Dillon, Joshua V.

9 publications

TMLR 2024 Federated Variational Inference: Towards Improved Personalization and Generalization Elahe Vedadi, Joshua V. Dillon, Philip Andrew Mansfield, Karan Singhal, Arash Afkanpour, Warren Richard Morningstar

ICML 2024 VideoPoet: A Large Language Model for Zero-Shot Video Generation Dan Kondratyuk, Lijun Yu, Xiuye Gu, Jose 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

UAI 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

ICLR 2023 Weighted Ensemble Self-Supervised Learning Yangjun Ruan, Saurabh Singh, Warren Richard Morningstar, Alexander A Alemi, Sergey Ioffe, Ian Fischer, Joshua V. Dillon

AISTATS 2022 PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime Warren R. Morningstar, Alex Alemi, Joshua V. Dillon

ICLR 2017 Deep Variational Information Bottleneck Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy

ICML 2010 Asymptotic Analysis of Generative Semi-Supervised Learning Joshua V. Dillon, Krishnakumar Balasubramanian, Guy Lebanon

JMLR 2010 Stochastic Composite Likelihood Joshua V. Dillon, Guy Lebanon

UAI 2007 Statistical Translation, Heat Kernels and Expected Distances Joshua V. Dillon, Yi Mao, Guy Lebanon, Jian Zhang