About Me
I am a postdoctoral researcher working with Thomas Griffiths at the Department of Computer Science, Princeton University. I received my Ph.D. in Computer Science in 2021 from the University of Toronto under the supervision of Richard Zemel, where I also completed a postdoctoral fellowship.
My recent research interests include nonparametric Bayesian approaches to few-shot and continual learning. I am also interested in uncertainty quantification for reliable machine learning.
Preprints
- Few-Shot Attribute Learning.
Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, and Richard Zemel.
Publications
-
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes. Jake Snell and Richard Zemel. ICLR 2021.
arxiv | code -
Lorentzian Distance Learning for Hyperbolic Representations.
Marc T. Law, Renjie Liao, Jake Snell, and Richard Zemel. ICML 2019.
retrieval code | binary classification code | supplemental -
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models.
Marc T. Law, Jake Snell, Amir-Massoud Farahmand, Raquel Urtasun, and Richard Zemel. ICLR 2019.
visualization code | flowers zero-shot code | CUB zero-shot code -
Learning Latent Subspaces in Variational Autoencoders.
Jack Klys, Jake Snell, and Richard Zemel. NeurIPS 2018. -
Meta-Learning for Semi-Supervised Few-Shot Classification.
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Josh B. Tenenbaum, Hugo Larochelle, and Richard Zemel. ICLR 2018.
code -
Prototypical Networks for Few-shot Learning.
Jake Snell, Kevin Swersky, and Richard Zemel. NeurIPS 2017.
code | supplemental -
Stochastic Segmentation Trees for Multiple Ground Truths.
Jake Snell and Richard Zemel. UAI 2017.
supplemental -
Learning to Generate Images with Perceptual Similarity Metrics.
Jake Snell, Karl Ridgeway, Renjie Liao, Brett Roads, Michael C. Mozer, and Richard Zemel. ICIP 2017.
supplemental
Ph.D. Thesis
- Learning to Build Probabilistic Models with Limited Data.
Jake Snell. Ph.D. Thesis, Dept. of Computer Science, Univ. of Toronto, 2021.
Service
Conference Reviewing
- AutoML-Conf: 2022 (area chair)
- CoLLAs: 2022
- CVPR: 2021
- ICLR: 2020, 2018, 2017
- ICML: 2022, 2019, 2018
- NeurIPS: 2022, 2021 (top 8% reviewer), 2019 (top 6% reviewer), 2018, 2017
Journal Reviewing
Workshop Reviewing
- NeurIPS Workshop on Meta-Learning: 2021 (area chair), 2020 (meta-reviewer), 2018, 2017