NIPS 2013 Workshop: Perturbations, Optimization, and Statistics
Schedule
7:30-7:50 | Danny Tarlow Microsoft Research | Introduction |
7:50-8:20 | Andrea Montanari Stanford (invited) | High-Dimensional Robust Estimation [abstract] |
8:20-8:50 | Stefano Ermon Cornell (invited) | Probabilistic Inference by Universal Hashing and Optimization [abstract] |
8:50-9:05 | Discussion Time | (to be used as appropriate in above window) |
9:05-9:30 | Coffee Break | |
9:30-10:00 | Moshe Ben-Akiva MIT (invited) | Discrete Choice Models with Large Choice Sets |
10:00-10:30 | Karthik Sridharan UPenn (invited) | Power of Randomization and Perturbations in Online Learning [abstract] |
10:30-10:45 | Gergely Neu INRIA (contributed) | An Efficient Algorithm for Learning with Semi-Bandit Feedback |
10:45-11:05 | Spotlight Session | Training Restricted Boltzmann Machine by Perturbation Efficient Feature Learning Using Perturb-and-MAP Does Better Inference mean Better Learning? On Robustness and Regularization of Structural Support Vector Machines |
11:05-break | Poster Session | (all contributed papers) |
15:30-16:00 | Percy Liang Stanford (invited) | Dropout Regularization [abstract] |
16:00-16:30 | David McAllester TTI-C (invited) | PAC-Bayes and Model Perturbations [abstract] |
16:30-16:45 | Ben London UMD (contributed) | PAC-Bayes Generalization Bounds for Randomized Structured Prediction |
16:45-17:05 | Discussion Time | (to be used as appropriate in above window) |
17:05-17:30 | Coffee Break | |
17:30-18:00 | Subhransu Maji TTI-C (invited) | Efficient Multi-scale Boundary Annotation using Random Maximum A-Posteriori Perturbations [abstract] |
18:00-18:30 | Panel discussion + Wrapup | |
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Accepted Contributed Papers
The following contributed papers will be presented at the poster session and
also as spotlights or short talks.
-
Andrew E. Gelfand, Rina Dechter, and Alexander Ihler
Does Better Inference mean Better Learning?
(spotlight)
-
Gergely Neu and Gabor Bartok
An Efficient Algorithm for Learning with Semi-Bandit Feedback
(short talk)
-
Ben London, Bert Huang, Ben Taskar, and Lise Getoor
PAC-Bayes Generalization Bounds for Randomized Structured Prediction
(short talk)
-
Siamak Ravanbakhsh, Russell Greiner, and Brendan J. Frey
Training Restricted Boltzmann Machine by Perturbation
(spotlight)
-
Ke Li, Kevin Swersky, and Richard Zemel
Efficient Feature Learning Using Perturb-and-MAP
(spotlight)
-
MohamadAli Torkamani and Daniel Lowd
On Robustness and Regularization of Structural Support Vector Machines
(spotlight)
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