Search Results for author: Andre Wibisono

Found 17 papers, 2 papers with code

Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time

no code implementations5 Jul 2022 Jun-Kun Wang, Andre Wibisono

When the potential $f$ is $L$-smooth and $m$-strongly convex, i. e.\ for sampling from a log-smooth and strongly log-concave target distribution $\pi$, it is known that under a constant integration time, the number of iterations that ideal HMC takes to get an $\epsilon$ Wasserstein-2 distance to the target $\pi$ is $O( \kappa \log \frac{1}{\epsilon} )$, where $\kappa := \frac{L}{m}$ is the condition number.

Alternating Mirror Descent for Constrained Min-Max Games

no code implementations8 Jun 2022 Andre Wibisono, Molei Tao, Georgios Piliouras

In this paper we study two-player bilinear zero-sum games with constrained strategy spaces.

Improved analysis for a proximal algorithm for sampling

no code implementations13 Feb 2022 Yongxin Chen, Sinho Chewi, Adil Salim, Andre Wibisono

We study the proximal sampler of Lee, Shen, and Tian (2021) and obtain new convergence guarantees under weaker assumptions than strong log-concavity: namely, our results hold for (1) weakly log-concave targets, and (2) targets satisfying isoperimetric assumptions which allow for non-log-concavity.

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

no code implementations29 Jan 2022 Haizhou Du, Ryan Yang, Yijian Chen, Qiao Xiang, Andre Wibisono, Wei Huang

In this paper, we analyze properties of the WPM and rigorously prove convergence properties of our aggregation mechanism.

Autonomous Driving

The Mirror Langevin Algorithm Converges with Vanishing Bias

no code implementations24 Sep 2021 Ruilin Li, Molei Tao, Santosh S. Vempala, Andre Wibisono

The Mirror Langevin Diffusion (MLD) is a sampling analogue of mirror flow in continuous time, and it has nice convergence properties under log-Sobolev or Poincare inequalities relative to the Hessian metric, as shown by Chewi et al. (2020).

Proximal Langevin Algorithm: Rapid Convergence Under Isoperimetry

no code implementations4 Nov 2019 Andre Wibisono

We study the Proximal Langevin Algorithm (PLA) for sampling from a probability distribution $\nu = e^{-f}$ on $\mathbb{R}^n$ under isoperimetry.

Last-iterate convergence rates for min-max optimization

no code implementations ICLR 2020 Jacob Abernethy, Kevin A. Lai, Andre Wibisono

While classic work in convex-concave min-max optimization relies on average-iterate convergence results, the emergence of nonconvex applications such as training Generative Adversarial Networks has led to renewed interest in last-iterate convergence guarantees.

Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices

no code implementations NeurIPS 2019 Santosh S. Vempala, Andre Wibisono

We also prove convergence guarantees in R\'enyi divergence of order $q > 1$ assuming the limit of ULA satisfies either the log-Sobolev or Poincar\'e inequality.

Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions

1 code implementation NeurIPS 2019 Ashia Wilson, Lester Mackey, Andre Wibisono

We also introduce a new first-order algorithm, called rescaled gradient descent (RGD), and show that RGD achieves a faster convergence rate than gradient descent provided the function is strongly smooth -- a natural generalization of the standard smoothness assumption on the objective function.

Optimization and Control

A Variational Perspective on Accelerated Methods in Optimization

no code implementations14 Mar 2016 Andre Wibisono, Ashia C. Wilson, Michael. I. Jordan

We show that there is a Lagrangian functional that we call the \emph{Bregman Lagrangian} which generates a large class of accelerated methods in continuous time, including (but not limited to) accelerated gradient descent, its non-Euclidean extension, and accelerated higher-order gradient methods.

Concavity of reweighted Kikuchi approximation

no code implementations NeurIPS 2014 Po-Ling Loh, Andre Wibisono

We establish sufficient conditions for the concavity of our reweighted objective function in terms of weight assignments in the Kikuchi expansion, and show that a reweighted version of the sum product algorithm applied to the Kikuchi region graph will produce global optima of the Kikuchi approximation whenever the algorithm converges.

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

no code implementations7 Dec 2013 John C. Duchi, Michael. I. Jordan, Martin J. Wainwright, Andre Wibisono

We consider derivative-free algorithms for stochastic and non-stochastic convex optimization problems that use only function values rather than gradients.

How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal

no code implementations NeurIPS 2013 Jacob Abernethy, Peter L. Bartlett, Rafael Frongillo, Andre Wibisono

We consider a popular problem in finance, option pricing, through the lens of an online learning game between Nature and an Investor.

online learning

Streaming Variational Bayes

2 code implementations NeurIPS 2013 Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael. I. Jordan

We present SDA-Bayes, a framework for (S)treaming, (D)istributed, (A)synchronous computation of a Bayesian posterior.

Variational Inference

Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods

no code implementations NeurIPS 2012 Andre Wibisono, Martin J. Wainwright, Michael. I. Jordan, John C. Duchi

We consider derivative-free algorithms for stochastic optimization problems that use only noisy function values rather than gradients, analyzing their finite-sample convergence rates.

Stochastic Optimization

Cannot find the paper you are looking for? You can Submit a new open access paper.