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Talks

2016

  1. Jeffrey Regier, Approximate Bayesian inference for generative models of astronomical images. Statistics Department, Ohio State University, March 2016.
  2. Jeffrey Regier, Approximate Bayesian inference for generative models of astronomical images. Bin Yu's lab group, Statistics Department, UC Berkeley, March 2016.
  3. Jeffrey Regier, Approximate Bayesian inference for generative models of astronomical images. Pieter Abbeel's lab group, Computer Science Department, UC Berkeley, March 2016.
  4. Jon McAuliffe, Approximate Bayesian inference for generative models of astronomical images. Statistical Challenges in Modern Astronomy VI, Carnegie Mellon University, June 2016.

2015

  1. Aydın Buluc, Scalable algorithms for complex genome assembly, alignment, and genetic mapping, School of CSE, Georgia Tech (invited talk). January 2015
  2.  Aydın Buluc, Distributed-Memory Parallel Algorithms for Graph Traversal & Genome Assembly, Computer Science Department, Stony Brook University (invited talk). December 2014
  3. Jon McAuliffe, Celeste: Scalable variational inference for a generative model of astronomical images. Information Theory and Applications, February 2015.
  4. 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.
  5. Jeffrey Regier, Celeste: Variational inference for a generative model of astronomical images. International Conference on Machine Learning, June 2015.
  6. Jeffrey Regier, Celeste: Variational inference for a generative model of astronomical images. MANTISSA Day, August 2015.

2014

  1. Ryan Adams, 17 April 2014, Department of Mathematics and Statistics, Boston University, “Exact MCMC with Subsets of Data”
  2. Ryan Adams, 5 May 2014, Department of Statistics, University of Washington, “Exact MCMC with Subsets of Data”.
  3. Ryan Adams, 12 September 2014, Department of Statistics and Data Mining, University of Texas at Austin. “Exact MCMC with Subsets of Data”.
  4. Michael Mahoney, “Eigenvector localization, implicit regularization, and algorithmic anti-differentiation for large-scale graphs and networked data” ICERM, May 2014
  5. Michael Mahoney, “Implicit regularization in sublinear approximation algorithms” Bertinoro Sublinear Algorithms 2014
  6. Fritz Sommer, September 2014, International Traveling Workshop on Interactions Between Sparse Models and Technology, Namur, Belgium, “Combining compressed sampling and representational learning”
  7. Fritz Sommer, September 2014, LMU, Munich, Germany, “Sparse methods for decoding the theta wave in the rat hippocampus”
  8. Prabhat, Nov 2013, “Machine Learning opportunities in climate science”, LBL Machine Learning workshop.