49 citations · 72 across the 5 of their papers we have counts for
6 papers
Nonsmooth nonconvex stochastic heavy ball
Tam Le
Motivated by the conspicuous use of momentum-based algorithms in deep learning, we study a nonsmooth nonconvex stochastic heavy ball method and show its convergence. Our approach b…
Subgradient sampling for nonsmooth nonconvex minimization
Jérôme Bolte, Tam Le, Edouard Pauwels
Risk minimization for nonsmooth nonconvex problems naturally leads to first-order sampling or, by an abuse of terminology, to stochastic subgradient descent. We establish the conve…
Nonsmooth Implicit Differentiation for Machine Learning and Optimization
Jérôme Bolte, Tam Le, Edouard Pauwels +1
In view of training increasingly complex learning architectures, we establish a nonsmooth implicit function theorem with an operational calculus. Our result applies to most practic…
Topological Bayesian Optimization with Persistence Diagrams
Tatsuya Shiraishi, Tam Le, Hisashi Kashima +1
Finding an optimal parameter of a black-box function is important for searching stable material structures and finding optimal neural network structures, and Bayesian optimization…
Safe Grid Search with Optimal Complexity
Eugene Ndiaye, Tam Le, Olivier Fercoq +2
Popular machine learning estimators involve regularization parameters that can be challenging to tune, and standard strategies rely on grid search for this task. In this paper, we…
Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams
Tam Le, Makoto Yamada
Algebraic topology methods have recently played an important role for statistical analysis with complicated geometric structured data such as shapes, linked twist maps, and materia…