Directed Networks
Relative Entropic Optimal Transport: a (Prior-aware) Matching Perspective to (Unbalanced) Classification
Classification is a fundamental problem in machine learning, and considerable efforts have been recently devoted to the demanding long-tailed setting due to its prevalence in nature.
Pseudo-Likelihood Inference
's likelihood kernel that bounds the loss of information based on information-geometric
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks
Learning curve extrapolation aims to predict model performance in later epochs of training, based on the performance in earlier epochs. In this work, we argue that, while the inherent uncertainty in the extrapolation of learning curves warrants a Bayesian approach, existing methods are (i) overly restrictive, and/or (ii) computationally expensive.
Appendix for " Learning Dynamic Attribute-Factored World Models for Efficient Multi-object Reinforcement Learning " A Detailed Discussion and Comparison with Related Work
In this section, we first provide a summary of the notation in Table A2.