Mark Schmidt

According to our database, Mark Schmidt authored at least 100 papers between 2005 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Why Line Search when you can Plane Search? SO-Friendly Neural Networks allow Per-Iteration Optimization of Learning and Momentum Rates for Every Layer.

Betty Shea, Mark Schmidt

CoRR, 2024

BlockLLM: Memory-Efficient Adaptation of LLMs by Selecting and Optimizing the Right Coordinate Blocks.

Amrutha Varshini Ramesh, Vignesh Ganapathiraman, Issam H. Laradji, Mark Schmidt

CoRR, 2024

Enhancing Policy Gradient with the Polyak Step-Size Adaption.

Yunxiang Li, Rui Yuan, Chen Fan, Mark Schmidt, Samuel Horváth, Robert M. Gower, Martin Takác

CoRR, 2024

Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation.

Aaron Mishkin, Mert Pilanci, Mark Schmidt

CoRR, 2024

Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models.

Frederik Kunstner, Robin Yadav, Alan Milligan, Mark Schmidt, Alberto Bietti

CoRR, 2024

2023

Predicting DNA kinetics with a truncated continuous-time Markov chain method.

Sedigheh Zolaktaf, Frits Dannenberg, Mark Schmidt, Anne Condon, Erik Winfree

Comput. Biol. Chem., June, 2023

Analyzing and Improving Greedy 2-Coordinate Updates for Equality-Constrained Optimization via Steepest Descent in the 1-Norm.

Amrutha Varshini Ramesh, Aaron Mishkin, Mark Schmidt, Yihan Zhou, Jonathan Wilder Lavington, Jennifer She

CoRR, 2023

Simplifying Momentum-based Riemannian Submanifold Optimization.

Wu Lin, Valentin Duruisseaux, Melvin Leok, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt

CoRR, 2023

Optimistic Thompson Sampling-based algorithms for episodic reinforcement learning.

Bingshan Hu, Tianyue H. Zhang, Nidhi Hegde, Mark Schmidt

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Fast Convergence of Random Reshuffling Under Over-Parameterization and the Polyak-Łojasiewicz Condition.

Chen Fan, Christos Thrampoulidis, Mark Schmidt

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking.

Frederik Kunstner, Victor Sanches Portella, Mark Schmidt, Nicholas J. A. Harvey

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

Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models.

Leonardo Galli, Holger Rauhut, Mark Schmidt

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

BiSLS/SPS: Auto-tune Step Sizes for Stable Bi-level Optimization.

Chen Fan, Gaspard Choné-Ducasse, Mark Schmidt, Christos Thrampoulidis

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

Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning.

Wu Lin, Valentin Duruisseaux, Melvin Leok, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt

Proceedings of the International Conference on Machine Learning, 2023

Target-based Surrogates for Stochastic Optimization.

Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad Harikandeh, Mark Schmidt, Nicolas Le Roux

Proceedings of the International Conference on Machine Learning, 2023

Noise Is Not the Main Factor Behind the Gap Between Sgd and Adam on Transformers, But Sign Descent Might Be.

Frederik Kunstner, Jacques Chen, Jonathan Wilder Lavington, Mark Schmidt

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

SVRG meets AdaGrad: painless variance reduction.

Benjamin Dubois-Taine, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Simon Lacoste-Julien

Mach. Learn., 2022

Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence.

Julie Nutini, Issam H. Laradji, Mark Schmidt

J. Mach. Learn. Res., 2022

Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent (Extended Abstract).

Frederik Kunstner, Raunak Kumar, Mark Schmidt

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Improved Policy Optimization for Online Imitation Learning.

Jonathan Wilder Lavington, Sharan Vaswani, Mark Schmidt

Proceedings of the Conference on Lifelong Learning Agents, 2022

2021

Structured second-order methods via natural gradient descent.

Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt

CoRR, 2021

AutoRetouch: Automatic Professional Face Retouching.

Alireza Shafaei, James J. Little, Mark Schmidt

Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021

Robust Asymmetric Learning in POMDPs.

Andrew Warrington, Jonathan Wilder Lavington, Adam Scibior, Mark Schmidt, Frank Wood

Proceedings of the 38th International Conference on Machine Learning, 2021

Tractable structured natural-gradient descent using local parameterizations.

Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt

Proceedings of the 38th International Conference on Machine Learning, 2021

Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent.

Frederik Kunstner, Raunak Kumar, Mark Schmidt

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Variance-Reduced Methods for Machine Learning.

Robert M. Gower, Mark Schmidt, Francis R. Bach, Peter Richtárik

Proc. IEEE, 2020

Combining Bayesian optimization and Lipschitz optimization.

Mohamed Osama Ahmed, Sharan Vaswani, Mark Schmidt

Mach. Learn., 2020

Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search).

Sharan Vaswani, Frederik Kunstner, Issam H. Laradji, Si Yi Meng, Mark Schmidt, Simon Lacoste-Julien

CoRR, 2020

Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses.

Yihan Zhou, Victor S. Portella, Mark Schmidt, Nicholas J. A. Harvey

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Handling the Positive-Definite Constraint in the Bayesian Learning Rule.

Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan

Proceedings of the 37th International Conference on Machine Learning, 2020

Proposal-Based Instance Segmentation With Point Supervision.

Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vázquez, Mark Schmidt

Proceedings of the IEEE International Conference on Image Processing, 2020

Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation.

Si Yi Meng, Sharan Vaswani, Issam Hadj Laradji, Mark Schmidt, Simon Lacoste-Julien

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

"Active-set complexity" of proximal gradient: How long does it take to find the sparsity pattern?

Julie Nutini, Mark Schmidt, Warren L. Hare

Optim. Lett., 2019

Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures.

Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt

CoRR, 2019

Instance Segmentation with Point Supervision.

Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vázquez, Mark Schmidt

CoRR, 2019

Efficient Deep Gaussian Process Models for Variable-Sized Input.

Issam H. Laradji, Mark Schmidt, Vladimir Pavlovic, Minyoung Kim

CoRR, 2019

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

Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien

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

Efficient Deep Gaussian Process Models for Variable-Sized Inputs.

Issam H. Laradji, Mark Schmidt, Vladimir Pavlovic, Minyoung Kim

Proceedings of the International Joint Conference on Neural Networks, 2019

Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations.

Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt

Proceedings of the 36th International Conference on Machine Learning, 2019

Efficient Parameter Estimation for DNA Kinetics Modeled as Continuous-Time Markov Chains.

Sedigheh Zolaktaf, Frits Dannenberg, Erik Winfree, Alexandre Bouchard-Côté, Mark Schmidt, Anne Condon

Proceedings of the DNA Computing and Molecular Programming - 25th International Conference, 2019

A Less Biased Evaluation of Out-of-distribution Sample Detectors.

Alireza Shafaei, Mark Schmidt, James J. Little

Proceedings of the 30th British Machine Vision Conference 2019, 2019

Where are the Masks: Instance Segmentation with Image-level Supervision.

Issam H. Laradji, David Vázquez, Mark Schmidt

Proceedings of the 30th British Machine Vision Conference 2019, 2019

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

Sharan Vaswani, Francis R. Bach, Mark Schmidt

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Are we there yet? Manifold identification of gradient-related proximal methods.

Yifan Sun, Halyun Jeong, Julie Nutini, Mark Schmidt

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Distributed Maximization of "Submodular plus Diversity" Functions for Multi-label Feature Selection on Huge Datasets.

Mehrdad Ghadiri, Mark Schmidt

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Does Your Model Know the Digit 6 Is Not a Cat? A Less Biased Evaluation of "Outlier" Detectors.

Alireza Shafaei, Mark Schmidt, James J. Little

CoRR, 2018

New Insights into Bootstrapping for Bandits.

Sharan Vaswani, Branislav Kveton, Zheng Wen, Anup Rao, Mark Schmidt, Yasin Abbasi-Yadkori

CoRR, 2018

MASAGA: A Linearly-Convergent Stochastic First-Order Method for Optimization on Manifolds.

Reza Babanezhad, Issam H. Laradji, Alireza Shafaei, Mark Schmidt

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient.

Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Online Learning Rate Adaptation with Hypergradient Descent.

Atilim Gunes Baydin, Robert Cornish, David Martínez-Rubio, Mark Schmidt, Frank Wood

Proceedings of the 6th International Conference on Learning Representations, 2018

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

Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vázquez, Mark Schmidt

Proceedings of the Computer Vision - ECCV 2018, 2018

2017

Erratum to: Minimizing finite sums with the stochastic average gradient.

Mark Schmidt, Nicolas Le Roux, Francis R. Bach

Math. Program., 2017

Minimizing finite sums with the stochastic average gradient.

Mark Schmidt, Nicolas Le Roux, Francis R. Bach

Math. Program., 2017

Diffusion Independent Semi-Bandit Influence Maximization.

Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan, Mark Schmidt

CoRR, 2017

Model-Independent Online Learning for Influence Maximization.

Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan, Mark Schmidt

Proceedings of the 34th International Conference on Machine Learning, 2017

Inferring Parameters for an Elementary Step Model of DNA Structure Kinetics with Locally Context-Dependent Arrhenius Rates.

Sedigheh Zolaktaf, Frits Dannenberg, Xander Rudelis, Anne Condon, Joseph M. Schaeffer, Mark Schmidt, Chris Thachuk, Erik Winfree

Proceedings of the DNA Computing and Molecular Programming - 23rd International Conference, 2017

Horde of Bandits using Gaussian Markov Random Fields.

Sharan Vaswani, Mark Schmidt, Laks V. S. Lakshmanan

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Convergence Rates for Greedy Kaczmarz Algorithms, and Faster Randomized Kaczmarz Rules Using the Orthogonality Graph.

Julie Nutini, Behrooz Sepehry, Issam H. Laradji, Mark Schmidt, Hoyt A. Koepke, Alim Virani

CoRR, 2016

Fast Patch-based Style Transfer of Arbitrary Style.

Tian Qi Chen, Mark Schmidt

CoRR, 2016

Convergence Rates for Greedy Kaczmarz Algorithms, and Randomized Kaczmarz Rules Using the Orthogonality Graph.

Julie Nutini, Behrooz Sepehry, Issam H. Laradji, Mark Schmidt, Hoyt A. Koepke, Alim Virani

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions.

Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

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

Hamed Karimi, Julie Nutini, Mark Schmidt

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016

Play and Learn: Using Video Games to Train Computer Vision Models.

Alireza Shafaei, James J. Little, Mark Schmidt

Proceedings of the British Machine Vision Conference 2016, 2016

2015

Hierarchical Maximum-Margin Clustering.

Guang-Tong Zhou, Sung Ju Hwang, Mark Schmidt, Leonid Sigal, Greg Mori

CoRR, 2015

Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence.

Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama

CoRR, 2015

Stop Wasting My Gradients: Practical SVRG.

Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen

CoRR, 2015

StopWasting My Gradients: Practical SVRG.

Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

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

Julie Nutini, Mark Schmidt, Issam H. Laradji, Michael P. Friedlander, Hoyt A. Koepke

Proceedings of the 32nd International Conference on Machine Learning, 2015

Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields.

Mark Schmidt, Reza Babanezhad, Mohamed Osama Ahmed, Aaron Defazio, Ann Clifton, Anoop Sarkar

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

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

Volkan Cevher, Stephen Becker, Mark Schmidt

IEEE Signal Process. Mag., 2014

Convex Optimization for Big Data.

Volkan Cevher, Stephen Becker, Mark Schmidt

CoRR, 2014

2013

Erratum: Hybrid Deterministic-Stochastic Methods for Data Fitting.

Michael P. Friedlander, Mark Schmidt

SIAM J. Sci. Comput., 2013

Block-Coordinate Frank-Wolfe Optimization for Structural SVMs.

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

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Hybrid Deterministic-Stochastic Methods for Data Fitting.

Michael P. Friedlander, Mark Schmidt

SIAM J. Sci. Comput., 2012

On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models.

David Buchman, Mark Schmidt, Shakir Mohamed, David Poole, Nando de Freitas

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

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

Simon Lacoste-Julien, Mark Schmidt, Francis R. Bach

CoRR, 2012

Stochastic Block-Coordinate Frank-Wolfe Optimization for Structural SVMs

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

CoRR, 2012

A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets

Nicolas Le Roux, Mark Schmidt, Francis R. Bach

CoRR, 2012

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

Nicolas Le Roux, Mark Schmidt, Francis R. Bach

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

2011

Generalized Fast Approximate Energy Minimization via Graph Cuts: Alpha-Expansion Beta-Shrink Moves

Mark Schmidt, Karteek Alahari

CoRR, 2011

Generalized Fast Approximate Energy Minimization via Graph Cuts: a-Expansion b-Shrink Moves.

Mark Schmidt, Karteek Alahari

Proceedings of the UAI 2011, 2011

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

Mark Schmidt, Nicolas Le Roux, Francis R. Bach

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

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

Yan Yan, Rómer Rosales, Glenn Fung, Mark Schmidt, Gerardo Hermosillo Valadez, Luca Bogoni, Linda Moy, Jennifer G. Dy

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials.

Mark Schmidt, Kevin P. Murphy

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Causal learning without DAGs.

David Duvenaud, Daniel Eaton, Kevin P. Murphy, Mark Schmidt

Proceedings of the Causality: Objectives and Assessment (NIPS 2008 Workshop), 2010

2009

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

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

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Modeling Discrete Interventional Data using Directed Cyclic Graphical Models.

Mark Schmidt, Kevin P. Murphy

Proceedings of the UAI 2009, 2009

Group Sparse Priors for Covariance Estimation.

Benjamin M. Marlin, Mark Schmidt, Kevin P. Murphy

Proceedings of the UAI 2009, 2009

Increased discrimination in level set methods with embedded conditional random fields.

Dana Cobzas, Mark Schmidt

Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 2009

2008

An interior-point stochastic approximation method and an L1-regularized delta rule.

Peter Carbonetto, Mark Schmidt, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Structure learning in random fields for heart motion abnormality detection.

Mark Schmidt, Kevin P. Murphy, Glenn Fung, Rómer Rosales

Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 2008

2007

3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set.

Dana Cobzas, Neil Birkbeck, Mark Schmidt, Martin Jägersand, Albert Murtha

Proceedings of the IEEE 11th International Conference on Computer Vision, 2007

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

Mark Schmidt, Glenn Fung, Rómer Rosales

Proceedings of the Machine Learning: ECML 2007, 2007

Learning Graphical Model Structure Using L1-Regularization Paths.

Mark Schmidt, Alexandru Niculescu-Mizil, Kevin P. Murphy

Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007

2006

Learning a Classification-based Glioma Growth Model Using MRI Data.

Marianne Morris, Russell Greiner, Jörg Sander, Albert Murtha, Mark Schmidt

J. Comput., 2006

Accelerated training of conditional random fields with stochastic gradient methods.

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

Proceedings of the Machine Learning, 2006

A Classification-Based Glioma Diffusion Model Using MRI Data.

Marianne Morris, Russell Greiner, Jörg Sander, Albert Murtha, Mark Schmidt

Proceedings of the Advances in Artificial Intelligence, 2006

2005

Support Vector Random Fields for Spatial Classification.

Chi-Hoon Lee, Russell Greiner, Mark Schmidt

Proceedings of the Knowledge Discovery in Databases: PKDD 2005, 2005

Segmenting brain tumors using alignment-based features.

Mark Schmidt, Ilya Levner, Russell Greiner, Albert Murtha, Aalo Bistritz

Proceedings of the Fourth International Conference on Machine Learning and Applications, 2005

Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines.

Chi-Hoon Lee, Mark Schmidt, Albert Murtha, Aalo Bistritz, Jörg Sander, Russell Greiner

Proceedings of the Computer Vision for Biomedical Image Applications, 2005