← Authors

Anant Raj

23 papers · 2014–2025 · 6 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (10) 🧭 Keyword Pioneer 🌈 Renaissance Researcher (5) 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (30) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (11) πŸ”¬ Deep Specialist (11) πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (97) πŸš€ Conference Pioneer πŸ’Ž Century Club (23) πŸ”₯ Unstoppable (9) πŸ“ˆ Trend Setter ⚑ Prolific Year (6)

Conferences

NIPS (7) AISTATS (6) ICML (6) COLT (2) ALT (1) ICLR (1)

Top co-authors

Csaba SzepesvΓ‘ri (3) Lingjiong Zhu (3) Martin Jaggi (3) Francis Bach (3) Mert Gurbuzbalaban (3) Umut Simsekli (3) Youssef Mroueh (3) Pooria Joulani (2) Bo Dai (2) Sharan Vaswani (2)

Keywords

stochastic gradient descent (6) convex optimization (5) coordinate descent (4) stochastic optimization (4) wasserstein distance (3) kernel methods (3) algorithmic stability (3) online learning (2) variance reduction (2) heavy-tailed distribution (2) stochastic differential equation (2) generalization bound (2) importance sampling (2) optimization algorithm (2) explicit regularization (2) ridge regression (1) kl divergence (1) optimal transport (1) scalable learning (1) approximate inference (1)

Papers

Beyond propagation of chaos: A stochastic algorithm for mean field optimization COLT 2025
Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates NIPS 2024
From Inverse Optimization to Feasibility to ERM ICML 2024
Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models NIPS 2023
Utilising the CLT Structure in Stochastic Gradient based Sampling : Improved Analysis and Faster Algorithms COLT 2023
Explicit Regularization in Overparametrized Models via Noise Injection AISTATS 2023
Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares ALT 2023
Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions ICML 2023
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent NIPS 2023
Faster Rates, Adaptive Algorithms, and Finite-Time Bounds for Linear Composition Optimization and Gradient TD Learning AISTATS 2022
Convergence of Uncertainty Sampling for Active Learning ICML 2022
Explicit Regularization of Stochastic Gradient Methods through Duality AISTATS 2021
Dual Instrumental Variable Regression NIPS 2020
Importance Sampling via Local Sensitivity AISTATS 2020
Stochastic Stein Discrepancies NIPS 2020
A simpler approach to accelerated optimization: iterative averaging meets optimism ICML 2020
Sobolev Descent AISTATS 2019
Sobolev GAN ICLR 2018
On Matching Pursuit and Coordinate Descent ICML 2018
Approximate Steepest Coordinate Descent ICML 2017
Local Group Invariant Representations via Orbit Embeddings AISTATS 2017
Safe Adaptive Importance Sampling NIPS 2017
Scalable Kernel Methods via Doubly Stochastic Gradients NIPS 2014