2 citations · 3 across the 15 of their papers we have counts for
17 papers
Data Reuse in Non-Stationary Learning
Tomer Gafni, Garud Iyengar, Assaf Zeevi
We consider online learning in non-stationary environments, where the goal is to track an unknown parameter that switches abruptly between a finite set of recurring values. Recurre…
AltGDA Achieves Global Ergodic Convergence in Matrix Games
Tianlong Nan, Garud Iyengar, Christian Kroer +1
Alternating gradient descent-ascent (AltGDA) is a simple and practically effective method for solving finite two-player zero-sum matrix games. However, the theory of AltGDA remains…
Assortment and Procurement Design in Dual-Mode Content Platforms
Garud Iyengar, Yuanzhe Ma, Jay Sethuraman
We study assortment and procurement design for a digital content platform offering both ad-supported and subscription access. Users are heterogeneous in content preferences and ad…
Variance-Adaptive Optimal Algorithm for Reinforcement Learning with Multinomial Logit Function Approximation
Wonyoung Kim, Min-Hwan Oh, Garud Iyengar +1
Reinforcement learning with multinomial logistic (MNL) function approximation has become an important framework due to its flexibility and broad applicability. While existing studi…
The Cost of Learning Under Multiple Change Points
Tomer Gafni, Garud Iyengar, Assaf Zeevi
We consider an online learning problem in environments with multiple change points. In contrast to the single change point problem that is widely studied using classical "high conf…
Model-Free Assessment of Simulator Fidelity via Quantile Curves
Garud Iyengar, Yu-Shiou Willy Lin, Kaizheng Wang
As generative AI models are increasingly used to simulate real-world systems, quantifying the ``sim-to-real'' gap is critical. For each input setting of interest -- which we call a…
On the Convergence of Alternating Gradient Descent-Ascent in Bilinear Games
Tianlong Nan, Shuvomoy Das Gupta, Garud Iyengar +1
We study the alternating gradient descent-ascent (AltGDA) algorithm in two-player zero-sum games. Alternating methods, where players take turns to update their strategies, have lon…
Virtual Trading in Multi-Settlement Electricity Markets
Agostino Capponi, Garud Iyengar, Bo Yang +1
In the Day-Ahead (DA) market, suppliers sell and load-serving entities (LSEs) purchase energy commitments, with both sides adjusting for imbalances between contracted and actual de…
Contested Route Planning
Jakub Černý, Garud Iyengar, Christian Kroer
We consider the problem of routing for logistics purposes, in a contested environment where an adversary attempts to disrupt the vehicle along the chosen route. We construct a game…
Linear Bandits with Partially Observable Features
Wonyoung Kim, Sungwoo Park, Garud Iyengar +2
We study the linear bandit problem that accounts for partially observable features. Without proper handling, unobserved features can lead to linear regret in the decision horizon $…
Contested Logistics: A Game-Theoretic Approach
Jakub Cerny, Chun Kai Ling, Darshan Chakrabarti +4
We introduce Contested Logistics Games, a variant of logistics problems that account for the presence of an adversary that can disrupt the movement of goods in selected areas. We m…
Is Cross-Validation the Gold Standard to Evaluate Model Performance?
Garud Iyengar, Henry Lam, Tianyu Wang
Cross-Validation (CV) is the default choice for evaluating the performance of machine learning models. Despite its wide usage, their statistical benefits have remained half-underst…
Layered Graph Security Games
Jakub Černý, Chun Kai Ling, Christian Kroer +1
Security games model strategic interactions in adversarial real-world applications. Such applications often involve extremely large but highly structured strategy sets (e.g., selec…
Model-Free Approximate Bayesian Learning for Large-Scale Conversion Funnel Optimization
Garud Iyengar, Raghav Singal
The flexibility of choosing the ad action as a function of the consumer state is critical for modern-day marketing campaigns. We study the problem of identifying the optimal sequen…
Decentralized Finance: Protocols, Risks, and Governance
Agostino Capponi, Garud Iyengar, Jay Sethuraman
Financial markets are undergoing an unprecedented transformation. Technological advances have brought major improvements to the operations of financial services. While these advanc…
A Doubly Robust Approach to Sparse Reinforcement Learning
Wonyoung Kim, Garud Iyengar, Assaf Zeevi
We propose a new regret minimization algorithm for episodic sparse linear Markov decision process (SMDP) where the state-transition distribution is a linear function of observed fe…
A Unified Approach for Minimizing Composite Norms
Necdet Serhat Aybat, Garud Iyengar
We propose a first-order augmented Lagrangian algorithm (FALC) to solve the composite norm minimization problem min |sigma(F(X)-G)|_alpha + |C(X)- d|_beta subject to A(X)-b in Q; w…