Journal Publications (α-β: alphabetical order)

[J1] 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. 2024. Operations Research.

• Selected as an oral presentation at MSOM Supply Chain SIG 2022.

• Selected as an oral presentation at RMP Conference 2022. 

[J2] 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 (*equal contribution)

[C1] 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.

[C2] Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data. [ArXiv]

Xuxing Chen, Abhishek Roy, Yifan Hu, Krishna Balasubramanian. NeurIPS 2024.

[C3] 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.

• Preliminary version in ELLIS Robust LLMs Workshop, 2024.

[C4] Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization. [Link]

Siqi Zhang*, Yifan Hu*, Liang Zhang, Niao He. AISTATS 2024.

• Preliminary version in NeurIPS OPT for Machine Learning Workshop 2022.

[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.

• Preliminary version in NeurIPS ReALML Workshop 2023. 

[C6] Contextual Stochastic Bilevel Optimization. [Link] [Slides]

Yifan Hu, Jie Wang, Yao Xie, Andreas Krause, Daniel Kuhn. NeurIPS 2023.

[C7] On the Bias-Variance-Cost Tradeoff of Stochastic Optimization. [Link][Slides]

Yifan Hu, Xin Chen, and Niao He. NeurIPS 2021. 

[C8] 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.  3rd edition, Encyclopedia of Optimization.

Working Papers

[6] Contextual Stochastic Bilevel Optimization and Three-Stage Stochastic Programming. 

Yifan Hu, Jie Wang, Yao Xie, Andreas Krause, Daniel Kuhn. 

• To be submitted to Operations Research. 

• Conference version see "[C6] Hu et al., NeurIPS 2023. [Link] [Slides]".

[5] Causal Invariance Learning via Efficient Optimization of a Nonconvex Objective. [ArXiv]

Zhenyu Wang*, Yifan Hu*,  Peter Bühlmann, Zijian Guo.

[4] 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 review at Operations Research. 

[3] Multi-level Monte-Carlo Gradient Methods for Stochastic Optimization with Biased Oracles. [ArXiv]

Yifan Hu, Jie Wang, Xin Chen, and Niao He. 2024.

• Under review at Operations Research. 

• Conference version see "[C7] Hu et al., NeurIPS 2021. [Link][Slides]"

[2] Metric-Regularized Group Fairness in Federated Learning. 

Yves Rychener, Daniel Kuhn, Yifan Hu.

• Under review. (Preprint available upon request).

[1] Stochastic Optimization under Hidden Convexity. [ArXiv] [Slides]

(α-β) Ilyas Fatkhullin, Niao He, Yifan Hu. 2023. 

• Major revision in journal.

• Preliminary version in NeurIPS OPT for Machine Learning Workshop 2023. [Link]

Technical Notes

Nonconvex Variance Reduction: General Analysis, Biased Oracle and Optimal Algorithms for Stochastic Bilevel Optimization.

Liang Zhang, Yifan Hu, and Niao He. 2021.