Daniel Roy
Professor
Studies foundations of machine learning and statistics, including statistical learning theory and deep learning generalization.
Daniel Roy's lab at Toronto works on learning theory & optimization. 14 papers since 2023, 5 at top venues, mostly NeurIPS and AISTATS. 1 PhD student. 15 alumni, most often to Google. Applications close .
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 36%
- PhD students
- 1
- MS students
- 0
- alumni listed
- 15
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 4 papers2025 · T1 3
- 5 papers2024 · T1 1
- 5 papers2023 · T1 1
T1 venuesOtherVenues: NeurIPS, AISTATS, COLT, ICLR
Research focuspaper titles that use each topic's words
- Learning theory & optimization3 papers
- generalization2 papers
- studies foundations0 papers
- mathematical0 papers
- statistical learning0 papers
Paper list
From the lab homepage.
20254 papers
- COLT (T1)Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-offs (opens in a new tab)
- Proc. International Conf. on Machine LearningLeveraging Per-Instance Privacy for Machine Unlearning (opens in a new tab)
- ICLR (T1)Selective Unlearning via Representation Erasure Using Adversarial Training (opens in a new tab)
- AISTATS (T1)The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws (opens in a new tab)
20245 papers
- Proc. International Conf. on Machine LearningCausal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals (opens in a new tab)
- Proc. International Conf. on Machine LearningInformation Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization (opens in a new tab)
- Proc. ACM SIGPLAN Symposium on Principles of Programming LanguagesProbabilistic programming interfaces for random graphs: Markov categories, graphons, and nominal sets (opens in a new tab)
- NeurIPS (T1)Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood (opens in a new tab)
- Proc. European Conference in Machine LearningSimultaneous linear connectivity of neural networks modulo permutation (opens in a new tab)
20235 papers
- BiometrikaExistence of matching priors on compact spaces (opens in a new tab)
- Proc. International Conference on Algorithmic Learning TheoryLimitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization (opens in a new tab)
- Annals of StatisticsMinimax Rates for Conditional Density Estimation via Empirical Entropy (opens in a new tab)
- Annals of StatisticsRelaxing the I.I.D. Assumption: Adaptive Minimax Optimal Sequential Prediction with Expert Advice (opens in a new tab)
- NeurIPS (T1)The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit (opens in a new tab)
Where alumni went15 listed, 7% to industry
Industry 1Academia 13
Most often to
Also to
- MIT
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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- Papers
- The lab homepage
- Members
- Lab homepage
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- Lab homepage
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