Mark Schmidt

Overview

Best Publications

• Minimizing finite sums with the stochastic average gradient

  Mark Schmidt;Nicolas Le Roux;Francis Bach

  1278
  Citations

• Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition

  Hamed Karimi;Julie Nutini;Mark Schmidt

  1073
  Citations

• A Stochastic Gradient Method with an Exponential Convergence _Rate for Finite Training Sets

  Nicolas L. Roux;Mark Schmidt;Francis R. Bach

  906
  Citations

• Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization

  Mark Schmidt;Nicolas L. Roux;Francis R. Bach

  518
  Citations

• Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches

  Mark Schmidt;Glenn Fung;Rómer Rosales

  459
  Citations

• Hybrid Deterministic-Stochastic Methods for Data Fitting

  Michael P. Friedlander;Mark W. Schmidt

  427
  Citations

• Accelerated training of conditional random fields with stochastic gradient methods

  S. V. N. Vishwanathan;Nicol N. Schraudolph;Mark W. Schmidt;Kevin P. Murphy

  420
  Citations

• Block-Coordinate Frank-Wolfe Optimization for Structural SVMs

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

  399
  Citations

• Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics

  Volkan Cevher;Stephen Becker;Mark W. Schmidt

  357
  Citations

• Fast Patch-based Style Transfer of Arbitrary Style

  Tian Qi Chen;Mark Schmidt

  344
  Citations

• Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm

  Mark W. Schmidt;Ewout van den Berg;Michael P. Friedlander;Kevin P. Murphy

  301
  Citations

• Learning graphical model structure using L1-regularization paths

  Mark Schmidt;Alexandru Niculescu-Mizil;Kevin Murphy

  270
  Citations

• 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

  245
  Citations

• Modeling annotator expertise: Learning when everybody knows a bit of something

  Yan Yan;Rómer Rosales;Glenn Fung;Mark W. Schmidt

  241
  Citations

• A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets

  Nicolas Le Roux;Mark Schmidt;Francis Bach

  241
  Citations

• Where Are the Blobs: Counting by Localization with Point Supervision

  Issam H. Laradji;Negar Rostamzadeh;Pedro O. Pinheiro;David Vazquez

  230
  Citations

• Segmenting brain tumors with conditional random fields and support vector machines

  Chi-Hoon Lee;Mark Schmidt;Albert Murtha;Aalo Bistritz

  214
  Citations

• Method and System for Automatic Detection and Segmentation of Tumors and Associated Edema (Swelling) in Magnetic Resonance (Mri) Images

  Mark Schmidt;Russell Greiner;Albert Douglas Murtha

  200
  Citations

• Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection

  Julie Nutini;Mark Schmidt;Issam Laradji;Michael Friedlander

  199
  Citations

• Convex Optimization for Big Data

  Volkan Cevher;Stephen Becker;Mark Schmidt

  180
  Citations

• Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron

  Sharan Vaswani;Francis R. Bach;Mark Schmidt

  144
  Citations