Murat Erdogdu
Associate Professor
Studies optimization algorithms, sampling and the statistical theory of machine learning.
Murat Erdogdu's lab at Toronto works on learning theory & optimization. 25 papers since 2023, 22 at top venues, mostly NeurIPS and COLT. 4 PhD students. 8 alumni. Applications close .
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 88%
- PhD students
- 4
- MS students
- 0
- alumni listed
- 8
Papers by yearfrom the lab homepage, 2023–2026
- 1 papers2026 · T1 1 · collecting
- 8 papers2025 · T1 8
- 7 papers2024 · T1 6
- 9 papers2023 · T1 7
T1 venuesOtherVenues: NeurIPS, COLT, ICLR, ICML, JMLR
Research focuspaper titles that use each topic's words
- Learning theory & optimization10 papers
- sampling4 papers
- statistical1 paper
- optimization algorithms0 papers
- studies optimization0 papers
Paper list
From the lab homepage.
20261 paper
20258 papers
- NeurIPS (T1)A Geometric Analysis of PCA (opens in a new tab)
- ICML (T1)Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics (opens in a new tab)
- NeurIPS (T1)Distributional Training Data Attribution: What do Influence Functions Sample? (opens in a new tab)
- NeurIPS (T1)From Information to Generative Exponent: Learning Rate Induces Phase Transitions in SGD (opens in a new tab)
- ICLR (T1)Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics (opens in a new tab)
- NeurIPS (T1)Learning Quadratic Neural Networks in High Dimensions: SGD Dynamics and Scaling Laws (opens in a new tab)
- ICLR (T1)Robust Feature Learning for Multi-Index Models in High Dimensions (opens in a new tab)
- NeurIPS (T1)When Do Transformers Outperform Feedforward and Recurrent Networks? A Statistical Perspective (opens in a new tab)
20247 papers
- NeurIPS (T1)A Separation in Heavy-Tailed Sampling: Gaussian vs. Stable Oracles for Proximal Samplers (opens in a new tab)
- Journal of Foundations of Computational MathematicsAnalysis of Langevin Monte Carlo from Poincaré to Log-Sobolev (opens in a new tab)
- JMLR (T1)Mean-square Analysis of Discretized Ito Diffusions for Heavy-tailed Sampling (opens in a new tab)
- COLT (T1)Minimax Linear Regression under the Quantile Risk (opens in a new tab)
- NeurIPS (T1)On the Efficiency of ERM in Feature Learning (opens in a new tab)
- COLT (T1)Pruning is Optimal for Learning Sparse Features in High-Dimensions (opens in a new tab)
- COLT (T1)Sampling from the Mean-Field Stationary Distribution (opens in a new tab)
20239 papers
- IEEE Transactions on Information TheoryAn analysis of Transformed Unadjusted Langevin Algorithm for Heavy-tailed Sampling (opens in a new tab)
- NeurIPS (T1)Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning (opens in a new tab)
- NeurIPS (T1)Gradient-Based Feature Learning under Structured Data (opens in a new tab)
- COLT (T1)Improved Discretization Analysis for Underdamped Langevin Monte Carlo (opens in a new tab)
- NeurIPS (T1)Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective (opens in a new tab)
- ICLR (T1)Neural Networks Efficiently Learn Low-Dimensional Representations with SGD (opens in a new tab)
- NeurIPS (T1)Optimal Excess Risk Bounds for Empirical Risk Minimization on p-Norm Linear Regression (opens in a new tab)
- Bernoulli JournalRiemannian Langevin Algorithm for Solving Semidefinite Programs (opens in a new tab)
- COLT (T1)Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincare Inequality (opens in a new tab)
Where alumni went8 listed, 0% to industry
Industry 0Academia 4
Also to
- Korea University
- MIT
- NYU
Apply to TorontoFall 2027
Computer Science MSc/PhD
- Round
- Single round (Dec 1)
- Opens
- —
- GRE
- optional
- English
- TOEFL/IELTS if applicable
- Fee
- CAD 135
Confirmed on the program page.
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