Learning to generate images with perceptual similarity metrics. - Karl Ridgeway; Jake Snell; Brett Roads; Richard S. Zemel; Michael C. Mozer (CoRR, 2015)

Learning to generate images with perceptual similarity metrics. - Jake Snell; Karl Ridgeway; Renjie Liao; Brett D. Roads; Michael C. Mozer; Richard S. Zemel (ICIP, 2017)

Prototypical Networks for Few-shot Learning. - Jake Snell; Kevin Swersky; Richard S. Zemel (NIPS, 2017)

Stochastic Segmentation Trees for Multiple Ground Truths. - Jake Snell; Richard S. Zemel (UAI, 2017)

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 (ICLR (Poster), 2018)

Learning Latent Subspaces in Variational Autoencoders. - Jack Klys; Jake Snell; Richard S. Zemel (NeurIPS, 2018)

Dimensionality Reduction for Representing the Knowledge of Probabilistic Models. - Marc T. Law; Jake Snell; Amir-massoud Farahmand; Raquel Urtasun; Richard S. Zemel (ICLR (Poster), 2019)

Lorentzian Distance Learning for Hyperbolic Representations. - Marc Teva Law; Renjie Liao; Jake Snell; Richard S. Zemel (ICML, 2019)

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)

Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes. - Jake Snell; Richard S. Zemel (ICLR, 2021)

Learning to Build Probabilistic Models with Limited Data. - Jake Snell (2021)

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 (ICLR, 2023)

Im-Promptu: In-Context Composition from Image Prompts. - Bhishma Dedhia; Michael Chang 003; Jake Snell; Tom Griffiths 001; Niraj K. Jha (NeurIPS, 2023)

Distribution-Free Statistical Dispersion Control for Societal Applications. - Zhun Deng; Thomas P. Zollo; Jake Snell; Toniann Pitassi; Richard S. Zemel (NeurIPS, 2023)

Implicit Maximum a Posteriori Filtering via Adaptive Optimization. - Gianluca M. Bencomo; Jake Snell; Thomas L. Griffiths 001 (ICLR, 2024)

Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models. - Thomas P. Zollo; Todd Morrill; Zhun Deng; Jake Snell; Toniann Pitassi; Richard S. Zemel (ICLR, 2024)

A Metalearned Neural Circuit for Nonparametric Bayesian Inference. - Jake Snell; Gianluca M. Bencomo; Tom Griffiths 001 (NeurIPS, 2024)

Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases. - Raja Marjieh; Sreejan Kumar; Declan Campbell; Liyi Zhang; Gianluca M. Bencomo; Jake Snell; Thomas L. Griffiths 001 (CoRR, 2024)

Improving Predictor Reliability with Selective Recalibration. - Thomas P. Zollo; Zhun Deng; Jake Snell; Toniann Pitassi; Richard S. Zemel (Trans. Mach. Learn. Res., 2024)

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. - Brennen Mills; Martin Masek; Julie Boston; Wyatt de Souza; Jake Snell; Stuart Bender; Matthew B. Thompson; Billy Sung; Sara Hansen (Virtual Real., 2024)

Conformal Prediction as Bayesian Quadrature. - Jake C. Snell; Thomas L. Griffiths 001 (CoRR, 2025)