This paper proposes a novel approach towards better interpretability of a trained text-based
ranking model in a post-hoc manner. A popular approach for post-hoc interpretability text …

ABSTRACT SQL is the standard language for retrieving and manipulating relational data.
Although SQL is ubiquitous for simple analytical queries, it is rarely used for more complex …

Computing derivatives of tensor expressions, also known as tensor calculus, is a
fundamental task in machine learning. A key concern is the efficiency of evaluating the …

Buyun Liang Plan B MS final defense Page 1 NCVX: A General-Purpose Optimization
Solver for Constrained Machine and Deep Learning Buyun Liang Apr 7, 2023 Page 2 …

Empirical robustness evaluation (RE) of deep learning models against adversarial
perturbations entails solving nontrivial constrained optimization problems. Existing …

Constrained optimization problems arise frequently in classical machine learning. There
exist frameworks addressing constrained optimization, for instance, CVXPY and GENO …

Optimizing nonconvex (NCVX) problems, especially nonsmooth and constrained ones, is an
essential part of machine learning. However, it can be hard to reliably solve such problems …

Frequently, equality constraints are imposed on the objective function of machine learning
algorithms aiming at increasing their robustness and generalization. In addition, non …

Empirical evaluation of deep learning models against adversarial attacks entails solving
nontrivial constrained optimization problems. Popular algorithms for solving these …

Empirical evaluation of the adversarial robustness of deep learning models involves solving
non-trivial constrained optimization problems. Popular numerical algorithms to solve these …