Learning Graphical Models
Smoothed Analysis of Sequential Probability Assignment
Our approach establishes a general-purpose reduction from minimax rates for sequential probability assignment for smoothed adversaries to minimax rates for transductive learning. This leads to optimal (logarithmic) fast rates for parametric classes and classes with finite VC dimension.
Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses
However, patient outcomes with current technologies are limited.
Self-Correcting Bayesian Optimization through Bayesian Active Learning
Gaussian processes are the model of choice in Bayesian optimization and active learning.
One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning
Offline reinforcement learning (RL) is suitable for safety-critical domains where online exploration is not feasible.
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization
Despite the success of neural-based combinatorial optimization methods for end-to-end heuristic learning, out-of-distribution generalization remains a challenge.
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning Jianzhun Shao, Y un Qu
MARL in real scenarios is still challenging due to the same safety and efficiency concerns in single-agent setting, then it is worth conducting investigation for offline RL in multi-agent setting.
Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes
Our study of the convergence of PMD avoids the use of the performance difference lemma, which leads to a direct analysis of independent interest.