Anant Raj
23 papers · 2014–2025 · 6 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (6) π Interdisciplinary Bridge π§ Keyword Pioneer π£ Hot Topic Early Bird π Academic Marathon (11)
π
Interdisciplinary Bridge
π
Conference Polyglot
(6)
π
Academic Marathon
(11)
π¬
Deep Specialist
(11)
π
Keyword Champion
(2)
ποΈ
Keyword Collector
(97)
π
Conference Pioneer
π
Century Club
(23)
π₯
Unstoppable
(9)
π
Trend Setter
β‘
Prolific Year
(6)
Conferences
NIPS (7)
AISTATS (6)
ICML (6)
COLT (2)
ALT (1)
ICLR (1)
Top co-authors
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