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20232025
most citedA Bilevel Optimization Method for Inverse Mean-Field Games

5 citations · 5 across the 7 of their papers we have counts for

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6 papers · 1 filter

math.OC2025

Efficient Penalty-Based Bilevel Methods: Improved Analysis, Novel Updates, and Flatness Condition

Liuyuan Jiang, Quan Xiao, Lisha Chen +1

Penalty-based methods have become popular for solving bilevel optimization (BLO) problems, thanks to their effective first-order nature. However, they often require inner-loop iter…

math.OC2025

Beyond Value Functions: Single-Loop Bilevel Optimization under Flatness Conditions

Liuyuan Jiang, Quan Xiao, Lisha Chen +1

Bilevel optimization, a hierarchical optimization paradigm, has gained significant attention in a wide range of practical applications, notably in the fine-tuning of generative mod…

math.OC2024

Unlocking Global Optimality in Bilevel Optimization: A Pilot Study

Quan Xiao, Tianyi Chen

Bilevel optimization has witnessed a resurgence of interest, driven by its critical role in trustworthy and efficient AI applications. While many recent works have established conv…

math.OC2024

A Primal-Dual-Assisted Penalty Approach to Bilevel Optimization with Coupled Constraints

Liuyuan Jiang, Quan Xiao, Victor M. Tenorio +3

Interest in bilevel optimization has grown in recent years, partially due to its applications to tackle challenging machine-learning problems. Several exciting recent works have be…

math.OC2024★ 5 cited

A Bilevel Optimization Method for Inverse Mean-Field Games

Jiajia Yu, Quan Xiao, Tianyi Chen +1

In this paper, we introduce a bilevel optimization framework for addressing inverse mean-field games, alongside an exploration of numerical methods tailored for this bilevel proble…

math.OC2023

A Generalized Alternating Method for Bilevel Learning under the Polyak-Łojasiewicz Condition

Quan Xiao, Songtao Lu, Tianyi Chen

Bilevel optimization has recently regained interest owing to its applications in emerging machine learning fields such as hyperparameter optimization, meta-learning, and reinforcem…