Jake Snell

According to our database, Jake Snell authored at least 17 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

A Metalearned Neural Circuit for Nonparametric Bayesian Inference.

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

CoRR, 2023

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 S. Zemel

CoRR, 2023

Implicit Maximum a Posteriori Filtering via Adaptive Optimization.

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

CoRR, 2023

Distribution-Free Statistical Dispersion Control for Societal Applications.

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Im-Promptu: In-Context Composition from Image Prompts.

Bhishma Dedhia, Michael Chang, Jake Snell, Tom Griffiths, Niraj K. Jha

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2021

Learning to Build Probabilistic Models with Limited Data.

Jake Snell

PhD thesis, 2021

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

Jake Snell, Richard S. Zemel

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Flexible Few-Shot Learning with Contextual Similarity.

Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard S. Zemel

CoRR, 2020

2019

Lorentzian Distance Learning for Hyperbolic Representations.

Marc Teva Law, Renjie Liao, Jake Snell, Richard S. Zemel

Proceedings of the 36th International Conference on Machine Learning, 2019

Dimensionality Reduction for Representing the Knowledge of Probabilistic Models.

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

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Learning Latent Subspaces in Variational Autoencoders.

Jack Klys, Jake Snell, Richard S. Zemel

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Meta-Learning for Semi-Supervised Few-Shot Classification.

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

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Stochastic Segmentation Trees for Multiple Ground Truths.

Jake Snell, Richard S. Zemel

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Prototypical Networks for Few-shot Learning.

Jake Snell, Kevin Swersky, Richard S. Zemel

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Learning to generate images with perceptual similarity metrics.

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

Proceedings of the 2017 IEEE International Conference on Image Processing, 2017

2015

Learning to generate images with perceptual similarity metrics.

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

CoRR, 2015