Learning Graphical Models
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks Alexander Immer
Further, we emphasize the significance of principled regularization of the network parameters and prediction.
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics Leon Klein
MD is often used to compute equilibrium properties, which requires sampling from an equilibrium distribution such as the Boltzmann distribution.
Reducing Blackwell and Average Optimality to Discounted MDPs via the Blackwell Discount Factor
We introduce the Blackwell discount factor for Markov Decision Processes (MDPs). Classical objectives for MDPs include discounted, average, and Blackwell opti-mality.
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization Xiangsen Wang
Despite some success in the single-agent setting, offline multi-agent RL (MARL) remains to be a challenge. The large joint state-action space and the coupled multi-agent behaviors pose extra complexities for offline policy optimization.
Generalization in the Face of Adaptivity: A Bayesian Perspective
However, differential privacy's worst-case nature entails scaling such noise to the range of the queries even for
Enhancing Adversarial Robustness via Score-Based Optimization
Adversarial attacks have the potential to mislead deep neural network classifiers by introducing slight perturbations.