CFP: ICML 2013 Workshop on Inferning: Interactions between Inference and Learning

[Ruslan Salakhutdinov]

Call for Papers

ICML 2013 Workshop on Inferning: Interactions between Inference and
Learning

http://inferning.cs.umass.edu
infern...@gmail.com

Important Dates:
Submission Deadline: Mar 30th, 2013 (11:59pm PST)
Author Notification: April 21st, 2013
Workshop: June 20-21, 2013, Atlanta, GA


There are strong interactions between learning algorithms which estimate
the parameters of a model from data, and inference algorithms which use a
model to make predictions about data. Understanding the intricacies of
these interactions is crucial for advancing the state-of-the-art on
real-world tasks in natural language processing, computer vision,
computation biology, etc. Yet, many facets of these interactions remain
unknown. In this workshop, we study the interactions between inference and
learning using two reciprocating perspectives.

Perspective one: how does inference affect learning? The first perspective
studies the influence of the choice of inference technique during learning
on the resulting model. When faced with models for which exact inference
is intractable, efficient approximate inference techniques may be used,
such as MCMC sampling, stochastic approximation, belief propagation,
beam-search, dual decomposition, etc. The workshop will focus on work that
evaluates the impact of the approximations on the resulting parameters, in
terms of both the generalization of the model, the effect it has on the
objective functions, and the convergence properties. We will also study
approaches that attempt to correct for the approximations in inference by
modifying the objective and/or the learning algorithm (for example,
contrastive divergence for deep architectures), and approaches that
minimize the dependence on the inference algorithms by exploring
inference-free methods (e.g., piece-wise training, pseudo-max and
decomposed learning).

Perspective two: how does learning affect inference? Traditionally, the
goal of learning has been to find a model for which prediction (i.e.,
inference) accuracy is as high as possible. However, an increasing
emphasis on modeling complexity has shifted the goal of learning: find
models for which prediction (i.e., inference) is as efficient as possible.
Thus, there has been recent interest in more unconventional approaches to
learning that combine generalization accuracy with other desiderata such
as faster inference. Some examples of this kind are: learning classifiers
for greedy inference (e.g., Searn, Dagger); structured cascade models that
learn a cost function to perform multiple runs of inference from coarse to
fine level of abstraction by trading-off accuracy and efficiency at each
level; learning cost function to search in the space of complete outputs
(e.g., SampleRank, search in Limited Discrepancy Search space); learning
structures that exhibit efficient exact inference etc. Similarly, there
has been work that learns operators for efficient search-based inference,
approaches that trade-off speed and accuracy by incorporating resource
constraints such as run-time and memory into the learning objective.


This workshop brings together practitioners from different fields
(information extraction, machine vision, natural language processing,
computational biology, etc.) in order to study a unified framework for
understanding and formalizing the interactions between learning and
inference. The following is a partial list of relevant keywords for the
workshop:

* learning with approximate inference
* cost-aware learning
* learning sparse structures
* pseudo-likelihood, composite likelihood training
* contrastive divergence
* piece-wise and decomposed training
* decomposed learning
* coarse to fine learning and inference
* score matching
* stochastic approximation
* incremental gradient methods
* adaptive proposal distributions
* learning for anytime inference
* learning approaches that trade-off speed and accuracy
* learning to speed up inference
* learning structures that exhibit efficient exact inference
* lifted inference for first-order models
* more ...

New benchmark problems: This line of research can hugely benefit from new
challenge problems from various fields (e.g., computer vision, natural
language processing, speech, computational biology, computational
sustainability, etc.). Therefore, we especially request relevant papers
describing such problems, main challenges, evaluations and public data
sets.


Invited Speakers:

Dan Roth, University of Illinois, Urbana-Champaign
Rina Dechter, University of California, Irvine
Ben Taskar, University of Washington
Hal Daume, University of Maryland, College Park
Alan Fern, Oregon State University


Important Dates:

Submission Deadline: Mar 30th, 2013 (11:59pm PST)
Author Notification: April 21st, 2013
Workshop: June 20-21, 2013


Author Guidelines:

Submissions are encouraged as extended abstracts of ongoing research. The
recommended page length is 4-6 pages. Additional supplementary content may
be included, but may not be considered during the review process.
Previously published or currently in submission papers are also encouraged
(we will confirm with authors before publishing the papers online).

The format of the submissions should follow the ICML 2013 style, available
here:
http://icml.cc/2013/wp-content/uploads/2012/12/icml2013stylefiles.tar.gz
However, since the review process is not double-blind, submissions need
not be anonymized and author names may be included.

Submission site: https://www.easychair.org/conferences/?conf=inferning2013


Organizers:

Janardhan Rao (Jana) Doppa, Oregon State University
Pawan Kumar, Ecole Centrale Paris
Michael Wick, University of Massachusetts, Amherst
Sameer Singh, University of Massachusetts, Amherst
Ruslan Salakhutdinov, University of Toronto