CFProposal AISB2018

  The Society for the Study of Artificial Intelligence and Simulation for Behaviour (AISB) is soliciting proposals for symposia to be held at the AISB 2018 convention.The longest running convention on Artificial Intelligence, A...


Insurance AI Analy...

Insurance AI Analytics Summit, October 9-10, London Join us for Europe’s only AI event dedicated to insurance where 300 attendees will unite from analytics, pricing, marketing, claims and underwriting. You’ll find out how advan...


AISB 2018 Convention

  The longest running convention on Artificial Intelligence, AISB 2018 will be held at the University of Liverpool, chaired by Floriana Grasso and Louise Dennis. As in the past years, AISB 2018 will provide a unique forum for p...


AI Summit London

     The AI Summit London: The World’s Number One AI Event for Business  Date: 9-10 May 2017 Venue: Business Design Centre, London. The AI Summit is the world’s first and largest/number one conference exhibition dedicated to t...


AISB Wired Health

    AISB and WIRED events have partnered to bring together inspirational high-profile speakers. Join hundreds of healthcare, pharmaceutical and technology influencers and leaders at the 4th Annual WIRED Health event, taking pl...


Hugh Gene Loebner

  The AISB were sad to learn last week of the passing of philanthropist and inventor Hugh Gene Loebner PhD, who died peacefully in his home in New York at the age of 74.  Hugh was founder and sponsor of The Loebner Prize, an an...


AI Europe 2016

  Partnership between AISB and AI Europe 2016: Next December 5th and 6th in London, AI Europe will bring together the European AI eco-system by gathering new tools and future technologies appearing in professional fields for th...


AISB convention 2017

  In the run up to AISB2017 convention (, I've asked Joanna Bryson, from the organising team, to answer few questions about the convention and what comes with it. Mohammad Majid...


Harold Cohen

Harold Cohen, tireless computer art pioneer dies at 87   Harold Cohen at the Tate (1983) Aaron image in background   Harold Cohen died at 87 in his studio on 27th April 2016 in Encintias California, USA.The first time I hear...


Dancing with Pixies?...

At TEDx Tottenham, London Mark Bishop (the former chair of the Society) demonstrates that if the ongoing EU flagship science project - the 1.6 billion dollar "Human Brain Project” - ultimately succeeds in understanding all as...



AISB event Bulletin Item

CALL FOR PAPERS: Computational Trade-offs in Statistical Learning, SPAIN

Computational Trade-offs in Statistical Learning NIPS 2011 Workshop, Sierra Nevada, Spain

Since its early days, the field of Machine Learning has focused on developing computationally 
tractable algorithms with good learning guarantees. The vast literature on statistical learning 
theory has led to a good understanding of how the predictive performance of different algorithms 
improves as a function of the number of training samples.
By the same token, the well-developed theories of optimization and sampling methods have yielded 
efficient computational techniques at the core of most modern learning methods. The separate 
developments in these fields mean that given an algorithm we have a sound understanding of its 
statistical and computational behavior. However, there hasn't been much joint study of the 
computational and statistical complexities of learning, as a consequence of which, little is 
known about the interaction and trade-offs between statistical accuracy and computational 
complexity. Indeed a systematic joint treatment can answer some very interesting questions: 
what is the best attainable statistical error given a finite computational budget? What is the 
best learning method to use given different computational constraints and desired statistical 
yardsticks? Is it the case that simple methods outperform complex ones in computationally 
impoverished scenarios?

The goal of our workshop is to draw the attention of machine learning researchers to this rich 
and emerging area of problems and to establish a community of researchers that are interested 
in understanding computational and statistical trade-offs. We aim to define a number of common 
problems in this area and to encourage future research.

We would like to welcome high-quality submissions on topics including but not limited to:

* Fundamental statistical limits with bounded computation
* Trade-offs between statistical accuracy and computational costs
* Computation-preserving reductions between statistical problems
* Algorithms to learn under budget constraints
* Budget constraints on other resources (e.g. bounded memory)
* Computationally aware approaches such as coarse-to-fine learning

Interesting submissions in other relevant topics not listed above are welcome too. Due to the 
time constraints, most accepted submissions will be presented as poster spotlights.

* Shai Shalev-Shwartz
* Ben Taskar

Submissions should be written as extended abstracts, no longer than 4 pages in the NIPS latex 
style. NIPS style files and formatting instructions can be found at The submissions should include the authors' name and 
affiliation since the review process will not be double blind. 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 by the program committee. The papers can be submitted 
at by Oct 17, 5PM PST.
Authors will be notified on or before Nov 4.

Alekh Agarwal
Alexander Rakhlin

Lon Bottou, Olivier Chapelle , John Duchi, Claudio Gentile, John Langford, Maxim Raginsky, 
Pradeep Ravikumar, Ohad Shamir, Karthik Sridharan, David Weiss, Nati Srebro