Joshua V. Dillon

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 29

Top Topics

Joint Distribution

Variational Inference

Information Bottleneck

Top Venues

Publications

Elahe Vedadi, Joshua V. Dillon, Philip Andrew Mansfield, Karan Singhal, Arash Afkanpour, Warren Richard Morningstar
Federated Variational Inference: Towards Improved Personalization and Generalization. AAAI Spring Symposia (2024)
Elahe Vedadi, Joshua V. Dillon, Philip Andrew Mansfield, Karan Singhal, Arash Afkanpour, Warren Richard Morningstar
Federated Variational Inference: Towards Improved Personalization and Generalization. CoRR (2023)
Sami Abu-El-Haija, Joshua V. Dillon, Bahare Fatemi, Kyriakos Axiotis, Neslihan Bulut, Johannes Gasteiger, Bryan Perozzi, MohammadHossein Bateni
SubMix: Learning to Mix Graph Sampling Heuristics. UAI (2023)
Matthew Streeter, Joshua V. Dillon
Sharp Taylor Polynomial Enclosures in One Dimension. CoRR (2023)
Yangjun Ruan, Saurabh Singh, Warren Richard Morningstar, Alexander A. Alemi, Sergey Ioffe, Ian Fischer, Joshua V. Dillon
Weighted Ensemble Self-Supervised Learning. ICLR (2023)
Yangjun Ruan, Saurabh Singh, Warren R. Morningstar, Alexander A. Alemi, Sergey Ioffe, Ian Fischer, Joshua V. Dillon
Weighted Ensemble Self-Supervised Learning. CoRR (2022)
Matthew Streeter, Joshua V. Dillon
Automatically Bounding the Taylor Remainder Series: Tighter Bounds and New Applications. CoRR (2022)
Warren R. Morningstar, Alex Alemi, Joshua V. Dillon
PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime. AISTATS (2022)
Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alex Alemi, Joshua V. Dillon
Density of States Estimation for Out of Distribution Detection. AISTATS (2021)
Warren R. Morningstar, Sharad M. Vikram, Cusuh Ham, Andrew G. Gallagher, Joshua V. Dillon
Automatic Differentiation Variational Inference with Mixtures. AISTATS (2021)
Junpeng Lao, Christopher Suter, Ian Langmore, Cyril Chimisov, Ashish Saxena, Pavel Sountsov, Dave Moore, Rif A. Saurous, Matthew D. Hoffman, Joshua V. Dillon
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware. CoRR (2020)
Warren R. Morningstar, Sharad M. Vikram, Cusuh Ham, Andrew G. Gallagher, Joshua V. Dillon
Automatic Differentiation Variational Inference with Mixtures. CoRR (2020)
Linh Tran, Bastiaan S. Veeling, Kevin Roth, Jakub Swiatkowski, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Sebastian Nowozin, Rodolphe Jenatton
Hydra: Preserving Ensemble Diversity for Model Distillation. CoRR (2020)
Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. CoRR (2020)
Dan Piponi, Dave Moore, Joshua V. Dillon
Joint Distributions for TensorFlow Probability. CoRR (2020)
Alexander A. Alemi, Warren R. Morningstar, Ben Poole, Ian Fischer, Joshua V. Dillon
VIB is Half Bayes. CoRR (2020)
Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. ICML (2020)
Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon
Density of States Estimation for Out-of-Distribution Detection. CoRR (2020)
Warren R. Morningstar, Alexander A. Alemi, Joshua V. Dillon
-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime. CoRR (2020)
Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D. Sculley, Joshua V. Dillon, Jie Ren, Zachary Nado
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift. NeurIPS (2019)
Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan
Likelihood Ratios for Out-of-Distribution Detection. CoRR (2019)
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, Jasper Snoek
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift. CoRR (2019)
Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan
Likelihood Ratios for Out-of-Distribution Detection. NeurIPS (2019)
Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy
Fixing a Broken ELBO. ICML (2018)
Alexander A. Alemi, Ian Fischer, Joshua V. Dillon
Uncertainty in the Variational Information Bottleneck. CoRR (2018)
Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy
Deep Variational Information Bottleneck. ICLR (Poster) (2017)
Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matthew D. Hoffman, Rif A. Saurous
TensorFlow Distributions. CoRR (2017)
Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy
An Information-Theoretic Analysis of Deep Latent-Variable Models. CoRR (2017)
Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy
Deep Variational Information Bottleneck. CoRR (2016)
Seungyeon Kim, Joshua V. Dillon, Guy Lebanon
Cumulative Revision Map CoRR (2012)
Joshua V. Dillon, Yi Mao, Guy Lebanon, Jian Zhang
Statistical Translation, Heat Kernels and Expected Distances CoRR (2012)
Joshua V. Dillon, Kevyn Collins-Thompson
A unified optimization framework for robust pseudo-relevance feedback algorithms. CIKM (2010)
Joshua V. Dillon, Guy Lebanon
Stochastic Composite Likelihood. J. Mach. Learn. Res. 11 (2010)
Joshua V. Dillon, Krishnakumar Balasubramanian, Guy Lebanon
Asymptotic Analysis of Generative Semi-Supervised Learning. ICML (2010)
Joshua V. Dillon, Guy Lebanon
Statistical and Computational Tradeoffs in Stochastic Composite Likelihood CoRR (2010)
Joshua V. Dillon, Krishnakumar Balasubramanian, Guy Lebanon
Asymptotic Analysis of Generative Semi-Supervised Learning CoRR (2010)
Joshua V. Dillon, Guy Lebanon
Statistical and Computational Tradeoffs in Stochastic Composite Likelihood. AISTATS (2009)
Guy Lebanon, Yi Mao, Joshua V. Dillon
The Locally Weighted Bag of Words Framework for Document Representation. J. Mach. Learn. Res. 8 (2007)
Yi Mao, Joshua V. Dillon, Guy Lebanon
Sequential Document Visualization. IEEE Trans. Vis. Comput. Graph. 13 (6) (2007)
Joshua V. Dillon, Yi Mao, Guy Lebanon, Jian Zhang
Statistical Translation, Heat Kernels and Expected Distances. UAI (2007)