| Painless stochastic gradient: Interpolation, line-search, and convergence rates S Vaswani, A Mishkin, I Laradji, M Schmidt, G Gidel, S Lacoste-Julien Advances in neural information processing systems 32, 2019 | 250 | 2019 |
| Slang: Fast structured covariance approximations for bayesian deep learning with natural gradient A Mishkin, F Kunstner, D Nielsen, M Schmidt, ME Khan Advances in neural information processing systems 31, 2018 | 74 | 2018 |
| Fast convex optimization for two-layer relu networks: Equivalent model classes and cone decompositions A Mishkin, A Sahiner, M Pilanci International Conference on Machine Learning, 15770-15816, 2022 | 33 | 2022 |
| Interpolation, Growth Conditions, and Stochastic Gradient Descent A Mishkin University of British Columbia, 2020 | 9 | 2020 |
| Optimal sets and solution paths of relu networks A Mishkin, M Pilanci International Conference on Machine Learning, 24888-24924, 2023 | 8 | 2023 |
| To each optimizer a norm, to each norm its generalization S Vaswani, R Babanezhad, J Gallego-Posada, A Mishkin, ... arXiv preprint arXiv:2006.06821, 2020 | 7 | 2020 |
| Directional smoothness and gradient methods: Convergence and adaptivity A Mishkin, A Khaled, Y Wang, A Defazio, R Gower Advances in Neural Information Processing Systems 37, 14810-14848, 2025 | 5 | 2025 |
| A library of mirrors: Deep neural nets in low dimensions are convex lasso models with reflection features E Zeger, Y Wang, A Mishkin, T Ergen, E Candès, M Pilanci arXiv preprint arXiv:2403.01046, 2024 | 3 | 2024 |
| Web ValueCharts AP Mishkin, EA Hindalong | 3 | 2018 |
| Faster convergence of stochastic accelerated gradient descent under interpolation A Mishkin, M Pilanci, M Schmidt arXiv preprint arXiv:2404.02378, 2024 | 2 | 2024 |
| Exploring the loss landscape of regularized neural networks via convex duality S Kim, A Mishkin, M Pilanci arXiv preprint arXiv:2411.07729, 2024 | 1 | 2024 |
| Level Set Teleportation: An Optimization Perspective A Mishkin, A Bietti, RM Gower arXiv preprint arXiv:2403.03362, 2024 | 1 | 2024 |
| Analyzing and Improving Greedy 2-Coordinate Updates for Equality-Constrained Optimization via Steepest Descent in the 1-Norm AV Ramesh, A Mishkin, M Schmidt, Y Zhou, JW Lavington, J She arXiv preprint arXiv:2307.01169, 2023 | 1 | 2023 |
| The Solution Path of the Group Lasso A Mishkin, M Pilanci OPT 2022: Optimization for Machine Learning (NeurIPS 2022 Workshop), 2022 | 1 | 2022 |
| Web ValueCharts: Analyzing Individual and Group Preferences with Interactive, Web-based Visualizations A Mishkin | | 2017 |
| A novel analysis of gradient descent under directional smoothness A Mishkin, A Khaled, A Defazio, RM Gower OPT 2023: Optimization for Machine Learning, 0 | | |
| Level Set Teleportation: the Good, the Bad, and the Ugly A Mishkin, A Bietti, RM Gower OPT 2023: Optimization for Machine Learning, 0 | | |
| Strong Duality via Convex Conjugacy A Mishkin | | |
| Solving Projection Problems using Lagrangian Duality A Mishkin | | |
| Fast Convergence of Greedy 2-Coordinate Updates for Optimizing with an Equality Constraint AV Ramesh, A Mishkin, M Schmidt OPT 2022: Optimization for Machine Learning (NeurIPS 2022 Workshop), 0 | | |
Mark SchmidtProfessor of Computer Science, University of British ColumbiaPatvirtintas el. paštas cs.ubc.ca
