[ML-news] Last CFP: ICML Workshop on Learning from Multi-Label Data

[Grigorios Tsoumakas]

Apologies if you receive this CFP more than once.

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MLD '10

Second International Workshop on Learning from Multi-Label
Data

in conjunction with ICML/COLT 2010, June 25, 2010 - Haifa, Isreal

[http://cies.hhu.edu.cn/conf/MLD10/]

**The BEST SELECTED papers of MLD'10 may be invited to submit an
extended version to a special issue at Machine Learning Journal on the
topic of Learning from Multi-Label Data (c.f. [http://mlkd.csd.auth.gr/
events/ml2010si.html])**

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BACKGROUND AND MOTIVATION

Traditional supervised learning works under the single-label scenario,
i.e. each example is associated with one single label characterizing
its property. However, in many real-world applications, objects are
usually associated with multiple labels simultaneously. One natural
example is text categorization, where each document may belong to
several predefined topics, such as Shanghai World Expo, economics and
even volunteers.

In multi-label learning, each example in the training set is
associated with a set of labels and two main tasks are: a) to predict
the label set of unseen examples, and b) to rank all labels according
to relevance with unseen examples, through analyzing training examples
with known label sets. Following the earlier work on multi-label text
categorization since 1999, multi-label learning has gradually
attracted more and more attentions from machine learning and other
related communities.

Specifically, the ever-increasing interest on learning from multi-
label data is witnessed by the remarkable amount of works on: a) novel
multi-label learning algorithms; b) applications of multi-label
learning techniques such as (semi)automated annotation of large
collections of image/video, music, text, web and biology objects, drug
discovery, query categorization, medical diagnosis, tag
recommendation, and direct marketing; c) learning tasks related to
multi-label learning, such as dimensionality reduction for multi-label
data, hierarchical multi-label learning, semi-supervised multi-label
learning, active multi-label learning, multi-instance multi-label
learning; and others.

However, despite the encouraging progress in multi-label learning
research, there are still many open issues to be addressed in this
emerging learning scenario (see topics of interest for some
examples).


AIMS, SCOPE AND FORMAT

Following the success of MLD'09 (c.f. http://lpis.csd.auth.gr/workshops/mld09/),
we organize the 2nd edition of this workshop to provide an open and
interactive forum for people with diverse backgrounds that are
interested in multi-label learning, to share their expertise, exchange
their ideas, and discuss on related issues. The goal of this workshop
is to bring researchers and practitioners that work on various aspects
of multi-label learning into a fruitful discussion about the state-of-
the-art and the remaining open problems, and to offer them an
opportunity to identify new promising research directions. To achieve
this goal we are soliciting two types of contributions: a) mature
research results, and b) interesting preliminary results or
stimulating position statements. The workshop will feature sessions
for oral presentation of the accepted contributions, invited talks and
at least one discussion session to allow for a more interactive and
engaging experience.


TOPICS OF INTEREST (non-exhaustive list)

* Theoretical analysis of multi-label learning
* Novel methodologies/algorithms to learn from multi-label data
* Scalable multi-label learning (presence of large number of labels)
* Learning and/or exploiting label structure (constraints,
hierarchies, ontologies)
* Learning from data with multiple non-binary target variables
(nominal, real-valued, mixed)
* Evaluation in multi-label learning
* New applications of multi-label learning
* Related learning tasks
- Feature selection from multi-label data
- Multi-instance multi-label learning
- Active multi-label learning
- Semi-supervised multi-label learning


IMPORTANT DATES

* Full paper submission due : May 2, 2010
* Acceptance notification : May 23, 2010
* Camera-ready paper due : May 30, 2010
* Early registration due : May 31, 2010
* Date of MLD'10 workshop : June 25, 2010


SUBMISSION

Papers must be in English and formatted according to the ICML 2010
stylefiles (available at http://www.icml2010.org/papers/icml2010stylefiles.zip).
The maximum number of pages allowed is eight in this format. At the
time of submission, papers should not be under review or accepted for
publication elsewhere.

Each paper will undergo rigorous review (single-blind) by at least two
reviewers, whose quality be evaluated based on its novelty of content,
clarity of presentation, thoroughness of experiments, as well as
other aspects.

The submission system of MLD'10 is managed by EasyChair. To submit
your contribution, please visit http://www.easychair.org/conferences/?conf=mld10


PROGRAM COMMITTEE

* Hendrik Blockeel, Katholieke Universiteit Leuven
* Koby Crammer, Israel Institute of Technology
* Johannes Fuernkranz, Technische Universitat Darmstadt
* Shantanu Godbole, IBM Research India
* Xian-Sheng Hua, Microsoft Research Asia
* Eyke Hullermeier, Philipps-Universitat Marburg
* Rong Jin, Michigan State University
* Ioannis Katakis, Aristotle University of Thessaloniki
* Eneldo Loza Mencia, Technische Universitat Darmstadt
* Sang-Hyeun Park, Technische Universitat Darmstadt
* Jose M. Pena, Universidad Politecnica de Madrid
* Jesse Read, University of Waikato
* Naonori Ueda, NTT Communication Science Laboratories
* Rong Yan, Facebook, Inc.
* Jieping Ye, Arizona State University
* Kai Yu, NEC Laboratories America, Inc.
* Shipeng Yu, Siemens Medical Solutions USA, Inc.
* Zheng-Jun Zha, National University of Singapore


WORKSHOP CO-CHAIRS

Min-Ling Zhang,
Hohai University, China
Email: zha...@hhu.edu.cn
Url: http://cies.hhu.edu.cn/pweb/zhangml/

Grigorios Tsoumakas,
Aristotle University of Thessaloniki, Greece
Email: gr...@csd.auth.gr
Url: http://mlkd.csd.auth.gr/greg.html

Zhi-Hua Zhou,
Nanjing University, China
Email: zho...@nju.edu.cn
Url: http://cs.nju.edu.cn/zhouzh/

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