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Tianbao Yang

118 papers · 2012–2026 · 13 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (31) 🌈 Renaissance Researcher (9) 🐝 Cross-Pollinator (4) πŸƒ Academic Marathon (13) 🌍 Conference Polyglot (13) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (4) 🏠 Conference Loyalist (38) πŸ† Grand Slam πŸ‘‘ Triple Crown 🌱 Topic Pioneer πŸ”¬ Deep Specialist (23) 🀝 Dynamic Duo (32) πŸ† Keyword Champion (12) πŸ“ˆ Trend Setter ⚑ Prolific Year (5) πŸ’Ž Century Club (117) πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (151) πŸ”₯ Unstoppable (14) ❓ The Questioner (2)

Conferences

ICML (39) NIPS (38) JMLR (9) AISTATS (7) ICLR (5) ECCV (4) IJCAI (4) AAAI (3) COLT (3) CVPR (3) ICCV (1) MICCAI (1) UAI (1)

Top co-authors

Lijun Zhang (32) Rong Jin (26) Qihang Lin (16) Yi Xu (15) Zhuoning Yuan (13) Mingrui Liu (12) Yan Yan (9) Zhishuai Guo (9) Yiming Ying (9) Bokun Wang (9)

Keywords

stochastic optimization (33) non-convex optimization (17) convex optimization (14) stochastic gradient descent (11) convergence rate (10) compositional optimization (9) online convex optimization (8) regret bound (8) online learning (8) min-max optimization (7) dynamic regret (6) auc maximization (6) gradient descent (6) convergence analysis (6) strong convexity (6) variance reduction (5) iteration complexity (5) empirical risk minimization (5) distributionally robust optimization (5) matrix completion (5)

Papers

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
NystrΓΆm 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