Dunson, David

11 publications

ICML 2024 Position: Bayesian Deep Learning Is Needed in the Age of Large-Scale AI Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang

AISTATS 2021 Statistical Guarantees for Transformation Based Models with Applications to Implicit Variational Inference Sean Plummer, Shuang Zhou, Anirban Bhattacharya, David Dunson, Debdeep Pati

ICML 2020 Fiedler Regularization: Learning Neural Networks with Graph Sparsity Edric Tam, David Dunson

ICML 2016 No Penalty No Tears: Least Squares in High-Dimensional Linear Models Xiangyu Wang, David Dunson, Chenlei Leng

ICML 2014 Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors Piyush Rai, Yingjian Wang, Shengbo Guo, Gary Chen, David Dunson, Lawrence Carin

ICML 2014 Scalable and Robust Bayesian Inference via the Median Posterior Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David Dunson

AISTATS 2012 Beta-Negative Binomial Process and Poisson Factor Analysis Mingyuan Zhou, Lauren Hannah, David Dunson, Lawrence Carin

AISTATS 2012 Hierarchical Latent Dictionaries for Models of Brain Activation Alona Fyshe, Emily Fox, David Dunson, Tom Mitchell

AISTATS 2011 Dependent Hierarchical Beta Process for Image Interpolation and Denoising Mingyuan Zhou, Hongxia Yang, Guillermo Sapiro, David Dunson, Lawrence Carin

JMLR 2011 Logistic Stick-Breaking Process Lu Ren, Lan Du, Lawrence Carin, David Dunson

AISTATS 2011 Preface Geoffrey Gordon, David Dunson