Learning Graphical Models
On the Curses of Future and History in Future-dependent Value Functions for OPE
We study off-policy evaluation (OPE) in partially observable environments with complex observations, with the goal of developing estimators whose guarantee avoids exponential dependence on the horizon.
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits
OFUGLB outperforms or is at par with prior algorithms for logistic bandits.
Distributional Successor Features Enable Zero-Shot Policy Optimization
Intelligent agents must be generalists, capable of quickly adapting to various tasks.
Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales?
Our findings on NoRa dataset reveal a prevalent vulnerability to such noise among current LLMs, with existing robust methods like self-correction and self-consistency showing limited efficacy.
df22a19686a558e74f038e6277a51f68-Paper-Conference.pdf
In tasks aiming for long-term returns, planning becomes essential.
MetaCURL: Non-stationary Concave Utility Reinforcement Learning Bianca Marin Moreno Inria
We explore online learning in episodic Markov decision processes on non-stationary environments (changing losses and probability transitions).
Prospective Learning: Learning for a Dynamic Future
In real-world applications, the distribution of the data, and our goals, evolve over time.
A Simple Framework for Generalization in Visual RL under Dynamic Scene Perturbations
Imbalanced saliency is a phenomenon where an RL agent disproportionately identifies salient features across consecutive frames in a frame stack.
Imitation learning (IL) aims to mimic the behavior of an expert in a sequential decision making task by learning from demonstrations, and has been widely applied
Full experimental details are provided in Appendix C.