Blogposts
[Blog1] Avoid Overclaims - Summary of Complexity Bounds for Algorithms in Minimization and Minimax Optimization. [Link] [Updating version]
Siqi Zhang, Yifan Hu.
ICLR Blogposts 2025.
Preprints (α-β: alphabetical order, *equal contribution)
[4] Statistical Inference for Conditional Group Distributionally Robust Optimization with Cross-Entropy Loss. [ArXiv]
Zijian Guo, Zhenyu Wang, Yifan Hu, Francis Bach. 2025.
[3] Causal Invariance Learning via Efficient Optimization of a Nonconvex Objective. [ArXiv]
Zhenyu Wang*, Yifan Hu*, Peter Bühlmann, Zijian Guo. 2024.
[2] Landscape of Policy Optimization for Finite Horizon MDPs with General State and Action. [ArXiv]
(α-β) Xin Chen, Yifan Hu, and Minda Zhao. 2024.
Under revision at Operations Research.
[1] Multi-level Monte-Carlo Gradient Methods for Stochastic Optimization with Biased Oracles. [ArXiv]
Yifan Hu, Jie Wang, Xin Chen, and Niao He. 2024.
Major revision at Operations Research.
[J4] Stochastic Optimization under Hidden Convexity. [ArXiv] [Slides]
(α-β) Ilyas Fatkhullin, Niao He, Yifan Hu.
SIAM Journal on Optimization. 2025.
[J3] Real-time vehicle relocation, personnel dispatch and trip pricing for carsharing systems under supply and demand uncertainties. [Link]
Mengjie Li, Haoning Xi, Chi Xie, Zuo-Jun Max Shen, Yifan Hu.
Transportation Research Part B: Methodological. 2025.
[J2] Efficient Algorithms for A Class of Stochastic Hidden Convex Optimization and Its Applications in Network Revenue Management. [Link] [Slides]
(α-β) Xin Chen, Niao He, Yifan Hu, and Zikun Ye.
Operations Research. 2024.
Selected as an oral presentation at MSOM Supply Chain SIG 2022.
Selected as an oral presentation at RMP Conference 2022.
[J1] Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization. [Link]
Yifan Hu, Xin Chen, and Niao He.
SIAM Journal on Optimization. 2020.
Conference Proceedings
[C10] MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignment. [ArXiv]
Tianze Wang, Dongnan Gui, Yifan Hu, Shuhang Lin, Linjun Zhang.
ICML 2025.
[C9] Global Group Fairness in Federated Learning via Function Tracking. [ArXiv]
Yves Rychener, Daniel Kuhn, Yifan Hu.
AISTATS 2025.
[C8] Contextual Bilevel Reinforcement Learning for Incentive Alignment. [ArXiv]
Vinzenz Thoma*, Barna Pasztor*, Andreas Krause, Giorgia Ramponi, Yifan Hu.
NeurIPS 2024.
Preliminary version "Stochastic Bilevel Optimization with Lower-Level Contextual Markov Decision Processes." in ICML Agentic Markets Workshop, 2024.
[C7] Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data. [ArXiv]
Xuxing Chen*, Abhishek Roy*, Yifan Hu, Krishna Balasubramanian.
NeurIPS 2024.
[C6] Group Robust Preference Optimization in Reward-free RLHF. [ArXiv]
Shyam Sundhar Ramesh, Yifan Hu, Iason Chaimalas, Viraj Mehta, Pier Giuseppe Sessa, Haitham Bou Ammar, Ilija Bogunovic.
NeurIPS 2024.
[C5] Distributionally Robust Model-based Reinforcement Learning with Large State Spaces. [Link]
Shyam Sundhar Ramesh, Pier Giuseppe Sessa, Yifan Hu, Andreas Krause, Ilija Bogunovic.
AISTATS 2024.
[C4] Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization. [Link]
Siqi Zhang*, Yifan Hu*, Liang Zhang, Niao He.
AISTATS 2024.
[C3] Contextual Stochastic Bilevel Optimization. [Link] [Slides]
Yifan Hu, Jie Wang, Yao Xie, Andreas Krause, Daniel Kuhn.
NeurIPS 2023.
[C2] On the Bias-Variance-Cost Tradeoff of Stochastic Optimization. [Link][Slides]
Yifan Hu, Xin Chen, and Niao He.
NeurIPS 2021.
[C1] Biased Stochastic First-order Methods for Conditional Stochastic Optimization and Its Applications in Meta Learning. [Link]
Yifan Hu*, Siqi Zhang*, Xin Chen, and Niao He.
NeurIPS 2020.
Invited Book Chapter
[B1] Stochastic Biased Gradient Methods. [Link]
Yifan Hu.
Encyclopedia of Optimization, 3rd edition.
Technical Notes
Nonconvex Variance Reduction: General Analysis, Biased Oracle and Optimal Algorithms for Stochastic Bilevel Optimization.
Liang Zhang, Yifan Hu, and Niao He. 2021.