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Simon Lacoste-Julien

Simon Lacoste-Julien

D-Index & Metrics

Computer Science

D-Index
45
Citations
8513
World Ranking
7191
National Ranking
287

Overview

Simon Lacoste-Julien is affiliated with the University of Montreal in Canada, focusing primarily on research within the field of Computer Science. Their work spans across several subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mechanics, Statistics and Probability, and Computational Theory and Mathematics.

The scientist has contributed extensively to topics such as Domain Adaptation and Few-Shot Learning, Stochastic Gradient Optimization Techniques, Advanced Neural Network Applications, Machine Learning and Algorithms, Sparse and Compressive Sensing Techniques, Neural Networks and Applications, and Markov Chains and Monte Carlo Methods.

Recent publications by Simon Lacoste-Julien include:

  • A Survey of Self-Supervised and Few-Shot Object Detection, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence, 2020, arXiv (Cornell University)
  • Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information, 2022, arXiv (Cornell University)
  • Bayesian Structure Learning with Generative Flow Networks, 2022, arXiv (Cornell University)
  • Predicting Tactical Solutions to Operational Planning Problems Under Imperfect Information, 2021, INFORMS journal on computing

Lacoste-Julien's work has been published frequently in the venue arXiv (Cornell University), with 51 publications, alongside contributions to the IEEE Transactions on Pattern Analysis and Machine Intelligence, INFORMS journal on computing, Machine Learning, and EURO Journal on Transportation and Logistics.

Collaboration appears as a significant aspect of this researcher's output, with frequent coauthors including Sébastien Lachapelle, Gauthier Gidel, Ioannis Mitliagkas, Yoshua Bengio, and Nicolas Loizou, reflecting ongoing partnerships within their research network.

Best Publications

  • SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives

    Aaron Defazio;Francis Bach;Simon Lacoste-Julien

  • A closer look at memorization in deep networks

    Devansh Arpit;Stanisław Jastrzębski;Nicolas Ballas;David Krueger

  • DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification

    Simon Lacoste-Julien;Fei Sha;Michael I. Jordan

  • Block-Coordinate Frank-Wolfe Optimization for Structural SVMs

    Simon Lacoste-Julien;Martin Jaggi;Mark Schmidt;Patrick Pletscher

  • On the global linear convergence of Frank-Wolfe optimization variants

    Simon Lacoste-Julien;Martin Jaggi

  • Unsupervised Learning from Narrated Instruction Videos

    Jean-Baptiste Alayrac;Piotr Bojanowski;Nishant Agrawal;Nishant Agrawal;Josef Sivic

  • A Discriminative Matching Approach to Word Alignment

    Ben Taskar;Lacoste-Julien Simon;Klein Dan

  • A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method

    Simon Lacoste-Julien;Mark W. Schmidt;Francis R. Bach

  • A Variational Inequality Perspective on Generative Adversarial Networks

    Gauthier Gidel;Hugo Berard;Gaëtan Vignoud;Pascal Vincent

  • SIGMa: simple greedy matching for aligning large knowledge bases

    Simon Lacoste-Julien;Konstantina Palla;Alex Davies;Gjergji Kasneci

  • On pairwise costs for network flow multi-object tracking

    Visesh Chari;Simon Lacoste-Julien;Ivan Laptev;Josef Sivic

  • Structured Prediction, Dual Extragradient and Bregman Projections

    Ben Taskar;Simon Lacoste-Julien;Michael I. Jordan

  • Convergence Rate of Frank-Wolfe for Non-Convex Objectives.

    Simon Lacoste-Julien;Simon Lacoste-Julien

  • On the Equivalence between Herding and Conditional Gradient Algorithms

    Simon Lacoste-julien;Francis R. Bach;Guillaume R. Obozinski

  • On the Equivalence between Herding and Conditional Gradient Algorithms

    Francis Bach;Simon Lacoste-Julien;Guillaume Obozinski

  • Negative Momentum for Improved Game Dynamics

    Gauthier Gidel;Reyhane Askari Hemmat;Mohammad Pezeshki;Gabriel Huang

  • PAC-Bayesian theory meets Bayesian inference

    Pascal Germain;Francis Bach;Alexandre Lacoste;Simon Lacoste-Julien

  • Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates

    Sharan Vaswani;Aaron Mishkin;Issam H. Laradji;Mark Schmidt

  • Variance reduced stochastic gradient descent with neighbors

    Thomas Hofmann;Aurelien Lucchi;Simon Lacoste-Julien;Brian McWilliams

  • ASAGA: Asynchronous Parallel SAGA

    Rémi Leblond;Fabian Pedregosa;Simon Lacoste-Julien

  • A Modern Take on the Bias-Variance Tradeoff in Neural Networks

    Brady Neal;Sarthak Mittal;Aristide Baratin;Vinayak Tantia

  • Differentiable Causal Discovery from Interventional Data

    Philippe Brouillard;Sébastien Lachapelle;Alexandre Lacoste;Simon Lacoste-Julien

Frequent Co-Authors

Francis Bach
Francis Bach École Normale Supérieure
Pascal Vincent
Pascal Vincent Facebook (United States)
Ivan Laptev
Ivan Laptev Mohamed bin Zayed University of Artificial Intelligence
Josef Sivic
Josef Sivic Czech Technical University in Prague
Mark Schmidt
Mark Schmidt University of British Columbia
Yoshua Bengio
Yoshua Bengio University of Montreal
François Soumis
François Soumis Polytechnique Montréal
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Piotr Bojanowski
Piotr Bojanowski Facebook (United States)
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge

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