Ohad Shamir
Professor
Studies the theory of machine learning, including optimization and the expressiveness of neural networks.
Ohad Shamir's lab at Toronto works on learning theory & optimization. 16 papers since 2023, 14 at top venues, mostly COLT and NeurIPS. 2 PhD students. 11 alumni, most often to IBM. Applications close .
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 88%
- PhD students
- 2
- MS students
- 0
- alumni listed
- 11
Papers by yearfrom DBLP, 2023–2026
- 1 papers2026 · T1 1 · collecting
- 5 papers2025 · T1 4
- 4 papers2024 · T1 4
- 6 papers2023 · T1 5
T1 venuesOtherVenues: COLT, NeurIPS, JMLR, ALT, ICML
Research focuspaper titles that use each topic's words
- Learning theory & optimization4 papers
- optimization4 papers
- networks4 papers
- neural networks3 papers
- theory0 papers
Paper list
From DBLP.
20261 paper
20255 papers
- Trans. Mach. Learn. Res.Are Convex Optimization Curves Convex? (opens in a new tab)
- NeurIPS (T1)Beyond Benign Overfitting in Nadaraya-Watson Interpolators (opens in a new tab)
- COLT (T1)Logarithmic Width Suffices for Robust Memorization (opens in a new tab)
- COLT (T1)The Oracle Complexity of Simplex-based Matrix Games: Linear Separability and Nash Equilibria (opens in a new tab)
- NeurIPS (T1)When Models Don't Collapse: On the Consistency of Iterative MLE (opens in a new tab)
20244 papers
- JMLR (T1)An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization (opens in a new tab)
- COLT (T1)Depth Separation in Norm-Bounded Infinite-Width Neural Networks (opens in a new tab)
- ICML (T1)Generalization in Kernel Regression Under Realistic Assumptions (opens in a new tab)
- COLT (T1)Open Problem: Anytime Convergence Rate of Gradient Descent (opens in a new tab)
20236 papers
- NeurIPS (T1)Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance (opens in a new tab)
- COLT (T1)Deterministic Nonsmooth Nonconvex Optimization (opens in a new tab)
- NeurIPS (T1)From Tempered to Benign Overfitting in ReLU Neural Networks (opens in a new tab)
- ALTImplicit Regularization Towards Rank Minimization in ReLU Networks (opens in a new tab)
- NeurIPS (T1)Initialization-Dependent Sample Complexity of Linear Predictors and Neural Networks (opens in a new tab)
- JMLR (T1)The Implicit Bias of Benign Overfitting (opens in a new tab)
Where alumni went11 listed, 25% to industry
Industry 1Academia 3
Most often to
- IBM
Also to
- Ben Gurion University
- Hebrew University of Jerusalem
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.
Similar labsnearest by research summary
- CNSJTU
Zheng Huang
- Trustworthy & safe ML
- CNZhejiang
Haobo Wang
78 T1 - CNTsinghua
Jian Li
- Learning theory & optimization
- USUW
Jamie Morgenstern
- Trustworthy & safe ML
- GBEdinburgh
Siddharth N
- Trustworthy & safe ML
- USUMD
Yaodong Yu
- Generative & foundation models
Your notes
Save this lab to keep notes.
Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- DBLP
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
Torontocloses