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Jake C. Snell

Postdoctoral Researcher

Department of Computer Science

Princeton University

For a fully up-to-date list, see my Google Scholar page.

Preprints

P1

Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases

Raja Marjieh, Sreejan Kumar, Declan Campbell, Liyi Zhang, Gianluca Bencomo, Jake C. Snell, Thomas L. Griffiths

arXiv

Publications

C14

A Metalearned Neural Circuit for Nonparametric Bayesian Inference

Jake C. Snell, Gianluca Bencomo, Thomas L. Griffiths

To appear in: Neural Information Processing Systems (NeurIPS) 2024.
arXiv

J1

Improving Predictor Reliability with Selective Recalibration

Thomas P. Zollo, Zhun Deng, Jake C. Snell, Toniann Pitassi, Richard Zemel

Transactions on Machine Learning Research (TMLR), 2024.
PDF

C13

Implicit Maximum a Posteriori Filtering via Adaptive Optimization

Gianluca M. Bencomo, Jake C. Snell, Thomas L. Griffiths

International Conference on Learning Representations (ICLR) 2024.
PDF

C12

Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models

Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake C. Snell, Toniann Pitassi, Richard Zemel

International Conference on Learning Representations (ICLR) 2024.
PDF

C11

Im-Promptu: In-Context Composition from Image Prompts

Bhishma Dedhia, Michael Chang, Jake C. Snell, Thomas L. Griffiths, Niraj K. Jha

Neural Information Processing Systems (NeurIPS) 2023.
PDF
arXiv
Code
Link

C10

Distribution-Free Statistical Dispersion Control for Societal Applications

Zhun Deng, Thomas P. Zollo, Jake C. Snell, Toniann Pitassi, Richard Zemel

Neural Information Processing Systems (NeurIPS) 2023.
Selected for NeurIPS spotlight presentation
PDF

C9

Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions

Jake C. Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard Zemel

International Conference on Learning Representations (ICLR) 2023.
PDF

C8

Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes

Jake C. Snell, Richard Zemel

International Conference on Learning Representations (ICLR) 2021.
PDF
arXiv
Code

C7

Lorentzian Distance Learning for Hyperbolic Representations

Marc T. Law, Renjie Liao, Jake C. Snell, Richard S. Zemel

International Conference on Machine Learning (ICML) 2019.
PDF
supplementary material retrieval code classification code

C6

Dimensionality Reduction for Representing the Knowledge of Probabilistic Models

Marc T. Law, Jake C. Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard S. Zemel

International Conference on Learning Representations (ICLR) 2019.
PDF
visualization code flowers zero-shot code CUB zero-shot code

C5

Learning Latent Subspaces in Variational Autoencoders

Jack Klys, Jake C. Snell, Richard Zemel

Neural Information Processing Systems (NeurIPS) 2018.
PDF

C4

Meta-learning for Semi-supervised Few-shot Classification

Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake C. Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel

International Conference on Learning Representations (ICLR) 2018.
PDF
Code

C3

Prototypical Networks for Few-shot Learning

Jake C. Snell, Kevin Swersky, Richard Zemel

Neural Information Processing Systems (NeurIPS) 2017.
PDF
Code
supplementary material

C2

Stochastic Segmentation Trees for Multiple Ground Truths

Jake C. Snell, Richard S. Zemel

Uncertainty in Artificial Intelligence (UAI) 2017.
PDF
supplementary material

C1

Learning to Generate Images With Perceptual Similarity Metrics

Jake C. Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel

IEEE International Conference on Image Processing (ICIP) 2017.
Finalist for ICIP 2017 best student paper award
PDF
supplementary material

Thesis

T1

Learning to Build Probabilistic Models with Limited Data

Jake C. Snell

Ph.D. Thesis, Department of Computer Science, University of Toronto, 2021.
Link