Zhi-Hua Zhou

A simple, optimal and efficient algorithm for online exp-concave optimization COLT 2026 CoRE-Learning with Look-Ahead and Immediate Resource Allocation AAAI 2026 Reparameterized Complex-valued Neurons Can Efficiently Learn More than Real-valued Neurons via Gradient Descent JMLR 2026 Tabular Learnwares Can Be Repurposed for Seemingly Irrelevant New Tasks AAAI 2026 Avoiding Undesired Future with Sequential Decisions IJCAI 2025 Efficient Methods for Non-stationary Online Learning JMLR 2025 Efficient Rectification of Neuro-Symbolic Reasoning Inconsistencies by Abductive Reflection AAAI 2025 Efficient Rectification of Neuro-Symbolic Reasoning Inconsistencies by Abductive Reflection (Extended Abstract) IJCAI 2025 Enabling Optimal Decisions in Rehearsal Learning under CARE Condition ICML 2025 Gradient-Based Nonlinear Rehearsal Learning with Multivariate Alterations AAAI 2025 Heavy-Tailed Linear Bandits: Huber Regression with One-Pass Update ICML 2025 Identifying and Reusing Learnwares Across Different Label Spaces IJCAI 2025 One-Pass Feature Evolvable Learning with Theoretical Guarantees ICML 2025 On the Diversity of Adversarial Ensemble Learning ICML 2025 Polynomial-Delay MAG Listing with Novel Locally Complete Orientation Rules ICML 2025 TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree ICML 2025 Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization JMLR 2024 Analysis for Abductive Learning and Neural-Symbolic Reasoning Shortcuts ICML 2024 An Efficient Maximal Ancestral Graph Listing Algorithm ICML 2024 A Simple and Optimal Approach for Universal Online Learning with Gradient Variations NeurIPS 2024 Avoiding Undesired Future with Minimal Cost in Non-Stationary Environments NeurIPS 2024 Dynamic Regret of Adversarial MDPs with Unknown Transition and Linear Function Approximation AAAI 2024 Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation ICML 2024 Gradient-Variation Online Learning under Generalized Smoothness NeurIPS 2024 Handling Learnwares from Heterogeneous Feature Spaces with Explicit Label Exploitation NeurIPS 2024 Improved Algorithm for Adversarial Linear Mixture MDPs with Bandit Feedback and Unknown Transition AISTATS 2024 Learning Only When It Matters: Cost-Aware Long-Tailed Classification AAAI 2024 Learning with Adaptive Resource Allocation ICML 2024 Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs NeurIPS 2024 On the Ability of Developers' Training Data Preservation of Learnware NeurIPS 2024 On the Intrinsic Structures of Spiking Neural Networks JMLR 2024 Policy Rehearsing: Training Generalizable Policies for Reinforcement Learning ICLR 2024 Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation NeurIPS 2024 Safe Abductive Learning in the Presence of Inaccurate Rules AAAI 2024 Towards Making Learnware Specification and Market Evolvable AAAI 2024 Beyond Performative Prediction: Open-environment Learning with Presence of Corruptions AISTATS 2023 Complex-valued Neurons Can Learn More but Slower than Real-valued Neurons via Gradient Descent NeurIPS 2023 Dynamic Regret of Adversarial Linear Mixture MDPs NeurIPS 2023 Enabling Abductive Learning to Exploit Knowledge Graph IJCAI 2023 Enabling Knowledge Refinement upon New Concepts in Abductive Learning AAAI 2023 Estimating Possible Causal Effects with Latent Variables via Adjustment ICML 2023 Fast Rates in Time-Varying Strongly Monotone Games ICML 2023 Handling Learnwares Developed from Heterogeneous Feature Spaces without Auxiliary Data IJCAI 2023 Identifying Useful Learnwares for Heterogeneous Label Spaces ICML 2023 Non-stationary Online Learning with Memory and Non-stochastic Control JMLR 2023 Online Non-stochastic Control with Partial Feedback JMLR 2023 On the Consistency Rate of Decision Tree Learning Algorithms AISTATS 2023 On the Gini-impurity Preservation For Privacy Random Forests NeurIPS 2023 Rehearsal Learning for Avoiding Undesired Future NeurIPS 2023 Revisiting Weighted Strategy for Non-stationary Parametric Bandits AISTATS 2023 Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning ICLR 2023 Stochastic Approximation Approaches to Group Distributionally Robust Optimization NeurIPS 2023 Universal Online Learning with Gradient Variations: A Multi-layer Online Ensemble Approach NeurIPS 2023 Adapting to Online Label Shift with Provable Guarantees NeurIPS 2022 Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits COLT 2022 Depth is More Powerful than Width with Prediction Concatenation in Deep Forest NeurIPS 2022 Dynamic Regret of Online Markov Decision Processes ICML 2022 Efficient Methods for Non-stationary Online Learning NeurIPS 2022 Fast Provably Robust Decision Trees and Boosting ICML 2022 Non-stationary Online Learning with Memory and Non-stochastic Control AISTATS 2022 No-Regret Learning in Time-Varying Zero-Sum Games ICML 2022 Pre-Trained Model Reusability Evaluation for Small-Data Transfer Learning NeurIPS 2022 Real-Valued Backpropagation is Unsuitable for Complex-Valued Neural Networks NeurIPS 2022 Sound and Complete Causal Identification with Latent Variables Given Local Background Knowledge NeurIPS 2022 Theoretically Provable Spiking Neural Networks NeurIPS 2022 Actively Identifying Causal Effects with Latent Variables Given Only Response Variable Observable NeurIPS 2021 Bandit Convex Optimization in Non-stationary Environments JMLR 2021 Bifurcation Spiking Neural Network JMLR 2021 Budgeted Heterogeneous Treatment Effect Estimation ICML 2021 Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions NeurIPS 2021 Exploratory Machine Learning with Unknown Unknowns AAAI 2021 Storage Fit Learning with Feature Evolvable Streams AAAI 2021 Towards Enabling Learnware to Handle Unseen Jobs AAAI 2021 An Unbiased Risk Estimator for Learning with Augmented Classes NeurIPS 2020 A Simple Approach for Non-stationary Linear Bandits AISTATS 2020 A Simple Online Algorithm for Competing with Dynamic Comparators UAI 2020 Bandit Convex Optimization in Non-stationary Environments AISTATS 2020 Boosting-Based Reliable Model Reuse ACML 2020 Control Flow Graph Embedding Based on Multi-Instance Decomposition for Bug Localization AAAI 2020 Cost-effectively Identifying Causal Effects When Only Response Variable is Observable ICML 2020 Differentially Private Learning with Small Public Data AAAI 2020 Dynamic Regret of Convex and Smooth Functions NeurIPS 2020 Learning with Feature and Distribution Evolvable Streams ICML 2020 Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data ICML 2020 Towards Convergence Rate Analysis of Random Forests for Classification NeurIPS 2020 Adaptive Regret of Convex and Smooth Functions ICML 2019 A Refined Margin Distribution Analysis for Forest Representation Learning NeurIPS 2019 Bridging Machine Learning and Logical Reasoning by Abductive Learning NeurIPS 2019 Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin ICML 2019 Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder NeurIPS 2019 Partial Label Learning with Unlabeled Data IJCAI 2019 Reinforcement Learning Experience Reuse with Policy Residual Representation IJCAI 2019 Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion JMLR 2019 Stochastic Approximation of Smooth and Strongly Convex Functions: Beyond the $O(1/T)$ Convergence Rate COLT 2019 ℓ_1-regression with Heavy-tailed Distributions NeurIPS 2018 Adaptive Online Learning in Dynamic Environments NeurIPS 2018 Dynamic Regret of Strongly Adaptive Methods ICML 2018 Experienced Optimization with Reusable Directional Model for Hyper-Parameter Search IJCAI 2018 Learning Environmental Calibration Actions for Policy Self-Evolution IJCAI 2018 Multi-Layered Gradient Boosting Decision Trees NeurIPS 2018 Preference Based Adaptation for Learning Objectives NeurIPS 2018 Rectify Heterogeneous Models with Semantic Mapping ICML 2018 Semi-Supervised Optimal Margin Distribution Machines IJCAI 2018 Tri-net for Semi-Supervised Deep Learning IJCAI 2018 Unorganized Malicious Attacks Detection NeurIPS 2018 A Unified View of Multi-Label Performance Measures ICML 2017 Cost-Effective Active Learning from Diverse Labelers IJCAI 2017 Deep Descriptor Transforming for Image Co-Localization IJCAI 2017 Deep Forest: Towards An Alternative to Deep Neural Networks IJCAI 2017 Improved Dynamic Regret for Non-degenerate Functions NeurIPS 2017 Incomplete Label Distribution Learning IJCAI 2017 Learning with Feature Evolvable Streams NeurIPS 2017 Multi-Class Optimal Margin Distribution Machine ICML 2017 Multi-Instance Learning with Key Instance Shift IJCAI 2017 Obtaining High-Quality Label by Distinguishing between Easy and Hard Items in Crowdsourcing IJCAI 2017 Optimizing Ratio of Monotone Set Functions IJCAI 2017 Storage Fit Learning with Unlabeled Data IJCAI 2017 Subset Selection under Noise NeurIPS 2017 Cost-Saving Effect of Crowdsourcing Learning IJCAI 2016 Graph Quality Judgement: A Large Margin Expedition IJCAI 2016 Learnability of Non-I.I.D. ACML 2016 Learning Unified Features from Natural and Programming Languages for Locating Buggy Source Code IJCAI 2016 Online Stochastic Linear Optimization under One-bit Feedback ICML 2016 Parallel Pareto Optimization for Subset Selection IJCAI 2016 What Makes Objects Similar: A Unified Multi-Metric Learning Approach NeurIPS 2016 Active Learning from Crowds with Unsure Option IJCAI 2015 A Simple Homotopy Algorithm for Compressive Sensing AISTATS 2015 CUR Algorithm for Partially Observed Matrices ICML 2015 Multi-Label Active Learning: Query Type Matters IJCAI 2015 On Constrained Boolean Pareto Optimization IJCAI 2015 One-Pass Multi-View Learning ACML 2015 On the Consistency of AUC Pairwise Optimization IJCAI 2015 Statistical Unfolded Logic Learning ACML 2015 Subset Selection by Pareto Optimization NeurIPS 2015 Learning with Augmented Multi-Instance View ACML 2014 Top Rank Optimization in Linear Time NeurIPS 2014 Convex and Scalable Weakly Labeled SVMs JMLR 2013 Co-Training with Insufficient Views ACML 2013 Multi-Instance Multi-Label Learning with Weak Label IJCAI 2013 Multi-Modal Image Annotation with Multi-Instance Multi-Label LDA IJCAI 2013 One-Pass AUC Optimization ICML 2013 On the Approximation Ability of Evolutionary Optimization with Application to Minimum Set Cover: Extended Abstract IJCAI 2013 Speedup Matrix Completion with Side Information: Application to Multi-Label Learning NeurIPS 2013 Uniform Convergence, Stability and Learnability for Ranking Problems IJCAI 2013 Key Instance Detection in Multi-Instance Learning ACML 2012 Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison NeurIPS 2012 A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin JMLR 2011 On the Consistency of Multi-Label Learning COLT 2011 Active Learning by Querying Informative and Representative Examples NeurIPS 2010 Multi-View Active Learning in the Non-Realizable Case NeurIPS 2010 Tighter and Convex Maximum Margin Clustering AISTATS 2005