Jake C. Snell

Conformal Prediction as Bayesian Quadrature

Jake C. Snell, Thomas L. Griffiths

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

Binary Climate Data Heightens Perceived Impact of Climate Change

Grace Liu, Jake C. Snell, Thomas L. Griffiths, Rachit Dubey

A Metalearned Neural Circuit for Nonparametric Bayesian Inference

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

Improving Predictor Reliability with Selective Recalibration

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

Implicit Maximum a Posteriori Filtering via Adaptive Optimization

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

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

Im-Promptu: In-Context Composition from Image Prompts

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

Distribution-Free Statistical Dispersion Control for Societal Applications

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

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

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

Jake C. Snell, Richard Zemel

Lorentzian Distance Learning for Hyperbolic Representations

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

Dimensionality Reduction for Representing the Knowledge of Probabilistic Models

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

Learning Latent Subspaces in Variational Autoencoders

Jack Klys, Jake C. Snell, Richard Zemel

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

Prototypical Networks for Few-shot Learning

Jake C. Snell, Kevin Swersky, Richard Zemel

Stochastic Segmentation Trees for Multiple Ground Truths

Jake C. Snell, Richard S. Zemel

Learning to Generate Images With Perceptual Similarity Metrics

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

Learning to Build Probabilistic Models with Limited Data

Jake C. Snell