default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDJoshua V. Dillon
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2024
[c16]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- [c15]Dan Kondratyuk, Lijun Yu, Xiuye Gu, José Lezama, Jonathan Huang, Grant Schindler, Rachel Hornung, Vighnesh Birodkar, Jimmy Yan, Ming-Chang Chiu, Krishna Somandepalli, Hassan Akbari, Yair Alon, Yong Cheng, Joshua V. Dillon, Agrim Gupta, Meera Hahn, Anja Hauth, David Hendon, Alonso Martinez, David Minnen, Mikhail Sirotenko, Kihyuk Sohn, Xuan Yang, Hartwig Adam, Ming-Hsuan Yang, Irfan Essa, Huisheng Wang, David A. Ross, Bryan Seybold, Lu Jiang:
VideoPoet: A Large Language Model for Zero-Shot Video Generation. ICML 2024 - [i24]Vighnesh Birodkar, Gabriel Barcik, James Lyon, Sergey Ioffe, David Minnen, Joshua V. Dillon:
Sample what you cant compress. CoRR abs/2409.02529 (2024) - 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 - [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
- [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 - [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) - 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 - [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)
2010 – 2019
- 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 - [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
- [c6]Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy:
Fixing a Broken ELBO. ICML 2018: 159-168 - [i8]Alexander A. Alemi, Ian Fischer, Joshua V. Dillon:
Uncertainty in the Variational Information Bottleneck. CoRR abs/1807.00906 (2018) - 2017
- [c5]Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy:
Deep Variational Information Bottleneck. ICLR (Poster) 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
- [j3]Joshua V. Dillon, Guy Lebanon:
Stochastic Composite Likelihood. J. Mach. Learn. Res. 11: 2597-2633 (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 - [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)
2000 – 2009
- 2009
- [c2]Joshua V. Dillon, Guy Lebanon:
Statistical and Computational Tradeoffs in Stochastic Composite Likelihood. AISTATS 2009: 129-136 - 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) - [c1]Joshua V. Dillon, Yi Mao, Guy Lebanon, Jian Zhang:
Statistical Translation, Heat Kernels and Expected Distances. UAI 2007: 93-100
Coauthor Index
aka: Alex Alemi
aka: Warren Richard Morningstar
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-01-09 13:22 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
