3 citations · 9 across the 31 of their papers we have counts for
11 papers · 1 filter
Gaussian-Width Bounds for Offline-to-Online Linear Bandits
Kushagra Chandak, Volodymyr Tkachuk, Xiaoqi Tan +1
We study how offline data can help online learning in stochastic linear bandits. We focus on partially covered offline data, where only of the feature directions are poorly…
Foundations of Reinforcement Learning and Control:Connections and New Perspectives
Claire Vernade, Onno Eberhard, Martha White +4
Reinforcement learning and control theory are two adjacent scientific fields that focus on optimizing the controller of unknown dynamical systems using feedback. While both fields…
Randomized Exploration for Linear Bandits via Absolute Perturbations
Toshinori Kitamura, Shuai Liu, Csaba Szepesvári
In stochastic linear bandits, the canonical Upper Confidence Bound (UCB) algorithm admits a simple frequentist regret analysis but can be computationally demanding, while Thompson…
Sharper Guarantees for Misspecified Kernelized Bandit Optimization
Davide Maran, Csaba Szepesvári
Existing guarantees for misspecified kernelized bandit optimization pay for misspecification through kernel complexity: in generic offline bounds, the misspecification level $\vare…
To See the Unseen: on the Generalization Ability of Transformers in Symbolic Reasoning
Nevena Lazić, Liam Fowl, András György +1
We investigate the ability of decoder-only transformer models to perform abstract symbolic reasoning; specifically solving propositional logic reasoning problems given in-context.…
Revisiting Subgradient Dominance in Robust MDPs: Counterexamples, Hardness, and Sufficient Conditions
Toshinori Kitamura, Arnob Ghosh, Alex Ayoub +2
Projected subgradient descent (PSD) has gained popularity for solving robust Markov decision processes (RMDPs) because it applies to a broader class of uncertainty sets than tradit…
Optimistic Actor-Critic with Parametric Policies for Linear Markov Decision Processes
Max Qiushi Lin, Reza Asad, Kevin Tan +3
Although actor-critic methods have been successful in practice, their theoretical analyses have several limitations. Specifically, existing theoretical work either sidesteps the ex…
LACONIC: Length-Aware Constrained Reinforcement Learning for LLM
Chang Liu, Yiran Zhao, Lawrence Liu +3
Reinforcement learning (RL) has enhanced the capabilities of large language models (LLMs) through reward-driven training. Nevertheless, this process can introduce excessively long…
Sharp analysis of linear ensemble sampling
David Janz, Arya Akhavan, Csaba Szepesvári
We analyse linear ensemble sampling (ES) with standard Gaussian perturbations in stochastic linear bandits. We show that for ensemble size , ES attains $\tilde O(d^{3…
Learning What to Recommend: Minimax Optimal Simple Regret in Logistic Bandits
Shuai Liu, Alireza Bakhtiari, Alex Ayoub +2
We study stochastic logistic bandits with -dimensional action features under the simple-regret objective, where a learner uses rounds of exploration to output a single final…
Eluder dimension: localise it!
Alireza Bakhtiari, Alex Ayoub, Samuel Robertson +2
We establish a lower bound on the eluder dimension of generalised linear model classes, showing that standard eluder dimension-based analysis cannot lead to first-order regret boun…