45 citations · 89 across the 5 of their papers we have counts for
9 papers
LLM Value Functions for Credit Assignment in Agentic RL
Kaijie Zhu, Weixiang Yan, Omar Attia +3
Reinforcement learning (RL) for large language model (LLM) agents remains challenging in long-horizon tasks with sparse outcome rewards, where assigning credit to individual action…
RLTLDR: Self-improvement by Internalizing Self-generated Feedback
Michael Kirchhof, Eleonora Gualdoni, Andrew Szot +6
The common paradigm of reinforcement learning with verifiable rewards (RLVR) is to let agents make multiple attempts at a task, and optimize towards the successful ones. This becom…
Practical Policy Optimization with Personalized Experimentation
Mia Garrard, Hanson Wang, Ben Letham +9
Many organizations measure treatment effects via an experimentation platform to evaluate the casual effect of product variations prior to full-scale deployment. However, standard e…
Multi-armed Bandits with Cost Subsidy
Deeksha Sinha, Karthik Abinav Sankararama, Abbas Kazerouni +1
In this paper, we consider a novel variant of the multi-armed bandit (MAB) problem, MAB with cost subsidy, which models many real-life applications where the learning agent has to…
Active Learning for Skewed Data Sets
Abbas Kazerouni, Qi Zhao, Jing Xie +2
Consider a sequential active learning problem where, at each round, an agent selects a batch of unlabeled data points, queries their labels and updates a binary classifier. While t…
Best Arm Identification in Generalized Linear Bandits
Abbas Kazerouni, Lawrence M. Wein
Motivated by drug design, we consider the best-arm identification problem in generalized linear bandits. More specifically, we assume each arm has a vector of covariates, there is…
Learning to Price with Reference Effects
Abbas Kazerouni, Benjamin Van Roy
As a firm varies the price of a product, consumers exhibit reference effects, making purchase decisions based not only on the prevailing price but also the product's price history.…
A Tutorial on Thompson Sampling
Daniel Russo, Benjamin Van Roy, Abbas Kazerouni +2
Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immed…
Conservative Contextual Linear Bandits
Abbas Kazerouni, Mohammad Ghavamzadeh, Yasin Abbasi-Yadkori +1
Safety is a desirable property that can immensely increase the applicability of learning algorithms in real-world decision-making problems. It is much easier for a company to deplo…