Talks
2016
- Jeffrey Regier, Approximate Bayesian inference for generative models of astronomical images. Statistics Department, Ohio State University, March 2016.
- Jeffrey Regier, Approximate Bayesian inference for generative models of astronomical images. Bin Yu's lab group, Statistics Department, UC Berkeley, March 2016.
- Jeffrey Regier, Approximate Bayesian inference for generative models of astronomical images. Pieter Abbeel's lab group, Computer Science Department, UC Berkeley, March 2016.
- Jon McAuliffe, Approximate Bayesian inference for generative models of astronomical images. Statistical Challenges in Modern Astronomy VI, Carnegie Mellon University, June 2016.
2015
- Aydın Buluc, Scalable algorithms for complex genome assembly, alignment, and genetic mapping, School of CSE, Georgia Tech (invited talk). January 2015
- Aydın Buluc, Distributed-Memory Parallel Algorithms for Graph Traversal & Genome Assembly, Computer Science Department, Stony Brook University (invited talk). December 2014
- Jon McAuliffe, Celeste: Scalable variational inference for a generative model of astronomical images. Information Theory and Applications, February 2015.
- Jeffrey Regier, Celeste: Scalable variational inference for a generative model of astronomical images. Poster spotlight talk. Neural Information Processing Systems (NIPS), Workshop on Advances in Variational Inference, December 2014.
- Jeffrey Regier, Celeste: Variational inference for a generative model of astronomical images. International Conference on Machine Learning, June 2015.
- Jeffrey Regier, Celeste: Variational inference for a generative model of astronomical images. MANTISSA Day, August 2015.
2014
- Ryan Adams, 17 April 2014, Department of Mathematics and Statistics, Boston University, “Exact MCMC with Subsets of Data”
- Ryan Adams, 5 May 2014, Department of Statistics, University of Washington, “Exact MCMC with Subsets of Data”.
- Ryan Adams, 12 September 2014, Department of Statistics and Data Mining, University of Texas at Austin. “Exact MCMC with Subsets of Data”.
- Michael Mahoney, “Eigenvector localization, implicit regularization, and algorithmic anti-differentiation for large-scale graphs and networked data” ICERM, May 2014
- Michael Mahoney, “Implicit regularization in sublinear approximation algorithms” Bertinoro Sublinear Algorithms 2014
- Fritz Sommer, September 2014, International Traveling Workshop on Interactions Between Sparse Models and Technology, Namur, Belgium, “Combining compressed sampling and representational learning”
- Fritz Sommer, September 2014, LMU, Munich, Germany, “Sparse methods for decoding the theta wave in the rat hippocampus”
- Prabhat, Nov 2013, “Machine Learning opportunities in climate science”, LBL Machine Learning workshop.