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ORCIDMark Schmidt 0001
Mark W. Schmidt
Person information
- affiliation: University of British Columbia, Department of Computer Science, Vancouver, Canada
- affiliation: École Normale Supérieure, INRIA SIERRA project team, Paris, France
- affiliation: University of Alberta, Department of Computing Science, Edmonton, Canada
Other persons with the same name
- Mark Schmidt 0002
![[0000-0002-2872-1534]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-2872-1534")
(aka: Mark T. Schmidt, Mark Thomas Schmidt) — Eberhard Karls University of Tübingen, Department of Computer Science, Tübingen, Germany - Mark Schmidt 0003 (aka: Mark E. Schmidt) — Johnson and Johnson, Janssen Pharmaceutica, Pharmaceutical Research and Development, Beerse, Belgium (and 1 more)
- Mark Schmidt 0004 — ARRIS Inc.
- Mark Schmidt 0005 — GTE/GTEL Business Communication Systems, Fremont, Canada
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2020 – today
- 2024
[i59]Frederik Kunstner, Robin Yadav, Alan Milligan, Mark Schmidt, Alberto Bietti:
Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models. CoRR abs/2402.19449 (2024)- [i58]Aaron Mishkin, Mert Pilanci, Mark Schmidt:
Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation. CoRR abs/2404.02378 (2024) - [i57]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 abs/2404.07525 (2024) - [i56]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 abs/2406.17296 (2024) - [i55]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 abs/2406.17954 (2024) - 2023
- [j12]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: 107837 (2023) - [c63]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 - [c62]Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad Harikandeh, Mark Schmidt, Nicolas Le Roux:
Target-based Surrogates for Stochastic Optimization. ICML 2023: 18614-18651 - [c61]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: 21026-21050 - [c60]Chen Fan, Gaspard Choné-Ducasse, Mark Schmidt, Christos Thrampoulidis:
BiSLS/SPS: Auto-tune Step Sizes for Stable Bi-level Optimization. NeurIPS 2023 - [c59]Leonardo Galli, Holger Rauhut, Mark Schmidt:
Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models. NeurIPS 2023 - [c58]Frederik Kunstner, Victor Sanches Portella, Mark Schmidt, Nicholas J. A. Harvey:
Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking. NeurIPS 2023 - [c57]Chen Fan, Christos Thrampoulidis, Mark Schmidt:
Fast Convergence of Random Reshuffling Under Over-Parameterization and the Polyak-Łojasiewicz Condition. ECML/PKDD (4) 2023: 301-315 - [c56]Bingshan Hu, Tianyue H. Zhang, Nidhi Hegde, Mark Schmidt:
Optimistic Thompson Sampling-based algorithms for episodic reinforcement learning. UAI 2023: 890-899 - [i54]Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Nicolas Le Roux:
Target-based Surrogates for Stochastic Optimization. CoRR abs/2302.02607 (2023) - [i53]Wu Lin, Valentin Duruisseaux, Melvin Leok, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt:
Simplifying Momentum-based Riemannian Submanifold Optimization. CoRR abs/2302.09738 (2023) - [i52]Chen Fan, Christos Thrampoulidis, Mark Schmidt:
Fast Convergence of Random Reshuffling under Over-Parameterization and the Polyak-Łojasiewicz Condition. CoRR abs/2304.00459 (2023) - [i51]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 abs/2304.13960 (2023) - [i50]Chen Fan, Gaspard Choné-Ducasse, Mark Schmidt, Christos Thrampoulidis:
BiSLS/SPS: Auto-tune Step Sizes for Stable Bi-level Optimization. CoRR abs/2305.18666 (2023) - [i49]Frederik Kunstner, Victor S. Portella, Mark Schmidt, Nick Harvey:
Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking. CoRR abs/2306.02527 (2023) - [i48]Leonardo Galli, Holger Rauhut, Mark Schmidt:
Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models. CoRR abs/2306.12747 (2023) - [i47]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 abs/2307.01169 (2023) - 2022
- [j11]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: 131:1-131:74 (2022) - [j10]Benjamin Dubois-Taine, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Simon Lacoste-Julien:
SVRG meets AdaGrad: painless variance reduction. Mach. Learn. 111(12): 4359-4409 (2022) - [c55]Jonathan Wilder Lavington, Sharan Vaswani, Mark Schmidt:
Improved Policy Optimization for Online Imitation Learning. CoLLAs 2022: 1146-1173 - [c54]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: 5294-5298 - [i46]Jonathan Wilder Lavington, Sharan Vaswani, Mark Schmidt:
Improved Policy Optimization for Online Imitation Learning. CoRR abs/2208.00088 (2022) - 2021
- [c53]Frederik Kunstner, Raunak Kumar, Mark Schmidt:
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent. AISTATS 2021: 3295-3303 - [c52]Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt:
Tractable structured natural-gradient descent using local parameterizations. ICML 2021: 6680-6691 - [c51]Andrew Warrington, Jonathan Wilder Lavington, Adam Scibior, Mark Schmidt, Frank Wood:
Robust Asymmetric Learning in POMDPs. ICML 2021: 11013-11023 - [c50]Alireza Shafaei, James J. Little, Mark Schmidt:
AutoRetouch: Automatic Professional Face Retouching. WACV 2021: 989-997 - [i45]Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt:
Tractable structured natural gradient descent using local parameterizations. CoRR abs/2102.07405 (2021) - [i44]Benjamin Dubois-Taine, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Simon Lacoste-Julien:
SVRG Meets AdaGrad: Painless Variance Reduction. CoRR abs/2102.09645 (2021) - [i43]Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt:
Structured second-order methods via natural gradient descent. CoRR abs/2107.10884 (2021) - 2020
- [j9]Mohamed Osama Ahmed, Sharan Vaswani, Mark Schmidt:
Combining Bayesian optimization and Lipschitz optimization. Mach. Learn. 109(1): 79-102 (2020) - [j8]Robert M. Gower, Mark Schmidt, Francis R. Bach, Peter Richtárik:
Variance-Reduced Methods for Machine Learning. Proc. IEEE 108(11): 1968-1983 (2020) - [c49]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: 1375-1386 - [c48]Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vázquez, Mark Schmidt:
Proposal-Based Instance Segmentation With Point Supervision. ICIP 2020: 2126-2130 - [c47]Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan:
Handling the Positive-Definite Constraint in the Bayesian Learning Rule. ICML 2020: 6116-6126 - [c46]Yihan Zhou, Victor S. Portella, Mark Schmidt, Nicholas J. A. Harvey:
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses. NeurIPS 2020 - [i42]Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan:
Handling the Positive-Definite Constraint in the Bayesian Learning Rule. CoRR abs/2002.10060 (2020) - [i41]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 abs/2006.06835 (2020) - [i40]Robert M. Gower, Mark Schmidt, Francis R. Bach, Peter Richtárik:
Variance-Reduced Methods for Machine Learning. CoRR abs/2010.00892 (2020) - [i39]Yihan Zhou, Victor S. Portella, Mark Schmidt, Nicholas J. A. Harvey:
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses. CoRR abs/2010.12033 (2020) - [i38]Frederik Kunstner, Raunak Kumar, Mark Schmidt:
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent. CoRR abs/2011.01170 (2020) - [i37]Andrew Warrington, J. Wilder Lavington, Adam Scibior, Mark Schmidt, Frank Wood:
Robust Asymmetric Learning in POMDPs. CoRR abs/2012.15566 (2020)
2010 – 2019
- 2019
- [j7]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): 645-655 (2019) - [c45]Yifan Sun, Halyun Jeong, Julie Nutini, Mark Schmidt:
Are we there yet? Manifold identification of gradient-related proximal methods. AISTATS 2019: 1110-1119 - [c44]Sharan Vaswani, Francis R. Bach, Mark Schmidt:
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron. AISTATS 2019: 1195-1204 - [c43]Mehrdad Ghadiri, Mark Schmidt:
Distributed Maximization of "Submodular plus Diversity" Functions for Multi-label Feature Selection on Huge Datasets. AISTATS 2019: 2077-2086 - [c42]Alireza Shafaei, Mark Schmidt, James J. Little:
A Less Biased Evaluation of Out-of-distribution Sample Detectors. BMVC 2019: 3 - [c41]Issam H. Laradji, David Vázquez, Mark Schmidt:
Where are the Masks: Instance Segmentation with Image-level Supervision. BMVC 2019: 255 - [c40]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: 80-99 - [c39]Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt:
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations. ICML 2019: 3992-4002 - [c38]Issam H. Laradji, Mark Schmidt, Vladimir Pavlovic, Minyoung Kim:
Efficient Deep Gaussian Process Models for Variable-Sized Inputs. IJCNN 2019: 1-7 - [c37]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: 3727-3740 - [i36]Mehrdad Ghadiri, Mark Schmidt:
Distributed Maximization of "Submodular plus Diversity" Functions for Multi-label Feature Selection on Huge Datasets. CoRR abs/1903.08351 (2019) - [i35]Issam H. Laradji, Mark Schmidt, Vladimir Pavlovic, Minyoung Kim:
Efficient Deep Gaussian Process Models for Variable-Sized Input. CoRR abs/1905.06982 (2019) - [i34]Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien:
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. CoRR abs/1905.09997 (2019) - [i33]Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt:
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations. CoRR abs/1906.02914 (2019) - [i32]Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vázquez, Mark Schmidt:
Instance Segmentation with Point Supervision. CoRR abs/1906.06392 (2019) - [i31]Issam H. Laradji, David Vázquez, Mark Schmidt:
Where are the Masks: Instance Segmentation with Image-level Supervision. CoRR abs/1907.01430 (2019) - [i30]Si Yi Meng, Sharan Vaswani, Issam H. Laradji, Mark Schmidt, Simon Lacoste-Julien:
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation. CoRR abs/1910.04920 (2019) - [i29]Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt:
Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures. CoRR abs/1910.13398 (2019) - 2018
- [c36]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: 560-576 - [c35]Atilim Gunes Baydin, Robert Cornish, David Martínez-Rubio, Mark Schmidt, Frank Wood:
Online Learning Rate Adaptation with Hypergradient Descent. ICLR (Poster) 2018 - [c34]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: 6248-6258 - [c33]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: 344-359 - [i28]Sharan Vaswani, Branislav Kveton, Zheng Wen, Anup Rao, Mark Schmidt, Yasin Abbasi-Yadkori:
New Insights into Bootstrapping for Bandits. CoRR abs/1805.09793 (2018) - [i27]Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vázquez, Mark Schmidt:
Where are the Blobs: Counting by Localization with Point Supervision. CoRR abs/1807.09856 (2018) - [i26]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 abs/1809.04729 (2018) - [i25]Mohamed Osama Ahmed, Sharan Vaswani, Mark Schmidt:
Combining Bayesian Optimization and Lipschitz Optimization. CoRR abs/1810.04336 (2018) - [i24]Sharan Vaswani, Francis R. Bach, Mark Schmidt:
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron. CoRR abs/1810.07288 (2018) - [i23]Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan:
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. CoRR abs/1811.04504 (2018) - 2017
- [j6]Mark Schmidt, Nicolas Le Roux, Francis R. Bach:
Minimizing finite sums with the stochastic average gradient. Math. Program. 162(1-2): 83-112 (2017) - [j5]Mark Schmidt, Nicolas Le Roux, Francis R. Bach:
Erratum to: Minimizing finite sums with the stochastic average gradient. Math. Program. 162(1-2): 113 (2017) - [c32]Sharan Vaswani, Mark Schmidt, Laks V. S. Lakshmanan:
Horde of Bandits using Gaussian Markov Random Fields. AISTATS 2017: 690-699 - [c31]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: 172-187 - [c30]Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan, Mark Schmidt:
Model-Independent Online Learning for Influence Maximization. ICML 2017: 3530-3539 - [i22]Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan, Mark Schmidt:
Diffusion Independent Semi-Bandit Influence Maximization. CoRR abs/1703.00557 (2017) - [i21]Sharan Vaswani, Mark Schmidt, Laks V. S. Lakshmanan:
Horde of Bandits using Gaussian Markov Random Fields. CoRR abs/1703.02626 (2017) - [i20]Atilim Gunes Baydin, Robert Cornish, David Martínez-Rubio, Mark Schmidt, Frank D. Wood:
Online Learning Rate Adaptation with Hypergradient Descent. CoRR abs/1703.04782 (2017) - 2016
- [c29]Alireza Shafaei, James J. Little, Mark Schmidt:
Play and Learn: Using Video Games to Train Computer Vision Models. BMVC 2016 - [c28]Hamed Karimi, Julie Nutini, Mark Schmidt:
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition. ECML/PKDD (1) 2016: 795-811 - [c27]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 - [c26]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 - [i19]Alireza Shafaei, James J. Little, Mark Schmidt:
Play and Learn: Using Video Games to Train Computer Vision Models. CoRR abs/1608.01745 (2016) - [i18]Hamed Karimi, Julie Nutini, Mark Schmidt:
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition. CoRR abs/1608.04636 (2016) - [i17]Tian Qi Chen, Mark Schmidt:
Fast Patch-based Style Transfer of Arbitrary Style. CoRR abs/1612.04337 (2016) - [i16]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 abs/1612.07838 (2016) - 2015
- [c25]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 - [c24]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: 1632-1641 - [c23]Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen:
StopWasting My Gradients: Practical SVRG. NIPS 2015: 2251-2259 - [i15]Guang-Tong Zhou, Sung Ju Hwang, Mark Schmidt, Leonid Sigal, Greg Mori:
Hierarchical Maximum-Margin Clustering. CoRR abs/1502.01827 (2015) - [i14]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 abs/1504.04406 (2015) - [i13]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 abs/1506.00552 (2015) - [i12]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 abs/1511.00146 (2015) - [i11]Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen:
Stop Wasting My Gradients: Practical SVRG. CoRR abs/1511.01942 (2015) - 2014
- [j4]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): 32-43 (2014) - [i10]Volkan Cevher, Stephen Becker, Mark Schmidt:
Convex Optimization for Big Data. CoRR abs/1411.0972 (2014) - 2013
- [j3]Michael P. Friedlander, Mark Schmidt:
Erratum: Hybrid Deterministic-Stochastic Methods for Data Fitting. SIAM J. Sci. Comput. 35(4) (2013) - [c22]Simon Lacoste-Julien, Martin Jaggi, Mark Schmidt, Patrick Pletscher:
Block-Coordinate Frank-Wolfe Optimization for Structural SVMs. ICML (1) 2013: 53-61 - [i9]Mark Schmidt, Nicolas Le Roux, Francis R. Bach:
Minimizing Finite Sums with the Stochastic Average Gradient. CoRR abs/1309.2388 (2013) - 2012
- [j2]Michael P. Friedlander, Mark Schmidt:
Hybrid Deterministic-Stochastic Methods for Data Fitting. SIAM J. Sci. Comput. 34(3) (2012) - [c21]Nicolas Le Roux, Mark Schmidt, Francis R. Bach:
A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets. NIPS 2012: 2672-2680 - [c20]David Buchman, Mark Schmidt, Shakir Mohamed, David Poole, Nando de Freitas:
On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models. AISTATS 2012: 173-181 - [i8]Nicolas Le Roux, Mark Schmidt, Francis R. Bach:
A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets. CoRR abs/1202.6258 (2012) - [i7]Mark Schmidt, Kevin P. Murphy:
Modeling Discrete Interventional Data using Directed Cyclic Graphical Models. CoRR abs/1205.2617 (2012) - [i6]Benjamin M. Marlin, Mark Schmidt, Kevin P. Murphy:
Group Sparse Priors for Covariance Estimation. CoRR abs/1205.2626 (2012) - [i5]Simon Lacoste-Julien, Martin Jaggi, Mark Schmidt, Patrick Pletscher:
Stochastic Block-Coordinate Frank-Wolfe Optimization for Structural SVMs. CoRR abs/1207.4747 (2012) - [i4]Simon Lacoste-Julien, Mark Schmidt, Francis R. Bach:
A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method. CoRR abs/1212.2002 (2012) - 2011
- [c19]Mark Schmidt, Nicolas Le Roux, Francis R. Bach:
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization. NIPS 2011: 1458-1466 - [c18]Mark Schmidt, Karteek Alahari:
Generalized Fast Approximate Energy Minimization via Graph Cuts: a-Expansion b-Shrink Moves. UAI 2011: 653-660 - [i3]Michael P. Friedlander, Mark Schmidt:
Hybrid Deterministic-Stochastic Methods for Data Fitting. CoRR abs/1104.2373 (2011) - [i2]Mark Schmidt, Karteek Alahari:
Generalized Fast Approximate Energy Minimization via Graph Cuts: Alpha-Expansion Beta-Shrink Moves. CoRR abs/1108.5710 (2011) - [i1]Mark Schmidt, Nicolas Le Roux, Francis R. Bach:
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization. CoRR abs/1109.2415 (2011) - 2010
- [c17]David Duvenaud, Daniel Eaton, Kevin P. Murphy, Mark Schmidt:
Causal learning without DAGs. NIPS Causality: Objectives and Assessment 2010: 177-190 - [c16]Mark Schmidt, Kevin P. Murphy:
Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials. AISTATS 2010: 709-716 - [c15]Yan Yan, Rómer Rosales, Glenn Fung, Mark Schmidt, Gerardo Hermosillo Valadez, Luca Bogoni, Linda Moy, Jennifer G. Dy:
Modeling annotator expertise: Learning when everybody knows a bit of something. AISTATS 2010: 932-939
2000 – 2009
- 2009
- [c14]Dana Cobzas, Mark Schmidt:
Increased discrimination in level set methods with embedded conditional random fields. CVPR 2009: 328-335 - [c13]Benjamin M. Marlin, Mark Schmidt, Kevin P. Murphy:
Group Sparse Priors for Covariance Estimation. UAI 2009: 383-392 - [c12]Mark Schmidt, Kevin P. Murphy:
Modeling Discrete Interventional Data using Directed Cyclic Graphical Models. UAI 2009: 487-495 - [c11]Mark Schmidt, Ewout van den Berg, Michael P. Friedlander, Kevin P. Murphy:
Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm. AISTATS 2009: 456-463 - 2008
- [c10]Mark Schmidt, Kevin P. Murphy, Glenn Fung, Rómer Rosales:
Structure learning in random fields for heart motion abnormality detection.