NIPS Workshops, Lake Tahoe 2013
The Post-Conference Workshop Program covered a wide range of topics from Neuroscience to Machine Learning.
Detailed information can be found at NIPS 2013 Workshops homepage.
Crowdsourcing: Theory, Algorithms and Applications
In Crowdsourcing: Theory, Algorithms and Applications workshop, we call attention back to sources of data, discussing cheap and fast data collection methods based on crowdsourcing, and how it could impact subsequent machine learning stages. Furthermore, we emphasize how the data sourcing paradigm interacts with the most recent emerging trends of machine learning in NIPS community.
Deep Learning algorithms attempt to discover good representations, at multiple levels of abstraction. There has been rapid progress in this area in recent years, both in terms of algorithms and in terms of applications, but many challenges remain. The Deep Learning workshop aims at bringing together researchers in that field and discussing these challenges, brainstorming about new solutions.
Discrete Optimization in Machine Learning: Connecting Theory and Practice
One of the primary goals of Discrete Optimization in Machine Learning: Connecting Theory and Practice workshop is to provide a platform for exchange of ideas -- between machine learning, algorithms, discrete mathematics and combinatorics as well as application areas of computer vision, speech, NLP, biology and network analysis -- on how to discover, exploit, and deploy such structure.
Large Scale Matrix Analysis and Inference
Large Scale Matrix Analysis and Inference workshop aims to bring closer researchers in large scale machine learning and large scale numerical linear algebra to foster cross-talk between the two fields. The goal is to encourage machine learning researchers to work on numerical linear algebra problems, to inform machine learning researchers about new developments on large scale matrix analysis, and to identify unique challenges and opportunities.
MLINI-13: Machine Learning and Interpretation in Neuroimaging
In MLINI-13: Machine Learning and Interpretation in Neuroimaging workshop, we intend to investigate the implications that follow from adopting multivariate machine-learning methods for studying brain function. In particular, this concerns the question how these methods may be used to represent cognitive states, and what ramifications this has for consequent theories of cognition. Besides providing a rationale for the use of machine-learning methods in studying brain function, a further goal of this workshop is to identify shortcomings of state-of-the-art approaches and initiate research efforts that increase the impact of machine learning on cognitive neuroscience.
Modern Nonparametric Methods in Machine Learning
Modern pattern recognition problems arising in various disciplines are characterized by large data sizes, large number of observed variables, and increased pattern complexity. Therefore, nonparametric methods which can handle generally complex patterns are ever more relevant for modern data analysis. However, the larger data sizes and number of variables constitute new challenges for nonparametric methods in general. The aim of Modern Nonparametric Methods in Machine Learning workshop is to bring together both theoretical and applied researchers to discuss these modern challenges in detail, share insight on existing solutions, and lay out some of the important future directions.
NIPS 2013 Workshop on Causality: Large-scale Experiment Design and Inference of Causal Mechanisms
The goal of NIPS 2013 Workshop on Causality: Large-scale Experiment Design and Inference of Causal Mechanisms workshop is to discuss new methods of large scale experiment design and their application to the inference of causal mechanisms and promote their evaluation via a series of challenges. Emphasis is put on capitalizing on massive amounts of available observational data to cut down the number of experiments needed, pseudo- or quasi-experiments, iterative designs, and the on-line acquisition of data with minimal perturbation of the system under study.
Output Representation Learning
The aim of Output Representation Learning workshop is to bring these relevant research communities together to identify fundamental strategies, highlight differences, and identify the prospects for developing a set of systematic theory and methods for output representation learning. The target communities include researchers working on image tagging, document categorization, natural language processing, large vocabulary speech recognition, deep learning, latent variable modeling, and large scale multi-label learning.
Data Driven Education
The first goal of Data Driven Education workshop is to highlight some of the exciting and impactful ways that our community can bring tools from machine learning to bear on educational technology. The second goal is to accelerate the progress of research in these areas by addressing the challenges of data availability. At the moment, there are several barriers to entry including the lack of open and accessible datasets as well as unstandardized formats for such datasets. We hope that by (1) surveying a number of the publicly available datasets, and (2) proposing ways to distribute other datasets such as MOOC data in a spirited panel discussion we can make real progress on this issue as a community, thus lowering the barrier for researchers aspiring to make a big impact in this important area.
Knowledge Extraction from Text (KET)
The goal of Knowledge Extraction from Text (KET) workshop is to collect key researchers and practitioners from the area to exchange ideas, approaches and techniques used to deal with text understanding and related knowledge acquisition problems.
Learning Faster From Easy Data
The aim of Learning Faster From Easy Data workshop is threefold: to map, by means of a series of invited talks and poster presentations, the existing landscape of "easiness criteria" in relation to the efficiency of their corresponding algorithms; to identify, by means of a panel discussion led by the organizers, obstacles and promising directions; and through interaction foster partnerships for future research.
Machine Learning for Sustainability
While the significance of the problem is apparent, more involvement from the machine learning community in sustainability problems is required. Not surprisingly, sustainability problems bring along interesting challenges and opportunities for machine learning in terms of complexity, scalability and impact in areas such as prediction, modeling and control. Machine Learning for Sustainability workshop aims at bringing together scientists in machine learning, operations research, applied mathematics and statistics with a strong interest in sustainability to discuss how to use existing techniques and how to develop novel methods in order to address such challenges.
Machine Learning in Computational Biology
The goal of Machine Learning in Computational Biology workshop is to present emerging problems and machine learning techniques in computational biology. We invite contributed talks on novel learning approaches in computational biology. We encourage contributions describing either progress on new bioinformatics problems or work on established problems using methods that are substantially different from standard approaches. Kernel methods, graphical models, feature selection, and other techniques applied to relevant bioinformatics problems would all be appropriate for the workshop. The targeted audience are people with interest in learning and applications to relevant problems from the life sciences.
New Directions in Transfer and Multi-Task: Learning Across Domains and Tasks
New Directions in Transfer and Multi-Task: Learning Across Domains and Tasks workshop focuses on closing this gap by providing an opportunity for theoreticians and practitioners to get together in one place, to share and debate over current theories and empirical results. The goal is to promote a fruitful exchange of ideas and methods between the different communities, leading to a global advancement of the field.
Workshop on Spectral Learning
The focus of Spectral Learning workshop is on spectral learning algorithms, broadly construed as any method that fits a model by way of a spectral decomposition of moments of (features of) observations.