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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.uni-trier.de/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
[i57]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)- [i56]Aaron Mishkin, Mert Pilanci, Mark Schmidt:
Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation. CoRR abs/2404.02378 (2024) - [i55]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) - 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. CVPR 2008 - [c9]Peter Carbonetto, Mark Schmidt, Nando de Freitas:
An interior-point stochastic approximation method and an L1-regularized delta rule. NIPS 2008: 233-240 - 2007
- [c8]Mark Schmidt, Alexandru Niculescu-Mizil, Kevin P. Murphy:
Learning Graphical Model Structure Using L1-Regularization Paths. AAAI 2007: 1278-1283 - [c7]Mark Schmidt, Glenn Fung, Rómer Rosales:
Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches. ECML 2007: 286-297 - [c6]Dana Cobzas, Neil Birkbeck, Mark Schmidt, Martin Jägersand, Albert Murtha:
3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set. ICCV 2007: 1-8 - 2006
- [j1]Marianne Morris, Russell Greiner, Jörg Sander, Albert Murtha, Mark Schmidt:
Learning a Classification-based Glioma Growth Model Using MRI Data. J. Comput. 1(7): 21-31 (2006) - [c5]Marianne Morris, Russell Greiner, Jörg Sander, Albert Murtha, Mark Schmidt:
A Classification-Based Glioma Diffusion Model Using MRI Data. Canadian AI 2006: 98-109 - [c4]S. V. N. Vishwanathan, Nicol N. Schraudolph, Mark W. Schmidt, Kevin P. Murphy:
Accelerated training of conditional random fields with stochastic gradient methods. ICML 2006: 969-976 - 2005
- [c3]Chi-Hoon Lee, Mark Schmidt, Albert Murtha, Aalo Bistritz, Jörg Sander, Russell Greiner:
Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines. CVBIA 2005: 469-478 - [c2]Mark Schmidt, Ilya Levner, Russell Greiner, Albert Murtha, Aalo Bistritz:
Segmenting brain tumors using alignment-based features. ICMLA 2005 - [c1]Chi-Hoon Lee, Russell Greiner, Mark Schmidt:
Support Vector Random Fields for Spatial Classification. PKDD 2005: 121-132
Coauthor Index
Reza Babanezhad 0001
aka: Reza Babanezhad Harikandeh
aka: Reza Babanezhad Harikandeh
Issam H. Laradji
aka: Issam Hadj Laradji
aka: Issam Hadj Laradji
J. Wilder Lavington
aka: Jonathan Wilder Lavington
aka: Jonathan Wilder Lavington
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Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-05-31 04:10 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
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since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
