Jake Snell

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 20

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Publications

Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake Snell, Toniann Pitassi, Richard S. Zemel
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models. ICLR (2024)
Gianluca M. Bencomo, Jake Snell, Thomas L. Griffiths
Implicit Maximum a Posteriori Filtering via Adaptive Optimization. ICLR (2024)
Raja Marjieh, Sreejan Kumar, Declan Campbell, Liyi Zhang, Gianluca M. Bencomo, Jake Snell, Thomas L. Griffiths
Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases. CoRR (2024)
Jake Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard S. Zemel
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions. ICLR (2023)
Bhishma Dedhia, Michael Chang, Jake Snell, Tom Griffiths, Niraj K. Jha
Im-Promptu: In-Context Composition from Image Prompts. NeurIPS (2023)
Zhun Deng, Thomas P. Zollo, Jake Snell, Toniann Pitassi, Richard S. Zemel
Distribution-Free Statistical Dispersion Control for Societal Applications. NeurIPS (2023)
Jake Snell, Richard S. Zemel
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes. ICLR (2021)
Jake Snell, Richard S. Zemel
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes. CoRR (2020)
Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard S. Zemel
Flexible Few-Shot Learning with Contextual Similarity. CoRR (2020)
Marc T. Law, Jake Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard S. Zemel
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models. ICLR (Poster) (2019)
Marc Teva Law, Renjie Liao, Jake Snell, Richard S. Zemel
Lorentzian Distance Learning for Hyperbolic Representations. ICML (2019)
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel
Meta-Learning for Semi-Supervised Few-Shot Classification. CoRR (2018)
Jack Klys, Jake Snell, Richard S. Zemel
Learning Latent Subspaces in Variational Autoencoders. NeurIPS (2018)
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel
Meta-Learning for Semi-Supervised Few-Shot Classification. ICLR (Poster) (2018)
Jack Klys, Jake Snell, Richard S. Zemel
Learning Latent Subspaces in Variational Autoencoders. CoRR (2018)
Jake Snell, Richard S. Zemel
Stochastic Segmentation Trees for Multiple Ground Truths. UAI (2017)
Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel
Learning to generate images with perceptual similarity metrics. ICIP (2017)
Jake Snell, Kevin Swersky, Richard S. Zemel
Prototypical Networks for Few-shot Learning. NIPS (2017)
Jake Snell, Kevin Swersky, Richard S. Zemel
Prototypical Networks for Few-shot Learning. CoRR (2017)
Karl Ridgeway, Jake Snell, Brett Roads, Richard S. Zemel, Michael C. Mozer
Learning to generate images with perceptual similarity metrics. CoRR (2015)