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Jiawei Li

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27 papers

2 author rows

TMLR Journal 2026 Journal Article

Delta-Influence: Identifying Poisons via Influence Functions

Wenjie Li
Jiawei Li
Pengcheng Zeng
Christian Schroeder de Witt
Ameya Prabhu
Amartya Sanyal

Addressing data integrity challenges, such as unlearning the effects of targeted data poisoning after model training, is necessary for the reliable deployment of machine learning models. State-of-the-art influence functions, such as EK-FAC and TRAK, often fail to accurately attribute abnormal model behavior to the specific poisoned training data responsible for the data poisoning attack. In addition, traditional unlearning algorithms often struggle to effectively remove the influence of poisoned samples, particularly when only a few affected examples can be identified. To address these challenge, we introduce $\Delta$-Influence, a novel approach that leverages influence functions to trace abnormal model behavior back to the responsible poisoned training data using just one poisoned test example, without assuming any prior knowledge of the attack. $\Delta$-Influence applies data transformations that sever the link between poisoned training data and compromised test points without significantly affecting clean data. This allows detecting large negative shifts in influence scores following data transformations, a phenomenon we term as influence collapse, thereby accurately identifying poisoned training data. Unlearning this subset, e.g. through retraining, effectively eliminates the data poisoning. We validate our method across three vision-based poisoning attacks and three datasets, benchmarking against five detection algorithms and five unlearning strategies. We show that $\Delta$-Influence consistently achieves the best unlearning across all settings, showing the promise of influence functions for corrective unlearning.

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AAAI Conference 2026 Conference Paper

Simulated Rewards, Skewed Strategies: Tracing the Acquired Preference Bias in LLM-Based Dialogue Planners

Heyan Huang
Yizhe Yang
Huashan Sun
Jiawei Li
Yang Gao

Large language models have enabled sophisticated dialogue planning policy, but their reliance on LLM-generated simulation and feedback for policy optimization may introduce systematic preference bias. We present the first comprehensive analysis of preference bias in LLM-based dialogue planners, evaluating four state-of-the-art planning policies across three dialogue domains using multiple LLM families at varying scales. Our investigation reveals that all tested planners exhibit significant preference bias, systematically favoring narrow strategy sets rather than maintaining balanced distributions. User simulation emerges as the primary bias driver, while diverse persona simulation fails as an effective mitigation strategy. Most concerning, preference bias drives planners toward ethically problematic strategies that achieve short-term success while undermining real-world effectiveness and ethical standards. Our findings establish fundamental challenges for responsible deployment of LLM-based dialogue systems and provide crucial insights for developing more reliable and ethically-aligned planning approaches.

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AAAI Conference 2025 Conference Paper

A²RNet: Adversarial Attack Resilient Network for Robust Infrared and Visible Image Fusion

Jiawei Li
Hongwei Yu
Jiansheng Chen
Xinlong Ding
Jinlong Wang
Jinyuan Liu
Bochao Zou
Huimin Ma

Infrared and visible image fusion (IVIF) is a crucial technique for enhancing visual performance by integrating unique information from different modalities into one fused image. Exiting methods pay more attention to conducting fusion with undisturbed data, while overlooking the impact of deliberate interference on the effectiveness of fusion results. To investigate the robustness of fusion models, in this paper, we propose a novel adversarial attack resilient network, called A2RNet. Specifically, we develop an adversarial paradigm with an anti-attack loss function to implement adversarial attacks and training. It is constructed based on the intrinsic nature of IVIF and provide a robust foundation for future research advancements. We adopt a Unet as the pipeline with a transformer-based defensive refinement module (DRM) under this paradigm, which guarantees fused image quality in a robust coarse-to-fine manner. Compared to previous works, our method mitigates the adverse effects of adversarial perturbations, consistently maintaining high-fidelity fusion results. Furthermore, the performance of downstream tasks can also be well maintained under adversarial attacks.

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AAAI Conference 2025 Conference Paper

Predicting User Behavior in Smart Spaces with LLM-Enhanced Logs and Personalized Prompts

Yunpeng Song
Jiawei Li
Yiheng Bian
Zhongmin Cai

Enhancing the intelligence of smart systems, such as smart homes, smart vehicles, and smart grids, critically depends on developing sophisticated planning capabilities that can anticipate the next desired function based on historical interactions. While existing methods view user behaviors as sequential data and apply models like RNNs and Transformers to predict future actions, they often fail to incorporate domain knowledge and capture personalized user preferences. In this paper, we propose a novel approach that incorporates LLM-enhanced logs and personalized prompts. Our approach first constructs a graph that captures individual behavior preferences derived from their interaction histories. This graph effectively transforms into a soft continuous prompt that precedes the sequence of user behaviors. Then our approach leverages the vast general knowledge and robust reasoning capabilities of a pretrained LLM to enrich the oversimplified and incomplete log records. By enhancing these logs semantically, our approach better understands the user's actions and intentions, especially for those rare events in the dataset. We evaluate the method across four real-world datasets from both smart vehicle and smart home settings. The findings validate the effectiveness of our LLM-enhanced description and personalized prompt, shedding light on potential ways to advance the intelligence of smart space.

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ICML Conference 2025 Conference Paper

SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator

Guoxuan Chen
Han Shi
Jiawei Li
Yihang Gao
Xiaozhe Ren
Yimeng Chen
Xin Jiang
Zhenguo Li

Large Language Models (LLMs) have exhibited exceptional performance across a spectrum of natural language processing tasks. However, their substantial sizes pose considerable challenges, particularly in computational demands and inference speed, due to their quadratic complexity. In this work, we have identified a key pattern: certain seemingly meaningless separator tokens (i. e. , punctuations) contribute disproportionately to attention scores compared to semantically meaningful tokens. This observation suggests that information of the segments between these separator tokens can be effectively condensed into the separator tokens themselves without significant information loss. Guided by this insight, we introduce SepLLM, a plug-and-play framework that accelerates inference by compressing these segments and eliminating redundant tokens. Additionally, we implement efficient kernels for training acceleration. Experimental results across training-free, training-from-scratch, and post-training settings demonstrate SepLLM’s effectiveness. Notably, using the Llama-3-8B backbone, SepLLM achieves over 50% reduction in KV cache on the GSM8K-CoT benchmark while maintaining comparable performance. Furthermore, in streaming settings, SepLLM effectively processes sequences of up to 4 million tokens or more while maintaining consistent language modeling capabilities.

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IJCAI Conference 2025 Conference Paper

T2S: High-resolution Time Series Generation with Text-to-Series Diffusion Models

Yunfeng Ge
Jiawei Li
Yiji Zhao
Haomin Wen
Zhao Li
Meikang Qiu
Hongyan Li
Ming Jin

Text-to-Time Series generation holds significant potential to address challenges such as data sparsity, imbalance, and limited availability of multimodal time series data across domains. While diffusion models have achieved remarkable success in Text-to-X (e. g. , vision and audio data) generation, their use in time series generation remains limit. Existing approaches face two critical limitations: (1) reliance on domain-specific captions that generalize poorly, and (2) inability to generate time series of arbitrary length, limiting real-world use. In this work, we first introduce a new multimodal dataset containing over 600, 000 high-resolution text-time series pairs. Second, we propose Text-to-Series (T2S), a diffusion-based framework that bridges the gap between natural language and time series in a domain-agnostic manner. It employs a length-adaptive VAE to encode time series of varying lengths into consistent latent embeddings. On top of that, T2S effectively aligns textual representations with latent embeddings by utilizing Flow Matching and employing DiT as the denoiser. We train T2S in an interleaved paradigm across multiple lengths, allowing it to generate sequences of arbitrary lengths. Extensive evaluations demonstrate that T2S achieves state-of-the-art performance across 13 datasets spanning 12 domains.

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ICML Conference 2024 Conference Paper

Adversarial Attacks on Combinatorial Multi-Armed Bandits

Rishab Balasubramanian
Jiawei Li
Prasad Tadepalli
Huazheng Wang
Qingyun Wu
Haoyu Zhao

We study reward poisoning attacks on Combinatorial Multi-armed Bandits (CMAB). We first provide a sufficient and necessary condition for the attackability of CMAB, a notion to capture the vulnerability and robustness of CMAB. The attackability condition depends on the intrinsic properties of the corresponding CMAB instance such as the reward distributions of super arms and outcome distributions of base arms. Additionally, we devise an attack algorithm for attackable CMAB instances. Contrary to prior understanding of multi-armed bandits, our work reveals a surprising fact that the attackability of a specific CMAB instance also depends on whether the bandit instance is known or unknown to the adversary. This finding indicates that adversarial attacks on CMAB are difficult in practice and a general attack strategy for any CMAB instance does not exist since the environment is mostly unknown to the adversary. We validate our theoretical findings via extensive experiments on real-world CMAB applications including probabilistic maximum covering problem, online minimum spanning tree, cascading bandits for online ranking, and online shortest path.

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IJCAI Conference 2024 Conference Paper

GladCoder: Stylized QR Code Generation with Grayscale-Aware Denoising Process

Yuqiu Xie
Bolin Jiang
Jiawei Li
Naiqi Li
Bin Chen
Tao Dai
Yuang Peng
Shu-Tao Xia

Traditional QR codes consist of a grid of black-and-white square modules, which lack aesthetic appeal and meaning for human perception. This has motivated recent research to beautify the visual appearance of QR codes. However, there exists a trade-off between the visual quality and scanning-robustness of the image, causing outputs of previous works are simple and of low quality to ensure scanning-robustness. In this paper, we introduce a novel approach GladCoder to generate stylized QR codes that are personalized, natural, and text-driven. Its pipeline includes a Depth-guided Aesthetic QR code Generator (DAG) to improve quality of image foreground, and a GrayscaLe-Aware Denoising (GLAD) process to enhance scanning-robustness. The overall pipeline is based on diffusion models, which allow users to create stylized QR images from a textual prompt to describe the image and a textual input to be encoded. Experiments demonstrate that our method can generate stylized QR code with appealing perception details, while maintaining robust scanning reliability under real world applications.

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EAAI Journal 2024 Journal Article

Improving semantic similarity computation via subgraph feature fusion based on semantic awareness

Yuanfei Deng
Wen Bai
Jiawei Li
Shun Mao
Yuncheng Jiang

Semantic similarity is a critical aspect of natural language processing, as it evaluates the degree of similarity within a knowledge graph. Various computational methods, including distance-based and feature-based approaches, have been proposed to accurately measure this similarity. While existing methods can leverage diverse features within heterogeneous knowledge graphs, representing the overall structure, which encompasses a wide array of heterogeneous elements such as abstract descriptions and hidden relationships, remains challenging. To address the aforementioned challenges, our approach begins by employing the same text embedding method to map both abstract and category features into a unified vector space. We then extract features from DBpedia to construct concept and category graphs. Subsequently, we introduce a k -truss method based on semantic awareness within the DBpedia Concept Graph. This method identifies the significance of neighbouring concept nodes and assigns varying weights to enhance the representation of abstract features. Additionally, we propose a k -core method based on semantic awareness within the DBpedia Category Graph. This method identifies the importance of neighbouring category nodes and assigns different weights to enhance the representation of category features. Finally, we employ a hybrid weighting approach based on a feature fusion model to calculate semantic similarity. Experimental results demonstrate that our methods achieve a 5. 33% improvement compared to existing approaches.

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NeurIPS Conference 2023 Conference Paper

A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective

Chenhang Cui
Yazhou Ren
Jingyu Pu
Jiawei Li
Xiaorong Pu
Tianyi Wu
Yutao Shi
Lifang He

Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources. However, existing methods primarily focus on acquiring consistent information while often neglecting the issue of redundancy across multiple views. This study presents a new approach called Sufficient Multi-View Clustering (SUMVC) that examines the multi-view clustering framework from an information-theoretic standpoint. Our proposed method consists of two parts. Firstly, we develop a simple and reliable multi-view clustering method SCMVC (simple consistent multi-view clustering) that employs variational analysis to generate consistent information. Secondly, we propose a sufficient representation lower bound to enhance consistent information and minimise unnecessary information among views. The proposed SUMVC method offers a promising solution to the problem of multi-view clustering and provides a new perspective for analyzing multi-view data. To verify the effectiveness of our model, we conducted a theoretical analysis based on the Bayes Error Rate, and experiments on multiple multi-view datasets demonstrate the superior performance of SUMVC.

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AAMAS Conference 2023 Conference Paper

Cedric: A Collaborative DDoS Defense System Using Credit

Jiawei Li
Hui Wang
Jilong Wang

Distributed denial of service (DDoS) is one of the most common and damaging cyber attacks, and its impact grows rapidly with the massive use of Internet. Collaborative DDoS defense across countries enables faster and more efficient DDoS attack mitigation. Collaboration requires countries that are not target victims to help detect and block the malicious flow, but selfish countries may refuse to do so because lacking individual gain compared with individual cost. In this paper, we model a stochastic game where selfish countries interact repeatedly and form coalitions to defend DDoS attacks. We design a multi-agent system, Cedric, to simulate and solve this complex stochastic game. Each agent adopts Q-learning to find their long-term optimal strategies, and credits are used to encourage efficient collaboration. The Shapley Value based reward assignment of Cedric satisfies several desired properties about fairness and stability. Simulations with trace data of over 7 years’ global DDoS attacks support the superiority of Cedric empirically.

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NeurIPS Conference 2023 Conference Paper

Complexity Matters: Rethinking the Latent Space for Generative Modeling

Tianyang Hu
Fei Chen
Haonan Wang
Jiawei Li
Wenjia Wang
Jiacheng Sun
Zhenguo Li

In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e. g. , Stable Diffusion models the latent space induced by an encoder and generates images through a paired decoder. Although the selection of the latent space is empirically pivotal, determining the optimal choice and the process of identifying it remain unclear. In this study, we aim to shed light on this under-explored topic by rethinking the latent space from the perspective of model complexity. Our investigation starts with the classic generative adversarial networks (GANs). Inspired by the GAN training objective, we propose a novel "distance" between the latent and data distributions, whose minimization coincides with that of the generator complexity. The minimizer of this distance is characterized as the optimal data-dependent latent that most effectively capitalizes on the generator's capacity. Then, we consider parameterizing such a latent distribution by an encoder network and propose a two-stage training strategy called Decoupled Autoencoder (DAE), where the encoder is only updated in the first stage with an auxiliary decoder and then frozen in the second stage while the actual decoder is being trained. DAE can improve the latent distribution and as a result, improve the generative performance. Our theoretical analyses are corroborated by comprehensive experiments on various models such as VQGAN and Diffusion Transformer, where our modifications yield significant improvements in sample quality with decreased model complexity.

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AAAI Conference 2023 Conference Paper

DAMix: Exploiting Deep Autoregressive Model Zoo for Improving Lossless Compression Generalization

Qishi Dong
Fengwei Zhou
Ning Kang
Chuanlong Xie
Shifeng Zhang
Jiawei Li
Heng Peng
Zhenguo Li

Deep generative models have demonstrated superior performance in lossless compression on identically distributed data. However, in real-world scenarios, data to be compressed are of various distributions and usually cannot be known in advance. Thus, commercially expected neural compression must have strong Out-of-Distribution (OoD) generalization capabilities. Compared with traditional compression methods, deep learning methods have intrinsic flaws for OoD generalization. In this work, we make the attempt to tackle this challenge via exploiting a zoo of Deep Autoregressive models (DAMix). We build a model zoo consisting of autoregressive models trained on data from diverse distributions. In the test phase, we select useful expert models by a simple model evaluation score and adaptively aggregate the predictions of selected models. By assuming the outputs from each expert model are biased in favor of their training distributions, a von Mises-Fisher based filter is proposed to recover the value of unbiased predictions that provides more accurate density estimations than a single model. We derive the posterior of unbiased predictions as well as concentration parameters in the filter, and a novel temporal Stein variational gradient descent for sequential data is proposed to adaptively update the posterior distributions. We evaluate DAMix on 22 image datasets, including in-distribution and OoD data, and demonstrate that making use of unbiased predictions has up to 45.6% improvement over the single model trained on ImageNet.

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AAAI Conference 2023 Conference Paper

Fair-CDA: Continuous and Directional Augmentation for Group Fairness

Rui Sun
Fengwei Zhou
Zhenhua Dong
Chuanlong Xie
Lanqing Hong
Jiawei Li
Rui Zhang
Zhen Li

In this work, we propose Fair-CDA, a fine-grained data augmentation strategy for imposing fairness constraints. We use a feature disentanglement method to extract the features highly related to the sensitive attributes. Then we show that group fairness can be achieved by regularizing the models on transition paths of sensitive features between groups. By adjusting the perturbation strength in the direction of the paths, our proposed augmentation is controllable and auditable. To alleviate the accuracy degradation caused by fairness constraints, we further introduce a calibrated model to impute labels for the augmented data. Our proposed method does not assume any data generative model and ensures good generalization for both accuracy and fairness. Experimental results show that Fair-CDA consistently outperforms state-of-the-art methods on widely-used benchmarks, e.g., Adult, CelebA and MovieLens. Especially, Fair-CDA obtains an 86.3% relative improvement for fairness while maintaining the accuracy on the Adult dataset. Moreover, we evaluate Fair-CDA in an online recommendation system to demonstrate the effectiveness of our method in terms of accuracy and fairness.

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AAAI Conference 2023 Conference Paper

Improving Robotic Tactile Localization Super-resolution via Spatiotemporal Continuity Learning and Overlapping Air Chambers

Xuyang Li
Yipu Zhang
Xuemei Xie
Jiawei Li
Guangming Shi

Human hand has amazing super-resolution ability in sensing the force and position of contact and this ability can be strengthened by practice. Inspired by this, we propose a method for robotic tactile super-resolution enhancement by learning spatiotemporal continuity of contact position and a tactile sensor composed of overlapping air chambers. Each overlapping air chamber is constructed of soft material and seals the barometer inside to mimic adapting receptors of human skin. Each barometer obtains the global receptive field of the contact surface with the pressure propagation in the hyperelastic seal overlapping air chambers. Neural networks with causal convolution are employed to resolve the pressure data sampled by barometers and to predict the contact position. The temporal consistency of spatial position contributes to the accuracy and stability of positioning. We obtain an average super-resolution (SR) factor of over 2500 with only four physical sensing nodes on the rubber surface (0.1 mm in the best case on 38 × 26 mm²), which outperforms the state-of-the-art. The effect of time series length on the location prediction accuracy of causal convolution is quantitatively analyzed in this article. We show that robots can accomplish challenging tasks such as haptic trajectory following, adaptive grasping, and human-robot interaction with the tactile sensor. This research provides new insight into tactile super-resolution sensing and could be beneficial to various applications in the robotics field.

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YNIMG Journal 2023 Journal Article

Leading and following: Noise differently affects semantic and acoustic processing during naturalistic speech comprehension

Xinmiao Zhang
Jiawei Li
Zhuoran Li
Bo Hong
Tongxiang Diao
Xin Ma
Guido Nolte
Andreas K. Engel

Despite the distortion of speech signals caused by unavoidable noise in daily life, our ability to comprehend speech in noisy environments is relatively stable. However, the neural mechanisms underlying reliable speech-in-noise comprehension remain to be elucidated. The present study investigated the neural tracking of acoustic and semantic speech information during noisy naturalistic speech comprehension. Participants listened to narrative audio recordings mixed with spectrally matched stationary noise at three signal-to-ratio (SNR) levels (no noise, 3 dB, -3 dB), and 60-channel electroencephalography (EEG) signals were recorded. A temporal response function (TRF) method was employed to derive event-related-like responses to the continuous speech stream at both the acoustic and the semantic levels. Whereas the amplitude envelope of the naturalistic speech was taken as the acoustic feature, word entropy and word surprisal were extracted via the natural language processing method as two semantic features. Theta-band frontocentral TRF responses to the acoustic feature were observed at around 400 ms following speech fluctuation onset over all three SNR levels, and the response latencies were more delayed with increasing noise. Delta-band frontal TRF responses to the semantic feature of word entropy were observed at around 200 to 600 ms leading to speech fluctuation onset over all three SNR levels. The response latencies became more leading with increasing noise and decreasing speech comprehension and intelligibility. While the following responses to speech acoustics were consistent with previous studies, our study revealed the robustness of leading responses to speech semantics, which suggests a possible predictive mechanism at the semantic level for maintaining reliable speech comprehension in noisy environments.

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AAAI Conference 2023 Conference Paper

Learned Distributed Image Compression with Multi-Scale Patch Matching in Feature Domain

Yujun Huang
Bin Chen
Shiyu Qin
Jiawei Li
Yaowei Wang
Tao Dai
Shu-Tao Xia

Beyond achieving higher compression efficiency over classical image compression codecs, deep image compression is expected to be improved with additional side information, e.g., another image from a different perspective of the same scene. To better utilize the side information under the distributed compression scenario, the existing method only implements patch matching at the image domain to solve the parallax problem caused by the difference in viewing points. However, the patch matching at the image domain is not robust to the variance of scale, shape, and illumination caused by the different viewing angles, and can not make full use of the rich texture information of the side information image. To resolve this issue, we propose Multi-Scale Feature Domain Patch Matching (MSFDPM) to fully utilizes side information at the decoder of the distributed image compression model. Specifically, MSFDPM consists of a side information feature extractor, a multi-scale feature domain patch matching module, and a multi-scale feature fusion network. Furthermore, we reuse inter-patch correlation from the shallow layer to accelerate the patch matching of the deep layer. Finally, we find that our patch matching in a multi-scale feature domain further improves compression rate by about 20% compared with the patch matching method at image domain.

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NeurIPS Conference 2023 Conference Paper

Red Teaming Deep Neural Networks with Feature Synthesis Tools

Stephen Casper
Tong Bu
Yuxiao Li
Jiawei Li
Kevin Zhang
Kaivalya Hariharan
Dylan Hadfield-Menell

Interpretable AI tools are often motivated by the goal of understanding model behavior in out-of-distribution (OOD) contexts. Despite the attention this area of study receives, there are comparatively few cases where these tools have identified previously unknown bugs in models. We argue that this is due, in part, to a common feature of many interpretability methods: they analyze model behavior by using a particular dataset. This only allows for the study of the model in the context of features that the user can sample in advance. To address this, a growing body of research involves interpreting models using feature synthesis methods that do not depend on a dataset. In this paper, we benchmark the usefulness of interpretability tools for model debugging. Our key insight is that we can implant human-interpretable trojans into models and then evaluate these tools based on whether they can help humans discover them. This is analogous to finding OOD bugs, except the ground truth is known, allowing us to know when a user's interpretation is correct. We make four contributions. (1) We propose trojan discovery as an evaluation task for interpretability tools and introduce a benchmark with 12 trojans of 3 different types. (2) We demonstrate the difficulty of this benchmark with a preliminary evaluation of 16 state-of-the-art feature attribution/saliency tools. Even under ideal conditions, given direct access to data with the trojan trigger, these methods still often fail to identify bugs. (3) We evaluate 7 feature-synthesis methods on our benchmark. (4) We introduce and evaluate 2 new variants of the best-performing method from the previous evaluation.

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AAAI Conference 2021 Conference Paper

Amodal Segmentation Based on Visible Region Segmentation and Shape Prior

Yuting Xiao
Yanyu Xu
Ziming Zhong
Weixin Luo
Jiawei Li
Shenghua Gao

Almost all existing amodal segmentation methods make the inferences of occluded regions by using features corresponding to the whole image. This is against the human’s amodal perception, where human uses the visible part and the shape prior knowledge of the target to infer the occluded region. To mimic the behavior of the human and solve the ambiguity in the learning, we propose a framework, it firstly estimates a coarse visible mask and a coarse amodal mask. Then based on the coarse prediction, our model infers the amodal mask by concentrating on the visible region and utilizing the shape prior in the memory. In this way, features corresponding to background and occlusion can be suppressed for amodal mask estimation. Consequently, the amodal mask would not be affected by the occlusion when given the same visible regions. The leverage of shape prior makes the amodal mask estimation more robust and reasonable. Our proposed model is evaluated on three datasets. Experiments show that our proposed model outperforms existing state-of-the-art methods. The visualization of shape prior indicates that the category-specific feature in the codebook has certain interpretability. The code is available at https: //github. com/YutingXiao/Amodal-Segmentation- Based-on-Visible-Region-Segmentation-and-Shape-Prior.

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AAAI Conference 2021 Conference Paper

MetaAugment: Sample-Aware Data Augmentation Policy Learning

Fengwei Zhou
Jiawei Li
Chuanlong Xie
Fei Chen
Lanqing Hong
Rui Sun
Zhenguo Li

Automated data augmentation has shown superior performance in image recognition. Existing works search for datasetlevel augmentation policies without considering individual sample variations, which are likely to be sub-optimal. On the other hand, learning different policies for different samples naively could greatly increase the computing cost. In this paper, we learn a sample-aware data augmentation policy efficiently by formulating it as a sample reweighting problem. Specifically, an augmentation policy network takes a transformation and the corresponding augmented image as inputs, and outputs a weight to adjust the augmented image loss computed by a task network. At training stage, the task network minimizes the weighted losses of augmented training images, while the policy network minimizes the loss of the task network on a validation set via meta-learning. We theoretically prove the convergence of the training procedure and further derive the exact convergence rate. Superior performance is achieved on widely-used benchmarks including CIFAR-10/100, Omniglot, and ImageNet.

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NeurIPS Conference 2021 Conference Paper

MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps

Awais Muhammad
Fengwei Zhou
Chuanlong Xie
Jiawei Li
Sung-Ho Bae
Zhenguo Li

Deep neural networks are susceptible to adversarially crafted, small, and imperceptible changes in the natural inputs. The most effective defense mechanism against these examples is adversarial training which constructs adversarial examples during training by iterative maximization of loss. The model is then trained to minimize the loss on these constructed examples. This min-max optimization requires more data, larger capacity models, and additional computing resources. It also degrades the standard generalization performance of a model. Can we achieve robustness more efficiently? In this work, we explore this question from the perspective of knowledge transfer. First, we theoretically show the transferability of robustness from an adversarially trained teacher model to a student model with the help of mixup augmentation. Second, we propose a novel robustness transfer method called Mixup-Based Activated Channel Maps (MixACM) Transfer. MixACM transfers robustness from a robust teacher to a student by matching activated channel maps generated without expensive adversarial perturbations. Finally, extensive experiments on multiple datasets and different learning scenarios show our method can transfer robustness while also improving generalization on natural images.

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AAAI Conference 2021 Conference Paper

On the Approximation of Nash Equilibria in Sparse Win-Lose Multi-player Games

Zhengyang Liu
Jiawei Li
Xiaotie Deng

A polymatrix game is a multi-player game over n players, where each player chooses a pure strategy from a list of its own pure strategies. The utility of each player is a sum of payoffs it gains from the two player’s game from all its neighbors, under its chosen strategy and that of its neighbor. As a natural extension to two-player games (a. k. a. bimatrix games), polymatrix games are widely used for multi-agent games in real world scenarios. In this paper we show that the problem of approximating a Nash equilibrium in a polymatrix game within the polynomial precision is PPAD-hard, even in sparse and win-lose ones. This result further challenges the predictability of Nash equilibria as a solution concept in the multi-agent setting. We also propose a simple and efficient algorithm, when the game is further restricted. Together, we establish a new dichotomy theorem for this class of games. It is also of independent interest for exploring the computational and structural properties in Nash equilibria.

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JBHI Journal 2020 Journal Article

DeepAVP: A Dual-Channel Deep Neural Network for Identifying Variable-Length Antiviral Peptides

Jiawei Li
Yuqian Pu
Jijun Tang
Quan Zou
Fei Guo

Antiviral peptides (AVPs) have been experimentally verified to block virus into host cells, which have antiviral activity with decapeptide amide. Therefore, utilization of experimentally validated antiviral peptides is a potential alternative strategy for targeting medically important viruses. In this article, we propose a dual-channel deep neural network ensemble method for analyzing variable-length antiviral peptides. The LSTM channel can capture long-term dependencies for effectively studying original variable-length sequence data. The CONV channel can build dynamic neural network for analyzing the local evolution information. Also, our model can fine-tune the substitution matrix for specifically functional peptides. Applying it to a novel experimentally verified dataset, our AVPs predictor, DeepAVP, demonstrates state-of-the-art performance of $\text{92. 4}\%$ accuracy and 0. 85 MCC, which is far better than existing prediction methods for identifying antiviral peptides. Therefore, DeepAVP, web server for predicting the effective AVPs, would make significantly contributions to peptide-based antiviral research.

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IJCAI Conference 2019 Conference Paper

Variation Generalized Feature Learning via Intra-view Variation Adaptation

Jiawei Li
Mang Ye
Andy Jinhua Ma
Pong C Yuen

This paper addresses the variation generalized feature learning problem in unsupervised video-based person re-identification (re-ID). With advanced tracking and detection algorithms, large-scale intra-view positive samples can be easily collected by assuming that the image frames within the tracking sequence belong to the same person. Existing methods either directly use the intra-view positives to model cross-view variations or simply minimize the intra-view variations to capture the invariant component with some discriminative information loss. In this paper, we propose a Variation Generalized Feature Learning (VGFL) method to learn adaptable feature representation with intra-view positives. The proposed method can learn a discriminative re-ID model without any manually annotated cross-view positive sample pairs. It could address the unseen testing variations with a novel variation generalized feature learning algorithm. In addition, an Adaptability-Discriminability (AD) fusion method is introduced to learn adaptable video-level features. Extensive experiments on different datasets demonstrate the effectiveness of the proposed method.

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AAAI Conference 2018 Conference Paper

Hierarchical Discriminative Learning for Visible Thermal Person Re-Identification

Mang Ye
Xiangyuan Lan
Jiawei Li
Pong Yuen

Person re-identiﬁcation is widely studied in visible spectrum, where all the person images are captured by visible cameras. However, visible cameras may not capture valid appearance information under poor illumination conditions, e. g, at night. In this case, thermal camera is superior since it is less dependent on the lighting by using infrared light to capture the human body. To this end, this paper investigates a cross-modal re-identiﬁcation problem, namely visible-thermal person reidentiﬁcation (VT-REID). Existing cross-modal matching methods mainly focus on modeling the cross-modality discrepancy, while VT-REID also suffers from cross-view variations caused by different camera views. Therefore, we propose a hierarchical cross-modality matching model by jointly optimizing the modality-speciﬁc and modality-shared metrics. The modality-speciﬁc metrics transform two heterogenous modalities into a consistent space that modality-shared metric can be subsequently learnt. Meanwhile, the modalityspeciﬁc metric compacts features of the same person within each modality to handle the large intra-modality intra-person variations (e. g. viewpoints, pose). Additionally, an improved two-stream CNN network is presented to learn the multimodality sharable feature representations. Identity loss and contrastive loss are integrated to enhance the discriminability and modality-invariance with partially shared layer parameters. Extensive experiments illustrate the effectiveness and robustness of the proposed method.

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ICRA Conference 2004 Conference Paper

Calibrating Human Hand for Teleoperating the HIT/DLR Hand

Haiying Hu
Xiaohui Gao
Jiawei Li
Jie Wang
Hong Liu 0002

Using human action to guide robot execution can greatly reduce the planning complexity. We calibrate a human hand model and map its motion to a four-finger dexterous robot hand. The parameters of human hand model are determined by open-loop kinematic calibration method based on a vision system. We analyze the kinematic difference between the human hand and dexterous robot hand, and present a modified fingertip mapping to solve the partial overlap of the fingertip workspaces. 3D graphic simulation and manipulation experiments show that the accuracy of the human hand model and the mapping method are sufficiently precise for teleoperation tasks.

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ICRA Conference 2003 Conference Paper

A new algorithm for three-finger force-closure grasp of polygonal objects

Jiawei Li
Minghe Jin
Hong Liu 0002

We prove a new necessary and sufficient condition for 2D three-finger equilibrium grasps and implement a geometrical algorithm for computing force-closure grasps of polygonal objects in this article. The algorithm is quite simple and only needs some algebraic calculations. An easily computable measure of how far a grasp is from losing force-closure is provided as well. Finally, we implement the algorithm and demonstrate its usefulness by an example.

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