1 citations · 2 across the 10 of their papers we have counts for
12 papers
Partition, Prompt, Aggregate: Statistical Self-Consistency in Language Models
Patrik Wolf, Thomas Kleine Buening, Andreas Krause +1
In-context learning is commonly interpreted as a form of conditional inference, in which the prompt specifies a context and the model's output is treated as an estimate of the corr…
Reinforcement Learning via Self-Distillation
Jonas Hübotter, Frederike Lübeck, Lejs Behric +8
Large language models are increasingly post-trained with reinforcement learning in verifiable domains such as code and math. Yet, current methods for reinforcement learning with ve…
Stackelberg Learning from Human Feedback: Preference Optimization as a Sequential Game
Barna Pásztor, Thomas Kleine Buening, Andreas Krause
We introduce Stackelberg Learning from Human Feedback (SLHF), a new framework for preference optimization. SLHF frames the alignment problem as a sequential-move game between two p…
Strategyproof Reinforcement Learning from Human Feedback
Thomas Kleine Buening, Jiarui Gan, Debmalya Mandal +1
We study Reinforcement Learning from Human Feedback (RLHF) in settings where multiple labelers may strategically misreport feedback to steer the learned policy toward their own pre…
A Minimax Approach to Ad Hoc Teamwork
Victor Villin, Thomas Kleine Buening, Christos Dimitrakakis
We propose a minimax-Bayes approach to Ad Hoc Teamwork (AHT) that optimizes policies against an adversarial prior over partners, explicitly accounting for uncertainty about partner…
Causal Imitation Learning under Expert-Observable and Expert-Unobservable Confounding
Daqian Shao, Thomas Kleine Buening, Marta Kwiatkowska
We propose a general framework for causal Imitation Learning (IL) with hidden confounders, which subsumes several existing settings. Our framework accounts for two types of hidden…
Strategic Linear Contextual Bandits
Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis +1
Motivated by the phenomenon of strategic agents gaming a recommender system to maximize the number of times they are recommended to users, we study a strategic variant of the linea…
Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation
Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis +1
We study a strategic variant of the multi-armed bandit problem, which we coin the strategic click-bandit. This model is motivated by applications in online recommendation where the…
Minimax-Bayes Reinforcement Learning
Thomas Kleine Buening, Christos Dimitrakakis, Hannes Eriksson +2
While the Bayesian decision-theoretic framework offers an elegant solution to the problem of decision making under uncertainty, one question is how to appropriately select the prio…
ANACONDA: An Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Bandits
Thomas Kleine Buening, Aadirupa Saha
We study the problem of non-stationary dueling bandits and provide the first adaptive dynamic regret algorithm for this problem. The only two existing attempts in this line of work…
Environment Design for Inverse Reinforcement Learning
Thomas Kleine Buening, Victor Villin, Christos Dimitrakakis
Learning a reward function from demonstrations suffers from low sample-efficiency. Even with abundant data, current inverse reinforcement learning methods that focus on learning fr…
Interactive Inverse Reinforcement Learning for Cooperative Games
Thomas Kleine Buening, Anne-Marie George, Christos Dimitrakakis
We study the problem of designing autonomous agents that can learn to cooperate effectively with a potentially suboptimal partner while having no access to the joint reward functio…