Mert Gurbuzbalaban
23 papers · 2017–2024 · 5 venues · across top CS/AI venues
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Research topics
Machine Learning
(23)
Optimization & Theory Β· Machine Learning
(23)
Mathematics & Optimization
(11)
Optimization
(11)
Deep Learning
(7)
Optimization & Theory Β· Deep Learning
(5)
Artificial Intelligence
(3)
Bayesian & Probabilistic
(2)
Techniques
(2)
Application Areas
(1)
Core AI
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Learning Paradigms Β· Artificial Intelligence
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Learning Paradigms Β· Machine Learning
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Learning Types
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Keywords
stochastic gradient descent
(14)
heavy-tailed distribution
(5)
stochastic gradient
(4)
langevin dynamics
(4)
non-convex optimization
(4)
stochastic optimization
(4)
algorithmic stability
(3)
convergence rate
(3)
stochastic differential equation
(3)
accelerated gradient
(3)
generalization bound
(3)
markov chain monte carlo
(3)
wasserstein distance
(3)
convex optimization
(3)
minimax optimization
(2)
neural network optimization
(2)
decentralized optimization
(2)
bayesian inference
(2)
variance reduction
(2)
generalization error
(2)
Papers
High Probability and Risk-Averse Guarantees for a Stochastic Accelerated Primal-Dual Method
JMLR 2024
High-probability complexity bounds for stochastic non-convex minimax optimization
NeurIPS 2024
Penalized Overdamped and Underdamped Langevin Monte Carlo Algorithms for Constrained Sampling
JMLR 2024
Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions
ICML 2023
Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares
ALT 2023
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent
NeurIPS 2023
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks
JMLR 2022
SAPD+: An Accelerated Stochastic Method for Nonconvex-Concave Minimax Problems
NeurIPS 2022
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections
ICML 2021
Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance
NeurIPS 2021
Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo
JMLR 2021
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms
NeurIPS 2021
Fractional moment-preserving initialization schemes for training deep neural networks
AISTATS 2021
The Heavy-Tail Phenomenon in SGD
ICML 2021
Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization
NeurIPS 2020
DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate
AISTATS 2020
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise
ICML 2020
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
NeurIPS 2020
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances
ICML 2019
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks
ICML 2019
A Universally Optimal Multistage Accelerated Stochastic Gradient Method
NeurIPS 2019
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise
NeurIPS 2019
When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent
NeurIPS 2017