Statistical Learning
Self-Supervised Learning Disentangled Group Representation as Feature T an Wang
In this paper, we formulate the notion of "good" representation from a group-theoretic view using Higgins' definition of
Appendices In Appendix A, we present definitions for f-DiGraphs and f
Appendix C, we prove our main theoretical result on Boolean-rank instability under edge perturbation. In Appendix E, we present the implementation details for DCD-FG. Definition 3. (Half-square graphs and induced feature graph) Therefore, we focus on the direct set inclusion. We define the number of such possible adjacency patterns as I . By comparing coefficients inside the exponential, for fixed m, this upper bound tends to 0 as d .
Large-Scale Differentiable Causal Discovery of Factor Graphs Romain Lopez 1,2, Jan-Christian Hütter 1, Jonathan K. Pritchard
A common theme in causal inference is learning causal relationships between observed variables, also known as causal discovery. This is usually a daunting task, given the large number of candidate causal graphs and the combinatorial nature of the search space.
Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution Shift Christina Baek
OOD benchmarks -- a phenomenon they dubbed "accuracy-on-the-line".
Convex optimization based on global lower second-order models
The next point in the iteration process is defined as the minimizer of this model.
