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Andre Wibisono

According to our database¹, Andre Wibisono authored at least 40 papers between 2012 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

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Links

On csauthors.net:

Bibliography

2024

Fast Convergence of Ψ-Divergence Along the Unadjusted Langevin Algorithm and Proximal Sampler.

[BibT_eX]

[DOI]

Siddharth Mitra

,

CoRR, 2024

A Symplectic Analysis of Alternating Mirror Descent.

[BibT_eX]

[DOI]

,

,

CoRR, 2024

On Independent Samples Along the Langevin Diffusion and the Unadjusted Langevin Algorithm.

[BibT_eX]

[DOI]

,

Siddharth Mitra

,

CoRR, 2024

Optimal score estimation via empirical Bayes smoothing.

[BibT_eX]

[DOI]

,

,

Kaylee Yingxi Yang

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Fast sampling from constrained spaces using the Metropolis-adjusted Mirror Langevin algorithm.

[BibT_eX]

[DOI]

Vishwak Srinivasan

,

,

Ashia C. Wilson

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Extragradient Type Methods for Riemannian Variational Inequality Problems.

[BibT_eX]

[DOI]

,

,

,

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Jacob D. Abernethy

,

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Mitigating Catastrophic Forgetting in Long Short-Term Memory Networks.

[BibT_eX]

[DOI]

,

Raghavendra Pradyumna Pothukuchi

,

,

Abhishek Bhattacharjee

CoRR, 2023

Learning Exponential Families from Truncated Samples.

[BibT_eX]

[DOI]

,

,

Emmanouil Zampetakis

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time.

[BibT_eX]

[DOI]

,

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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

[BibT_eX]

[DOI]

,

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Continuized Acceleration for Quasar Convex Functions in Non-Convex Optimization.

[BibT_eX]

[DOI]

,

Proceedings of the Eleventh International Conference on Learning Representations, 2023

On a Class of Gibbs Sampling over Networks.

[BibT_eX]

[DOI]

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,

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Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

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

[BibT_eX]

[DOI]

,

Kaylee Yingxi Yang

CoRR, 2022

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

[BibT_eX]

[DOI]

,

,

,

,

,

CoRR, 2022

Alternating Mirror Descent for Constrained Min-Max Games.

[BibT_eX]

[DOI]

,

,

Georgios Piliouras

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

[BibT_eX]

[DOI]

,

,

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Proceedings of the IEEE Military Communications Conference, 2022

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

[BibT_eX]

[DOI]

,

,

,

Proceedings of the International Conference on Machine Learning, 2022

Improved analysis for a proximal algorithm for sampling.

[BibT_eX]

[DOI]

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,

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Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

The Mirror Langevin Algorithm Converges with Vanishing Bias.

[BibT_eX]

[DOI]

,

,

Santosh S. Vempala

,

Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

2021

Fast Convergence of Fictitious Play for Diagonal Payoff Matrices.

[BibT_eX]

[DOI]

Jacob D. Abernethy

,

,

Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms, 2021

Last-Iterate Convergence Rates for Min-Max Optimization: Convergence of Hamiltonian Gradient Descent and Consensus Optimization.

[BibT_eX]

[DOI]

Jacob D. Abernethy

,

,

Proceedings of the Algorithmic Learning Theory, 2021

2019

Fictitious Play: Convergence, Smoothness, and Optimism.

[BibT_eX]

[DOI]

Jacob D. Abernethy

,

,

CoRR, 2019

Proximal Langevin Algorithm: Rapid Convergence Under Isoperimetry.

[BibT_eX]

[DOI]

CoRR, 2019

Last-iterate convergence rates for min-max optimization.

[BibT_eX]

[DOI]

Jacob D. Abernethy

,

,

CoRR, 2019

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

[BibT_eX]

[DOI]

Santosh S. Vempala

,

CoRR, 2019

Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions.

[BibT_eX]

[DOI]

Ashia C. Wilson

,

,

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices.

[BibT_eX]

[DOI]

Santosh S. Vempala

,

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Convexity of mutual information along the Ornstein-Uhlenbeck flow.

[BibT_eX]

[DOI]

,

Proceedings of the International Symposium on Information Theory and Its Applications, 2018

Convexity of Mutual Information Along the Heat Flow.

[BibT_eX]

[DOI]

,

Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

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

[BibT_eX]

[DOI]

Proceedings of the Conference On Learning Theory, 2018

2017

Information and estimation in Fokker-Planck channels.

[BibT_eX]

[DOI]

,

,

Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017

2016

Variational and Dynamical Perspectives On Learning and Optimization.

[BibT_eX]

[DOI]

PhD thesis, 2016

A Variational Perspective on Accelerated Methods in Optimization.

[BibT_eX]

[DOI]

,

Ashia C. Wilson

,

Michael I. Jordan

CoRR, 2016

2015

Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations.

[BibT_eX]

[DOI]

,

Michael I. Jordan

,

Martin J. Wainwright

,

IEEE Trans. Inf. Theory, 2015

2014

Concavity of reweighted Kikuchi approximation.

[BibT_eX]

[DOI]

,

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

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

[BibT_eX]

[DOI]

,

Michael I. Jordan

,

Martin J. Wainwright

,

CoRR, 2013

Streaming Variational Bayes.

[BibT_eX]

[DOI]

Tamara Broderick

,

,

,

Ashia C. Wilson

,

Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

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

[BibT_eX]

[DOI]

Jacob D. Abernethy

,

Peter L. Bartlett

,

Rafael M. Frongillo

,

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012

Minimax option pricing meets black-scholes in the limit.

[BibT_eX]

[DOI]

Jacob D. Abernethy

,

Rafael M. Frongillo

,

Proceedings of the 44th Symposium on Theory of Computing Conference, 2012

Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods.

[BibT_eX]

[DOI]

,

Michael I. Jordan

,

Martin J. Wainwright

,

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

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