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Mark Schmidt

37 papers · 2008–2024 · 9 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (9) πŸƒ Academic Marathon (16) πŸ—ΊοΈ Taxonomy Completionist (18) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (15) 🌈 Renaissance Researcher (6) 🌟 Keyword Trendsetter Combo (5) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (10) πŸ† Keyword Champion πŸ—ƒοΈ Keyword Collector (62) ⚑ Prolific Year (5) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter ❓ The Questioner πŸ’Ž Century Club (37) πŸ”₯ Unstoppable (8)

Conferences

NIPS (11) AISTATS (10) ICML (9) ICLR (2) ECCV (1) IJCAI (1) JMLR (1) UAI (1) WACV (1)

Top co-authors

Sharan Vaswani (6) Frederik Kunstner (6) Mohammad Emtiyaz Khan (4) Wu Lin (4) Issam Laradji (3) Julie Nutini (3) Simon Lacoste-Julien (3) Nicolas L. Roux (2) Reza Babanezhad Harikandeh (2) Frank Wood (2)

Keywords

convergence rate (8) convex optimization (6) variational inference (4) gradient descent (4) stochastic gradient descent (4) regret bound (3) natural gradient (3) stochastic gradient (3) second-order optimization (2) online learning (2) thompson sampling (2) stochastic optimization (2) imitation learning (2) kl divergence (2) bayesian inference (2) deep learning (2) submodular optimization (2) expectation maximization (2) exponential family (2) mirror descent (2)

Papers

Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models NIPS 2024
Noise Is Not the Main Factor Behind the Gap Between Sgd and Adam on Transformers, But Sign Descent Might Be ICLR 2023
Optimistic Thompson Sampling-based algorithms for episodic reinforcement learning UAI 2023
Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking NIPS 2023
Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models NIPS 2023
BiSLS/SPS: Auto-tune Step Sizes for Stable Bi-level Optimization NIPS 2023
Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning ICML 2023
Target-based Surrogates for Stochastic Optimization ICML 2023
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent (Extended Abstract) IJCAI 2022
Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence JMLR 2022
Robust Asymmetric Learning in POMDPs ICML 2021
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent AISTATS 2021
Tractable structured natural-gradient descent using local parameterizations ICML 2021
AutoRetouch: Automatic Professional Face Retouching WACV 2021
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation AISTATS 2020
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses NIPS 2020
Handling the Positive-Definite Constraint in the Bayesian Learning Rule ICML 2020
Distributed Maximization of "Submodular plus Diversity" Functions for Multi-label Feature Selection on Huge Datasets AISTATS 2019
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates NIPS 2019
Are we there yet? Manifold identification of gradient-related proximal methods AISTATS 2019
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron AISTATS 2019
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations ICML 2019
Online Learning Rate Adaptation with Hypergradient Descent ICLR 2018
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient NIPS 2018
Where are the blobs: Counting by Localization with Point Supervision ECCV 2018
Model-Independent Online Learning for Influence Maximization ICML 2017
Horde of Bandits using Gaussian Markov Random Fields AISTATS 2017
StopWasting My Gradients: Practical SVRG NIPS 2015
Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection ICML 2015
Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields AISTATS 2015
Block-Coordinate Frank-Wolfe Optimization for Structural SVMs ICML 2013
A Stochastic Gradient Method with an Exponential Convergence _Rate for Finite Training Sets NIPS 2012
On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models AISTATS 2012
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization NIPS 2011
Modeling annotator expertise: Learning when everybody knows a bit of something AISTATS 2010
Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials AISTATS 2010
An interior-point stochastic approximation method and an L1-regularized delta rule NIPS 2008