Call for Papers
Call for Contributions:
ICML Workshop on Data-Efficient Machine Learning
Workshop Date: 24 June 2016 Submission deadline: 1 May 2016
1. Call for Papers
We invite researchers to submit their recent work on the development and analysis of methods leading towards more data-efficient machine learning. A submission should take the form of an extended abstract of 2 pages in PDF format using the ICML style. Author names do not need to be anonymized and references may extend as far as needed beyond the 2 page upper limit. If authors' research has previously appeared in a journal, workshop, or conference (including the ICML 2016 conference), their workshop submission should extend that previous work.
Submissions will be accepted as contributed talks or poster presentations. Extended abstracts should be submitted by 1 May; see website for submission details. Final versions will be posted on the workshop website (and are archival but do not constitute a proceedings).
2. Workshop Abstract
Recent efforts in machine learning have addressed the problem of learning from massive amounts data. We now have highly scalable solutions for problems in object detection and recognition, machine translation, text-to-speech, recommender systems, and information retrieval, all of which attain state-of-the-art performance when trained with large amounts of data. In these domains, the question that is increasingly asked, is how we can design machine learning systems that achieve the same performance, but that learn efficiently using less data. Other problem domains, such as personalized healthcare, robot reinforcement learning, sentiment analysis, and community detection, are characterized as either small-data problems for which data will always be scarce, or big-data problems that are a collection of small-data problems. The ability to learn in a sample-efficient manner is a necessity in these data-limited domains.
Collectively, these problems highlight the increasing need for data-efficient machine learning: the ability to learn in complex domains without requiring large quantities of data. The types of inductive biases and generalization properties we use, the degree of prior knowledge and side information that is incorporated, and the specifics of the models and inference approaches, all have significant impact in this regime.
Data-efficiency has become an increasingly important requirement for modern machine learning and AI systems. This workshop will discuss the diversity of approaches that exist and the practical challenges that we face. There are many approaches that demonstrate that data-efficient machine learning is possible, including methods that
Consider trade-offs between incorporating explicit domain knowledge and more general-purpose approaches,
Exploit structural knowledge of our data, such as symmetry and other invariance properties,
Apply bootstrapping and data augmentation techniques that make statistically efficient reuse of available data,
Use semi-supervised learning techniques, e.g., where we can use generative models to better guide the training of discriminative models,
Generalize knowledge across domains (transfer learning),
Use active learning and Bayesian optimization for experimental design and data-efficient black-box optimization,
Apply non-parametric methods, one-shot learning and Bayesian deep learning.
The objective of this interdisciplinary workshop is to provide a platform for researchers from a variety of areas, spanning transfer learning, Bayesian optimization, bandits, deep learning, approximate inference, robot learning, healthcare, computational neuroscience, active learning, reinforcement learning, and social network analysis, to share insights and perspectives on the problem, discuss challenges and to debate the roadmap towards more data-efficient machine learning.
3. Key Dates
Paper submission: 1 May 2016
Acceptance notification: 5 May 2016
Final paper submission: 22 May 2015
Workshop: 24 June 2016
4. Workshop Organizers
Marc Deisenroth (Imperial College London)
Shakir Mohamed (Google DeepMind)
Finale Doshi-Velez (Harvard University)
Andreas Krause (ETH Zürich)
Max Welling (University of Amsterdam)