Learning Graphical Models
On the Statistical Consistency of Risk-Sensitive Bayesian Decision-Making
We compute the convergence rates of the RSVB approximate posterior and the corresponding optimal value.
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.
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.
Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers Lorenzo Tiberi 1,2 Francesca Mignacco
Second, generalization--what specific aspects of the transformer architecture are responsible for their effective learning?
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.