Uncertainty
Credal Deep Ensembles for Uncertainty Quantification
This paper presents an innovative approach to classification tasks called Credal Deep Ensembles (CreDEs), ensembles of novel Credal-Set Neural Networks (CreNets), aiming to improve EU quantification in the framework of credal inference.
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds Jiayi Huang 1 Han Zhong 1 Liwei Wang
Our contributions are summarized as follows.
Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning Tyler Kastner
We consider the problem of learning models for risk-sensitive reinforcement learning.
L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference
However, evaluating whether or not these approximations can be trusted remains a challenge. Most approaches evaluate the posterior estimator only in expectation over the observation space.
Learning via Wasserstein-Based High Probability Generalisation Bounds
The authors contributed equally to this work 37th Conference on Neural Information Processing Systems (NeurIPS 2023). Developing upper bounds on the generalisation gap, i.e., generalisation bounds has been a longstanding topic in statistical learning.