank (LTR) is widely employed in web searches to prioritize pertinent webpages from the retrieved contents based on input queries, traditional LTR models stumble over two principal stumbling blocks leading to subpar performance: 1) the lack of well-annotated query-webpage pairs with ranking scores to cover search queries of various popularity, debilitating their coverage of search queries across the popularity spectrum, and 2) ill-trained models that are incapable of inducing generalized representations for LTR, culminating in overfitting. To tackle above challenges, we proposed a Generative Semi-supervised Pre-trained (GS2P) LTR model. Specifically, GS2P first generates pseudo-labels for the unlabeled samples using tree-based LTR models after a series of co-training procedures, then learns the representations of query-webpage pairs with self-attentive transformers via both discriminative and generative losses. Finally, GS2P boosts the performance of LTR through incorporating Random Fourier Features to over-parameterize the models into "interpolating regime", so as to enjoy the further descent of generalization errors with learned representations. We conduct extensive offline experiments on a publicly available dataset and a real-world dataset collected from a large-scale search engine. The results show that GS2P can achieve the best performance on both datasets, compared to baselines. We also deploy GS2P at a large-scale web search engine with realistic traffic, where we can still observe significant improvement in real-world applications.

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NeurIPS Conference 2024 Conference Paper

HyperPrism: An Adaptive Non-linear Aggregation Framework for Distributed Machine Learning over Non-IID Data and Time-varying Communication Links

Haizhou Du
Yijian Chen
Ryan Yang
Yuchen Li
Linghe Kong

While Distributed Machine Learning (DML) has been widely used to achieve decent performance, it is still challenging to take full advantage of data and devices distributed at multiple vantage points to adapt and learn, especially it is non-trivial to address dynamic and divergence challenges based on the linear aggregation framework as follows: (1) heterogeneous learning data at different devices (i. e. , non-IID data) resulting in model divergence and (2) in the case of time-varying communication links, the limited ability for devices to reconcile model divergence. In this paper, we contribute a non-linear class aggregation framework HyperPrism that leverages distributed mirror descent with averaging done in the mirror descent dual space and adapts the degree of Weighted Power Mean (WPM) used in each round. Moreover, HyperPrism could adaptively choose different mapping for different layers of the local model with a dedicated hypernetwork per device, achieving automatic optimization of DML in high divergence settings. We perform rigorous analysis and experimental evaluations to demonstrate the effectiveness of adaptive, mirror-mapping DML. In particular, we extend the generalizability of existing related works and position them as special cases within HyperPrism. Our experimental results show that HyperPrism can improve the convergence speed up to 98. 63% and scale well to more devices compared with the state-of-the-art, all with little additional computation overhead compared to traditional linear aggregation.

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IJCAI Conference 2024 Conference Paper

MPGraf: a Modular and Pre-trained Graphformer for Learning to Rank at Web-scale (Extended Abstract)

Yuchen Li
Haoyi Xiong
Linghe Kong
Zeyi Sun
Hongyang Chen
Shuaiqiang Wang
Dawei Yin

Both Transformer and Graph Neural Networks (GNNs) have been used in learning to rank (LTR), however, they adhere to two distinct yet complementary problem formulations, i. e. , ranking score regression based on query-webpage pairs and link prediction within query-webpage bipartite graphs, respectively. Though it is possible to pre-train GNNs or Transformers on source datasets and fine-tune them subject to sparsely annotated LTR datasets separately, the source-target distribution shifts across the pairs and bipartite graphs domains make it extremely difficult to integrate these diverse models into a single LTR framework at a web-scale. We introduce the novel MPGraf model, which utilizes a modular and capsule-based pre-training approach, aiming to incorporate regression capacities from Transformers and link prediction capabilities of GNNs cohesively. We conduct extensive experiments to evaluate the performance of MPGraf using real-world datasets collected from large-scale search engines. The results show that MPGraf can outperform baseline algorithms on several major metrics. Further, we deploy and evaluate MPGraf atop a large-scale search engine with realistic web traffic via A/B tests, where we can still observe significant improvement. MPGraf performs consistently in both offline and online evaluations.

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ICML Conference 2024 Conference Paper

Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines

Yuchen Li
Alexandre Kirchmeyer
Aashay Mehta
Yilong Qin
Boris Dadachev
Kishore Papineni
Sanjiv Kumar
Andrej Risteski

Autoregressive language models are the currently dominant paradigm for text generation, however they have some fundamental limitations that cannot be remedied by scale—for example inherently sequential and unidirectional generation. While alternate classes of models have been explored, we have limited mathematical understanding of their fundamental power and limitations. In this paper we focus on Generative Masked Language Models (GMLMs), a non-autoregressive paradigm in which we train a model to fit conditional probabilities of the data distribution via masking, which are subsequently used as inputs to a Markov Chain to draw samples from the model. These models empirically strike a promising speed-quality trade-off as each step can be typically parallelized by decoding the entire sequence in parallel. We develop a mathematical framework for analyzing and improving such models which sheds light on questions of sample complexity and inference speed and quality. Empirically, we adapt the T5 model for iteratively-refined parallel decoding, achieving 2-3x speedup in machine translation with minimal sacrifice in quality compared with autoregressive models. We run careful ablation experiments to give recommendations on key design choices, and make fine-grained observations on the common error modes in connection with our theory. Our mathematical analyses and empirical observations characterize both potentials and limitations of this approach, and can be applied to future works on improving understanding and performance of GMLMs. We released codes for our experiments.

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JBHI Journal 2024 Journal Article

RED-Net: Residual and Enhanced Discriminative Network for Image Steganalysis in the Internet of Medical Things and Telemedicine

Kai Chen
Zhengyuan Zhou
Yuchen Li
Xu Ji
Jiasong Wu
Jean-Louis Coatrieux
Yang Chen
Gouenou Coatrieux

Internet of Medical Things (IoMT) and telemedicine technologies utilize computers, communications, and medical devices to facilitate off-site exchanges between specialists and patients, specialists, and medical staff. If the information communicated in IoMT is illegally steganography, tampered or leaked during transmission and storage, it will directly impact patient privacy or the consultation results with possible serious medical incidents. Steganalysis is of great significance for the identification of medical images transmitted illegally in IoMT and telemedicine. In this article, we propose a Residual and Enhanced Discriminative Network (RED-Net) for image steganalysis in the internet of medical things and telemedicine. RED-Net consists of a steganographic information enhancement module, a deep residual network, and steganographic information discriminative mechanism. Specifically, a steganographic information enhancement module is adopted by the RED-Net to boost the illegal steganographic signal in texturally complex high-dimensional medical image features. A deep residual network is utilized for steganographic feature extraction and compression. A steganographic information discriminative mechanism is employed by the deep residual network to enable it to recalibrate the steganographic features and drop high-frequency features that are mistaken for steganographic information. Experiments conducted on public and private datasets with data hiding payloads ranging from 0. 1bpp/bpnzac-0. 5bpp/bpnzac in the spatial and JPEG domain led to RED-Net's steganalysis error $P_{\mathrm{E}}$ in the range of 0. 0732-0. 0010 and 0. 231-0. 026, respectively. In general, qualitative and quantitative results on public and private datasets demonstrate that the RED-Net outperforms 8 state-of-art steganography detectors.

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NeurIPS Conference 2023 Conference Paper

Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars

Kaiyue Wen
Yuchen Li
Bingbin Liu
Andrej Risteski

Transformer interpretability aims to understand the algorithm implemented by a learned Transformer by examining various aspects of the model, such as the weight matrices or the attention patterns. In this work, through a combination of theoretical results and carefully controlled experiments on synthetic data, we take a critical viewof methods that exclusively focus on individual parts of the model, rather than consider the network as a whole. We consider a simple synthetic setup of learning a (bounded) Dyck language. Theoretically, we show that the set of models that (exactly or approximately) solve this task satisfy a structural characterization derived from ideas in formal languages (the pumping lemma). We use this characterization to show that the set of optima is qualitatively rich; in particular, the attention pattern of a single layer can be "nearly randomized", while preserving the functionality of the network. We also show via extensive experiments that these constructions are not merely a theoretical artifact: even with severe constraints to the architecture of the model, vastly different solutions can be reached via standard training. Thus, interpretability claims based on inspecting individual heads or weight matrices in the Transformer can be misleading.

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NeurIPS Conference 2022 Conference Paper

PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies

Guocheng Qian
Yuchen Li
Houwen Peng
Jinjie Mai
Hasan Hammoud
Mohamed Elhoseiny
Bernard Ghanem

PointNet++ is one of the most influential neural architectures for point cloud understanding. Although the accuracy of PointNet++ has been largely surpassed by recent networks such as PointMLP and Point Transformer, we find that a large portion of the performance gain is due to improved training strategies, i. e. data augmentation and optimization techniques, and increased model sizes rather than architectural innovations. Thus, the full potential of PointNet++ has yet to be explored. In this work, we revisit the classical PointNet++ through a systematic study of model training and scaling strategies, and offer two major contributions. First, we propose a set of improved training strategies that significantly improve PointNet++ performance. For example, we show that, without any change in architecture, the overall accuracy (OA) of PointNet++ on ScanObjectNN object classification can be raised from 77. 9% to 86. 1%, even outperforming state-of-the-art PointMLP. Second, we introduce an inverted residual bottleneck design and separable MLPs into PointNet++ to enable efficient and effective model scaling and propose PointNeXt, the next version of PointNets. PointNeXt can be flexibly scaled up and outperforms state-of-the-art methods on both 3D classification and segmentation tasks. For classification, PointNeXt reaches an overall accuracy of 87. 7 on ScanObjectNN, surpassing PointMLP by 2. 3%, while being 10x faster in inference. For semantic segmentation, PointNeXt establishes a new state-of-the-art performance with 74. 9% mean IoU on S3DIS (6-fold cross-validation), being superior to the recent Point Transformer. The code and models are available at https: //github. com/guochengqian/pointnext.

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AAAI Conference 2020 Conference Paper

Social Influence Does Matter: User Action Prediction for In-Feed Advertising

Hongyang Wang
Qingfei Meng
Ju Fan
Yuchen Li
Laizhong Cui
Xiaoman Zhao
Chong Peng
Gong Chen

Social in-feed advertising delivers ads that seamlessly ﬁt inside a user’s feed, and allows users to engage in social actions (likes or comments) with the ads. Many businesses pay higher attention to “engagement marketing” that maximizes social actions, as social actions can effectively promote brand awareness. This paper studies social action prediction for infeed advertising. Most existing works overlook the social in- ﬂuence as a user’s action may be affected by her friends’ actions. This paper introduces an end-to-end approach that leverages social inﬂuence for action prediction, and focuses on addressing the high sparsity challenge for in-feed ads. We propose to learn inﬂuence structure that models who tends to be inﬂuenced. We extract a subgraph with the near neighbors a user interacts with, and learn topological features of the subgraph by developing structure-aware graph encoding methods. We also introduce graph attention networks to learn inﬂuence dynamics that models how a user is inﬂuenced by neighbors’ actions. We conduct extensive experiments on real datasets from the commercial advertising platform of WeChat and a public dataset. The experimental results demonstrate that social inﬂuence learned by our approach can signiﬁcantly boost performance of social action prediction.

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