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.
- Few-Shot Attribute Learning.
Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, and Richard Zemel.
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.
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.
Learning to Generate Images with Perceptual Similarity Metrics.
Jake Snell, Karl Ridgeway, Renjie Liao, Brett Roads, Michael C. Mozer, and Richard Zemel. ICIP 2017.
- Learning to Build Probabilistic Models with Limited Data.
Jake Snell. Ph.D. Thesis, Dept. of Computer Science, Univ. of Toronto, 2021.
- 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
- NeurIPS Workshop on Meta-Learning: 2021 (area chair), 2020 (meta-reviewer), 2018, 2017