3 citations · 9 across the 31 of their papers we have counts for
7 papers · 1 filter
Almost Free: Self-concordance in Natural Exponential Families and an Application to Bandits
Shuai Liu, Alex Ayoub, Flore Sentenac +2
We prove that single-parameter natural exponential families with subexponential tails are self-concordant with polynomial-sized parameters. For subgaussian natural exponential fami…
To Believe or Not to Believe Your LLM
Yasin Abbasi Yadkori, Ilja Kuzborskij, András György +1
We explore uncertainty quantification in large language models (LLMs), with the goal to identify when uncertainty in responses given a query is large. We simultaneously consider bo…
Confident Natural Policy Gradient for Local Planning in -realizable Constrained MDPs
Tian Tian, Lin F. Yang, Csaba Szepesvári
The constrained Markov decision process (CMDP) framework emerges as an important reinforcement learning approach for imposing safety or other critical objectives while maximizing c…
Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear -Realizability and Concentrability
Volodymyr Tkachuk, Gellért Weisz, Csaba Szepesvári
We consider offline reinforcement learning (RL) in -horizon Markov decision processes (MDPs) under the linear -realizability assumption, where the action-value function of…
Mitigating LLM Hallucinations via Conformal Abstention
Yasin Abbasi Yadkori, Ilja Kuzborskij, David Stutz +9
We develop a principled procedure for determining when a large language model (LLM) should abstain from responding (e.g., by saying "I don't know") in a general domain, instead of…
Regret Minimization via Saddle Point Optimization
Johannes Kirschner, Seyed Alireza Bakhtiari, Kushagra Chandak +2
A long line of works characterizes the sample complexity of regret minimization in sequential decision-making by min-max programs. In the corresponding saddle-point game, the min-p…
Switching the Loss Reduces the Cost in Batch (Offline) Reinforcement Learning
Alex Ayoub, Kaiwen Wang, Vincent Liu +5
We propose training fitted Q-iteration with log-loss (FQI-log) for batch reinforcement learning (RL). We show that the number of samples needed to learn a near-optimal policy with…