20 citations · 36 across the 5 of their papers we have counts for
5 papers
Robust Distribution Learning with Local and Global Adversarial Corruptions
Sloan Nietert, Ziv Goldfeld, Soroosh Shafiee
We consider learning in an adversarial environment, where an -fraction of samples from a distribution are arbitrarily modified (global corruptions) and the remaini…
Outlier-Robust Wasserstein DRO
Sloan Nietert, Ziv Goldfeld, Soroosh Shafiee
Distributionally robust optimization (DRO) is an effective approach for data-driven decision-making in the presence of uncertainty. Geometric uncertainty due to sampling or localiz…
Max-Sliced Mutual Information
Dor Tsur, Ziv Goldfeld, Kristjan Greenewald
Quantifying the dependence between high-dimensional random variables is central to statistical learning and inference. Two classical methods are canonical correlation analysis (CCA…
Sliced Mutual Information: A Scalable Measure of Statistical Dependence
Ziv Goldfeld, Kristjan Greenewald
Mutual information (MI) is a fundamental measure of statistical dependence, with a myriad of applications to information theory, statistics, and machine learning. While it possesse…
Fourier-Motzkin Elimination Software for Information Theoretic Inequalities
Ido B. Gattegno, Ziv Goldfeld, Haim H. Permuter
We provide open-source software implemented in MATLAB, that performs Fourier-Motzkin elimination (FME) and removes constraints that are redundant due to Shannon-type inequalities (…