Tianbao Yang

CyPortQA: Benchmarking Multimodal Large Language Models for Cyclone Preparedness in Port Operation AAAI 2026 AdFair-CLIP: Adversarial Fair Contrastive Language-Image Pre-training for Chest X-rays MICCAI 2025 On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning ICLR 2025 Gradient Aligned Regression via Pairwise Losses ICML 2025 Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws ICML 2025 Universal Online Convex Optimization Meets Second-order Bounds JMLR 2025 Discovering Global False Negatives On the Fly for Self-supervised Contrastive Learning ICML 2025 A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization ICML 2025 Discriminative Finetuning of Generative Large Language Models without Reward Models and Human Preference Data ICML 2025 Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms ICML 2024 Communication-Efficient Federated Group Distributionally Robust Optimization NIPS 2024 To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO ICML 2024 Adaptive Preference Scaling for Reinforcement Learning with Human Feedback NIPS 2024 Single-Loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions NIPS 2024 FeDXL: Provable Federated Learning for Deep X-Risk Optimization ICML 2023 Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity ICML 2023 Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization NIPS 2023 Federated Compositional Deep AUC Maximization NIPS 2023 SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data NIPS 2023 Maximization of Average Precision for Deep Learning with Adversarial Ranking Robustness NIPS 2023 Stochastic Approximation Approaches to Group Distributionally Robust Optimization NIPS 2023 Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints AISTATS 2023 Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization ICML 2023 Provable Multi-instance Deep AUC Maximization with Stochastic Pooling ICML 2023 Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization ICML 2023 Generalization Analysis for Contrastive Representation Learning ICML 2023 Learning Unnormalized Statistical Models via Compositional Optimization ICML 2023 Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning JMLR 2023 Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition JMLR 2023 Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications ICML 2022 Optimal Algorithms for Stochastic Multi-Level Compositional Optimization ICML 2022 Compositional Training for End-to-End Deep AUC Maximization ICLR 2022 Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor NIPS 2022 Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization NIPS 2022 Large-scale Optimization of Partial AUC in a Range of False Positive Rates NIPS 2022 Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization NIPS 2022 When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee ICML 2022 Momentum Accelerates the Convergence of Stochastic AUPRC Maximization AISTATS 2022 A Simple yet Universal Strategy for Online Convex Optimization ICML 2022 Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance ICML 2022 GraphFM: Improving Large-Scale GNN Training via Feature Momentum ICML 2022 Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence ICML 2022 Stability and Generalization of Stochastic Gradient Methods for Minimax Problems ICML 2021 Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity ICML 2021 First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems JMLR 2021 Online Convex Optimization with Continuous Switching Constraint NIPS 2021 Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning NIPS 2021 Revisiting Smoothed Online Learning NIPS 2021 An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives NIPS 2021 Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence NIPS 2021 Large-Scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification ICCV 2021 Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets ICLR 2020 Improved Schemes for Episodic Memory-based Lifelong Learning NIPS 2020 Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization NIPS 2020 A Decentralized Parallel Algorithm for Training Generative Adversarial Nets NIPS 2020 Deep Unsupervised Binary Coding Networks for Multivariate Time Series Retrieval AAAI 2020 Adversarial Localized Energy Network for Structured Prediction AAAI 2020 Minimizing Dynamic Regret and Adaptive Regret Simultaneously AISTATS 2020 Accelerating Deep Learning with Millions of Classes ECCV 2020 A Simple and Effective Framework for Pairwise Deep Metric Learning ECCV 2020 Stochastic AUC Maximization with Deep Neural Networks ICLR 2020 Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks ICML 2020 Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints ICML 2020 Stochastic Optimization for Non-convex Inf-Projection Problems ICML 2020 A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints JMLR 2020 Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems NIPS 2019 Stagewise Training Accelerates Convergence of Testing Error Over SGD NIPS 2019 EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching From Scratch CVPR 2019 Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number ICML 2019 Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence ICML 2019 Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions ICLR 2019 Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion JMLR 2019 On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Minimization IJCAI 2019 A Robust Zero-Sum Game Framework for Pool-based Active Learning AISTATS 2019 Learning with Non-Convex Truncated Losses by SGD UAI 2019 RSG: Beating Subgradient Method without Smoothness and Strong Convexity JMLR 2018 Level-Set Methods for Finite-Sum Constrained Convex Optimization ICML 2018 Fast Stochastic AUC Maximization with $O(1/n)$-Convergence Rate ICML 2018 A Generic Approach for Accelerating Stochastic Zeroth-Order Convex Optimization IJCAI 2018 A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer AISTATS 2018 A Unified Analysis of Stochastic Momentum Methods for Deep Learning IJCAI 2018 Adaptive Negative Curvature Descent with Applications in Non-convex Optimization NIPS 2018 Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions NIPS 2018 Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization NIPS 2018 First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time NIPS 2018 Dynamic Regret of Strongly Adaptive Methods ICML 2018 How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization ECCV 2018 Improving Sequential Determinantal Point Processes for Supervised Video Summarization ECCV 2018 SADAGRAD: Strongly Adaptive Stochastic Gradient Methods ICML 2018 ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization NIPS 2017 Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition NIPS 2017 Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter NIPS 2017 A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates ICML 2017 SVD-free Convex-Concave Approaches for Nuclear Norm Regularization IJCAI 2017 Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence ICML 2017 Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$- and $O(1/n^2)$-type of Risk Bounds COLT 2017 Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement JMLR 2017 Improved Dynamic Regret for Non-degenerate Functions NIPS 2017 Improved Dropout for Shallow and Deep Learning NIPS 2016 Online Stochastic Linear Optimization under One-bit Feedback ICML 2016 Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient ICML 2016 Learning Attributes Equals Multi-Source Domain Generalization CVPR 2016 Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/\epsilon)$ NIPS 2016 Hyper-Class Augmented and Regularized Deep Learning for Fine-Grained Image Classification CVPR 2015 A Simple Homotopy Algorithm for Compressive Sensing AISTATS 2015 An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection ICML 2015 Theory of Dual-sparse Regularized Randomized Reduction ICML 2015 Efficient Low-Rank Stochastic Gradient Descent Methods for Solving Semidefinite Programs AISTATS 2014 Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities NIPS 2014 Recovering the Optimal Solution by Dual Random Projection COLT 2013 Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent NIPS 2013 Stochastic Convex Optimization with Multiple Objectives NIPS 2013 O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions ICML 2013 Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning NIPS 2012 Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison NIPS 2012 Stochastic Gradient Descent with Only One Projection NIPS 2012 Trading Regret for Efficiency: Online Convex Optimization with Long Term Constraints JMLR 2012 Online Optimization with Gradual Variations COLT 2012