Mark Schmidt

ORCID

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 89

Top Venues

Publications

Betty Shea, Mark Schmidt
Why Line Search when you can Plane Search? SO-Friendly Neural Networks allow Per-Iteration Optimization of Learning and Momentum Rates for Every Layer. CoRR (2024)
Frederik Kunstner, Robin Yadav, Alan Milligan, Mark Schmidt, Alberto Bietti
Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models. CoRR (2024)
Amrutha Varshini Ramesh, Vignesh Ganapathiraman, Issam H. Laradji, Mark Schmidt
BlockLLM: Memory-Efficient Adaptation of LLMs by Selecting and Optimizing the Right Coordinate Blocks. CoRR (2024)
Aaron Mishkin, Mert Pilanci, Mark Schmidt
Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation. CoRR (2024)
Yunxiang Li, Rui Yuan, Chen Fan, Mark Schmidt, Samuel Horváth, Robert M. Gower, Martin Takác
Enhancing Policy Gradient with the Polyak Step-Size Adaption. CoRR (2024)
Frederik Kunstner, Jacques Chen, Jonathan Wilder Lavington, Mark Schmidt
Noise Is Not the Main Factor Behind the Gap Between Sgd and Adam on Transformers, But Sign Descent Might Be. ICLR (2023)
Bingshan Hu, Tianyue H. Zhang, Nidhi Hegde, Mark Schmidt
Optimistic Thompson Sampling-based algorithms for episodic reinforcement learning. UAI (2023)
Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Nicolas Le Roux
Target-based Surrogates for Stochastic Optimization. CoRR (2023)
Frederik Kunstner, Victor S. Portella, Mark Schmidt, Nick Harvey
Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking. CoRR (2023)
Chen Fan, Christos Thrampoulidis, Mark Schmidt
Fast Convergence of Random Reshuffling Under Over-Parameterization and the Polyak-Łojasiewicz Condition. ECML/PKDD (4) (2023)
Wu Lin, Valentin Duruisseaux, Melvin Leok, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt
Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning. ICML (2023)
Frederik Kunstner, Jacques Chen, Jonathan Wilder Lavington, Mark Schmidt
Noise Is Not the Main Factor Behind the Gap Between SGD and Adam on Transformers, but Sign Descent Might Be. CoRR (2023)
Frederik Kunstner, Victor Sanches Portella, Mark Schmidt, Nicholas J. A. Harvey
Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking. NeurIPS (2023)
Chen Fan, Gaspard Choné-Ducasse, Mark Schmidt, Christos Thrampoulidis
BiSLS/SPS: Auto-tune Step Sizes for Stable Bi-level Optimization. CoRR (2023)
Leonardo Galli, Holger Rauhut, Mark Schmidt
Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models. CoRR (2023)
Amrutha Varshini Ramesh, Aaron Mishkin, Mark Schmidt, Yihan Zhou, Jonathan Wilder Lavington, Jennifer She
Analyzing and Improving Greedy 2-Coordinate Updates for Equality-Constrained Optimization via Steepest Descent in the 1-Norm. CoRR (2023)
Leonardo Galli, Holger Rauhut, Mark Schmidt
Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models. NeurIPS (2023)
Wu Lin, Valentin Duruisseaux, Melvin Leok, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt
Simplifying Momentum-based Riemannian Submanifold Optimization. CoRR (2023)
Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad Harikandeh, Mark Schmidt, Nicolas Le Roux
Target-based Surrogates for Stochastic Optimization. ICML (2023)
Sedigheh Zolaktaf, Frits Dannenberg, Mark Schmidt, Anne Condon, Erik Winfree
Predicting DNA kinetics with a truncated continuous-time Markov chain method. Comput. Biol. Chem. 104 (2023)
Chen Fan, Gaspard Choné-Ducasse, Mark Schmidt, Christos Thrampoulidis
BiSLS/SPS: Auto-tune Step Sizes for Stable Bi-level Optimization. NeurIPS (2023)
Frederik Kunstner, Raunak Kumar, Mark Schmidt
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent (Extended Abstract). IJCAI (2022)
Benjamin Dubois-Taine, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Simon Lacoste-Julien
SVRG meets AdaGrad: painless variance reduction. Mach. Learn. 111 (12) (2022)
Julie Nutini, Issam H. Laradji, Mark Schmidt
Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence. J. Mach. Learn. Res. 23 (2022)
Jonathan Wilder Lavington, Sharan Vaswani, Mark Schmidt
Improved Policy Optimization for Online Imitation Learning. CoRR (2022)
Jonathan Wilder Lavington, Sharan Vaswani, Mark Schmidt
Improved Policy Optimization for Online Imitation Learning. CoLLAs (2022)
Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt
Structured second-order methods via natural gradient descent. CoRR (2021)
Alireza Shafaei, James J. Little, Mark Schmidt
AutoRetouch: Automatic Professional Face Retouching. WACV (2021)
Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt
Tractable structured natural-gradient descent using local parameterizations. ICML (2021)
Frederik Kunstner, Raunak Kumar, Mark Schmidt
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent. AISTATS (2021)
Benjamin Dubois-Taine, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Simon Lacoste-Julien
SVRG Meets AdaGrad: Painless Variance Reduction. CoRR (2021)
Andrew Warrington, Jonathan Wilder Lavington, Adam Scibior, Mark Schmidt, Frank Wood
Robust Asymmetric Learning in POMDPs. ICML (2021)
Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt
Tractable structured natural gradient descent using local parameterizations. CoRR (2021)
Yihan Zhou, Victor S. Portella, Mark Schmidt, Nicholas J. A. Harvey
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses. NeurIPS (2020)
Si Yi Meng, Sharan Vaswani, Issam Hadj Laradji, Mark Schmidt, Simon Lacoste-Julien
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation. AISTATS (2020)
Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan
Handling the Positive-Definite Constraint in the Bayesian Learning Rule. ICML (2020)
Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vázquez, Mark Schmidt
Proposal-Based Instance Segmentation With Point Supervision. ICIP (2020)
Yihan Zhou, Victor S. Portella, Mark Schmidt, Nicholas J. A. Harvey
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses. CoRR (2020)
Robert M. Gower, Mark Schmidt, Francis R. Bach, Peter Richtárik
Variance-Reduced Methods for Machine Learning. CoRR (2020)
Andrew Warrington, J. Wilder Lavington, Adam Scibior, Mark Schmidt, Frank Wood
Robust Asymmetric Learning in POMDPs. CoRR (2020)
Sharan Vaswani, Frederik Kunstner, Issam H. Laradji, Si Yi Meng, Mark Schmidt, Simon Lacoste-Julien
Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search). CoRR (2020)
Mohamed Osama Ahmed, Sharan Vaswani, Mark Schmidt
Combining Bayesian optimization and Lipschitz optimization. Mach. Learn. 109 (1) (2020)
Robert M. Gower, Mark Schmidt, Francis R. Bach, Peter Richtárik
Variance-Reduced Methods for Machine Learning. Proc. IEEE 108 (11) (2020)
Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan
Handling the Positive-Definite Constraint in the Bayesian Learning Rule. CoRR (2020)
Frederik Kunstner, Raunak Kumar, Mark Schmidt
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent. CoRR (2020)
Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations. ICML (2019)
Sharan Vaswani, Francis R. Bach, Mark Schmidt
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron. AISTATS (2019)
Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. NeurIPS (2019)
Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vázquez, Mark Schmidt
Instance Segmentation with Point Supervision. CoRR (2019)
Julie Nutini, Mark Schmidt, Warren L. Hare
"Active-set complexity" of proximal gradient: How long does it take to find the sparsity pattern? Optim. Lett. 13 (4) (2019)
Alireza Shafaei, Mark Schmidt, James J. Little
A Less Biased Evaluation of Out-of-distribution Sample Detectors. BMVC (2019)
Issam H. Laradji, Mark Schmidt, Vladimir Pavlovic, Minyoung Kim
Efficient Deep Gaussian Process Models for Variable-Sized Input. CoRR (2019)
Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations. CoRR (2019)
Mehrdad Ghadiri, Mark Schmidt
Distributed Maximization of "Submodular plus Diversity" Functions for Multi-label Feature Selection on Huge Datasets. CoRR (2019)
Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt
Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures. CoRR (2019)
Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. CoRR (2019)
Issam H. Laradji, David Vázquez, Mark Schmidt
Where are the Masks: Instance Segmentation with Image-level Supervision. BMVC (2019)
Issam H. Laradji, David Vázquez, Mark Schmidt
Where are the Masks: Instance Segmentation with Image-level Supervision. CoRR (2019)
Sedigheh Zolaktaf, Frits Dannenberg, Erik Winfree, Alexandre Bouchard-Côté, Mark Schmidt, Anne Condon
Efficient Parameter Estimation for DNA Kinetics Modeled as Continuous-Time Markov Chains. DNA (2019)
Si Yi Meng, Sharan Vaswani, Issam H. Laradji, Mark Schmidt, Simon Lacoste-Julien
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation. CoRR (2019)
Mehrdad Ghadiri, Mark Schmidt
Distributed Maximization of "Submodular plus Diversity" Functions for Multi-label Feature Selection on Huge Datasets. AISTATS (2019)
Issam H. Laradji, Mark Schmidt, Vladimir Pavlovic, Minyoung Kim
Efficient Deep Gaussian Process Models for Variable-Sized Inputs. IJCNN (2019)
Yifan Sun, Halyun Jeong, Julie Nutini, Mark Schmidt
Are we there yet? Manifold identification of gradient-related proximal methods. AISTATS (2019)
Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vázquez, Mark Schmidt
Where are the Blobs: Counting by Localization with Point Supervision. CoRR (2018)
Sharan Vaswani, Branislav Kveton, Zheng Wen, Anup Rao, Mark Schmidt, Yasin Abbasi-Yadkori
New Insights into Bootstrapping for Bandits. CoRR (2018)
Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vázquez, Mark Schmidt
Where Are the Blobs: Counting by Localization with Point Supervision. ECCV (2) (2018)
Alireza Shafaei, Mark Schmidt, James J. Little
Does Your Model Know the Digit 6 Is Not a Cat? A Less Biased Evaluation of "Outlier" Detectors. CoRR (2018)
Mohamed Osama Ahmed, Sharan Vaswani, Mark Schmidt
Combining Bayesian Optimization and Lipschitz Optimization. CoRR (2018)
Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. NeurIPS (2018)
Reza Babanezhad, Issam H. Laradji, Alireza Shafaei, Mark Schmidt
MASAGA: A Linearly-Convergent Stochastic First-Order Method for Optimization on Manifolds. ECML/PKDD (2) (2018)
Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. CoRR (2018)
Sharan Vaswani, Francis R. Bach, Mark Schmidt
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron. CoRR (2018)
Atilim Gunes Baydin, Robert Cornish, David Martínez-Rubio, Mark Schmidt, Frank Wood
Online Learning Rate Adaptation with Hypergradient Descent. ICLR (Poster) (2018)
Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan, Mark Schmidt
Model-Independent Online Learning for Influence Maximization. ICML (2017)
Mark Schmidt, Nicolas Le Roux, Francis R. Bach
Erratum to: Minimizing finite sums with the stochastic average gradient. Math. Program. 162 (1-2) (2017)
Atilim Gunes Baydin, Robert Cornish, David Martínez-Rubio, Mark Schmidt, Frank D. Wood
Online Learning Rate Adaptation with Hypergradient Descent. CoRR (2017)
Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan, Mark Schmidt
Diffusion Independent Semi-Bandit Influence Maximization. CoRR (2017)
Sedigheh Zolaktaf, Frits Dannenberg, Xander Rudelis, Anne Condon, Joseph M. Schaeffer, Mark Schmidt, Chris Thachuk, Erik Winfree
Inferring Parameters for an Elementary Step Model of DNA Structure Kinetics with Locally Context-Dependent Arrhenius Rates. DNA (2017)
Mark Schmidt, Nicolas Le Roux, Francis R. Bach
Minimizing finite sums with the stochastic average gradient. Math. Program. 162 (1-2) (2017)
Sharan Vaswani, Mark Schmidt, Laks V. S. Lakshmanan
Horde of Bandits using Gaussian Markov Random Fields. CoRR (2017)
Sharan Vaswani, Mark Schmidt, Laks V. S. Lakshmanan
Horde of Bandits using Gaussian Markov Random Fields. AISTATS (2017)
Hamed Karimi, Julie Nutini, Mark Schmidt
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition. ECML/PKDD (1) (2016)
Hamed Karimi, Julie Nutini, Mark Schmidt
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition. CoRR (2016)
Julie Nutini, Behrooz Sepehry, Issam H. Laradji, Mark Schmidt, Hoyt A. Koepke, Alim Virani
Convergence Rates for Greedy Kaczmarz Algorithms, and Faster Randomized Kaczmarz Rules Using the Orthogonality Graph. CoRR (2016)
Julie Nutini, Behrooz Sepehry, Issam H. Laradji, Mark Schmidt, Hoyt A. Koepke, Alim Virani
Convergence Rates for Greedy Kaczmarz Algorithms, and Randomized Kaczmarz Rules Using the Orthogonality Graph. UAI (2016)
Alireza Shafaei, James J. Little, Mark Schmidt
Play and Learn: Using Video Games to Train Computer Vision Models. BMVC (2016)
Tian Qi Chen, Mark Schmidt
Fast Patch-based Style Transfer of Arbitrary Style. CoRR (2016)
Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama
Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions. UAI (2016)
Alireza Shafaei, James J. Little, Mark Schmidt
Play and Learn: Using Video Games to Train Computer Vision Models. CoRR (2016)
Julie Nutini, Mark Schmidt, Issam H. Laradji, Michael P. Friedlander, Hoyt A. Koepke
Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection. ICML (2015)
Mark Schmidt, Reza Babanezhad, Mohamed Osama Ahmed, Aaron Defazio, Ann Clifton, Anoop Sarkar
Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields. AISTATS (2015)
Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen
Stop Wasting My Gradients: Practical SVRG. CoRR (2015)
Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen
StopWasting My Gradients: Practical SVRG. NIPS (2015)
Guang-Tong Zhou, Sung Ju Hwang, Mark Schmidt, Leonid Sigal, Greg Mori
Hierarchical Maximum-Margin Clustering. CoRR (2015)
Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama
Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence. CoRR (2015)
Mark Schmidt, Reza Babanezhad, Mohamed Osama Ahmed, Aaron Defazio, Ann Clifton, Anoop Sarkar
Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields. CoRR (2015)
Julie Nutini, Mark Schmidt, Issam H. Laradji, Michael P. Friedlander, Hoyt A. Koepke
Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection. CoRR (2015)
Volkan Cevher, Stephen Becker, Mark Schmidt
Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics. IEEE Signal Process. Mag. 31 (5) (2014)
Volkan Cevher, Stephen Becker, Mark Schmidt
Convex Optimization for Big Data. CoRR (2014)
Simon Lacoste-Julien, Martin Jaggi, Mark Schmidt, Patrick Pletscher
Block-Coordinate Frank-Wolfe Optimization for Structural SVMs. ICML (1) (2013)

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Publication Activity (10 Years)

Top Topics

Top Venues

Mark Schmidt