Andre Wibisono | DeepAI

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∙ 06/23/2023

On a Class of Gibbs Sampling over Networks

We consider the sampling problem from a composite distribution whose pot...

0 Bo Yuan, et al. ∙

∙ 05/26/2023

Mitigating Catastrophic Forgetting in Long Short-Term Memory Networks

Continual learning on sequential data is critical for many machine learn...

0 Ketaki Joshi, et al. ∙

∙ 02/15/2023

Continuized Acceleration for Quasar Convex Functions in Non-Convex Optimization

Quasar convexity is a condition that allows some first-order methods to ...

0 Jun-Kun Wang, et al. ∙

∙ 11/02/2022

Convergence in KL Divergence of the Inexact Langevin Algorithm with Application to Score-based Generative Models

We study the Inexact Langevin Algorithm (ILA) for sampling using estimat...

0 Andre Wibisono, et al. ∙

∙ 10/28/2022

Aggregation in the Mirror Space (AIMS): Fast, Accurate Distributed Machine Learning in Military Settings

Distributed machine learning (DML) can be an important capability for mo...

0 Ryan Yang, et al. ∙

∙ 10/18/2022

Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation

We consider a setting that a model needs to adapt to a new domain under ...

0 Jun-Kun Wang, et al. ∙

∙ 07/05/2022

Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time

Hamiltonian Monte Carlo (HMC) is a popular method in sampling. While the...

0 Jun-Kun Wang, et al. ∙

∙ 06/22/2022

Provable Acceleration of Heavy Ball beyond Quadratics for a Class of Polyak-Łojasiewicz Functions when the Non-Convexity is Averaged-Out

Heavy Ball (HB) nowadays is one of the most popular momentum methods in ...

0 Jun-Kun Wang, et al. ∙

∙ 06/08/2022

Alternating Mirror Descent for Constrained Min-Max Games

In this paper we study two-player bilinear zero-sum games with constrain...

0 Andre Wibisono, et al. ∙

∙ 02/13/2022

Improved analysis for a proximal algorithm for sampling

We study the proximal sampler of Lee, Shen, and Tian (2021) and obtain n...

0 Yongxin Chen, et al. ∙

∙ 01/29/2022

Achieving Efficient Distributed Machine Learning Using a Novel Non-Linear Class of Aggregation Functions

Distributed machine learning (DML) over time-varying networks can be an ...

38 Haizhou Du, et al. ∙

∙ 09/24/2021

The Mirror Langevin Algorithm Converges with Vanishing Bias

The technique of modifying the geometry of a problem from Euclidean to H...

0 Ruilin Li, et al. ∙

∙ 11/19/2019

Fictitious Play: Convergence, Smoothness, and Optimism

We consider the dynamics of two-player zero-sum games, with the goal of ...

0 Jacob Abernethy, et al. ∙

∙ 11/04/2019

Proximal Langevin Algorithm: Rapid Convergence Under Isoperimetry

We study the Proximal Langevin Algorithm (PLA) for sampling from a proba...

0 Andre Wibisono, et al. ∙

∙ 06/05/2019

Last-iterate convergence rates for min-max optimization

We study the problem of finding min-max solutions for smooth two-input o...

0 Jacob Abernethy, et al. ∙

∙ 03/20/2019

Rapid Convergence of the Unadjusted Langevin Algorithm: Log-Sobolev Suffices

We prove a convergence guarantee on the unadjusted Langevin algorithm fo...

0 Santosh S. Vempala, et al. ∙

∙ 05/03/2018

Convexity of mutual information along the Ornstein-Uhlenbeck flow

We study the convexity of mutual information as a function of time along...

0 Andre Wibisono, et al. ∙

∙ 02/22/2018

Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem

We study sampling as optimization in the space of measures. We focus on ...

0 Andre Wibisono, et al. ∙

∙ 01/22/2018

Convexity of mutual information along the heat flow

We study the convexity of mutual information along the evolution of the ...

0 Andre Wibisono, et al. ∙

∙ 03/14/2016

A Variational Perspective on Accelerated Methods in Optimization

Accelerated gradient methods play a central role in optimization, achiev...

0 Andre Wibisono, et al. ∙

∙ 10/26/2014

Concavity of reweighted Kikuchi approximation

We analyze a reweighted version of the Kikuchi approximation for estimat...

0 Po-Ling Loh, et al. ∙

∙ 12/07/2013

Optimal rates for zero-order convex optimization: the power of two function evaluations

We consider derivative-free algorithms for stochastic and non-stochastic...

0 John C. Duchi, et al. ∙

∙ 07/25/2013

Streaming Variational Bayes

We present SDA-Bayes, a framework for (S)treaming, (D)istributed, (A)syn...

0 Tamara Broderick, et al. ∙

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