TLWorkshop 2015 : NIPS 2015 Workshop on Transfer and Multi-Task Learning: Trends and New Perspectives
Call For Papers
[CFP] NIPS 2015 Workshop on Transfer and Multi-Task Learning: Trends and New Perspectives
Call for Participation
We invite researchers and practitioners from machine learning, computer vision, natural language processing and related fields to participate in the:
NIPS 2015 Workshop on Transfer and Multi-Task Learning: Trends and New Perspectives
Saturday December 12th, 2015, Montreal, Canada
Submission deadline: October 16th, 2015
1. Call for Papers
Transfer and multi-task learning methods aim to better exploit the available data during training and adapt previously learned knowledge to new domains or tasks. This mitigates the burden of human labeling for emerging applications and enables learning from very few labeled examples. This workshop explores new discoveries and directions in the main areas of transfer and multi-task learning, as well as several related variants such as domain adaptation and dataset bias, and deep learning based approaches.
In the past years there has been increasing activity in these areas, mainly driven by practical applications (e.g. object recognition, sentiment analysis) as well as advances in deep learning. Of the recently proposed practical solutions, most lack theoretical justifications, especially approaches based on deep learning. On the other hand, most of the existing theoretically justified approaches are rarely used in practice.
This NIPS 2015 workshop will focus on closing the gap between theory and practice by providing an opportunity for researchers and practitioners to get together, to share ideas and debate current theories and empirical results. The goal is to promote a fruitful exchange of ideas across different communities, leading to a global advancement of the field.
We invite submission of extended abstracts to the workshop on all topics related to transfer and multi-task learning, with special interest in:
New perspectives or theories on transfer and multi-task learning
Dataset bias and concept drift
Transfer learning and domain adaptation
Zero-shot or one-shot learning
Feature based approaches
Instance based approaches
Deep architectures for transfer and multi-task learning
Transferability of deep representations
Transfer across different architectures, e.g. CNN to RNN
Transfer across different modalities, e.g. image to text
Transfer across different tasks, e.g. object recognition and detection
Transfer from weakly labeled or noisy data, e.g. Web data
Transfer in practical settings, e.g. online, active, and large-scale learning
Innovative applications, e.g. in machine translation, computational biology
Datasets, benchmarks, and open-source packages
Submissions should be no longer than 4 pages in the NIPS style (plus 1 additional page containing references only). However, it is the authors’ responsibility to make sure they did not violate any dual submission policy if they want to publish it in a future conference (e.g. CVPR 2016). Style files and formatting instructions can be found on the NIPS website. The extended abstract may be accompanied by an unlimited appendix and other supplementary material, with the understanding that anything beyond 4 pages may be ignored. Topics that were recently published or presented elsewhere are allowed, provided that the extended abstract mentions this explicitly.
Deadline for Paper Submission:
Fri Oct 16, 2015 23:00 PM UTC.
Submit at: https://cmt.research.microsoft.com/TLW2015/
Important: As per workshop tradition, reviews are not double-blind, and author names and affiliations should be listed.
Accepted papers will be made available on this website. However, the workshop's proceedings can be considered non-archival, meaning contributors are free to publish their work in archival journals or conferences. All accepted papers will have a poster presentation. A selected number of papers will be featured in an oral presentation or spotlight presentation. There will also be a best paper award.
Yoshua Bengio, University of Montreal
Shai Ben-David, University of Waterloo
Percy Liang, Stanford University
Mehryar Mohri, New York University
Massimiliano Pontil, University College London
Ruslan Salakhutdinov, University of Toronto
Qiang Yang, Hong Kong University of Science and Technology
3. Important Dates
Submission deadline: October 16th, 2015
Acceptance decision: October 23rd, 2015
Camera-ready: November 13rd, 2015
Workshop: Saturday December 12th, 2015
Anastasia Pentina, Institute of Science and Technology, Austria
Christoph Lampert, Institute of Science and Technology, Austria
Sinno Jialin Pan, Nanyang Technological University, Singapore
Mingsheng Long, Tsinghua University and UC Berkeley
Judy Hoffman, UC Berkeley
Baochen Sun, University of Massachusetts Lowell
Kate Saenko, University of Massachusetts Lowell