1 citations · 1 across the 11 of their papers we have counts for
12 papers
No One Size Fits All: QueryBandits for Hallucination Mitigation
Nicole Cho, William Watson, Alec Koppel +2
Advanced reasoning capabilities in Large Language Models (LLMs) have led to more frequent hallucinations; yet most mitigation work focuses on open-source models for post-hoc detect…
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…
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…
Learning Payment-Free Resource Allocation Mechanisms
Sihan Zeng, Sujay Bhatt, Eleonora Kreacic +3
We consider the design of mechanisms that allocate limited resources among self-interested agents using neural networks. Unlike the recent works that leverage machine learning for…