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Larkin Liu
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2020 – today
- 2026
[c3]Larkin Liu, Kashif Rasul, Yutong Chao, Jalal Etesami:
Riemannian Manifold Learning for Stackelberg Games with Neural Flow Representations. AAAI 2026: 23774-23782- 2025
[i8]Larkin Liu, Kashif Rasul, Yutong Chao, Jalal Etesami:
Riemannian Manifold Learning for Stackelberg Games with Neural Flow Representations. CoRR abs/2502.05498 (2025)
[i7]Larkin Liu, Jalal Etesami:
Online Mixture of Experts: No-Regret Learning for Optimal Collective Decision-Making. CoRR abs/2510.21788 (2025)- 2024
[j2]Larkin Liu, Shiqi Liu, Matej Jusup:
Optimizing Stochastic Control through State TransitionSeparability and Resource-Utility Exchange. SIGMETRICS Perform. Evaluation Rev. 52(2): 30-32 (2024)
[c2]Larkin Liu:
Approximate Nash Equilibrium Learning for n-Player Markov Games in Dynamic Pricing. EPIA (1) 2024: 360-371
[c1]Larkin Liu:
Dual-Sourcing via Dynamic Programming with Monte Carlo Value Approximation. ICSTCC 2024: 315-322
[i6]Larkin Liu, Yuming Rong:
A Stackelberg Regret Minimizing Framework for Online Learning in Newsvendor Pricing Games. CoRR abs/2404.00203 (2024)
[i5]Larkin Liu, Shiqi Liu, Matej Jusup:
Monte Carlo Planning for Stochastic Control on Constrained Markov Decision Processes. CoRR abs/2406.16151 (2024)- 2022
[j1]Larkin Liu
, Jun Tao Luo
:
mctreesearch4j: A Monte Carlo Tree Search Implementation for the JVM. J. Open Source Softw. 7(69): 3804 (2022)
[i4]Larkin Liu:
Approximate Nash Equilibrium Learning for n-Player Markov Games in Dynamic Pricing. CoRR abs/2207.06492 (2022)- 2021
[i3]Larkin Liu, Jun Tao Luo:
An Extensible and Modular Design and Implementation of Monte Carlo Tree Search for the JVM. CoRR abs/2108.10061 (2021)
2010 – 2019
- 2019
[i2]Larkin Liu, Richard Downe, Joshua Reid:
Multi-Armed Bandit Strategies for Non-Stationary Reward Distributions and Delayed Feedback Processes. CoRR abs/1902.08593 (2019)
[i1]Larkin Liu, Yu-Chung Lin, Joshua Reid:
Improving the Performance of the LSTM and HMM Models via Hybridization. CoRR abs/1907.04670 (2019)
Coauthor Index

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last updated on 2026-03-27 21:04 CET by the dblp team
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