Schmidt, Mark

72 publications

ICLRW 2025 BiD: Behavioral Agents in Dynamic Auctions Weitong Zhang, Chengqi Zang, Mark Schmidt, Richard Blythman

NeurIPS 2025 Implicit Bias of Spectral Descent and Muon on Multiclass Separable Data Chen Fan, Mark Schmidt, Christos Thrampoulidis

NeurIPS 2025 ReMA: Learning to Meta-Think for LLMs with Multi-Agent Reinforcement Learning Ziyu Wan, Yunxiang Li, Xiaoyu Wen, Yan Song, Hanjing Wang, Linyi Yang, Mark Schmidt, Jun Wang, Weinan Zhang, Shuyue Hu, Ying Wen

NeurIPSW 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

NeurIPSW 2024 Don't Be so Positive: Negative Step Sizes in Second-Order Methods Betty Shea, Mark Schmidt

NeurIPSW 2024 Glocal Smoothness: Line Search Can Really Help! Curtis Fox, Mark Schmidt

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

NeurIPSW 2024 Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models Frederik Kunstner, Alan Milligan, Robin Yadav, Mark Schmidt, Alberto Bietti

NeurIPSW 2024 Nonmonotone Line Searches Operate at the Edge of Stability Curtis Fox, Leonardo Galli, Mark Schmidt, Holger Rauhut

NeurIPSW 2024 Normalization Matters for Optimization Performance on Graph Neural Networks Alan Milligan, Frederik Kunstner, Hamed Shirzad, Mark Schmidt, Danica J. Sutherland

NeurIPS 2023 BiSLS/SPS: Auto-Tune Step Sizes for Stable Bi-Level Optimization Chen Fan, Gaspard Choné-Ducasse, Mark Schmidt, Christos Thrampoulidis

NeurIPS 2023 Don't Be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models Leonardo Galli, Holger Rauhut, Mark Schmidt

ECML-PKDD 2023 Fast Convergence of Random Reshuffling Under Over-Parameterization and the Polyak-Łojasiewicz Condition Chen Fan, Christos Thrampoulidis, Mark Schmidt

NeurIPSW 2023 Greedy Newton: Newton's Method with Exact Line Search Betty Shea, Mark Schmidt

NeurIPSW 2023 MSL: An Adaptive Momentem-Based Stochastic Line-Search Framework Chen Fan, Sharan Vaswani, Christos Thrampoulidis, Mark Schmidt

ICLR 2023 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

UAI 2023 Optimistic Thompson Sampling-Based Algorithms for Episodic Reinforcement Learning Bingshan Hu, Tianyue H. Zhang, Nidhi Hegde, Mark Schmidt

NeurIPS 2023 Searching for Optimal Per-Coordinate Step-Sizes with Multidimensional Backtracking Frederik Kunstner, Victor Sanches Portella, Mark Schmidt, Nicholas Harvey

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

ICML 2023 Target-Based Surrogates for Stochastic Optimization Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad Harikandeh, Mark Schmidt, Nicolas Le Roux

NeurIPSW 2023 Variance Reduced Model Based Methods: New Rates and Adaptive Step Sizes Robert M. Gower, Frederik Kunstner, Mark Schmidt

NeurIPSW 2023 Why Adam Outperforms Gradient Descent on Language Models: A Heavy-Tailed Class Imbalance Problem Robin Yadav, Frederik Kunstner, Mark Schmidt, Alberto Bietti

NeurIPSW 2022 Fast Convergence of Greedy 2-Coordinate Updates for Optimizing with an Equality Constraint Amrutha Varshini Ramesh, Aaron Mishkin, Mark Schmidt

NeurIPSW 2022 Fast Convergence of Random Reshuffling Under Interpolation and the Polyak-\l Ojasiewicz Condition Chen Fan, Christos Thrampoulidis, Mark Schmidt

IJCAI 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

CoLLAs 2022 Improved Policy Optimization for Online Imitation Learning Jonathan Wilder Lavington, Sharan Vaswani, Mark Schmidt

JMLR 2022 Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence Julie Nutini, Issam Laradji, Mark Schmidt

NeurIPSW 2022 Practical Structured Riemannian Optimization with Momentum by Using Generalized Normal Coordinates Wu Lin, Valentin Duruisseaux, Melvin Leok, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt

MLJ 2022 SVRG Meets AdaGrad: Painless Variance Reduction Benjamin Dubois-Taine, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Simon Lacoste-Julien

NeurIPSW 2022 Target-Based Surrogates for Stochastic Optimization Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad Harikandeh, Mark Schmidt, Nicolas Le Roux

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

NeurIPSW 2021 A Closer Look at Gradient Estimators with Reinforcement Learning as Inference Jonathan Wilder Lavington, Michael Teng, Mark Schmidt, Frank Wood

NeurIPSW 2021 An Empirical Study of Non-Uniform Sampling in Off-Policy Reinforcement Learning for Continuous Control Nicholas Ioannidis, Jonathan Wilder Lavington, Mark Schmidt

WACV 2021 AutoRetouch: Automatic Professional Face Retouching Alireza Shafaei, James J. Little, Mark Schmidt

ICML 2021 Robust Asymmetric Learning in POMDPs Andrew Warrington, Jonathan W Lavington, Adam Scibior, Mark Schmidt, Frank Wood

ICML 2021 Tractable Structured Natural-Gradient Descent Using Local Parameterizations Wu Lin, Frank Nielsen, Khan Mohammad Emtiyaz, Mark Schmidt

MLJ 2020 Combining Bayesian Optimization and Lipschitz Optimization Mohamed Osama Ahmed, Sharan Vaswani, Mark Schmidt

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

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

NeurIPS 2020 Regret Bounds Without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses Yihan Zhou, Victor Sanches Portella, Mark Schmidt, Nicholas Harvey

AISTATS 2019 Are We There yet? Manifold Identification of Gradient-Related Proximal Methods Yifan Sun, Halyun Jeong, Julie Nutini, Mark Schmidt

AISTATS 2019 Distributed Maximization of "Submodular Plus Diversity" Functions for Multi-Label Feature Selection on Huge Datasets Mehrdad Ghadiri, Mark Schmidt

AISTATS 2019 Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron Sharan Vaswani, Francis Bach, Mark Schmidt

ICML 2019 Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-Family Approximations Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt

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

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

ICLR 2018 Online Learning Rate Adaptation with Hypergradient Descent Atilim Gunes Baydin, Robert Cornish, David Martinez Rubio, Mark Schmidt, Frank Wood

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

ECCV 2018 Where Are the Blobs: Counting by Localization with Point Supervision Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vazquez, Mark Schmidt

AISTATS 2017 Horde of Bandits Using Gaussian Markov Random Fields Sharan Vaswani, Mark Schmidt, Laks V. S. Lakshmanan

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

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, Hoyt A. Koepke, Alim Virani

UAI 2016 Faster Stochastic Variational Inference Using Proximal-Gradient Methods with General Divergence Functions Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama

ECML-PKDD 2016 Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi, Julie Nutini, Mark Schmidt

ICML 2015 Coordinate Descent Converges Faster with the Gauss-Southwell Rule than Random Selection Julie Nutini, Mark Schmidt, Issam Laradji, Michael Friedlander, Hoyt Koepke

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

NeurIPS 2015 StopWasting My Gradients: Practical SVRG Reza Babanezhad Harikandeh, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konečný, Scott Sallinen

ICML 2013 Block-Coordinate Frank-Wolfe Optimization for Structural SVMs Simon Lacoste-Julien, Martin Jaggi, Mark Schmidt, Patrick Pletscher

NeurIPS 2012 A Stochastic Gradient Method with an Exponential Convergence _Rate for Finite Training Sets Nicolas L. Roux, Mark Schmidt, Francis R. Bach

AISTATS 2012 On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models David Buchman, Mark Schmidt, Shakir Mohamed, David Poole, Nando De Freitas

NeurIPS 2011 Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization Mark Schmidt, Nicolas L. Roux, Francis R. Bach

UAI 2011 Generalized Fast Approximate Energy Minimization via Graph Cuts: A-Expansion B-Shrink Moves Mark Schmidt, Karteek Alahari

AISTATS 2010 Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials Mark Schmidt, Kevin Murphy

AISTATS 2010 Modeling Annotator Expertise: Learning When Everybody Knows a Bit of Something Yan Yan, Romer Rosales, Glenn Fung, Mark Schmidt, Gerardo Hermosillo, Luca Bogoni, Linda Moy, Jennifer Dy

UAI 2009 Group Sparse Priors for Covariance Estimation Benjamin M. Marlin, Mark Schmidt, Kevin P. Murphy

CVPR 2009 Increased Discrimination in Level Set Methods with Embedded Conditional Random Fields Dana Cobzas, Mark Schmidt

UAI 2009 Modeling Discrete Interventional Data Using Directed Cyclic Graphical Models Mark Schmidt, Kevin P. Murphy

AISTATS 2009 Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm Mark Schmidt, Ewout Berg, Michael Friedlander, Kevin Murphy

NeurIPS 2008 An Interior-Point Stochastic Approximation Method and an L1-Regularized Delta Rule Peter Carbonetto, Mark Schmidt, Nando D. Freitas

CVPR 2008 Structure Learning in Random Fields for Heart Motion Abnormality Detection Mark Schmidt, Kevin P. Murphy, Glenn Fung, Rómer Rosales

ICCV 2007 3D Variational Brain Tumor Segmentation Using a High Dimensional Feature Set Dana Cobzas, Neil Birkbeck, Mark Schmidt, Martin Jägersand, Albert Murtha

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