Scholar
Richard Zemel
Google Scholar ID: iBeDoRAAAAAJ
Professor of Computer Science, University of Toronto
Citations & Impact
Citations
72,388
H-index
71
i10-index
145
Publications
20
Co-authors
0
Contact
Publications
Resume (English only)
Academic Achievements
- Published numerous papers, including but not limited to:
- - Environment inference for invariant learning (ICML 2021)
- - SketchEmbedNet: Learning novel concepts by imitating drawings (ICML 2021)
- - Universal template for few-shot dataset generalization (ICML 2021)
- - On monotonic linear interpolation of neural network parameters (ICML 2021)
- - A computational framework for slang generation (Transactions of the Association for Computational Linguistics, 2021)
- - Wandering within a world: Online contextualized few-shot learning (ICLR 2021)
- - Bayesian few-shot classification with one-vs-each Polya-Gamma augmented Gaussian Processes (ICLR 2021)
- - Theoretical bounds on estimation error for meta-learning (ICLR 2021)
- - A PAC-Bayesian approach to generalization bounds for graph neural networks (ICLR 2021)
- - Shortcut learning in deep neural networks (Nature Machine Intelligence, 2020)
- - Causal modeling for fairness in dynamical systems (ICML 2020)
- - Cutting out the middle-man: Training and evaluating energy-based models without sampling (ICML 2020)
- - Optimizing long-term social welfare in recommender systems: A constrained matching approach (ICML 2020)
- - Understanding the limitations of conditional generative models (ICLR 2020)
- - Incremental few-shot learning with attention attractor networks (NeurIPS 2019)
- - SMILe: Scalable meta inverse reinforcement learning through context-conditional policies (NeurIPS 2019)
- - Efficient graph generation with graph recurrent attention networks (NeurIPS 2019)
- - Flexibly fair representation learning by disentanglement (ICML 2019)
- - Understanding the origins of bias in word embedding (ICML 2019)
- - Lorentzian distance learning for hyperbolic representations (incomplete)
Research Experience
- Professor in the Department of Computer Science at the University of Toronto; Co-Founder of Vector Institute for Artificial Intelligence; Industrial Research Chair in Machine Learning; Senior Fellow at the Canadian Institute for Advanced Research.
Background
- Research interests include machine learning and artificial intelligence.
Miscellany
- Contact Information:
- - Email: zemel [at] cs [dot] toronto [dot] edu
- - Phone: (416) 978-7497
- - Fax: (416) 978-1455
- Note: On leave from the University of Toronto during 2021-2022, and not taking on new students.
Co-authors