Jake Snell

25 papers A* 9A 1B 1Journal 10Unranked 3
YearRankTypeTitle / Venue / Authors
2025 J jnl
Amortized Bayesian Meta-Learning for Low-Rank Adaptation of Large Language Models.
CoRR
Liyi Zhang, Jake Snell, Thomas L. Griffiths
2024 A* conf
A Metalearned Neural Circuit for Nonparametric Bayesian Inference.
NeurIPS
Jake Snell, Gianluca M. Bencomo, Tom Griffiths
2024 A* conf
Implicit Maximum a Posteriori Filtering via Adaptive Optimization.
ICLR
Gianluca M. Bencomo, Jake Snell, Thomas L. Griffiths
2024 J jnl
Improving Predictor Reliability with Selective Recalibration.
Trans. Mach. Learn. Res.
Thomas P. Zollo, Zhun Deng, Jake Snell, Toniann Pitassi, Richard S. Zemel
2024 J jnl
ParaVerse: co-design of a parachute rehearsal and training virtual-reality enhanced simulator for the Australian Defence Force: combining a generative co-design framework and an agile approach to development.
Virtual Real.
Brennen Mills, Martin Masek, Julie Boston, Wyatt de Souza, Jake Snell, Stuart Bender, Matthew B. Thompson, Billy Sung, Sara Hansen
2024 A* conf
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models.
ICLR
Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake Snell, Toniann Pitassi, Richard S. Zemel
2024 J jnl
Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases.
CoRR
Raja Marjieh, Sreejan Kumar, Declan Campbell, Liyi Zhang, Gianluca M. Bencomo, Jake Snell, Thomas L. Griffiths
2023 A* conf
Distribution-Free Statistical Dispersion Control for Societal Applications.
NeurIPS
Zhun Deng, Thomas P. Zollo, Jake Snell, Toniann Pitassi, Richard S. Zemel
2023 A* conf
Im-Promptu: In-Context Composition from Image Prompts.
NeurIPS
Bhishma Dedhia, Michael Chang, Jake Snell, Tom Griffiths, Niraj K. Jha
2023 A* conf
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions.
ICLR
Jake Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard S. Zemel
2021 A* conf
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes.
ICLR
Jake Snell, Richard S. Zemel
2021 — —
Learning to Build Probabilistic Models with Limited Data.
Jake Snell
2020 J jnl
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes.
CoRR
Jake Snell, Richard S. Zemel
2020 J jnl
Flexible Few-Shot Learning with Contextual Similarity.
CoRR
Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard S. Zemel
2019 — conf
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models.
ICLR (Poster)
Marc T. Law, Jake Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard S. Zemel
2019 A* conf
Lorentzian Distance Learning for Hyperbolic Representations.
ICML
Marc Teva Law, Renjie Liao, Jake Snell, Richard S. Zemel
2018 A* conf
Learning Latent Subspaces in Variational Autoencoders.
NeurIPS
Jack Klys, Jake Snell, Richard S. Zemel
2018 J jnl
Learning Latent Subspaces in Variational Autoencoders.
CoRR
Jack Klys, Jake Snell, Richard S. Zemel
2018 — conf
Meta-Learning for Semi-Supervised Few-Shot Classification.
ICLR (Poster)
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel
2018 J jnl
Meta-Learning for Semi-Supervised Few-Shot Classification.
CoRR
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel
2017 B conf
Learning to generate images with perceptual similarity metrics.
ICIP
Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel
2017 — conf
Prototypical Networks for Few-shot Learning.
NIPS
Jake Snell, Kevin Swersky, Richard S. Zemel
2017 J jnl
Prototypical Networks for Few-shot Learning.
CoRR
Jake Snell, Kevin Swersky, Richard S. Zemel
2017 A conf
Stochastic Segmentation Trees for Multiple Ground Truths.
UAI
Jake Snell, Richard S. Zemel
2015 J jnl
Learning to generate images with perceptual similarity metrics.
CoRR
Karl Ridgeway, Jake Snell, Brett Roads, Richard S. Zemel, Michael C. Mozer