Chris J. Maddison
Assistant Professor
Studies deep learning, Bayesian inference and optimization, including Monte Carlo methods.
Chris J. Maddison's lab at Toronto works on probabilistic & causal inference, learning theory & optimization and efficient training & inference. 18 papers since 2023, 16 at top venues, mostly NeurIPS and ICLR. 6 PhD students. 12 alumni, most often to Isomorphic Labs and Magic AI. Applications close .
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 89%
- PhD students
- 6
- MS students
- 0
- alumni listed
- 12
Papers by yearfrom DBLP, 2023–2026
- 1 papers2026 · T1 0 · collecting
- 7 papers2025 · T1 6
- 6 papers2024 · T1 6
- 4 papers2023 · T1 4
T1 venuesOtherVenues: NeurIPS, ICLR, ICML, ACL, COLT
Research focuspaper titles that use each topic's words
- Learning theory & optimization4 papers
- Probabilistic & causal inference3 papers
- Efficient training & inference1 paper
- carlo methods0 papers
- bayesian inference0 papers
Paper list
From DBLP.
20261 paper
20257 papers
- NeurIPS (T1)A Geometric Analysis of PCA (opens in a new tab)
- ACL (T1)APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts (opens in a new tab)
- NeurIPS (T1)BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model (opens in a new tab)
- NeurIPS (T1)Measuring Scientific Capabilities of Language Models with a Systems Biology Dry Lab (opens in a new tab)
- ICLR (T1)MixMax: Distributional Robustness in Function Space via Optimal Data Mixtures (opens in a new tab)
- ICML (T1)MixMin: Finding Data Mixtures via Convex Minimization (opens in a new tab)
- Trans. Mach. Learn. Res.Test-Time Fairness and Robustness in Large Language Models (opens in a new tab)
20246 papers
- NeurIPS (T1)End-To-End Causal Effect Estimation from Unstructured Natural Language Data (opens in a new tab)
- ICML (T1)Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs (opens in a new tab)
- ICLR (T1)Identifying the Risks of LM Agents with an LM-Emulated Sandbox (opens in a new tab)
- COLT (T1)Minimax Linear Regression under the Quantile Risk (opens in a new tab)
- NeurIPS (T1)Observational Scaling Laws and the Predictability of Langauge Model Performance (opens in a new tab)
- NeurIPS (T1)On the Efficiency of ERM in Feature Learning (opens in a new tab)
20234 papers
- ICLR (T1)Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions (opens in a new tab)
- NeurIPS (T1)MeGraph: Capturing Long-Range Interactions by Alternating Local and Hierarchical Aggregation on Multi-Scaled Graph Hierarchy (opens in a new tab)
- NeurIPS (T1)Probabilistic Invariant Learning with Randomized Linear Classifiers (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 went12 listed, 100% to industry
Industry 2Academia 0
Most often to
- Isomorphic Labs
- Magic AI
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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