Scaling Up Bayesian Inference for Big and Complex Data

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=swC2i2h7R5Y


Duration: 46:48
6,790 views
157

David Dunson, Duke University
Computational Challenges in Machine Learning
https://simons.berkeley.edu/talks/david-dunson-2017-5-1



Other Videos By Simons Institute for the Theory of Computing


2017-05-03Exponential Computational Improvement by Reduction
2017-05-03Machine Learning Combinatorial Optimization Algorithms
2017-05-03A Cost Function for Similarity-Based Hierarchical Clustering
2017-05-03Machine Learning for Healthcare Data
2017-05-02Efficient Distributed Deep Learning Using MXNet
2017-05-02Your Neural Network Can't Learn Mine
2017-05-02Sampling Polytopes: From Euclid to Riemann
2017-05-02Understanding Generalization in Adaptive Data Analysis
2017-05-02On Gradient-Based Optimization: Accelerated, Distributed, Asynchronous and Stochastic
2017-05-01Stochastic Gradient MCMC for Independent and Dependent Data Sources
2017-05-01Scaling Up Bayesian Inference for Big and Complex Data
2017-05-01Unbiased Estimation of the Spectral Properties of Large Implicit Matrices
2017-05-01Representational and Optimization Properties of Deep Residual Networks
2017-05-01Composing Graphical Models with Neural Networks for Structured Representations and Fast Inference
2017-05-01Variational Inference: Foundations and Innovations
2017-04-27How the Web Has Transformed Science and Technology Reporting
2017-04-26Machine Learning Today and Tomorrow: A Panel Discussion
2017-04-14True Complexity of Multilinear Systems
2017-04-14Unavoidable Patterns in Words
2017-04-13Labeling the Complete Bipartite Graph with No Zero Cycles
2017-04-13Positional Games and Randomness


Tags:
Simons Institute
theoretical computer science
UC Berkeley
Theory of Computing
computer science
David Dunson
Scaling Up Bayesian Inference for Big and Complex Data