[Conference Paper][Journal Article]

Conference Paper

[AAAI] [AAMAS] [AISTATS] [ASE] [COLT] [CVPR] [ECAI] [ECCV] [ECML] [GECCO] [ICLR] [ICML] [IJCAI] [KDD] [MICCAI] [NeurIPS] [PAKDD] [RecSys] [UAI] [WWW]

AAAI

• C.-X. Gao, C. Wu, M. Cao, R. Kong, Z. Zhang, and Y. Yu. ACT: Empowering decision transformer with dynamic programming via advantage conditioning. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

• J. Zhou, K. Zhu, and J. Wu. All You Need in Knowledge Distillation Is a Tailored Coordinate System. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

• Y. Liu, Z. Xie, and M. Li. AUC Optimization from Multiple Unlabeled Datasets. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

• L. Tao, Y.-X. Huang, W.-Z. Dai, and Y. Jiang. Deciphering Raw Data in Neuro-Symbolic Learning with Provable Guarantees. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

• M. Fu, K. Zhu, and J. Wu. DTL: Disentangled Transfer Learning for Visual Recognition. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

• L.-F. Li, P. Zhao, and Z.-H. Zhou. Dynamic Regret of Adversarial MDPs with Unknown Transition and Linear Function Approximation. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

• H. Lin, H. Wu, J. Zhang, Y. Sun, J. Ye, and Y. Yu. Episodic return decomposition by difference of implicitly assigned sub-trajectory reward. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

• C. Chen, J. Xu, W. Liao, H. Ding, Z. Zhang, Y. Yu, and R. Zhao. Focus-Then-Decide: Segmentation-assisted reinforcement learning. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

• R. Zhou, C.-X. Gao, Z. Zhang, and Y. Yu. Generalizable task representation learning for offline meta-reinforcement learning with data limitations. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

• H.-K. Zhang, Y.-G. Zhang, Z. Zhou, and Y.-F. Li. HONGAT: Graph Attention Networks in the Presence of High-Order Neighbors. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

• E.-H. Gao, Y.-X. Huang, W.-C. Hu, X.-H. Zhu, and W.-Z. Dai. Knowledge-Enhanced Historical Document Segmentation and Recognition. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

• X.-D. Bi, S.-Q. Zhang, and Y. Jiang. MEPSI: An MDL-based Ensemble Pruning Approach with Structural Information. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

• Y. Wang, W. Yang, W. Jiang, S. Lu, B. Wang, H. Tang, Y. Wan, and L. Zhang. Non-stationary Projection-Free Online Learning with Dynamic and Adaptive Regret Guarantees. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

• D.-C. Zhang, Z. Zhou, and Y.-F. Li. Robust Test-Time Adaptation for Zero-Shot Prompt Tuning. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

• X.-W. Yang, J.-J. Shao, W.-W. Tu, Y.-F. Li, W.-Z. Dai, and Z.-H. Zhou. Safe Abductive Learning in the Presence of Inaccurate Rules. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

• X. Huang, L. Song, K. Xue, and C. Qian. Stochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density Estimation. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

• J.-D. Liu, Z.-H. Tan, and Z.-H. Zhou. Towards Making Learnware Specification and Market Evolvable. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

• T. Lu, C. Bian, and C. Qian. Towards Running Time Analysis of Interactive Multi-objective Evolutionary Algorithms. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

• H. Yu, Y. Zhou, B. Chen, Z. Yang, S. Li, Y. Li, and J. Wu. Treasures in Discarded Weights for LLM Quantization. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

AAMAS

• C. Guan, R. Xue, Z. Zhang, L. Li, Y.-C. Li, L. Yuan, and Y. Yu. Cost-Aware Offline Safe Meta Reinforcement Learning With Robust In-Distribution Online Task Adaptation. In: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS'24), 2024.

• C. Chen, D. Wang, F. Mao, J. Xu, Z. Zhang, and Y. Yu. Deep Anomaly Detection via Active Anomaly Search. In: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS'24), 2024.

• C. Jia, F. Zhang, Y.-C. Li, C.-X. Gao, X.-H. Liu, L. Yuan, Z. Zhang, and Y. Yu. Disentangling Policy from Offline Task Representation Learning via Adversarial Data Augmentation. In: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS'24), 2024.

• R. Chen, X.-H. Liu, T.-S. Liu, S. Jiang, F. Xu, and Y. Yu. Foresight Distribution Adjustment for Off-Policy Reinforcement Learning. In: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS'24), 2024.

AISTATS

• L.-F. Li, P. Zhao, and Z.-H. Zhou. Improved Algorithm for Adversarial Linear Mixture MDPs with Bandit Feedback and Unknown Transition. In: Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS'24), 2024.

ASE

• Y.-L. Du, H. Sun, and M. Li. A Joint Learning Model with Variational Interaction for Multilingual Program Translation. In: Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering (ASE'24), 2024.

COLT

• Y. Wan, T. Wei, M. Song, and L. Zhang. Nearly Optimal Regret for Decentralized Online Convex Optimization. In: Proceedings of the 37th Annual Conference on Learning Theory (COLT'24), 2024.

CVPR

• H. Zhang, Y. Zhou, and G.-H. Wang. Dense Vision Transformer Compression with Few Samples. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'24), 2024.

• D.-W. Zhou, H.-L. Sun, Y.-H. He, and D.-C. Zhan. Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'24), 2024.

• M. Fu, K. Zhu, and G.-H. Wang. Instance-based Max-margin for Practical Few-shot Recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'24), 2024.

• C. Yi, R. Lu, D.-C. Zhan, and H.-J. Ye. Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'24), 2024.

ECAI

• S.-H. Lyu, J.-H. Wu, Q.-C. Zheng, B. Ye. The Role of Depth, Width, and Tree Size in Expressiveness of Deep Forest. In: Proceedings of the 27th European Conference on Artificial Intelligence (ECAI'24), 2024.

ECCV

• K. Zhu, M. Fu, J. Shao, T. Liu, J. Wu. Rectify the Regression Bias in Long-Tailed Object Detection. In: Proceedings of the 18th European Conference on Computer Vision (ECCV'24), 2024.

ECML

• R. Xue, Z. Zhang, L. Li, F. Chen, Y.-C. Li, Y. Yu, and L. Yuan. Dynamics adaptive safe reinforcement learning with a misspecified simulator. In: Proceedings of the 35th European Conference on Machine Learning (ECML'24), 2024.

GECCO

• Y. Zhou*, H. Shang, Y.-C. Wu, and C. Qian. Instance-Label Based Multi-Label Active Learning by Evolutionary Multi-Objective Optimization. In: Proceedings of the 26th ACM Conference on Genetic and Evolutionary Computation (GECCO'24), 2024.

• Z. Lv, C. Bian, C. Qian, and Y. Sun. Runtime Analysis of Population-based Evolutionary Neural Architecture Search for a Binary Classification Problem. In: Proceedings of the 26th ACM Conference on Genetic and Evolutionary Computation (GECCO'24), 2024.

ICLR

• W. Zhang, J. Wang, and Y. Yu. Distributional Reinforcement Learning With Sample-Set Bellman Update. In: Proceedings of the 12th International Conference on Learning Representations (ICLR'24), 2024.

• Z. Zhang, Y. Sun, J. Ye, T.-S. Liu, J. Zhang, and Y. Yu. Flow to Better: Offline Preference-Based Reinforcement Learning via Preferred Trajectory Generation. In: Proceedings of the 12th International Conference on Learning Representations (ICLR'24), 2024.

• J.-C. Pang, P. Wang, K. Li, X.-H. Chen, J. Xu, Z. Zhang, and Y. Yu. Language Model Self-Improvement by Reinforcement Learning Contemplation. In: Proceedings of the 12th International Conference on Learning Representations (ICLR'24), 2024.

• C. Jia, C. Gao, H. Yin, F. Zhang, X.-H. Chen, T. Xu, L. Yuan, Z. Zhang, Z.-H. Zhou, and Y. Yu. Policy Rehearsing: Training Generalizable Policies for Reinforcement Learning. In: Proceedings of the 12th International Conference on Learning Representations (ICLR'24), 2024.

• L.-H. Jia, L.-Z. Guo, Z. Zhou, and Y.-F. Li. Realistic Evaluation of Semi-Supervised Learning Algorithms in Open Environments. In: Proceedings of the 12th International Conference on Learning Representations (ICLR'24), 2024.

• F.-M. Luo, T. Xu, X. Cao, and Y. Yu. Reward-Consistent Dynamics Models Are Strongly Generalizable for Offline Reinforcement Learning. In: Proceedings of the 12th International Conference on Learning Representations (ICLR'24), 2024.

• K. Xue, R.-J. Wang, P. Li, D. Li, J. Hao, and C. Qian. Sample-Efficient Quality-Diversity by Cooperative Coevolution. In: Proceedings of the 12th International Conference on Learning Representations (ICLR'24), 2024.

• Z. Li, T. Xu, and Y. Yu. When Is RL Better Than DPO in RLHF? A Representation and Optimization Perspective. In: Proceedings of the 12th International Conference on Learning Representations (ICLR'24), 2024.

ICML

• Y.-C. Wang, S.-H. Wan, L. Gan, S. Feng, and D.-C. Zhan. AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• H.-Y. He, H. Sun, Z. Xie, and M. Li. Ambiguity-Aware Abductive Learning. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• T.-Z. Wang, W.-B. Du, and Z.-H. Zhou. An Efficient Maximal Ancestral Graph Listing Algorithm. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• X.-W. Yang, W.-D. Wei, J.-J. Shao, Y.-F. Li, and Z.-H. Zhou. Analysis for Abductive Learning and Neural-Symbolic Reasoning Shortcuts. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• Y.-C. Wu, S.-H. Lyu, H. Shang, X. Wang, and C. Qian. Confidence-Aware Contrastive Learning for Selective Classification. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• X. Zhang, W. Qiu, Y.-C. Li, L. Yuan, C. Jia, Z. Zhang, and Y. Yu. Debiased Offline Representation Learning for Fast Online Adaptation in Non-Stationary Dynamics. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• Z. Zhou, M. Yang, J.-X. Shi, L.-Z. Guo, and Y.-F. Li. DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• X.-H. Chen, J. Ye, H. Zhao, Y.-C. Li, X.-H. Liu, H. Shi, Y.-Y. Xu, Z. Ye, S.-H. Yang, Y. Yu, A. Huang, K. Xu, and Z. Zhang. Deep Demonstration Tracing: Learning Generalizable Imitator for Runtime One-Shot Imitation. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• D. Yu, Y. Cai, W. Jiang, and L. Zhang. Efficient Algorithms for Empirical Group Distributionally Robust Optimization and Beyond. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• Y.-Y. Qian, P. Zhao, Y.-J. Zhang, M. Sugiyama, and Z.-H. Zhou. Efficient Non-Stationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• L. Zhang, H. Bai, W.-W. Tu, P. Yang, and Y. Hu. Efficient Stochastic Approximation of Minimax Excess Risk Optimization. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• X.-H. Liu, T.-S. Liu, S. Jiang, R. Chen, Z. Zhang, X. Chen, and Y. Yu. Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• L. Li, X.-C. Li, H.-J. Ye, and D.-C. Zhan. Enhancing Class-Imbalanced Learning with Pre-Trained Guidance Through Class-Conditional Knowledge Distillation. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• Y.-H. Yan, J. Wang, and P. Zhao. Handling Heterogeneous Curvatures in Bandit LQR Control. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• L. Liu, Y. Wang, and L. Zhang. High-Probability Bound for Non-Smooth Non-Convex Stochastic Optimization with Heavy Tails. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• J. Wang, M. Yu, P. Zhao, and Z.-H. Zhou. Learning with Adaptive Resource Allocation. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• X. Cao, F.-M. Luo, J. Ye, T. Xu, Z. Zhang, and Y. Yu. Limited Preference Aided Imitation Learning from Imperfect. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• J.-X. Shi, T. Wei, Z. Zhou, J.-J. Shao, X.-Y. Han, and Y.-F. Li. Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• B.-W. Tao, X.-C. Li, and D.-C. Zhan. MLI Formula: A Nearly Scale-Invariant Solution with Noise Perturbation. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• B. Zheng, D.-W. Zhou, H.-J. Ye, and D.-C. Zhan. Multi-Layer Rehearsal Feature Augmentation for Class-Incremental Learning. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• Y. Wan, C. Yao, M. Song, and L. Zhang. Non-Stationary Online Convex Optimization with Arbitrary Delays. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• K. Xue, R.-X. Tan, X. Huang, and C. Qian. Offline Multi-Objective Optimization. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• R. Chen, C. Jia, Z. Huang, T.-S. Liu, X.-H. Liu, and Y. Yu. Offline Transition Modeling via Contrastive Energy Learning. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• R. Chen, X.-H. Chen, Y. Sun, S. Xiao, M. Li, and Y. Yu. Policy-Conditioned Environment Models Are More Generalizable. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• W. Jiang, S. Yang, W. Yang, Y. Wang, Y. Wan, and L. Zhang. Projection-Free Variance Reduction Methods for Stochastic Constrained Multi-Level Compositional Optimization. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• R.-J. Wang, K. Xue, C. Guan, and C. Qian. Quality-Diversity with Limited Resources. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• Z. Li, T. Xu, Y. Zhang, Z. Lin, Y. Yu, R. Sun, and Z.-Q. Luo. ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• W.-S. Fan, S. Lu, X.-C. Li, D.-C. Zhan, and L. Gan. Revisit the Essence of Distilling Knowledge through Calibration. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• S.-H. Wan, Z.-Y. Chen, S. Feng, L. Gan, and D.-C. Zhan. SeMOPO: Learning High-Quality Model and Policy from Low-Quality Offline Visual Datasets. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• L. Han, H.-J. Ye, and D.-C. Zhan. SIN: Selective and Interpretable Normalization for Long-Term Time Series Forecasting. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• W. Yang, W. Jiang, Y. Wang, P. Yang, Y. Hu, and L. Zhang. Small-Loss Adaptive Regret for Online Convex Optimization. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• J.-P. Jiang, H.-J. Ye, L. Wang, Y. Yang, Y. Jiang, and D.-C. Zhan. Tabular Insights, Visual Impacts: Transferring Expertise from Tables to Images. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

• Z.-H. Qiu, S. Guo, M. Xu, T. Zhao, L. Zhang, and T. Yang. To Cool or Not to Cool? Temperature Network Meets Large Foundation Models via DRO. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), 2024.

IJCAI

• H.-K. Zhang, Y.-G. Zhang, Z. Zhou, and Y.-F. Li. LSPAN: Spectrally Localized Augmentation for Graph Consistency Learning. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), 2024.

• C. Bian, S. Ren, M. Li, and C. Qian. An Archive Can Bring Provable Speed-Ups in Multi-Objective Evolutionary Algorithms. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), 2024.

• D.-W. Zhou, H.-L. Sun, Y.-H. He, and D.-C. Zhan. Continual Learning with Pre-Trained Models: A Survey. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), 2024.

• L. Zhang, Z.-H. Tian, W. Zhou, and W. Wang. Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), 2024.

• S. Ren, Z. Qiu, C. Bian, M. Li, and C. Qian. Maintaining Diversity Provably Helps in Evolutionary Multimodal Optimization. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), 2024.

• S.-H. Wan, H.-H. Sun, L. Gan, and D.-C. Zhan. MOSER: Learning Sensory Policy for Task-Specific Viewpoint via View-Conditional World Model. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), 2024.

• D.-X. Liu, Y.-H. Xu, and C. Qian. Peptide Vaccine Design by Evolutionary Multi-Objective Optimization. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), 2024.

• C. Qian, K. Xue, and R.-J. Wang. Quality-Diversity Algorithms Can Provably Be Helpful for Optimization. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), 2024.

• C. Gao, H. Shang, K. Xue, D. Li, and C. Qian. Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), 2024.

KDD

• Z.-H. Tan, J.-D. Liu, X.-D. Bi, P. Tan, Q.-C. Zheng, H.-T. Liu, Y. Xie, X.-C. Zou, Y. Yu, and Z.-H. Zhou. Beimingwu: A Learnware Dock System. In: Proceedings of the 30th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'24), 2024.

• J.-X. Shi, C. Zhang, T. Wei, and Y.-F. Li. Efficient and Long-Tailed Generalization for Pre-Trained Vision-Language Model. In: Proceedings of the 30th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'24), 2024.

• L. Ma, Z.-Y. Zhang, Y.-X. Ding, and Z.-H. Zhou. Handling Varied Objectives by Online Decision Making. In: Proceedings of the 30th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'24), 2024.

• J.-J. Shao, H.-S. Shi, L.-Z. Guo, and Y.-F. Li. Offline Imitation Learning with Model-Based Reverse Augmentation. In: Proceedings of the 30th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'24), 2024.

• H. Yu, M. Fu, J. Ding, Y. Zhou, and J. Wu. Unified Low-Rank Compression Framework for Click-Through Rate Prediction. In: Proceedings of the 30th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'24), 2024.

MICCAI

• Y. Shi, X. Tian, Y.-K. Wang, T. Zhang, B. Yao, H. Wang, Y. Shao, C. Wang, R. Zeng, and D.-C. Zhan. CS3: Cascade SAM for Sperm Segmentation. In: Proceedings of the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'24), 2024.

NeurIPS

• Y.-H. Yan, P. Zhao, and Z.-H. Zhou. A Simple and Optimal Approach for Universal Online Learning with Gradient Variations. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• W. Jiang, S. Yang, Y. Wang, and L. Zhang. Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• S. Chen, Y. Wang, Y.-F. Wu, Q.-G. Chen, Z. Xu, W. Luo, K. Zhang, and L. Zhang. Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• W.-B. Du, T. Qin, T.-Z. Wang, and Z.-H. Zhou. Avoiding Undesired Future with Minimal Cost in Non-Stationary Environments. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• C. Yi, Y.-H. He, D.-C. Zhan, and H.-J. Ye. Bridge the Modality and Capacity Gaps in Vision-Language Model Selection. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• J. Shao, K. Zhu, H. Zhang, and J. Wu. DiffuLT: Diffusion for Long-tail Recognition Without External Knowledge. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• F.-M. Luo, Z. Tu, Z. Huang, and Y. Yu. Efficient recurrent off-policy RL requires a context-encoder-specific learning rate. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• W. Jiang, S. Yang, Y. Wang, and L. Zhang. Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• X.-C. Li, J.-L. Tang, B. Zhang, L. Li, and D.-C. Zhan. Exploring and Exploiting the Asymmetric Valley of Deep Neural Networks. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• Y.-F. Xie, P. Zhao, and Z.-H. Zhou. Gradient-Variation Online Learning under Generalized Smoothness. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• P. Tan, H.-T. Liu, Z.-H. Tan, and Z.-H. Zhou. Handling Learnwares from Heterogeneous Feature Spaces with Explicit Label Exploitation. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• Y. Wan, C. Yao, M. Song, and L. Zhang. Improved Regret for Bandit Convex Optimization with Delayed Feedback. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• J.-C. Pang, S.-H. Yang, K. Li, J. Zhang, X.-H. Chen, N. Tang, and Y. Yu. KALM: Knowledgeable agents by offline reinforcement learning from large language model rollouts. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• K.-C. Huang, S.-H. Wan, M.-H. Shao, H.-H. Sun, L. Gan, S. Feng, and D.-C. Zhan. Leveraging Separated World Model for Exploration in Visually Distracted Environments. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• S. Wang, K. Xue, L. Song, X. Huang, and C. Qian. Monte Carlo Tree Search based Space Transfer for Black Box Optimization. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• T. Jiang, L. Yuan, L. Li, C. Guan, Z. Zhang, and Y. Yu. Multi-agent domain calibration with a handful of offline data. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• L.-F. Li, P. Zhao, and Z.-H. Zhou. Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• Z. Li, Z. Zhou, Y. Yao, X. Zhang, Y.-F. Li, C. Cao, F. Yang, X. Ma. Neuro-Symbolic Data Generation for Math Reasoning. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• H.-Y. Lei, Z.-H. Tan, and Z.-H. Zhou. On the Ability of Developers' Training Data Preservation of Learnware. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• Y. Wang, S. Chen, W. Jiang, W. Yang, Y. Wan, and L. Zhang. Online Composite Optimization Between Stochastic and Adversarial Environments. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• Z. Xu and L. Zhang. Online Non-convex Learning in Dynamic Environments. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• T. Xu, Z. Zhang, R. Chen, Y. Sun, and Y. Yu. Provably and practically efficient adversarial imitation learning with general function approximation. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• L.-F. Li, Y.-J. Zhang, P. Zhao, and Z.-H. Zhou. Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• K. Xue, R.-T. Chen, X. Lin, Y. Shi, S. Kai, S. Xu, and C. Qian. Reinforcement Learning Policy as Macro Regulator Rather than Macro Placer. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• L. Han, X.-Y. Chen, H.-J. Ye, and D.-C. Zhan. SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• X.-H. Chen, Z. Wang, Y. Du, S. Jiang, M. Fang, Y. Yu, and J. Wang. Understanding, rehearsing, and introspecting: Learn a policy from textual tutorial books in football games. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• W. Yang, Y. Wang, P. Zhao, and L. Zhang. Universal Online Convex Optimization with 1 Projection per Round. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• H.-T. Li, T. Wei, C.-S. Li, J.-X. Shi, Y.-F. Li, and M.-L. Zhang. Vision-Language Models are Strong Noisy Label Detectors. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

• Y.-K. Zhang, S. Lu, Y. Li, Y. Q. Ma, Q.-G. Chen, Z. Xu, W.-H. Luo, K.-F. Zhang, D.-C. Zhan, and H.-J. Ye. Wings: Learning Multimodal LLMs without Text-only Forgetting. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.

PAKDD

• M.-J. Yuan, Z. Zou, and W. Gao. Bi-CryptoNets: Leveraging Different-Level Privacy for Encrypted Inference. In: Advances in Knowledge Discovery and Data Mining (PAKDD'24), 2024.

• Y. Shang, K. M. Ting, Z. Wang, and Y. Wang. Distributional Kernel: An Effective and Efficient Means for Trajectory Retrieval. In: Advances in Knowledge Discovery and Data Mining (PAKDD'24), 2024.

• L. Gong, H. Zhang, Z. Liu, K. M. Ting, Y. Cao, and Y. Zhu. Local Subsequence-Based Distribution for Time Series Clustering. In: Advances in Knowledge Discovery and Data Mining (PAKDD'24), 2024.

• Z.-H. Zhuang and L. Zhang. Soft Contrastive Learning for Implicit Feedback Recommendations. In: Advances in Knowledge Discovery and Data Mining (PAKDD'24), 2024.

PPSN

• S. Ren, C. Bian, M. Li, and C. Qian. A First Running Time Analysis of the Strength Pareto Evolutionary Algorithm 2 (SPEA2). In: Proceedings of the 18th International Conference on Parallel Problem Solving from Nature (PPSN'24), 2024.

• D.-X. Liu and C. Qian. Biased Pareto Optimization for Subset Selection with Dynamic Cost Constraints. In: Proceedings of the 18th International Conference on Parallel Problem Solving from Nature (PPSN'24), 2024.

RecSys

• Y. Liu, Q.-L. Jia, S.-T. Shi, C.-H. Wu, Z.-C. Du, Z. Xie, R.-M. Tang, M.-Y. Zhang, and M. Li. Ranking-Aware Unbiased Post-Click Conversion Rate Estimation via AUC Optimization on Entire Exposure Space. In: Proceedings of the 18th ACM Conference on Recommender Systems (RecSys'24), 2024.

UAI

• S.-L. Wu, L. Du, J.-Q. Yang, Y. Wang, D.-C. Zhan, S. Zhao, and Z. Sun. RE-SORT: Removing Spurious Correlation in Multilevel Interaction for CTR Prediction. In: Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence (UAI'24), 2024.

WWW

• W. Yang, Y. Jian, Y. Wang, S. Lu, L. Shen, B. Wang, H. Tang, and L. Zhang. Not All Embeddings Are Created Equal: Towards Robust Cross-Domain Recommendation via Contrastive Learning. In: Proceedings of the ACM Web Conference 2024 (WWW'24), 2024.

Top

Journal Article and Book

[A] [F] [I] [J] [M] [P] [S] [T] [计] [软]

ACM Transactions on Evolutionary Learning and Optimization

• S. Liu, N. Lu, W. Hong, C. Qian, and K. Tang. Effective and Imperceptible Adversarial Textual Attack via Multi-Objectivization. In: ACM Transactions on Evolutionary Learning and Optimization, 2024, 4(3): 16.

ACM Transactions on Knowledge Discovery from Data

• Y.-X. He, S.-H. Lyu, and Y. Jiang. Interpreting Deep Forest Through Feature Contribution and MDI Feature Importance. In: ACM Transactions on Knowledge Discovery from Data, 2024, in press.

• Z.-H. Guan, J.-Q. Yang, Y. Yang, H. Zhu, W. Li, and H. Xiong. JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer. In: ACM Transactions on Knowledge Discovery from Data, 2024, 19(1): 1-20.

• Y.-Y. Qian, Z.-Y. Zhang, P. Zhao, and Z.-H. Zhou. Learning with Asynchronous Labels. In: ACM Transactions on Knowledge Discovery from Data, 2024, 18(8): 1-27.

Advanced Science

• Y.-C. Xu, J.-Q. Yang, K. Fan, S. Wang, J. Wu, C. Zhang, D.-C. Zhan, W. J. P., B. Jin, J. Chen, and P. Wu. Physics-Informed Inverse Design of Multi-Bit Programmable Metasurfaces. In: Advanced Science, 2024, 11(41).

Artificial Intelligence

• K. M. Ting, T. Washio, Y. Zhu, Y. Xu, and K. Zhang. Is It Possible to Find the Single Nearest Neighbor of a Query in High Dimensions? In: Artificial Intelligence, 2024, 336: 104206.

• C. Bian, Y. Zhou, M. Li, and C. Qian. Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms. In: Artificial Intelligence, 2024, in press.

Frontiers of Computer Science

• Y.-X. Huang, W.-C. Hu, E.-H. Gao, and Y. Jiang. ABLkit: A Python Toolkit for Abductive Learning. In: Frontiers of Computer Science, 2024, 18(6): 186354.

• J.-Q. Yang, C. Dai, D. Ou, D.-S. Li, J. Huang, D.-C. Zhan, X.-Y. Zeng, and Y. Yang. COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual Recommendation. In: Frontiers of Computer Science, 2024, in press.

• Z. Zhou, Y.-X. Jin, and Y.-F. Li. RTS: Learning Robustly from Time Series Data with Noisy Label. In: Frontiers of Computer Science, 2024, 18(6): 186332.

• Z.-C. Lyu, X.-Y. Li, Z. Xie, and M. Li. Top Pass: Improve Code Generation by Pass@k-Maximized Code Ranking. In: Frontiers of Computer Science, 2024, in press.

• D.-W. Zhou, H.-J. Ye, Z.-H. Qi, and D.-C. Zhan. TV100: A TV Series Dataset that Pre-Trained CLIP Has Not Seen. In: Frontiers of Computer Science, 2024, 18: 185349.

Fundamental Research

• D.-X. Liu and C. Qian. Result Diversification with Negative Type Distances by Multi-Objective Evolutionary Algorithms. In: Fundamental Research, 2024, in press.

IEEE Transactions on Artificial Intelligence

• D.-X. Liu, Y.-R. Gu, C. Qian, X. Mu, and K. Tang. Migrant Resettlement by Evolutionary Multi-Objective Optimization. In: IEEE Transactions on Artificial Intelligence, 2024, 6(1): 51-65.

IEEE Transactions on Emerging Topics in Computational Intelligence

• Z. Lv, C. Qian, G. G. Yen, and Y. Sun. Analyzing the Expected Hitting Time of Evolutionary Computation-Based Neural Architecture Search Algorithms. In: IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, 8(6): 3899-3911.

IEEE Transactions on Evolutionary Computation

• Z. Lv, C. Qian, and Y. Sun. Benchmarking Analysis of Evolutionary Neural Architecture Search. In: IEEE Transactions on Evolutionary Computation, 2024, 28(6): 1659-1673.

• C. Qian. Can Evolutionary Clustering Have Theoretical Guarantees? In: IEEE Transactions on Evolutionary Computation, 2024, 28(5): 1220-1234.

• K. Xue, Y. Wang, C. Guan, L. Yuan, H. Fu, Q. Fu, C. Qian, and Y. Yu. Heterogeneous Multi-Agent Zero-Shot Coordination by Coevolution. In: IEEE Transactions on Evolutionary Computation, 2024, in press.

• L. Zhao, X. Huang, C. Qian, and Q. Zhang. Many-to-Few Decomposition: Linking R2-Based and Decomposition-Based Multiobjective Efficient Global Optimization Algorithms. In: IEEE Transactions on Evolutionary Computation, 2024, in press.

• Y.-R. Gu, C. Bian, M. Li, and C. Qian. Subset Selection for Evolutionary Multi-Objective Optimization. In: IEEE Transactions on Evolutionary Computation, 2024, 28(2): 403-417.

IEEE Transactions on Knowledge and Data Engineering

• X.-C. Li, S. Song, Y. Li, B. Li, Y. Shao, Y. Yang, and D.-C. Zhan. MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes. In: IEEE Transactions on Knowledge and Data Engineering, 2024, 36(11): 6560-6573.

• L. Han, H.-J. Ye, and D.-C. Zhan. The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting. In: IEEE Transactions on Knowledge and Data Engineering, 2024, 36(11): 7129-7142.

IEEE Transactions on Neural Networks and Learning Systems

• L. Ma, Y.-X. Ding, P. Zhao, and Z.-H. Zhou. Learning Objective Adaptation by Correlation-Based Model Reuse. In: IEEE Transactions on Neural Networks and Learning Systems, 2024, in press.

• S.-Q. Zhang, F. Wang, and F.-L. Fan. Neural Network Gaussian Processes by Increasing Depth. In: IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(2): 2881-2886.

• G. Li, P. Yang, C. Qian, R. Hong, and K. Tang. Stage-Wise Magnitude-Based Pruning for Recurrent Neural Networks. In: IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(2): 1666-1680.

• J.-H. Wu, S.-Q. Zhang, Y. Jiang, and Z.-H. Zhou. Theoretical Exploration of Flexible Transmitter Model. In: IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(3): 3674-3688.

IEEE Transactions on Pattern Analysis and Machine Intelligence

• H.-J. Ye, D.-W. Zhou, L. Hong, Z. Li, X.-S. Wei, and D.-C. Zhan. Contextualizing Meta-Learning via Learning to Decompose. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(1): 117-133.

• H.-J. Ye, L. Ming, D.-C. Zhan, and W.-L. Chao. Few-Shot Learning with a Strong Teacher. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(3): 1425-1440.

• G.-H. Wang and J. Wu. Practical Network Acceleration with Tiny Sets: Hypothesis, Theory, and Algorithm. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(12): 9272-9285.

• Y.-H. Cao and J. Wu. Tobias: A Random CNN Sees Objects. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(2): 1290-1304.

• Z. Xie, Y. Liu, H.-Y. He, M. Li, and Z.-H. Zhou. Weakly Supervised AUC Optimization: A Unified Partial AUC Approach. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(7): 4780-4795.

Information Sciences

• Y.-X. He, D.-X. Liu, S.-H. Lyu, C. Qian, and Z.-H. Zhou. Multi-Class Imbalance Problem: A Multi-Objective Solution. In: Information Sciences, 2024, 680: 121156.

Journal of Artificial Intelligence Research

• Y. Wang, Z. Wang, K. M. Ting, and Y. Shang. A Principled Distributional Approach to Trajectory Similarity Measurement and Its Application to Anomaly Detection. In: Journal of Artificial Intelligence Research, 2024, 79: 865-893.

• Y. Cao, Y. Zhu, F. D. Salim, H. X. Li, L. Yang, and G. Li. Detecting Change Intervals with Isolation Distributional Kernel. In: Journal of Artificial Intelligence Research, 2024, 79: 273-306.

Journal of Computer Research and Development

• J.-H. Wu and Y. Jiang. Universal Approximation and Approximation Advantages of Quaternion-Valued Neural Networks. In: Journal of Computer Research and Development, 2024, in press.

Journal of Machine Learning Research

• S.-Q. Zhang, J.-Y. Chen, J.-H. Wu, G. Zhang, H. Xiong, B. Gu, and Z.-H. Zhou. On the Intrinsic Structures of Spiking Neural Networks. In: Journal of Machine Learning Research, 2024, 25(194): 1-74.

Machine Learning

• L. Li, D.-C. Zhan, and X.-C. Li. Aligning Model Outputs for Class Imbalanced Non-IID Federated Learning. In: Machine Learning, 2024, 113: 1861-1884.

• Y.-X. He, Y.-C. Wu, C. Qian, and Z.-H. Zhou. Margin Distribution and Structural Diversity Guided Ensemble Pruning. In: Machine Learning, 2024, 113: 3545-3567.

• Z.-H. Qiu, Q. Hu, Y. Zhong, W.-W. Tu, L. Zhang, and T. Yang. Optimal Large-Scale Stochastic Optimization of NDCG Surrogates for Deep Learning. In: Machine Learning, 2024, 114(42).

• Y.-L. Du, Y. Li, and M. Li. Capturing the Context-Aware Code Change via Dynamic Control Flow Graph for Commit Message Generation. In: Machine Learning, 2024, 114(4): 94.

• H.-Y. He, W.-Z. Dai, and M. Li. Reduced Implication-Bias Logic Loss for Neuro-Symbolic Learning. In: Machine Learning, 2024, 113(6): 3357-3377.

• P. Tan, Z.-H. Tan, Y. Jiang, and Z.-H. Zhou. Towards Enabling Learnware to Handle Heterogeneous Feature Spaces. In: Machine Learning, 2024, 113(4): 1839–1860.

• T. Qin, L.-F. Li, T.-Z. Wang, and Z.-H. Zhou. Tracking Treatment Effect Heterogeneity in Evolving Environments. In: Machine Learning, 2024, 113(6): 3653–3673.

Pattern Recognition

• K. Zhu, Y.-Y. He, and J. Wu. Coarse Is Better? A New Pipeline Towards Self-Supervised Learning with Uncurated Images. In: Pattern Recognition, 2024, 161: 111324.

• H. Yu, Y. Du, and J. Wu. Reviving Undersampling for Long-Tailed Learning. In: Pattern Recognition, 2024, 161: 111200.

Proceedings of the National Academy of Sciences

• H. Hu, C. Qian, K. Xue, R. G. Jörgensen, M. Keiluweit, C. Liang, X. Zhu, J. Chen, Y. Sun, H. Ni, J. Ding, W. Huang, J. Mao, R.-X. Tan, J. Zhou, T. W. Crowther, Z.-H. Zhou, J. Zhang, and Y. Liang. Reducing the Uncertainty in Estimating Soil Microbial Derived Carbon Storage. In: Proceedings of the National Academy of Sciences (PNAS), 2024, 121(35): e2401916121.

Science China: Information Sciences

• L.-H. Jia, L.-Z. Guo, Z. Zhou, and Y.-F. Li. LAMDA-SSL: A Comprehensive Semi-Supervised Learning Toolkit. In: Science China: Information Sciences, 2024, in press.

• Z.-H. Zhou and Z.-H. Tan. Learnware: Small Models Do Big. In: Science China: Information Sciences, 2024, 67(1): 112102.

• J.-X. Shi, T. Wei, and Y.-F. Li. Residual Diverse Ensemble for Long-Tailed Multi-Label Text Classification. In: Science China: Information Sciences, 2024, in press.

The International Journal on Very Large Data Bases

• K. M. Ting, Z. Liu, L. Gong, and Y. Zhu. A New Distributional Treatment for Time Series Anomaly Detection. In: The International Journal on Very Large Data Bases, 2024, 33(3): 753-780.

Transactions on Machine Learning Research

• C. Guan, F. Chen, K. Xue, C. Fan, L. Zhang, Z. Zhang, P. Zhao, Z. Zhang, C. Qian, L. Yuan, and Y. Yu. One by One, Continual Coordinating with Humans via Hyper-Teammate Identification. In: Transactions on Machine Learning Research, 2024, in press.

计算机学报

• 谭志豪, 史浩宇, 陈梓轩, 姜远. 基于神经切线核的学件RKME规约. In: 计算机学报, 2024, 47(6): 1232-1243.

软件学报

• Z. Zhou, D.-C. Zhang, Y.-F. Li, and M.-L. Zhang. Towards Robust Test-Time Adaptation for Open-Set Recognition. In: Journal of Software (软件学报), 2024, 35(4).

Top