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NIPS 2013 Workshop: Perturbations, Optimization, and Statistics

December 9, 2013 at Lake Tahoe, Nevada, U.S.A. Workshop in conjunction with NIPS 2013.
Schedule: Check out the workshop's program here.

Schedule

The schedule is here. The workshop will consist of a series of invited talks as well as poster, spotlight, and short oral presentation of contributed papers.

Confirmed Speakers

Description

In nearly all machine learning tasks, decisions must be made given current knowledge (e.g., choose which label to predict). Perhaps surprisingly, always making the best decision is not always the best strategy, particularly while learning. Recently, there is an emerging body of work on learning under different rules that apply perturbations to the decision procedure. These works provide simple and efficient learning rules with improved theoretical guarantees. This workshop will bring together the growing community of researchers interested in different aspects of this area, and it will broaden our understanding of why and how perturbation methods can be useful.

Last year, at the highly successful 2012 NIPS workshop on Perturbations, Optimization, and Statistics, we looked at how injecting perturbations (whether it be random or adversarial “noise”) into learning and inference procedures can be beneficial. The focus was on two angles: first, on how stochastic perturbations can be used to construct new types of probability models for structured data; and second, how deterministic perturbations affect the regularization and the generalization properties of learning algorithms.

The goal of this workshop is to expand the scope of last year and also explore different ways to apply perturbations within optimization and statistics to enhance and improve machine learning approaches. This year, we will (a) look at exciting new developments related to the above core themes, and (b) emphasize their implications on topics that received less coverage last year, specifically highlighting connections to decision theory, risk analysis, game theory, and economics.

More generally, we shall specifically be interested in understanding the following issues:

  • Repeated games and online learning: How to understand random perturbations in the context of exploring unseens options in repeated games? How to exploit connections to Bayesian risk?

  • Adversarial Uncertainty: How to play complex games with adversarial uncertainty? What are the computational qualities of such solutions, and do Nash-equilibria exists in these cases?

  • Stochastic risk: How to average predictions with random perturbations to get improved generalization guarantees? How stochastic perturbations imply approximated Bayesian risk and regularization?

  • Dropout: How stochastic dropout regularizes learning of complex models and what is its generalization power? What are the relationships between stochastic and adversarial dropouts?

  • Robust optimization: In what ways can learning be improved by perturbing the input measurements?

  • Choice theory: What is the best way to use perturbations to compensate lack of knowledge? What lessons in modeling can machine learning take from random utility theory?

  • Theory: How does the maximum of a random process relate to its complexity? How can the maximum of random perturbations be used to measure the uncertainty of a system?

Target Audience: The workshop should appeal to NIPS attendees interested in both theoretical aspects such as Bayesian modeling, Monte Carlo sampling, optimization, inference, and learning, as well as practical applications in computer vision and language modeling.

Call for Papers (Now Closed)

In addition to a program of invited presentations, we solicit contribution of short papers that explore perturbation-based methods in the context of topics such as: statistical modeling, sampling, inference, estimation, theory, robust optimization, robust learning. We are interested in both theoretical and application-oriented works. We also welcome papers that explore connections between alternative ways of using perturbations.

Contributed papers should adhere to the NIPS format and are encouraged to be up to four pages long (without counting the list of references). Papers submitted for review do not need to be anonymized. There will be no official proceedings. Thus, apart from papers reporting novel unpublished work, we also welcome submissions describing work in progress or summarizing a longer paper under review for a journal or conference (this should be clearly stated though). Accepted papers will be presented as posters; some may also be selected for spotlight talks.

Please submit papers in PDF format by email to posNIPS2013@gmail.com. The submission deadline is October 9 25, 2013 and notifications of acceptance will be sent by October 23, 2013. At least one of the authors must be attending the workshop to present the work.

Organizers

Last Year's Workshop

This is the second year of this workshop. The 2012 POS Workshop website is here.

References

We have assembled below a list of indicative references related to the workshop's theme.

Machine learning

Extreme value statistics

Discrete choice in psychology and economics

Mathematics and statistical physics

Optimization