Joshua V. Dillon

According to our database, Joshua V. Dillon authored at least 29 papers between 2007 and 2023.

Collaborative distances:

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

2023

Sharp Taylor Polynomial Enclosures in One Dimension.

Matthew Streeter, Joshua V. Dillon

CoRR, 2023

Federated Variational Inference: Towards Improved Personalization and Generalization.

Elahe Vedadi, Joshua V. Dillon, Philip Andrew Mansfield, Karan Singhal, Arash Afkanpour, Warren Richard Morningstar

CoRR, 2023

SubMix: Learning to Mix Graph Sampling Heuristics.

Sami Abu-El-Haija, Joshua V. Dillon, Bahare Fatemi, Kyriakos Axiotis, Neslihan Bulut, Johannes Gasteiger, Bryan Perozzi, MohammadHossein Bateni

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Weighted Ensemble Self-Supervised Learning.

Yangjun Ruan, Saurabh Singh, Warren Richard Morningstar, Alexander A. Alemi, Sergey Ioffe, Ian Fischer, Joshua V. Dillon

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Automatically Bounding the Taylor Remainder Series: Tighter Bounds and New Applications.

Matthew Streeter, Joshua V. Dillon

CoRR, 2022

PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime.

Warren R. Morningstar, Alex Alemi, Joshua V. Dillon

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Automatic Differentiation Variational Inference with Mixtures.

Warren R. Morningstar, Sharad M. Vikram, Cusuh Ham, Andrew G. Gallagher, Joshua V. Dillon

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Density of States Estimation for Out of Distribution Detection.

Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

VIB is Half Bayes.

Alexander A. Alemi, Warren R. Morningstar, Ben Poole, Ian Fischer, Joshua V. Dillon

CoRR, 2020

PAC^m-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime.

Warren R. Morningstar, Alexander A. Alemi, Joshua V. Dillon

CoRR, 2020

tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware.

Junpeng Lao, Christopher Suter, Ian Langmore, Cyril Chimisov, Ashish Saxena, Pavel Sountsov, Dave Moore, Rif A. Saurous, Matthew D. Hoffman, Joshua V. Dillon

CoRR, 2020

Joint Distributions for TensorFlow Probability.

Dan Piponi, Dave Moore, Joshua V. Dillon

CoRR, 2020

Hydra: Preserving Ensemble Diversity for Model Distillation.

Linh Tran, Bastiaan S. Veeling, Kevin Roth, Jakub Swiatkowski, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Sebastian Nowozin, Rodolphe Jenatton

CoRR, 2020

The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks.

Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift.

Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D. Sculley, Joshua V. Dillon, Jie Ren, Zachary Nado

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Likelihood Ratios for Out-of-Distribution Detection.

Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Uncertainty in the Variational Information Bottleneck.

Alexander A. Alemi, Ian Fischer, Joshua V. Dillon

CoRR, 2018

Fixing a Broken ELBO.

Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

TensorFlow Distributions.

Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matthew D. Hoffman, Rif A. Saurous

CoRR, 2017

An Information-Theoretic Analysis of Deep Latent-Variable Models.

Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy

CoRR, 2017

Deep Variational Information Bottleneck.

Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy

Proceedings of the 5th International Conference on Learning Representations, 2017

2012

Cumulative Revision Map

Seungyeon Kim, Joshua V. Dillon, Guy Lebanon

CoRR, 2012

2010

Stochastic Composite Likelihood.

Joshua V. Dillon, Guy Lebanon

J. Mach. Learn. Res., 2010

Asymptotic Analysis of Generative Semi-Supervised Learning.

Joshua V. Dillon, Krishnakumar Balasubramanian, Guy Lebanon

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

A unified optimization framework for robust pseudo-relevance feedback algorithms.

Joshua V. Dillon, Kevyn Collins-Thompson

Proceedings of the 19th ACM Conference on Information and Knowledge Management, 2010

2009

Statistical and Computational Tradeoffs in Stochastic Composite Likelihood.

Joshua V. Dillon, Guy Lebanon

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

2007

Sequential Document Visualization.

Yi Mao, Joshua V. Dillon, Guy Lebanon

IEEE Trans. Vis. Comput. Graph., 2007

The Locally Weighted Bag of Words Framework for Document Representation.

Guy Lebanon, Yi Mao, Joshua V. Dillon

J. Mach. Learn. Res., 2007

Statistical Translation, Heat Kernels and Expected Distances.

Joshua V. Dillon, Yi Mao, Guy Lebanon, Jian Zhang

Proceedings of the UAI 2007, 2007