Joshua V. Dillon

45 papers A* 7A 7Journal 29Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Speed is Confidence. CoRR Joshua V. Dillon
2024	—	`conf`	Federated Variational Inference: Towards Improved Personalization and Generalization. AAAI Spring Symposia Elahe Vedadi, Joshua V. Dillon, Philip Andrew Mansfield, Karan Singhal, Arash Afkanpour, Warren Richard Morningstar
2024	J	`jnl`	Federated Variational Inference: Towards Improved Personalization and Generalization. Trans. Mach. Learn. Res. Elahe Vedadi, Joshua V. Dillon, Philip Andrew Mansfield, Karan Singhal, Arash Afkanpour, Warren Richard Morningstar
2024	J	`jnl`	Sample what you cant compress. CoRR Vighnesh Birodkar, Gabriel Barcik, James Lyon, Sergey Ioffe, David Minnen, Joshua V. Dillon
2024	A*	`conf`	VideoPoet: A Large Language Model for Zero-Shot Video Generation. ICML 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
2023	J	`jnl`	Federated Variational Inference: Towards Improved Personalization and Generalization. CoRR Elahe Vedadi, Joshua V. Dillon, Philip Andrew Mansfield, Karan Singhal, Arash Afkanpour, Warren Richard Morningstar
2023	J	`jnl`	Sharp Taylor Polynomial Enclosures in One Dimension. CoRR Matthew Streeter, Joshua V. Dillon
2023	A	`conf`	SubMix: Learning to Mix Graph Sampling Heuristics. UAI Sami Abu-El-Haija, Joshua V. Dillon, Bahare Fatemi, Kyriakos Axiotis, Neslihan Bulut, Johannes Gasteiger, Bryan Perozzi, MohammadHossein Bateni
2023	J	`jnl`	VideoPoet: A Large Language Model for Zero-Shot Video Generation. CoRR 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
2023	A*	`conf`	Weighted Ensemble Self-Supervised Learning. ICLR Yangjun Ruan, Saurabh Singh, Warren Richard Morningstar, Alexander A. Alemi, Sergey Ioffe, Ian Fischer, Joshua V. Dillon
2022	J	`jnl`	Automatically Bounding the Taylor Remainder Series: Tighter Bounds and New Applications. CoRR Matthew Streeter, Joshua V. Dillon
2022	A	`conf`	PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime. AISTATS Warren R. Morningstar, Alex Alemi, Joshua V. Dillon
2022	J	`jnl`	Weighted Ensemble Self-Supervised Learning. CoRR Yangjun Ruan, Saurabh Singh, Warren R. Morningstar, Alexander A. Alemi, Sergey Ioffe, Ian Fischer, Joshua V. Dillon
2021	A	`conf`	Automatic Differentiation Variational Inference with Mixtures. AISTATS Warren R. Morningstar, Sharad M. Vikram, Cusuh Ham, Andrew G. Gallagher, Joshua V. Dillon
2021	A	`conf`	Density of States Estimation for Out of Distribution Detection. AISTATS Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon
2020	J	`jnl`	Automatic Differentiation Variational Inference with Mixtures. CoRR Warren R. Morningstar, Sharad M. Vikram, Cusuh Ham, Andrew G. Gallagher, Joshua V. Dillon
2020	J	`jnl`	Density of States Estimation for Out-of-Distribution Detection. CoRR Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon
2020	J	`jnl`	Hydra: Preserving Ensemble Diversity for Model Distillation. CoRR Linh Tran, Bastiaan S. Veeling, Kevin Roth, Jakub Swiatkowski, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Sebastian Nowozin, Rodolphe Jenatton
2020	J	`jnl`	Joint Distributions for TensorFlow Probability. CoRR Dan Piponi, Dave Moore, Joshua V. Dillon
2020	J	`jnl`	PAC CoRR Warren R. Morningstar, Alexander A. Alemi, Joshua V. Dillon
2020	A*	`conf`	The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. ICML Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
2020	J	`jnl`	The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. CoRR Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
2020	J	`jnl`	VIB is Half Bayes. CoRR Alexander A. Alemi, Warren R. Morningstar, Ben Poole, Ian Fischer, Joshua V. Dillon
2020	J	`jnl`	tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware. CoRR Junpeng Lao, Christopher Suter, Ian Langmore, Cyril Chimisov, Ashish Saxena, Pavel Sountsov, Dave Moore, Rif A. Saurous, Matthew D. Hoffman, Joshua V. Dillon
2019	J	`jnl`	Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift. CoRR Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, Jasper Snoek
2019	A*	`conf`	Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift. NeurIPS Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D. Sculley, Joshua V. Dillon, Jie Ren, Zachary Nado
2019	A*	`conf`	Likelihood Ratios for Out-of-Distribution Detection. NeurIPS Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan
2019	J	`jnl`	Likelihood Ratios for Out-of-Distribution Detection. CoRR Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan
2018	A*	`conf`	Fixing a Broken ELBO. ICML Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy
2018	J	`jnl`	Uncertainty in the Variational Information Bottleneck. CoRR Alexander A. Alemi, Ian Fischer, Joshua V. Dillon
2017	J	`jnl`	An Information-Theoretic Analysis of Deep Latent-Variable Models. CoRR Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy
2017	—	`conf`	Deep Variational Information Bottleneck. ICLR (Poster) Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy
2017	J	`jnl`	TensorFlow Distributions. CoRR Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matthew D. Hoffman, Rif A. Saurous
2016	J	`jnl`	Deep Variational Information Bottleneck. CoRR Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy
2012	J	`jnl`	Cumulative Revision Map CoRR Seungyeon Kim, Joshua V. Dillon, Guy Lebanon
2012	J	`jnl`	Statistical Translation, Heat Kernels and Expected Distances CoRR Joshua V. Dillon, Yi Mao, Guy Lebanon, Jian Zhang
2010	A	`conf`	A unified optimization framework for robust pseudo-relevance feedback algorithms. CIKM Joshua V. Dillon, Kevyn Collins-Thompson
2010	J	`jnl`	Asymptotic Analysis of Generative Semi-Supervised Learning CoRR Joshua V. Dillon, Krishnakumar Balasubramanian, Guy Lebanon
2010	A*	`conf`	Asymptotic Analysis of Generative Semi-Supervised Learning. ICML Joshua V. Dillon, Krishnakumar Balasubramanian, Guy Lebanon
2010	J	`jnl`	Statistical and Computational Tradeoffs in Stochastic Composite Likelihood CoRR Joshua V. Dillon, Guy Lebanon
2010	J	`jnl`	Stochastic Composite Likelihood. J. Mach. Learn. Res. Joshua V. Dillon, Guy Lebanon
2009	A	`conf`	Statistical and Computational Tradeoffs in Stochastic Composite Likelihood. AISTATS Joshua V. Dillon, Guy Lebanon
2007	J	`jnl`	Sequential Document Visualization. IEEE Trans. Vis. Comput. Graph. Yi Mao, Joshua V. Dillon, Guy Lebanon
2007	A	`conf`	Statistical Translation, Heat Kernels and Expected Distances. UAI Joshua V. Dillon, Yi Mao, Guy Lebanon, Jian Zhang
2007	J	`jnl`	The Locally Weighted Bag of Words Framework for Document Representation. J. Mach. Learn. Res. Guy Lebanon, Yi Mao, Joshua V. Dillon