Saturday Morning Video: Random Projections for Probabilistic Inference, Stefano Ermon

Here is another talk given at the Uncertainty in Computation workshop at the Simons Institute

 

Random Projections for Probabilistic Inference by Stefano Ermon, Stanford University

Probabilistic inference in high-dimensional probabilistic models (i.e., with many variables) is one of the central problems of statistical machine learning and stochastic decision making. To date, only a handful of distinct methods have been developed, most notably (MCMC) sampling, decomposition, and variational methods. In this talk, I will introduce a new approach where random projections are used to simplify a high-dimensional model while preserving some of its key properties. These random projections can be combined with traditional variational inference methods (information projections) and combinatorial optimization tools. These novel randomized approaches provide provable guarantees on the accuracy, and outperform traditional methods in a range of domains.

Recent papers by Stefano include

1. Xiaoyue Duan, Feifei Yang, Erin Antono, Wenge Yang, Piero Pianetta, Stefano Ermon, Apurva Mehta, Yijin Liu.
   Unsupervised Data Mining in Nanoscale X-ray Spectro-Microscopic Study of NdFeB Magnet [PDF]
   Scientific Reports. Scientific Reports, 6, 34406, 2016.
2. Neal Jean, Marshall Burke, Michael Xie, Matthew Davis, David Lobell, Stefano Ermon
   Combining Satellite Imagery and Machine Learning to Predict Poverty [PDF] [Project Website] [Commentary] [Nature Research Highlights] [Code]
   Science. Science, 353(6301), 790-794, 2016.
   
3. Jonathan Ho, Stefano Ermon
   Generative Adversarial Imitation Learning [PDF]
   NIPS-16. In Proc. 30th Annual Conference on Neural Information Processing Systems, December 2016.
   
4. Aditya Grover, Stefano Ermon
   Variational Bayes on Monte Carlo Steroids [PDF]
   NIPS-16. In Proc. 30th Annual Conference on Neural Information Processing Systems, December 2016.
   
5. Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon
   Adaptive Concentration Inequalities for Sequential Decision Problems [PDF]
   NIPS-16. In Proc. 30th Annual Conference on Neural Information Processing Systems, December 2016.
   
6. Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla Gomes, Bart Selman
   Solving Marginal MAP Problems with NP Oracles and Parity Constraints [PDF]
   NIPS-16. In Proc. 30th Annual Conference on Neural Information Processing Systems, December 2016.
   
7. Mitchell McIntire, Daniel Ratner, Stefano Ermon
   Sparse Gaussian Processes for Bayesian Optimization [PDF]
   UAI-16.
   
8. Jonathan Ho, Jayesh Gupta, Stefano Ermon
   Model-Free Imitation Learning with Policy Optimization [PDF]
   ICML-16. In Proc. 33rd International Conference on Machine Learning, 2016.
   
9. Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla Gomes, Bart Selman
   Variable Elimination in the Fourier Domain [PDF]
   ICML-16. In Proc. 33rd International Conference on Machine Learning, 2016.
   
10. Steve Mussmann, Stefano Ermon
    Learning and Inference via Maximum Inner Product Search [PDF]
    ICML-16. In Proc. 33rd International Conference on Machine Learning, 2016.
    
11. Tudor Achim, Ashish Sabharwal, Stefano Ermon
    Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference [PDF]
    ICML-16. In Proc. 33rd International Conference on Machine Learning, 2016.
    
12. Lun-Kai Hsu, Tudor Achim, Stefano Ermon
    Tight Variational Bounds via Random Projections and I-Projections [PDF]
    AISTATS-16.
    
13. Michael Xie, Neal Jean, Marshall Burke, David Lobell, Stefano Ermon
    Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping [PDF] [Stanford Report] [NYTimes]
    AAAI-16. In Proc. 30th AAAI Conference on Artificial Intelligence, 2016.
    
14. Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, Stefano Ermon
    Closing the Gap Between Short and Long XORs for Model Counting [PDF] [Code]
    AAAI-16. In Proc. 30th AAAI Conference on Artificial Intelligence, 2016.
    
15. Carolyn Kim, Ashish Sabharwal, Stefano Ermon
    Exact Sampling with Integer Linear Programs and Random Perturbations [PDF] [Code]
    AAAI-16. In Proc. 30th AAAI Conference on Artificial Intelligence, 2016.

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