NIPS 2012 Workshop: Perturbations, Optimization, and Statistics


7:30-7:40 Danny Tarlow
7:40-8:10 George Papandreou
UCLA (invited)
Perturb-and-MAP Random Fields
[abstract] [slides]
8:10-8:40 Max Welling
UCI and UvA (invited)
Herding versus Perturb-and-MAP
[abstract] [slides]
8:40-9:10 Amir Globerson
HUJI (invited)
A Simple Geometric Interpretation of SVM using Stochastic Adversaries
9:10-9:30 Coffee Break
9:30-10:00 Andrea Montanari
Stanford (invited keynote)
Approximate Message Passing Algorithms
[abstract] [slides]
10:00-10:30 Tamir Hazan
TTI-C (invited)
Learning with Random Maximum A-Posteriori Perturbations
[abstract] [slides]
10:30-10:40 Nitish Srivastava
Toronto (spotlight)
Improving Neural Networks by Preventing Co-adaptation of Feature Detectors
10:40-10:50 John Duchi
Berkeley (spotlight)
Randomized Smoothing for (Parallel) Stochastic Optimization
10:50-12:00 Poster Session (contributed papers)
15:30-16:00 Pascal Vincent
Montreal (invited)
Learning Robust Representations Through Input Perturbations: From Stochastic Perturbations to Analytic Criteria and Back
[abstract] [slides]
16:00-16:30 Ryan Adams
Harvard (invited)
Building Probabilistic Models Around Deterministic Optimization Procedures
[abstract] [slides]
16:30-17:00 Shie Mannor
Technion (invited)
All Learning is Robust
[abstract] [slides]
17:00-17:30 Coffee Break
17:30-18:30 Panel discussion + Wrapup

Accepted Contributed Papers

The following contributed papers will be presented at the poster session.

  1. John C. Duchi, Peter L. Bartlett, Martin J. Wainwright
    Randomized Smoothing for (Parallel) Stochastic Optimization (also spotlight)
  2. Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan R. Salakhutdinov
    Improving neural networks by preventing co-adaptation of feature detectors (also spotlight)
  3. Maayan Harel, Shie Mannor
    The Perturbed Variation
  4. Alexandre Lacoste, Francois Laviolette, Mario Marchand
    Model Averaging With Holdout Estimation of the Posterior Distribution
  5. Ying Liu, Oliver Kosut, Alan S. Willksy
    Sampling GMRFs by Subgraph Correction
  6. Varun Ramakrishna, Dhruv Batra
    Mode-Marginals: Expressing Uncertainty via Diverse M-Best Solutions
  7. Rajesh Ranganath, Chong Wang, David M. Blei, Eric P. Xing
    Adaptively Setting the Learning Rate in Stochastic Variational Inference
  8. Max Siegel, Josh Tenenbaum, Vikash Mansinghka
    Improving the Tractability of Bayesian Inverse Graphics by Injecting Noise