← Authors

Murat A Erdogdu

35 papers · 2013–2025 · 5 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (12) 🐝 Cross-Pollinator (8) 🌈 Renaissance Researcher (9) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer πŸ—ΊοΈ Taxonomy Completionist (49) 🌍 Conference Polyglot (5) 🏠 Conference Loyalist (24) πŸ† Grand Slam πŸ† Keyword Champion (4) πŸ”¬ Deep Specialist (10) πŸ—ƒοΈ Keyword Collector (147) πŸ’Ž Century Club (35) πŸ”₯ Unstoppable (11) πŸ“ˆ Trend Setter ⚑ Prolific Year (7)

Conferences

NIPS (24) COLT (5) ICLR (4) AAAI (1) ICML (1)

Top co-authors

Alireza Mousavi-Hosseini (5) Umut Simsekli (5) Denny Wu (5) Taiji Suzuki (4) Krishnakumar Balasubramanian (4) Andrea Montanari (3) Jimmy Ba (3) Sejun Park (2) Zhichao Wang (2) Mohsen Bayati (2)

Research topics

Privacy (1) Statistics (1) Probability (1)

Keywords

stochastic gradient descent (7) convergence rate (5) langevin monte carlo (4) markov chain monte carlo (4) generalization bound (4) feature learning (3) empirical risk minimization (3) sample complexity (3) gradient descent (3) asymptotic normality (2) non-convex optimization (2) stochastic gradient (2) statistical estimation (2) generalized linear model (2) stochastic process (2) convex optimization (2) maximum likelihood (2) learning theory (2) linear regression (2) sampling algorithm (2)

Papers

Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics ICLR 2025
Robust Feature Learning for Multi-Index Models in High Dimensions ICLR 2025
Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics ICML 2025
On the Efficiency of ERM in Feature Learning NIPS 2024
A Separation in Heavy-Tailed Sampling: Gaussian vs. Stable Oracles for Proximal Samplers NIPS 2024
Pruning is Optimal for Learning Sparse Features in High-Dimensions COLT 2024
Optimal Excess Risk Bounds for Empirical Risk Minimization on $p$-Norm Linear Regression NIPS 2023
Neural Networks Efficiently Learn Low-Dimensional Representations with SGD ICLR 2023
Gradient-Based Feature Learning under Structured Data NIPS 2023
Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning NIPS 2023
Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective NIPS 2023
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation NIPS 2022
Generalization Bounds for Stochastic Gradient Descent via Localized $\varepsilon$-Covers NIPS 2022
Convergence and Optimality of Policy Gradient Methods in Weakly Smooth Settings AAAI 2022
Analysis of Langevin Monte Carlo from Poincare to Log-Sobolev COLT 2022
Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance COLT 2022
Towards a Theory of Non-Log-Concave Sampling:First-Order Stationarity Guarantees for Langevin Monte Carlo COLT 2022
Understanding the Variance Collapse of SVGD in High Dimensions ICLR 2022
Manipulating SGD with Data Ordering Attacks NIPS 2021
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias NIPS 2021
On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness COLT 2021
On Empirical Risk Minimization with Dependent and Heavy-Tailed Data NIPS 2021
Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance NIPS 2021
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms NIPS 2021
Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks NIPS 2021
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method NIPS 2020
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks NIPS 2020
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond NIPS 2019
Global Non-convex Optimization with Discretized Diffusions NIPS 2018
Inference in Graphical Models via Semidefinite Programming Hierarchies NIPS 2017
Robust Estimation of Neural Signals in Calcium Imaging NIPS 2017
Scaled Least Squares Estimator for GLMs in Large-Scale Problems NIPS 2016
Convergence rates of sub-sampled Newton methods NIPS 2015
Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma NIPS 2015
Estimating LASSO Risk and Noise Level NIPS 2013