Learning Graphical Models
Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference
ASNPE brings an active learning scheme into the inference loop to estimate the utility of simulation parameter candidates to the underlying probabilistic model.
Identifying Latent State-Transition Processes for Individualized Reinforcement Learning Y uewen Sun
The application of reinforcement learning (RL) involving interactions with individuals has grown significantly in recent years.
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.
DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data
Specifically, we integrate two essential sub-tasks, i.e., traffic data imputation and decision-making, by leveraging a Partial Rewards Conditioned Diffusion (PRCD) model to prevent missing rewards
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).
