Search Results for author: Ya-Ping Hsieh

Found 14 papers, 1 papers with code

Aligned Diffusion Schrödinger Bridges

no code implementations22 Feb 2023 Vignesh Ram Somnath, Matteo Pariset, Ya-Ping Hsieh, Maria Rodriguez Martinez, Andreas Krause, Charlotte Bunne

Diffusion Schr\"odinger bridges (DSB) have recently emerged as a powerful framework for recovering stochastic dynamics via their marginal observations at different time points.

A Dynamical System View of Langevin-Based Non-Convex Sampling

no code implementations25 Oct 2022 Mohammad Reza Karimi, Ya-Ping Hsieh, Andreas Krause

Non-convex sampling is a key challenge in machine learning, central to non-convex optimization in deep learning as well as to approximate probabilistic inference.

Continuous-time Analysis for Variational Inequalities: An Overview and Desiderata

no code implementations14 Jul 2022 Tatjana Chavdarova, Ya-Ping Hsieh, Michael I. Jordan

Algorithms that solve zero-sum games, multi-objective agent objectives, or, more generally, variational inequality (VI) problems are notoriously unstable on general problems.

Riemannian stochastic approximation algorithms

no code implementations14 Jun 2022 Mohammad Reza Karimi, Ya-Ping Hsieh, Panayotis Mertikopoulos, Andreas Krause

We examine a wide class of stochastic approximation algorithms for solving (stochastic) nonlinear problems on Riemannian manifolds.

Riemannian optimization

Learning in games from a stochastic approximation viewpoint

no code implementations8 Jun 2022 Panayotis Mertikopoulos, Ya-Ping Hsieh, Volkan Cevher

We develop a unified stochastic approximation framework for analyzing the long-run behavior of multi-agent online learning in games.

The Schrödinger Bridge between Gaussian Measures has a Closed Form

no code implementations11 Feb 2022 Charlotte Bunne, Ya-Ping Hsieh, Marco Cuturi, Andreas Krause

The static optimal transport $(\mathrm{OT})$ problem between Gaussians seeks to recover an optimal map, or more generally a coupling, to morph a Gaussian into another.

Gaussian Processes MORPH

Conditional gradient methods for stochastically constrained convex minimization

no code implementations ICML 2020 Maria-Luiza Vladarean, Ahmet Alacaoglu, Ya-Ping Hsieh, Volkan Cevher

We propose two novel conditional gradient-based methods for solving structured stochastic convex optimization problems with a large number of linear constraints.

The limits of min-max optimization algorithms: convergence to spurious non-critical sets

no code implementations16 Jun 2020 Ya-Ping Hsieh, Panayotis Mertikopoulos, Volkan Cevher

Compared to ordinary function minimization problems, min-max optimization algorithms encounter far greater challenges because of the existence of periodic cycles and similar phenomena.

Finding Mixed Nash Equilibria of Generative Adversarial Networks

no code implementations ICLR 2019 Ya-Ping Hsieh, Chen Liu, Volkan Cevher

We reconsider the training objective of Generative Adversarial Networks (GANs) from the mixed Nash Equilibria (NE) perspective.

Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games

no code implementations ICML 2018 Ehsan Asadi Kangarshahi, Ya-Ping Hsieh, Mehmet Fatih Sahin, Volkan Cevher

We propose a simple algorithmic framework that simultaneously achieves the best rates for honest regret as well as adversarial regret, and in addition resolves the open problem of removing the logarithmic terms in convergence to the value of the game.

Mirrored Langevin Dynamics

no code implementations NeurIPS 2018 Ya-Ping Hsieh, Ali Kavis, Paul Rolland, Volkan Cevher

We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design.

Dimension-free Information Concentration via Exp-Concavity

no code implementations26 Feb 2018 Ya-Ping Hsieh, Volkan Cevher

Information concentration of probability measures have important implications in learning theory.

Learning Theory

Preconditioned Spectral Descent for Deep Learning

no code implementations NeurIPS 2015 David E. Carlson, Edo Collins, Ya-Ping Hsieh, Lawrence Carin, Volkan Cevher

These challenges include, but are not limited to, the non-convexity of learning objectives and estimating the quantities needed for optimization algorithms, such as gradients.

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