16 citations · 73 across the 18 of their papers we have counts for
19 papers
Riemannian stochastic optimization methods avoid strict saddle points
Ya-Ping Hsieh, Mohammad Reza Karimi, Andreas Krause +1
Many modern machine learning applications - from online principal component analysis to covariance matrix identification and dictionary learning - can be formulated as minimization…
Unbalanced Diffusion Schrödinger Bridge
Matteo Pariset, Ya-Ping Hsieh, Charlotte Bunne +2
Schrödinger bridges (SBs) provide an elegant framework for modeling the temporal evolution of populations in physical, chemical, or biological systems. Such natural processes are c…
Aligned Diffusion Schrödinger Bridges
Vignesh Ram Somnath, Matteo Pariset, Ya-Ping Hsieh +3
Diffusion Schrödinger bridges (DSB) have recently emerged as a powerful framework for recovering stochastic dynamics via their marginal observations at different time points. Despi…
A Dynamical System View of Langevin-Based Non-Convex Sampling
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. Despite its signi…
Continuous-time Analysis for Variational Inequalities: An Overview and Desiderata
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. Owing…
Riemannian stochastic approximation algorithms
Mohammad Reza Karimi, Ya-Ping Hsieh, Panayotis Mertikopoulos +1
We examine a wide class of stochastic approximation algorithms for solving (stochastic) nonlinear problems on Riemannian manifolds. Such algorithms arise naturally in the study of…
A unified stochastic approximation framework for learning in games
Panayotis Mertikopoulos, Ya-Ping Hsieh, Volkan Cevher
We develop a flexible stochastic approximation framework for analyzing the long-run behavior of learning in games (both continuous and finite). The proposed analysis template incor…
The Schrödinger Bridge between Gaussian Measures has a Closed Form
Charlotte Bunne, Ya-Ping Hsieh, Marco Cuturi +1
The static optimal transport problem between Gaussians seeks to recover an optimal map, or more generally a coupling, to morph a Gaussian into another. It has been…
Conditional gradient methods for stochastically constrained convex minimization
Maria-Luiza Vladarean, Ahmet Alacaoglu, Ya-Ping Hsieh +1
We propose two novel conditional gradient-based methods for solving structured stochastic convex optimization problems with a large number of linear constraints. Instances of this…
The limits of min-max optimization algorithms: convergence to spurious non-critical sets
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 phenom…
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics
Parameswaran Kamalaruban, Yu-Ting Huang, Ya-Ping Hsieh +3
We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents. Leveraging the powerful Stochastic Gradient Langevin Dynam…
Finding Mixed Nash Equilibria of Generative Adversarial Networks
Ya-Ping Hsieh, Chen Liu, Volkan Cevher
We reconsider the training objective of Generative Adversarial Networks (GANs) from the mixed Nash Equilibria (NE) perspective. Inspired by the classical prox methods, we develop a…
Dimension-free Information Concentration via Exp-Concavity
Ya-Ping Hsieh, Volkan Cevher
Information concentration of probability measures have important implications in learning theory. Recently, it is discovered that the information content of a log-concave distribut…
Mirrored Langevin Dynamics
Ya-Ping Hsieh, Ali Kavis, Paul Rolland +1
We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unif…
Let's be Honest: An Optimal No-Regret Framework for Zero-Sum Games
Ehsan Asadi Kangarshahi, Ya-Ping Hsieh, Mehmet Fatih Sahin +1
We revisit the problem of solving two-player zero-sum games in the decentralized setting. We propose a simple algorithmic framework that simultaneously achieves the best rates for…
An Efficient Streaming Algorithm for the Submodular Cover Problem
Ashkan Norouzi-Fard, Abbas Bazzi, Marwa El Halabi +3
We initiate the study of the classical Submodular Cover (SC) problem in the data streaming model which we refer to as the Streaming Submodular Cover (SSC). We show that any single…
Frank-Wolfe Works for Non-Lipschitz Continuous Gradient Objectives: Scalable Poisson Phase Retrieval
Gergely Odor, Yen-Huan Li, Alp Yurtsever +4
We study a phase retrieval problem in the Poisson noise model. Motivated by the PhaseLift approach, we approximate the maximum-likelihood estimator by solving a convex program with…
A Geometric View on Constrained M-Estimators
Yen-Huan Li, Ya-Ping Hsieh, Nissim Zerbib +1
We study the estimation error of constrained M-estimators, and derive explicit upper bounds on the expected estimation error determined by the Gaussian width of the constraint set.…
Mathematical Foundations for Information Theory in Diffusion-Based Molecular Communications
Ya-Ping Hsieh, Ping-Cheng Yeh
Molecular communication emerges as a promising communication paradigm for nanotechnology. However, solid mathematical foundations for information-theoretic analysis of molecular co…