1 citations · 2 across the 11 of their papers we have counts for
12 papers
Towards Learning Representations of Policies in Two-Player Zero-Sum Imperfect-Information Games
Kevin Wang, Kevin Yang, Arjun Prakash +1
We investigate the problem of learning useful policy representations (embeddings) in two-player zero-sum imperfect-information games. We make three contributions: First, we introdu…
Distilling Game Code World Model Generation into Lightweight Large Language Models
Tyrone Serapio, Arjun Prakash, Haoyang Xu +2
Large Language Models (LLMs) have shown great ability in generating executable code from natural language, opening the possibility of automatically constructing environments for AI…
Chronax: A Jax Library for Forecasting and Conformal Inference
Xan Carey, Arsh Singh, Yash Deshmukh +8
Time-series forecasting is central to many scientific and industrial domains, such as energy systems, climate modeling, finance, and retail. While forecasting methods have evolved…
TempusBench: An Evaluation Framework for Time-Series Forecasting
Denizalp Goktas, Gerardo Riaño-Briceño, Omkar Tekawade +14
Foundation models have transformed natural language processing and computer vision, and a rapidly growing literature on time-series foundation models (TSFMs) seeks to replicate thi…
Spectral Collapse Drives Loss of Plasticity in Deep Continual Learning
Arjun Prakash, Naicheng He, Kaicheng Guo +5
We investigate why deep neural networks suffer from loss of plasticity in continual learning, and thus fail to learn new tasks without reinitializing parameters. We show that this…
Bi-Level Policy Optimization with Nyström Hypergradients
Arjun Prakash, Naicheng He, Denizalp Goktas +2
The dependency of the actor on the critic in actor-critic (AC) reinforcement learning means that AC can be characterized as a bilevel optimization (BLO) problem, also called a Stac…
Efficient Inverse Multiagent Learning
Denizalp Goktas, Amy Greenwald, Sadie Zhao +2
In this paper, we study inverse game theory (resp. inverse multiagent learning) in which the goal is to find parameters of a game's payoff functions for which the expected (resp. s…
Infinite Horizon Markov Economies
Denizalp Goktas, Sadie Zhao, Yiling Chen +1
In this paper, we study a generalization of Markov games and pseudo-games that we call Markov pseudo-games, which, like the former, captures time and uncertainty, and like the latt…
Tractable General Equilibrium
Denizalp Goktas, Klára Chura, Alp Emin Guneri +1
We study Walrasian economies (or general equilibrium models) and their solution concept, the Walrasian equilibrium. A key challenge in this domain is identifying price-adjustment p…
Banzhaf Power in Hierarchical Voting Games
John Randolph, Denizalp Goktas, Amy Greenwald
The Banzhaf Power Index (BPI) is a method of measuring the power of voters in determining the outcome of a voting game. Some voting games exhibit a hierarchical structure, includin…
Convex-Concave Zero-sum Markov Stackelberg Games
Denizalp Goktas, Arjun Prakash, Amy Greenwald
Zero-sum Markov Stackelberg games can be used to model myriad problems, in domains ranging from economics to human robot interaction. In this paper, we develop policy gradient meth…
Stackelberg POMDP: Learning to Lead via Reinforcement Learning
Gianluca Brero, Alon Eden, Darshan Chakrabarti +6
Many real-world domains--including e-commerce platform design, security planning, and multi-agent coordination--feature leader-follower problems where one decision-maker commits to…