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Joshua V. Dillon
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Journal Articles
- 2010
- [j3]Joshua V. Dillon, Guy Lebanon:
Stochastic Composite Likelihood. J. Mach. Learn. Res. 11: 2597-2633 (2010) - 2007
- [j2]Guy Lebanon, Yi Mao, Joshua V. Dillon:
The Locally Weighted Bag of Words Framework for Document Representation. J. Mach. Learn. Res. 8: 2405-2441 (2007) - [j1]Yi Mao, Joshua V. Dillon, Guy Lebanon:
Sequential Document Visualization. IEEE Trans. Vis. Comput. Graph. 13(6): 1208-1215 (2007)
Conference and Workshop Papers
- 2024
- [c15]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: 323-327 - 2023
- [c14]Yangjun Ruan, Saurabh Singh, Warren Richard Morningstar, Alexander A. Alemi, Sergey Ioffe, Ian Fischer, Joshua V. Dillon:
Weighted Ensemble Self-Supervised Learning. ICLR 2023 - [c13]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: 1-10 - 2022
- [c12]Warren R. Morningstar, Alex Alemi, Joshua V. Dillon:
PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime. AISTATS 2022: 8270-8298 - 2021
- [c11]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. AISTATS 2021: 3232-3240 - [c10]Warren R. Morningstar, Sharad M. Vikram, Cusuh Ham, Andrew G. Gallagher, Joshua V. Dillon:
Automatic Differentiation Variational Inference with Mixtures. AISTATS 2021: 3250-3258 - 2020
- [c9]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: 9289-9299 - 2019
- [c8]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: 13969-13980 - [c7]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: 14680-14691 - 2018
- [c6]Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy:
Fixing a Broken ELBO. ICML 2018: 159-168 - 2017
- [c5]Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy:
Deep Variational Information Bottleneck. ICLR (Poster) 2017 - 2010
- [c4]Joshua V. Dillon, Kevyn Collins-Thompson:
A unified optimization framework for robust pseudo-relevance feedback algorithms. CIKM 2010: 1069-1078 - [c3]Joshua V. Dillon, Krishnakumar Balasubramanian, Guy Lebanon:
Asymptotic Analysis of Generative Semi-Supervised Learning. ICML 2010: 295-302 - 2009
- [c2]Joshua V. Dillon, Guy Lebanon:
Statistical and Computational Tradeoffs in Stochastic Composite Likelihood. AISTATS 2009: 129-136 - 2007
- [c1]Joshua V. Dillon, Yi Mao, Guy Lebanon, Jian Zhang:
Statistical Translation, Heat Kernels and Expected Distances. UAI 2007: 93-100
Informal and Other Publications
- 2023
- [i23]Elahe Vedadi, Joshua V. Dillon, Philip Andrew Mansfield, Karan Singhal, Arash Afkanpour, Warren Richard Morningstar:
Federated Variational Inference: Towards Improved Personalization and Generalization. CoRR abs/2305.13672 (2023) - [i22]Matthew Streeter, Joshua V. Dillon:
Sharp Taylor Polynomial Enclosures in One Dimension. CoRR abs/2308.00679 (2023) - [i21]Dan Kondratyuk, Lijun Yu
, Xiuye Gu, José Lezama, Jonathan Huang, Rachel Hornung, Hartwig Adam, Hassan Akbari, Yair Alon, Vighnesh Birodkar, Yong Cheng, Ming-Chang Chiu, Joshua V. Dillon, Irfan Essa, Agrim Gupta, Meera Hahn, Anja Hauth, David Hendon, Alonso Martinez, David Minnen, David A. Ross, Grant Schindler, Mikhail Sirotenko, Kihyuk Sohn, Krishna Somandepalli, Huisheng Wang, Jimmy Yan, Ming-Hsuan Yang, Xuan Yang, Bryan Seybold, Lu Jiang:
VideoPoet: A Large Language Model for Zero-Shot Video Generation. CoRR abs/2312.14125 (2023) - 2022
- [i20]Yangjun Ruan, Saurabh Singh, Warren R. Morningstar, Alexander A. Alemi, Sergey Ioffe, Ian Fischer, Joshua V. Dillon:
Weighted Ensemble Self-Supervised Learning. CoRR abs/2211.09981 (2022) - [i19]Matthew Streeter, Joshua V. Dillon:
Automatically Bounding the Taylor Remainder Series: Tighter Bounds and New Applications. CoRR abs/2212.11429 (2022) - 2020
- [i18]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 abs/2001.04694 (2020) - [i17]Dan Piponi, Dave Moore, Joshua V. Dillon:
Joint Distributions for TensorFlow Probability. CoRR abs/2001.11819 (2020) - [i16]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 abs/2002.01184 (2020) - [i15]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 abs/2002.02655 (2020) - [i14]Warren R. Morningstar, Sharad M. Vikram, Cusuh Ham, Andrew G. Gallagher, Joshua V. Dillon:
Automatic Differentiation Variational Inference with Mixtures. CoRR abs/2003.01687 (2020) - [i13]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 abs/2006.09273 (2020) - [i12]Warren R. Morningstar, Alexander A. Alemi, Joshua V. Dillon:
PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime. CoRR abs/2010.09629 (2020) - [i11]Alexander A. Alemi, Warren R. Morningstar, Ben Poole, Ian Fischer, Joshua V. Dillon:
VIB is Half Bayes. CoRR abs/2011.08711 (2020) - 2019
- [i10]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 abs/1906.02530 (2019) - [i9]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 abs/1906.02845 (2019) - 2018
- [i8]Alexander A. Alemi, Ian Fischer, Joshua V. Dillon:
Uncertainty in the Variational Information Bottleneck. CoRR abs/1807.00906 (2018) - 2017
- [i7]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 abs/1711.00464 (2017) - [i6]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 abs/1711.10604 (2017) - 2016
- [i5]Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy:
Deep Variational Information Bottleneck. CoRR abs/1612.00410 (2016) - 2012
- [i4]Seungyeon Kim, Joshua V. Dillon, Guy Lebanon:
Cumulative Revision Map. CoRR abs/1205.3205 (2012) - [i3]Joshua V. Dillon, Yi Mao, Guy Lebanon, Jian Zhang:
Statistical Translation, Heat Kernels and Expected Distances. CoRR abs/1206.5248 (2012) - 2010
- [i2]Joshua V. Dillon, Krishnakumar Balasubramanian, Guy Lebanon:
Asymptotic Analysis of Generative Semi-Supervised Learning. CoRR abs/1003.0024 (2010) - [i1]Joshua V. Dillon, Guy Lebanon:
Statistical and Computational Tradeoffs in Stochastic Composite Likelihood. CoRR abs/1003.0691 (2010)
Coauthor Index
aka: Alex Alemi
aka: Warren Richard Morningstar
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