1 citations · 1 across the 11 of their papers we have counts for
12 papers
Rethinking Neural Network Learning Rates: A Stackelberg Perspective
Sihan Zeng, Sujay Bhatt, Sumitra Ganesh
Neural networks are typically trained with a single learning rate across all layers. While recent empirical evidence suggests that assigning layer-specific learning rates can accel…
A Hessian-Free Actor-Critic Algorithm for Bi-Level Reinforcement Learning with Applications to LLM Fine-Tuning
Sihan Zeng, Sujay Bhatt, Sumitra Ganesh +1
We study a structured bi-level optimization problem where the upper-level objective is a smooth function and the lower-level problem is policy optimization in a Markov decision pro…
Learning in Stackelberg Mean Field Games: A Non-Asymptotic Analysis
Sihan Zeng, Benjamin Patrick Evans, Sujay Bhatt +3
We study policy optimization in Stackelberg mean field games (MFGs), a hierarchical framework for modeling the strategic interaction between a single leader and an infinitely large…
QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting
Nicole Cho, William Watson, Alec Koppel +2
Advanced reasoning capabilities in Large Language Models (LLMs) have caused higher hallucination prevalence; yet most mitigation work focuses on after-the-fact filtering rather tha…
Collab: Controlled Decoding using Mixture of Agents for LLM Alignment
Souradip Chakraborty, Sujay Bhatt, Udari Madhushani Sehwag +7
Alignment of Large Language models (LLMs) is crucial for safe and trustworthy deployment in applications. Reinforcement learning from human feedback (RLHF) has emerged as an effect…
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…
Decentralized Convergence to Equilibrium Prices in Trading Networks
Edwin Lock, Benjamin Patrick Evans, Eleonora Kreacic +4
We propose a decentralized market model in which agents can negotiate bilateral contracts. This builds on a similar, but centralized, model of trading networks introduced by Hatfie…
Regularized Proportional Fairness Mechanism for Resource Allocation Without Money
Sihan Zeng, Sujay Bhatt, Alec Koppel +1
Mechanism design in resource allocation studies dividing limited resources among self-interested agents whose satisfaction with the allocation depends on privately held utilities.…
Approximate Equivariance in Reinforcement Learning
Jung Yeon Park, Sujay Bhatt, Sihan Zeng +4
Equivariant neural networks have shown great success in reinforcement learning, improving sample efficiency and generalization when there is symmetry in the task. However, in many…
GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-time Alignment
Yuancheng Xu, Udari Madhushani Sehwag, Alec Koppel +4
Large Language Models (LLMs) exhibit impressive capabilities but require careful alignment with human preferences. Traditional training-time methods finetune LLMs using human prefe…
Partially Observable Contextual Bandits with Linear Payoffs
Sihan Zeng, Sujay Bhatt, Alec Koppel +1
The standard contextual bandit framework assumes fully observable and actionable contexts. In this work, we consider a new bandit setting with partially observable, correlated cont…
Learning in Herding Mean Field Games: Single-Loop Algorithm with Finite-Time Convergence Analysis
Sihan Zeng, Sujay Bhatt, Alec Koppel +1
We consider discrete-time stationary mean field games (MFG) with unknown dynamics and design algorithms for finding the equilibrium with finite-time complexity guarantees. Prior so…