Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines. - Chi-Hoon Lee; Mark Schmidt 001; Albert Murtha; Aalo Bistritz; Jörg Sander 001; Russell Greiner (CVBIA, 2005)

Segmenting brain tumors using alignment-based features. - Mark Schmidt 001; Ilya Levner; Russell Greiner; Albert Murtha; Aalo Bistritz (ICMLA, 2005)

Support Vector Random Fields for Spatial Classification. - Chi-Hoon Lee; Russell Greiner; Mark Schmidt 001 (PKDD, 2005)

A Classification-Based Glioma Diffusion Model Using MRI Data. - Marianne Morris; Russell Greiner; Jörg Sander 001; Albert Murtha; Mark Schmidt 001 (Canadian AI, 2006)

Accelerated training of conditional random fields with stochastic gradient methods. - S. V. N. Vishwanathan; Nicol N. Schraudolph; Mark W. Schmidt; Kevin P. Murphy (ICML, 2006)

Learning a Classification-based Glioma Growth Model Using MRI Data. - Marianne Morris; Russell Greiner; Jörg Sander 001; Albert Murtha; Mark Schmidt 001 (J. Comput., 2006)

Learning Graphical Model Structure Using L1-Regularization Paths. - Mark Schmidt 001; Alexandru Niculescu-Mizil; Kevin P. Murphy (AAAI, 2007)

Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches. - Mark Schmidt 001; Glenn Fung; Rómer Rosales (ECML, 2007)

3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set. - Dana Cobzas; Neil Birkbeck; Mark Schmidt 001; Martin Jägersand; Albert Murtha (ICCV, 2007)

Structure learning in random fields for heart motion abnormality detection. - Mark Schmidt 001; Kevin P. Murphy; Glenn Fung; Rómer Rosales (CVPR, 2008)

An interior-point stochastic approximation method and an L1-regularized delta rule. - Peter Carbonetto; Mark Schmidt 001; Nando de Freitas (NIPS, 2008)

Increased discrimination in level set methods with embedded conditional random fields. - Dana Cobzas; Mark Schmidt 001 (CVPR, 2009)

Group Sparse Priors for Covariance Estimation. - Benjamin M. Marlin; Mark Schmidt 001; Kevin P. Murphy (UAI, 2009)

Modeling Discrete Interventional Data using Directed Cyclic Graphical Models. - Mark Schmidt 001; Kevin P. Murphy (UAI, 2009)

Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm. - Mark Schmidt 001; Ewout van den Berg; Michael P. Friedlander; Kevin P. Murphy (AISTATS, 2009)

Causal learning without DAGs. - David Duvenaud; Daniel Eaton; Kevin P. Murphy; Mark Schmidt 001 (NIPS Causality: Objectives and Assessment, 2010)

Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials. - Mark Schmidt 001; Kevin P. Murphy (AISTATS, 2010)

Modeling annotator expertise: Learning when everybody knows a bit of something. - Yan Yan 024; Rómer Rosales; Glenn Fung; Mark Schmidt 001; Gerardo Hermosillo Valadez; Luca Bogoni; Linda Moy; Jennifer G. Dy (AISTATS, 2010)

Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization. - Mark Schmidt 001; Nicolas Le Roux; Francis R. Bach (NIPS, 2011)

Generalized Fast Approximate Energy Minimization via Graph Cuts: a-Expansion b-Shrink Moves. - Mark Schmidt 001; Karteek Alahari (UAI, 2011)

Generalized Fast Approximate Energy Minimization via Graph Cuts: Alpha-Expansion Beta-Shrink Moves - Mark Schmidt 001; Karteek Alahari (CoRR, 2011)

A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets. - Nicolas Le Roux; Mark Schmidt 001; Francis R. Bach (NIPS, 2012)

A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets - Nicolas Le Roux; Mark Schmidt 001; Francis R. Bach (CoRR, 2012)

Stochastic Block-Coordinate Frank-Wolfe Optimization for Structural SVMs - Simon Lacoste-Julien; Martin Jaggi; Mark Schmidt 001; Patrick Pletscher (CoRR, 2012)

A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method - Simon Lacoste-Julien; Mark Schmidt 001; Francis R. Bach (CoRR, 2012)

On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models. - David Buchman; Mark Schmidt 001; Shakir Mohamed; David Poole 001; Nando de Freitas (AISTATS, 2012)

Hybrid Deterministic-Stochastic Methods for Data Fitting. - Michael P. Friedlander; Mark Schmidt 001 (SIAM J. Sci. Comput., 2012)

Block-Coordinate Frank-Wolfe Optimization for Structural SVMs. - Simon Lacoste-Julien; Martin Jaggi; Mark Schmidt 001; Patrick Pletscher (ICML (1), 2013)

Erratum: Hybrid Deterministic-Stochastic Methods for Data Fitting. - Michael P. Friedlander; Mark Schmidt 001 (SIAM J. Sci. Comput., 2013)

Convex Optimization for Big Data. - Volkan Cevher; Stephen Becker; Mark Schmidt 001 (CoRR, 2014)

Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics. - Volkan Cevher; Stephen Becker; Mark Schmidt 001 (IEEE Signal Process. Mag., 2014)

Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields. - Mark Schmidt 001; Reza Babanezhad 001; Mohamed Osama Ahmed; Aaron Defazio; Ann Clifton; Anoop Sarkar (AISTATS, 2015)

Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection. - Julie Nutini; Mark Schmidt 001; Issam H. Laradji; Michael P. Friedlander; Hoyt A. Koepke (ICML, 2015)

StopWasting My Gradients: Practical SVRG. - Reza Babanezhad 001; Mohamed Osama Ahmed; Alim Virani; Mark Schmidt 001; Jakub Konecný; Scott Sallinen (NIPS, 2015)

Stop Wasting My Gradients: Practical SVRG. - Reza Babanezhad 001; Mohamed Osama Ahmed; Alim Virani; Mark Schmidt 001; Jakub Konecný; Scott Sallinen (CoRR, 2015)

Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence. - Mohammad Emtiyaz Khan; Reza Babanezhad 001; Wu Lin; Mark Schmidt 001; Masashi Sugiyama (CoRR, 2015)

Hierarchical Maximum-Margin Clustering. - Guang-Tong Zhou; Sung Ju Hwang; Mark Schmidt 001; Leonid Sigal; Greg Mori (CoRR, 2015)

Play and Learn: Using Video Games to Train Computer Vision Models. - Alireza Shafaei; James J. Little; Mark Schmidt 001 (BMVC, 2016)

Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition. - Hamed Karimi; Julie Nutini; Mark Schmidt 001 (ECML/PKDD (1), 2016)

Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions. - Mohammad Emtiyaz Khan; Reza Babanezhad 001; Wu Lin; Mark Schmidt 001; Masashi Sugiyama (UAI, 2016)

Convergence Rates for Greedy Kaczmarz Algorithms, and Randomized Kaczmarz Rules Using the Orthogonality Graph. - Julie Nutini; Behrooz Sepehry; Issam H. Laradji; Mark Schmidt 001; Hoyt A. Koepke; Alim Virani (UAI, 2016)

Fast Patch-based Style Transfer of Arbitrary Style. - Tian Qi Chen; Mark Schmidt 001 (CoRR, 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 001; Hoyt A. Koepke; Alim Virani (CoRR, 2016)

Horde of Bandits using Gaussian Markov Random Fields. - Sharan Vaswani; Mark Schmidt 001; Laks V. S. Lakshmanan (AISTATS, 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 001; Chris Thachuk; Erik Winfree (DNA, 2017)

Model-Independent Online Learning for Influence Maximization. - Sharan Vaswani; Branislav Kveton; Zheng Wen 002; Mohammad Ghavamzadeh; Laks V. S. Lakshmanan; Mark Schmidt 001 (ICML, 2017)

Diffusion Independent Semi-Bandit Influence Maximization. - Sharan Vaswani; Branislav Kveton; Zheng Wen 002; Mohammad Ghavamzadeh; Laks V. S. Lakshmanan; Mark Schmidt 001 (CoRR, 2017)

Minimizing finite sums with the stochastic average gradient. - Mark Schmidt 001; Nicolas Le Roux; Francis R. Bach (Math. Program., 2017)

Erratum to: Minimizing finite sums with the stochastic average gradient. - Mark Schmidt 001; Nicolas Le Roux; Francis R. Bach (Math. Program., 2017)

Where Are the Blobs: Counting by Localization with Point Supervision. - Issam H. Laradji; Negar Rostamzadeh; Pedro O. Pinheiro; David Vázquez 001; Mark Schmidt 001 (ECCV (2), 2018)

Online Learning Rate Adaptation with Hypergradient Descent. - Atilim Gunes Baydin; Robert Cornish; David Martínez-Rubio; Mark Schmidt 001; Frank Wood (ICLR (Poster), 2018)

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. - Aaron Mishkin; Frederik Kunstner; Didrik Nielsen; Mark Schmidt 001; Mohammad Emtiyaz Khan (NeurIPS, 2018)

MASAGA: A Linearly-Convergent Stochastic First-Order Method for Optimization on Manifolds. - Reza Babanezhad 001; Issam H. Laradji; Alireza Shafaei; Mark Schmidt 001 (ECML/PKDD (2), 2018)

New Insights into Bootstrapping for Bandits. - Sharan Vaswani; Branislav Kveton; Zheng Wen 002; Anup Rao 002; Mark Schmidt 001; Yasin Abbasi-Yadkori (CoRR, 2018)

Does Your Model Know the Digit 6 Is Not a Cat? A Less Biased Evaluation of "Outlier" Detectors. - Alireza Shafaei; Mark Schmidt 001; James J. Little (CoRR, 2018)

Distributed Maximization of "Submodular plus Diversity" Functions for Multi-label Feature Selection on Huge Datasets. - Mehrdad Ghadiri; Mark Schmidt 001 (AISTATS, 2019)

Are we there yet? Manifold identification of gradient-related proximal methods. - Yifan Sun 001; Halyun Jeong; Julie Nutini; Mark Schmidt 001 (AISTATS, 2019)

Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron. - Sharan Vaswani; Francis R. Bach; Mark Schmidt 001 (AISTATS, 2019)

Where are the Masks: Instance Segmentation with Image-level Supervision. - Issam H. Laradji; David Vázquez 001; Mark Schmidt 001 (BMVC, 2019)

A Less Biased Evaluation of Out-of-distribution Sample Detectors. - Alireza Shafaei; Mark Schmidt 001; James J. Little (BMVC, 2019)

Efficient Parameter Estimation for DNA Kinetics Modeled as Continuous-Time Markov Chains. - Sedigheh Zolaktaf; Frits Dannenberg; Erik Winfree; Alexandre Bouchard-Côté; Mark Schmidt 001; Anne Condon (DNA, 2019)

Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations. - Wu Lin; Mohammad Emtiyaz Khan; Mark Schmidt 001 (ICML, 2019)

Efficient Deep Gaussian Process Models for Variable-Sized Inputs. - Issam H. Laradji; Mark Schmidt 001; Vladimir Pavlovic 001; Minyoung Kim 001 (IJCNN, 2019)

Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. - Sharan Vaswani; Aaron Mishkin; Issam H. Laradji; Mark Schmidt 001; Gauthier Gidel; Simon Lacoste-Julien (NeurIPS, 2019)

Efficient Deep Gaussian Process Models for Variable-Sized Input. - Issam H. Laradji; Mark Schmidt 001; Vladimir Pavlovic 001; Minyoung Kim 001 (CoRR, 2019)

Instance Segmentation with Point Supervision. - Issam H. Laradji; Negar Rostamzadeh; Pedro O. Pinheiro; David Vázquez 001; Mark Schmidt 001 (CoRR, 2019)

Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures. - Wu Lin; Mohammad Emtiyaz Khan; Mark Schmidt 001 (CoRR, 2019)

"Active-set complexity" of proximal gradient: How long does it take to find the sparsity pattern? - Julie Nutini; Mark Schmidt 001; Warren L. Hare (Optim. Lett., 2019)

Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation. - Si Yi Meng; Sharan Vaswani; Issam Hadj Laradji; Mark Schmidt 001; Simon Lacoste-Julien (AISTATS, 2020)

Proposal-Based Instance Segmentation With Point Supervision. - Issam H. Laradji; Negar Rostamzadeh; Pedro O. Pinheiro; David Vázquez 001; Mark Schmidt 001 (ICIP, 2020)

Handling the Positive-Definite Constraint in the Bayesian Learning Rule. - Wu Lin; Mark Schmidt 001; Mohammad Emtiyaz Khan (ICML, 2020)

Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses. - Yihan Zhou; Victor S. Portella; Mark Schmidt 001; Nicholas J. A. Harvey (NeurIPS, 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 001; Simon Lacoste-Julien (CoRR, 2020)

Combining Bayesian optimization and Lipschitz optimization. - Mohamed Osama Ahmed; Sharan Vaswani; Mark Schmidt 001 (Mach. Learn., 2020)

Variance-Reduced Methods for Machine Learning. - Robert M. Gower; Mark Schmidt 001; Francis R. Bach; Peter Richtárik (Proc. IEEE, 2020)

Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent. - Frederik Kunstner; Raunak Kumar; Mark Schmidt 001 (AISTATS, 2021)

Tractable structured natural-gradient descent using local parameterizations. - Wu Lin; Frank Nielsen; Mohammad Emtiyaz Khan; Mark Schmidt 001 (ICML, 2021)

Robust Asymmetric Learning in POMDPs. - Andrew Warrington; Jonathan Wilder Lavington; Adam Scibior; Mark Schmidt 001; Frank Wood (ICML, 2021)

AutoRetouch: Automatic Professional Face Retouching. - Alireza Shafaei; James J. Little; Mark Schmidt 001 (WACV, 2021)

Structured second-order methods via natural gradient descent. - Wu Lin; Frank Nielsen; Mohammad Emtiyaz Khan; Mark Schmidt 001 (CoRR, 2021)

Improved Policy Optimization for Online Imitation Learning. - Jonathan Wilder Lavington; Sharan Vaswani; Mark Schmidt 001 (CoLLAs, 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 001 (IJCAI, 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 001 (J. Mach. Learn. Res., 2022)

SVRG meets AdaGrad: painless variance reduction. - Benjamin Dubois-Taine; Sharan Vaswani; Reza Babanezhad 001; Mark Schmidt 001; Simon Lacoste-Julien (Mach. Learn., 2022)

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 001 (ICLR, 2023)

Target-based Surrogates for Stochastic Optimization. - Jonathan Wilder Lavington; Sharan Vaswani; Reza Babanezhad Harikandeh; Mark Schmidt 001; Nicolas Le Roux (ICML, 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 001 (ICML, 2023)

BiSLS/SPS: Auto-tune Step Sizes for Stable Bi-level Optimization. - Chen Fan; Gaspard Choné-Ducasse; Mark Schmidt 001; Christos Thrampoulidis (NeurIPS, 2023)

Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models. - Leonardo Galli; Holger Rauhut; Mark Schmidt 001 (NeurIPS, 2023)

Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking. - Frederik Kunstner; Victor Sanches Portella; Mark Schmidt 001; Nicholas J. A. Harvey (NeurIPS, 2023)

Fast Convergence of Random Reshuffling Under Over-Parameterization and the Polyak-Łojasiewicz Condition. - Chen Fan; Christos Thrampoulidis; Mark Schmidt 001 (ECML/PKDD (4), 2023)

Optimistic Thompson Sampling-based algorithms for episodic reinforcement learning. - Bingshan Hu; Tianyue H. Zhang; Nidhi Hegde 001; Mark Schmidt 001 (UAI, 2023)

Simplifying Momentum-based Riemannian Submanifold Optimization. - Wu Lin; Valentin Duruisseaux; Melvin Leok; Frank Nielsen; Mohammad Emtiyaz Khan; Mark Schmidt 001 (CoRR, 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 001; Yihan Zhou; Jonathan Wilder Lavington; Jennifer She (CoRR, 2023)

Predicting DNA kinetics with a truncated continuous-time Markov chain method. - Sedigheh Zolaktaf; Frits Dannenberg; Mark Schmidt 001; Anne Condon; Erik Winfree (Comput. Biol. Chem., 2023)

Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models. - Frederik Kunstner; Alan Milligan; Robin Yadav; Mark Schmidt 001; Alberto Bietti (NeurIPS, 2024)

Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation. - Aaron Mishkin; Mert Pilanci; Mark Schmidt 001 (CoRR, 2024)

Enhancing Policy Gradient with the Polyak Step-Size Adaption. - Yunxiang Li; Rui Yuan; Chen Fan; Mark Schmidt 001; Samuel Horváth; Robert M. Gower; Martin Takác 001 (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 001 (CoRR, 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 001 (CoRR, 2024)

Don't Be So Positive: Negative Step Sizes in Second-Order Methods. - Betty Shea; Mark Schmidt 001 (CoRR, 2024)

Implicit Bias of SignGD and Adam on Multiclass Separable Data. - Chen Fan; Mark Schmidt 001; Christos Thrampoulidis (CoRR, 2025)

ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning. - Ziyu Wan; Yunxiang Li; Yan Song 003; Hanjing Wang; Linyi Yang; Mark Schmidt 001; Jun Wang 012; Weinan Zhang 001; Shuyue Hu; Ying Wen 001 (CoRR, 2025)

Glocal Smoothness: Line Search can really help! - Curtis Fox; Aaron Mishkin; Sharan Vaswani; Mark Schmidt 001 (CoRR, 2025)