Mark Schmidt - RankMe

141 papers A* 21A 21B 4C 1Journal 78Unranked 16

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Greedy Newton: Newton's method with exact line search. Optim. Lett. Betty Shea, Mark Schmidt
2026	J	`jnl`	Towards Parameter-Free Temporal Difference Learning. CoRR Yunxiang Li, Mark Schmidt, Reza Babanezhad, Sharan Vaswani
2025	J	`jnl`	A Unified Noise-Curvature View of Loss of Trainability. CoRR Gunbir Singh Baveja, Mark Schmidt
2025	J	`jnl`	Glocal Smoothness: Line Search can really help! CoRR Curtis Fox, Aaron Mishkin, Sharan Vaswani, Mark Schmidt
2025	J	`jnl`	Implicit Bias of SignGD and Adam on Multiclass Separable Data. CoRR Chen Fan, Mark Schmidt, Christos Thrampoulidis
2025	J	`jnl`	ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning. CoRR Ziyu Wan, Yunxiang Li, Yan Song, Hanjing Wang, Linyi Yang, Mark Schmidt, Jun Wang, Weinan Zhang, Shuyue Hu, Ying Wen
2024	J	`jnl`	BlockLLM: Memory-Efficient Adaptation of LLMs by Selecting and Optimizing the Right Coordinate Blocks. CoRR Amrutha Varshini Ramesh, Vignesh Ganapathiraman, Issam H. Laradji, Mark Schmidt
2024	J	`jnl`	Don't Be So Positive: Negative Step Sizes in Second-Order Methods. CoRR Betty Shea, Mark Schmidt
2024	J	`jnl`	Enhancing Policy Gradient with the Polyak Step-Size Adaption. CoRR Yunxiang Li, Rui Yuan, Chen Fan, Mark Schmidt, Samuel Horváth, Robert M. Gower, Martin Takác
2024	J	`jnl`	Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation. CoRR Aaron Mishkin, Mert Pilanci, Mark Schmidt
2024	A*	`conf`	Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models. NeurIPS Frederik Kunstner, Alan Milligan, Robin Yadav, Mark Schmidt, Alberto Bietti
2024	J	`jnl`	Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models. CoRR Frederik Kunstner, Robin Yadav, Alan Milligan, Mark Schmidt, Alberto Bietti
2024	J	`jnl`	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 Betty Shea, Mark Schmidt
2023	J	`jnl`	Analyzing and Improving Greedy 2-Coordinate Updates for Equality-Constrained Optimization via Steepest Descent in the 1-Norm. CoRR Amrutha Varshini Ramesh, Aaron Mishkin, Mark Schmidt, Yihan Zhou, Jonathan Wilder Lavington, Jennifer She
2023	A*	`conf`	BiSLS/SPS: Auto-tune Step Sizes for Stable Bi-level Optimization. NeurIPS Chen Fan, Gaspard Choné-Ducasse, Mark Schmidt, Christos Thrampoulidis
2023	J	`jnl`	BiSLS/SPS: Auto-tune Step Sizes for Stable Bi-level Optimization. CoRR Chen Fan, Gaspard Choné-Ducasse, Mark Schmidt, Christos Thrampoulidis
2023	A*	`conf`	Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models. NeurIPS Leonardo Galli, Holger Rauhut, Mark Schmidt
2023	J	`jnl`	Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models. CoRR Leonardo Galli, Holger Rauhut, Mark Schmidt
2023	—	`conf`	Fast Convergence of Random Reshuffling Under Over-Parameterization and the Polyak-Łojasiewicz Condition. ECML/PKDD (4) Chen Fan, Christos Thrampoulidis, Mark Schmidt
2023	J	`jnl`	Fast Convergence of Random Reshuffling under Over-Parameterization and the Polyak-Łojasiewicz Condition. CoRR Chen Fan, Christos Thrampoulidis, Mark Schmidt
2023	J	`jnl`	Noise Is Not the Main Factor Behind the Gap Between SGD and Adam on Transformers, but Sign Descent Might Be. CoRR Frederik Kunstner, Jacques Chen, Jonathan Wilder Lavington, Mark Schmidt
2023	A*	`conf`	Noise Is Not the Main Factor Behind the Gap Between Sgd and Adam on Transformers, But Sign Descent Might Be. ICLR Frederik Kunstner, Jacques Chen, Jonathan Wilder Lavington, Mark Schmidt
2023	A	`conf`	Optimistic Thompson Sampling-based algorithms for episodic reinforcement learning. UAI Bingshan Hu, Tianyue H. Zhang, Nidhi Hegde, Mark Schmidt
2023	J	`jnl`	Predicting DNA kinetics with a truncated continuous-time Markov chain method. Comput. Biol. Chem. Sedigheh Zolaktaf, Frits Dannenberg, Mark Schmidt, Anne Condon, Erik Winfree
2023	A*	`conf`	Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking. NeurIPS Frederik Kunstner, Victor Sanches Portella, Mark Schmidt, Nicholas J. A. Harvey
2023	J	`jnl`	Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking. CoRR Frederik Kunstner, Victor S. Portella, Mark Schmidt, Nick Harvey
2023	A*	`conf`	Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning. ICML Wu Lin, Valentin Duruisseaux, Melvin Leok, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt
2023	J	`jnl`	Simplifying Momentum-based Riemannian Submanifold Optimization. CoRR Wu Lin, Valentin Duruisseaux, Melvin Leok, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt
2023	A*	`conf`	Target-based Surrogates for Stochastic Optimization. ICML Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad Harikandeh, Mark Schmidt, Nicolas Le Roux
2023	J	`jnl`	Target-based Surrogates for Stochastic Optimization. CoRR Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Nicolas Le Roux
2022	A*	`conf`	Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent (Extended Abstract). IJCAI Frederik Kunstner, Raunak Kumar, Mark Schmidt
2022	—	`conf`	Improved Policy Optimization for Online Imitation Learning. CoLLAs Jonathan Wilder Lavington, Sharan Vaswani, Mark Schmidt
2022	J	`jnl`	Improved Policy Optimization for Online Imitation Learning. CoRR Jonathan Wilder Lavington, Sharan Vaswani, Mark Schmidt
2022	J	`jnl`	Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence. J. Mach. Learn. Res. Julie Nutini, Issam H. Laradji, Mark Schmidt
2022	J	`jnl`	SVRG meets AdaGrad: painless variance reduction. Mach. Learn. Benjamin Dubois-Taine, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Simon Lacoste-Julien
2021	A	`conf`	AutoRetouch: Automatic Professional Face Retouching. WACV Alireza Shafaei, James J. Little, Mark Schmidt
2021	A	`conf`	Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent. AISTATS Frederik Kunstner, Raunak Kumar, Mark Schmidt
2021	A*	`conf`	Robust Asymmetric Learning in POMDPs. ICML Andrew Warrington, Jonathan Wilder Lavington, Adam Scibior, Mark Schmidt, Frank Wood
2021	J	`jnl`	SVRG Meets AdaGrad: Painless Variance Reduction. CoRR Benjamin Dubois-Taine, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Simon Lacoste-Julien
2021	J	`jnl`	Structured second-order methods via natural gradient descent. CoRR Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt
2021	J	`jnl`	Tractable structured natural gradient descent using local parameterizations. CoRR Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt
2021	A*	`conf`	Tractable structured natural-gradient descent using local parameterizations. ICML Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt
2020	J	`jnl`	Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search). CoRR Sharan Vaswani, Frederik Kunstner, Issam H. Laradji, Si Yi Meng, Mark Schmidt, Simon Lacoste-Julien
2020	J	`jnl`	Combining Bayesian optimization and Lipschitz optimization. Mach. Learn. Mohamed Osama Ahmed, Sharan Vaswani, Mark Schmidt
2020	A	`conf`	Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation. AISTATS Si Yi Meng, Sharan Vaswani, Issam Hadj Laradji, Mark Schmidt, Simon Lacoste-Julien
2020	A*	`conf`	Handling the Positive-Definite Constraint in the Bayesian Learning Rule. ICML Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan
2020	J	`jnl`	Handling the Positive-Definite Constraint in the Bayesian Learning Rule. CoRR Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan
2020	J	`jnl`	Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent. CoRR Frederik Kunstner, Raunak Kumar, Mark Schmidt
2020	B	`conf`	Proposal-Based Instance Segmentation With Point Supervision. ICIP Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vázquez, Mark Schmidt
2020	A*	`conf`	Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses. NeurIPS Yihan Zhou, Victor S. Portella, Mark Schmidt, Nicholas J. A. Harvey
2020	J	`jnl`	Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses. CoRR Yihan Zhou, Victor S. Portella, Mark Schmidt, Nicholas J. A. Harvey
2020	J	`jnl`	Robust Asymmetric Learning in POMDPs. CoRR Andrew Warrington, J. Wilder Lavington, Adam Scibior, Mark Schmidt, Frank Wood
2020	J	`jnl`	Variance-Reduced Methods for Machine Learning. CoRR Robert M. Gower, Mark Schmidt, Francis R. Bach, Peter Richtárik
2020	J	`jnl`	Variance-Reduced Methods for Machine Learning. Proc. IEEE Robert M. Gower, Mark Schmidt, Francis R. Bach, Peter Richtárik
2019	J	`jnl`	"Active-set complexity" of proximal gradient: How long does it take to find the sparsity pattern? Optim. Lett. Julie Nutini, Mark Schmidt, Warren L. Hare
2019	A	`conf`	A Less Biased Evaluation of Out-of-distribution Sample Detectors. BMVC Alireza Shafaei, Mark Schmidt, James J. Little
2019	A	`conf`	Are we there yet? Manifold identification of gradient-related proximal methods. AISTATS Yifan Sun, Halyun Jeong, Julie Nutini, Mark Schmidt
2019	A	`conf`	Distributed Maximization of "Submodular plus Diversity" Functions for Multi-label Feature Selection on Huge Datasets. AISTATS Mehrdad Ghadiri, Mark Schmidt
2019	J	`jnl`	Distributed Maximization of "Submodular plus Diversity" Functions for Multi-label Feature Selection on Huge Datasets. CoRR Mehrdad Ghadiri, Mark Schmidt
2019	J	`jnl`	Efficient Deep Gaussian Process Models for Variable-Sized Input. CoRR Issam H. Laradji, Mark Schmidt, Vladimir Pavlovic, Minyoung Kim
2019	B	`conf`	Efficient Deep Gaussian Process Models for Variable-Sized Inputs. IJCNN Issam H. Laradji, Mark Schmidt, Vladimir Pavlovic, Minyoung Kim
2019	B	`conf`	Efficient Parameter Estimation for DNA Kinetics Modeled as Continuous-Time Markov Chains. DNA Sedigheh Zolaktaf, Frits Dannenberg, Erik Winfree, Alexandre Bouchard-Côté, Mark Schmidt, Anne Condon
2019	A	`conf`	Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron. AISTATS Sharan Vaswani, Francis R. Bach, Mark Schmidt
2019	J	`jnl`	Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation. CoRR Si Yi Meng, Sharan Vaswani, Issam H. Laradji, Mark Schmidt, Simon Lacoste-Julien
2019	A*	`conf`	Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations. ICML Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt
2019	J	`jnl`	Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations. CoRR Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt
2019	J	`jnl`	Instance Segmentation with Point Supervision. CoRR Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vázquez, Mark Schmidt
2019	A*	`conf`	Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. NeurIPS Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien
2019	J	`jnl`	Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. CoRR Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien
2019	J	`jnl`	Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures. CoRR Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt
2019	A	`conf`	Where are the Masks: Instance Segmentation with Image-level Supervision. BMVC Issam H. Laradji, David Vázquez, Mark Schmidt
2019	J	`jnl`	Where are the Masks: Instance Segmentation with Image-level Supervision. CoRR Issam H. Laradji, David Vázquez, Mark Schmidt
2018	J	`jnl`	Combining Bayesian Optimization and Lipschitz Optimization. CoRR Mohamed Osama Ahmed, Sharan Vaswani, Mark Schmidt
2018	J	`jnl`	Does Your Model Know the Digit 6 Is Not a Cat? A Less Biased Evaluation of "Outlier" Detectors. CoRR Alireza Shafaei, Mark Schmidt, James J. Little
2018	J	`jnl`	Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron. CoRR Sharan Vaswani, Francis R. Bach, Mark Schmidt
2018	—	`conf`	MASAGA: A Linearly-Convergent Stochastic First-Order Method for Optimization on Manifolds. ECML/PKDD (2) Reza Babanezhad, Issam H. Laradji, Alireza Shafaei, Mark Schmidt
2018	J	`jnl`	New Insights into Bootstrapping for Bandits. CoRR Sharan Vaswani, Branislav Kveton, Zheng Wen, Anup Rao, Mark Schmidt, Yasin Abbasi-Yadkori
2018	—	`conf`	Online Learning Rate Adaptation with Hypergradient Descent. ICLR (Poster) Atilim Gunes Baydin, Robert Cornish, David Martínez-Rubio, Mark Schmidt, Frank Wood
2018	A*	`conf`	SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. NeurIPS Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan
2018	J	`jnl`	SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. CoRR Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan
2018	—	`conf`	Where Are the Blobs: Counting by Localization with Point Supervision. ECCV (2) Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vázquez, Mark Schmidt
2018	J	`jnl`	Where are the Blobs: Counting by Localization with Point Supervision. CoRR Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vázquez, Mark Schmidt
2017	J	`jnl`	Diffusion Independent Semi-Bandit Influence Maximization. CoRR Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan, Mark Schmidt
2017	J	`jnl`	Erratum to: Minimizing finite sums with the stochastic average gradient. Math. Program. Mark Schmidt, Nicolas Le Roux, Francis R. Bach
2017	A	`conf`	Horde of Bandits using Gaussian Markov Random Fields. AISTATS Sharan Vaswani, Mark Schmidt, Laks V. S. Lakshmanan
2017	J	`jnl`	Horde of Bandits using Gaussian Markov Random Fields. CoRR Sharan Vaswani, Mark Schmidt, Laks V. S. Lakshmanan
2017	B	`conf`	Inferring Parameters for an Elementary Step Model of DNA Structure Kinetics with Locally Context-Dependent Arrhenius Rates. DNA Sedigheh Zolaktaf, Frits Dannenberg, Xander Rudelis, Anne Condon, Joseph M. Schaeffer, Mark Schmidt, Chris Thachuk, Erik Winfree
2017	J	`jnl`	Minimizing finite sums with the stochastic average gradient. Math. Program. Mark Schmidt, Nicolas Le Roux, Francis R. Bach
2017	A*	`conf`	Model-Independent Online Learning for Influence Maximization. ICML Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan, Mark Schmidt
2017	J	`jnl`	Online Learning Rate Adaptation with Hypergradient Descent. CoRR Atilim Gunes Baydin, Robert Cornish, David Martínez-Rubio, Mark Schmidt, Frank D. Wood
2016	J	`jnl`	Convergence Rates for Greedy Kaczmarz Algorithms, and Faster Randomized Kaczmarz Rules Using the Orthogonality Graph. CoRR Julie Nutini, Behrooz Sepehry, Issam H. Laradji, Mark Schmidt, Hoyt A. Koepke, Alim Virani
2016	A	`conf`	Convergence Rates for Greedy Kaczmarz Algorithms, and Randomized Kaczmarz Rules Using the Orthogonality Graph. UAI Julie Nutini, Behrooz Sepehry, Issam H. Laradji, Mark Schmidt, Hoyt A. Koepke, Alim Virani
2016	J	`jnl`	Fast Patch-based Style Transfer of Arbitrary Style. CoRR Tian Qi Chen, Mark Schmidt
2016	A	`conf`	Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions. UAI Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama
2016	—	`conf`	Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition. ECML/PKDD (1) Hamed Karimi, Julie Nutini, Mark Schmidt
2016	J	`jnl`	Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition. CoRR Hamed Karimi, Julie Nutini, Mark Schmidt
2016	A	`conf`	Play and Learn: Using Video Games to Train Computer Vision Models. BMVC Alireza Shafaei, James J. Little, Mark Schmidt
2016	J	`jnl`	Play and Learn: Using Video Games to Train Computer Vision Models. CoRR Alireza Shafaei, James J. Little, Mark Schmidt
2015	J	`jnl`	Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence. CoRR Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama
2015	A*	`conf`	Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection. ICML Julie Nutini, Mark Schmidt, Issam H. Laradji, Michael P. Friedlander, Hoyt A. Koepke
2015	J	`jnl`	Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection. CoRR Julie Nutini, Mark Schmidt, Issam H. Laradji, Michael P. Friedlander, Hoyt A. Koepke
2015	J	`jnl`	Hierarchical Maximum-Margin Clustering. CoRR Guang-Tong Zhou, Sung Ju Hwang, Mark Schmidt, Leonid Sigal, Greg Mori
2015	A	`conf`	Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields. AISTATS Mark Schmidt, Reza Babanezhad, Mohamed Osama Ahmed, Aaron Defazio, Ann Clifton, Anoop Sarkar
2015	J	`jnl`	Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields. CoRR Mark Schmidt, Reza Babanezhad, Mohamed Osama Ahmed, Aaron Defazio, Ann Clifton, Anoop Sarkar
2015	J	`jnl`	Stop Wasting My Gradients: Practical SVRG. CoRR Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen
2015	—	`conf`	StopWasting My Gradients: Practical SVRG. NIPS Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen
2014	J	`jnl`	Convex Optimization for Big Data. CoRR Volkan Cevher, Stephen Becker, Mark Schmidt
2014	J	`jnl`	Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics. IEEE Signal Process. Mag. Volkan Cevher, Stephen Becker, Mark Schmidt
2013	—	`conf`	Block-Coordinate Frank-Wolfe Optimization for Structural SVMs. ICML (1) Simon Lacoste-Julien, Martin Jaggi, Mark Schmidt, Patrick Pletscher
2013	J	`jnl`	Erratum: Hybrid Deterministic-Stochastic Methods for Data Fitting. SIAM J. Sci. Comput. Michael P. Friedlander, Mark Schmidt
2013	J	`jnl`	Minimizing Finite Sums with the Stochastic Average Gradient. CoRR Mark Schmidt, Nicolas Le Roux, Francis R. Bach
2012	—	`conf`	A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets. NIPS Nicolas Le Roux, Mark Schmidt, Francis R. Bach
2012	J	`jnl`	A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets CoRR Nicolas Le Roux, Mark Schmidt, Francis R. Bach
2012	J	`jnl`	A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method CoRR Simon Lacoste-Julien, Mark Schmidt, Francis R. Bach
2012	J	`jnl`	Group Sparse Priors for Covariance Estimation CoRR Benjamin M. Marlin, Mark Schmidt, Kevin P. Murphy
2012	J	`jnl`	Hybrid Deterministic-Stochastic Methods for Data Fitting. SIAM J. Sci. Comput. Michael P. Friedlander, Mark Schmidt
2012	J	`jnl`	Modeling Discrete Interventional Data using Directed Cyclic Graphical Models CoRR Mark Schmidt, Kevin P. Murphy
2012	A	`conf`	On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models. AISTATS David Buchman, Mark Schmidt, Shakir Mohamed, David Poole, Nando de Freitas
2012	J	`jnl`	Stochastic Block-Coordinate Frank-Wolfe Optimization for Structural SVMs CoRR Simon Lacoste-Julien, Martin Jaggi, Mark Schmidt, Patrick Pletscher
2011	J	`jnl`	Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization CoRR Mark Schmidt, Nicolas Le Roux, Francis R. Bach
2011	—	`conf`	Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization. NIPS Mark Schmidt, Nicolas Le Roux, Francis R. Bach
2011	J	`jnl`	Generalized Fast Approximate Energy Minimization via Graph Cuts: Alpha-Expansion Beta-Shrink Moves CoRR Mark Schmidt, Karteek Alahari
2011	A	`conf`	Generalized Fast Approximate Energy Minimization via Graph Cuts: a-Expansion b-Shrink Moves. UAI Mark Schmidt, Karteek Alahari
2011	J	`jnl`	Hybrid Deterministic-Stochastic Methods for Data Fitting CoRR Michael P. Friedlander, Mark Schmidt
2010	—	`conf`	Causal learning without DAGs. NIPS Causality: Objectives and Assessment David Duvenaud, Daniel Eaton, Kevin P. Murphy, Mark Schmidt
2010	A	`conf`	Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials. AISTATS Mark Schmidt, Kevin P. Murphy
2010	A	`conf`	Modeling annotator expertise: Learning when everybody knows a bit of something. AISTATS Yan Yan, Rómer Rosales, Glenn Fung, Mark Schmidt, Gerardo Hermosillo Valadez, Luca Bogoni, Linda Moy, Jennifer G. Dy
2009	A	`conf`	Group Sparse Priors for Covariance Estimation. UAI Benjamin M. Marlin, Mark Schmidt, Kevin P. Murphy
2009	A*	`conf`	Increased discrimination in level set methods with embedded conditional random fields. CVPR Dana Cobzas, Mark Schmidt
2009	A	`conf`	Modeling Discrete Interventional Data using Directed Cyclic Graphical Models. UAI Mark Schmidt, Kevin P. Murphy
2009	A	`conf`	Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm. AISTATS Mark Schmidt, Ewout van den Berg, Michael P. Friedlander, Kevin P. Murphy
2008	—	`conf`	An interior-point stochastic approximation method and an L1-regularized delta rule. NIPS Peter Carbonetto, Mark Schmidt, Nando de Freitas
2008	A*	`conf`	Structure learning in random fields for heart motion abnormality detection. CVPR Mark Schmidt, Kevin P. Murphy, Glenn Fung, Rómer Rosales
2007	A*	`conf`	3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set. ICCV Dana Cobzas, Neil Birkbeck, Mark Schmidt, Martin Jägersand, Albert Murtha
2007	—	`conf`	Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches. ECML Mark Schmidt, Glenn Fung, Rómer Rosales
2007	A*	`conf`	Learning Graphical Model Structure Using L1-Regularization Paths. AAAI Mark Schmidt, Alexandru Niculescu-Mizil, Kevin P. Murphy
2006	—	`conf`	A Classification-Based Glioma Diffusion Model Using MRI Data. Canadian AI Marianne Morris, Russell Greiner, Jörg Sander, Albert Murtha, Mark Schmidt
2006	J	`jnl`	Learning a Classification-based Glioma Growth Model Using MRI Data. J. Comput. Marianne Morris, Russell Greiner, Jörg Sander, Albert Murtha, Mark Schmidt
2005	—	`conf`	Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines. CVBIA Chi-Hoon Lee, Mark Schmidt, Albert Murtha, Aalo Bistritz, Jörg Sander, Russell Greiner
2005	C	`conf`	Segmenting brain tumors using alignment-based features. ICMLA Mark Schmidt, Ilya Levner, Russell Greiner, Albert Murtha, Aalo Bistritz
2005	—	`conf`	Support Vector Random Fields for Spatial Classification. PKDD Chi-Hoon Lee, Russell Greiner, Mark Schmidt