3 citations · 12 across the 17 of their papers we have counts for
18 papers
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…
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…
Flow-Based Synthesis of Reactive Tests for Discrete Decision-Making Systems with Temporal Logic Specifications
Josefine B. Graebener, Apurva S. Badithela, Denizalp Goktas +4
Designing tests to evaluate if a given autonomous system satisfies complex specifications is challenging due to the complexity of these systems. This work proposes a flow-based app…
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…
Tâtonnement in Homothetic Fisher Markets
Denizalp Goktas, Jiayi Zhao, Amy Greenwald
A prevalent theme in the economics and computation literature is to identify natural price-adjustment processes by which sellers and buyers in a market can discover equilibrium pri…
Fisher Markets with Social Influence
Jiayi Zhao, Denizalp Goktas, Amy Greenwald
A Fisher market is an economic model of buyer and seller interactions in which each buyer's utility depends only on the bundle of goods she obtains. Many people's interests, howeve…
Generative Adversarial Equilibrium Solvers
Denizalp Goktas, David C. Parkes, Ian Gemp +5
We introduce the use of generative adversarial learning to compute equilibria in general game-theoretic settings, specifically the generalized Nash equilibrium (GNE) in pseudo-game…
Zero-Sum Stochastic Stackelberg Games
Denizalp Goktas, Jiayi Zhao, Amy Greenwald
Zero-sum stochastic games have found important applications in a variety of fields, from machine learning to economics. Work on this model has primarily focused on the computation…
Exploitability Minimization in Games and Beyond
Denizalp Goktas, Amy Greenwald
Pseudo-games are a natural and well-known generalization of normal-form games, in which the actions taken by each player affect not only the other players' payoffs, as in games, bu…
Gradient Descent Ascent in Min-Max Stackelberg Games
Denizalp Goktas, Amy Greenwald
Min-max optimization problems (i.e., min-max games) have attracted a great deal of attention recently as their applicability to a wide range of machine learning problems has become…
Robust No-Regret Learning in Min-Max Stackelberg Games
Denizalp Goktas, Jiayi Zhao, Amy Greenwald
The behavior of no-regret learning algorithms is well understood in two-player min-max (i.e, zero-sum) games. In this paper, we investigate the behavior of no-regret learning in mi…
Convex-Concave Min-Max Stackelberg Games
Denizalp Goktas, Amy Greenwald
Min-max optimization problems (i.e., min-max games) have been attracting a great deal of attention because of their applicability to a wide range of machine learning problems. Alth…
A Consumer-Theoretic Characterization of Fisher Market Equilibria
Denizalp Goktas, Enrique Areyan Viqueira, Amy Greenwald
In this paper, we bring consumer theory to bear in the analysis of Fisher markets whose buyers have arbitrary continuous, concave, homogeneous (CCH) utility functions representing…