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Mingyang Liu

10 papers hereh-index 5155 citations15 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author9
  • middle author1

Across the 10 of 10 papers where every author was matched, so the position is known.

fields
  • cs.GT5
  • cs.LG4
  • cs.AI1
same name
  • Mingyang Liu — 10 papers, h 3
  • Mingyang Liu — 3 papers, h 2
  • Mingyang Liu — 2 papers, h 5
  • Mingyang Liu — 2 papers, h 2
  • Mingyang Liu — 2 papers, h 1
  • Mingyang Liu — 2 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedRLHF from Heterogeneous Feedback via Personalization and Preference Aggregation

2 citations · 2 across the 9 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

Constant Individual Regret in General Games

Mingyang Liu, Gabriele Farina, Asuman Ozdaglar

Uncoupled no-regret dynamics provide a decentralized route to equilibrium, but prior guarantees for individual regret retain a polylogarithmic dependence on the horizon. We remove…

cs.LG2026

Regret Minimization with Adaptive Opponents in Repeated Games

Mingyang Liu, Asuman Ozdaglar, Tiancheng Yu +1

In this paper, we study regret minimization in repeated games with \emph{adaptive} opponents who can respond based on histories of play. The standard metric of \emph{external regre…

cs.LG2026

Online Learning and Equilibrium Computation with Ranking Feedback

Mingyang Liu, Yongshan Chen, Zhiyuan Fan +3

Online learning in arbitrary, and possibly adversarial, environments has been extensively studied in sequential decision-making, and it is closely connected to equilibrium computat…

cs.LG2025

UFT: Unifying Supervised and Reinforcement Fine-Tuning

Mingyang Liu, Gabriele Farina, Asuman Ozdaglar

Post-training has demonstrated its importance in enhancing the reasoning capabilities of large language models (LLMs). The primary post-training methods can be categorized into sup…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.