Our workshop focuses on optimization theory and practice that is relevant to machine learning. This proposal builds on precedent established by two of our previously well-received NIPS workshops:

(@NIPS08) http://opt2008.kyb.tuebingen.mpg.de/
(@NIPS09) http://opt.kyb.tuebingen.mpg.de/

Both these workshops had packed (often overpacked) attendance almost throughout the day. This enthusiastic reception reflects the strong interest, relevance, and importance enjoyed by optimization in the greater ML community.

One could ask why does optimization attract such continued interest? The answer is simple but telling: optimization lies at the heart of almost every ML algorithm. For some algorithms textbook methods suffice, but the majority require tailoring algorithmic tools from optimization, which in turn depends on a deeper understanding of the ML requirements. In fact, ML applications and researchers are driving some of the most cutting-edge developments in optimization today. The intimate relation of optimization with ML is the key motivation for our workshop, which aims to foster discussion, discovery, and dissemination of the state-of-the-art in optimization, especially in the context of ML.

The workshop should realize its aims by:

* Providing a platform for increasing the interaction between researchers from optimization, operations research, statistics, scientific computing, and machine learning;
* Identifying key problems and challenges …

The field of computational biology has seen dramatic growth over the past few years, both in terms of new available data, new scientific questions, and new challenges for learning and inference. In particular, biological data is often relationally structured and highly diverse, well-suited to approaches that combine multiple weak evidence from heterogeneous sources. These data may include sequenced genomes of a variety of organisms, gene expression data from multiple technologies, protein expression data, protein sequence and 3D structural data, protein interactions, gene ontology and pathway databases, genetic variation data (such as SNPs), and an enormous amount of textual data in the biological and medical literature. New types of scientific and clinical problems require the development of novel supervised and unsupervised learning methods that can use these growing resources.

The goal of this workshop is to present emerging problems and machine learning techniques in computational biology. We invited several speakers from the biology/bioinformatics community who will present current research problems in bioinformatics, and 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 …

Monte Carlo methods have been the dominant form of approximate inference for Bayesian statistics over the last couple of decades. Monte Carlo methods are interesting as a technical topic of research in themselves, as well as enjoying widespread practical use. In a diverse number of application areas Monte Carlo methods have enabled Bayesian inference over classes of statistical models which previously would have been infeasible. Despite this broad and sustained attention, it is often still far from clear how best to set up a Monte Carlo method for a given problem, how to diagnose if it is working well, and how to improve under-performing methods. The impact of these issues is even more pronounced with new emerging applications. This workshop is aimed equally at practitioners and core Monte Carlo researchers. For practitioners we hope to identify what properties of applications are important for selecting, running and checking a Monte Carlo algorithm. Monte Carlo methods are applied to a broad variety of problems. The workshop aims to identify and explore what properties of these disparate areas are important to think about when applying Monte Carlo methods.
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The workshop wiki contains a more detailed list of discussion topics and recommended background …

Computational social science is an emerging academic research area at the intersection of computer science, statistics, and the social sciences, in which quantitative methods and computational tools are used to identify and answer social science questions. The field is driven by new sources of data from the Internet, sensor networks, government databases, crowdsourcing systems, and more, as well as by recent advances in computational modeling, machine learning, statistics, and social network analysis. \par

The related area of social computing deals with the mechanisms through which people interact with computational systems, examining how and why people contribute to crowdsourcing sites, and the Internet more generally. Examples of social computing systems include prediction markets, reputation systems, and collaborative filtering systems, all designed with the intent of capturing the wisdom of crowds. \par

Machine learning plays in important role in both of these research areas, but to make truly groundbreaking advances, collaboration is necessary: social scientists and economists are uniquely positioned to identify the most pertinent and vital questions and problems, as well as to provide insight into data generation, while computer scientists contribute significant expertise in developing novel, quantitative methods and tools. To date there have been few in-person venues for researchers …

This workshop seeks to excite and inform researchers to tackle the next level of problems in the area of Computer Vision. The idea is to both give Computer Vision researchers access to the latest Machine Learning research, and also to communicate to researchers in the machine learning community some of the latest challenges in computer vision, in order to stimulate the emergence of the next generation of learning techniques. The workshop itself is motivated from several different points of view:

\begin{enumerate}
\item There is a great interest in and take-up of machine learning techniques in the computer vision community. In top vision conferences such as CVPR, machine learning is prevalent: there is widespread use of Bayesian Techniques, Kernel Methods, Structured Prediction, Deep Learning, etc.; and many vision conferences have featured invited speakers from the machine learning community.

\item Despite the quality of this research and the significant adoption of machine learning techniques, often such techniques are used as black box'' parts of a pipeline, performing traditional tasks such as classification or feature selection, rather than fundamentally taking a learning approach to solving some of the unique problems arising in real-world vision applications. <br> <br>\item Beyond object recognition and robot navigation, …

An aging demographic has been identified as a challenge for healthcare provision, with technology tipped to play an increasingly significant role. Already, assistive technologies for cognitive and physical disabilities are being developed at an increasingly rapid rate. However, the use of complex technological solutions by specific and diverse user groups is a significant challenge for universal design. For example, 'smart homes' that recognise inhabitant activities for assessment and assistance have not seen significant uptake by target user groups. The reason for this is primarily that user requirements for this type of technology are very diverse, making a single universal design extremely challenging. Machine learning techniques are therefore playing an increasing role in allowing assistive technologies to be adaptive to persons with diverse needs. However, the ability to adapt to these needs carries a number of theoretical challenges and research directions, including but not limited to decision making under uncertainty, sequence modeling, activity recognition, active learning, hierarchical models, sensor networks, computer vision, preference elicitation, interface design and game theory. This workshop will expose the research area of assistive technology to machine learning specialists, will provide a forum for machine learning researchers and medical/industrial practitioners to brainstorm about the main challenges, and …

Modeling human communicative dynamics brings exciting new problems and challenges to the NIPS community. The first goal of this workshop is to raise awareness in the machine learning community of these problems, including some applications needs, the special properties of these input streams, and the modeling challenges. The second goal is to exchange information about methods, techniques, and algorithms suitable for modeling human communication dynamics.

Face-to-face communication is a highly interactive process in which the participants mutually exchange and interpret verbal and nonverbal messages. Both the interpersonal dynamics and the dynamic interactions among an individual\'s perceptual, cognitive, and motor processes are swift and complex. How people accomplish these feats of coordination is a question of great scientific interest. Models of human communication dynamics also have much potential practical value, for applications including the understanding of communications problems such as autism and the creation of socially intelligent robots able to recognize, predict, and analyze verbal and nonverbal behaviors in real-time interaction with humans.

Tensors are a generalization of vectors and matrices to high
dimensions. The goal of this workshop is to explore the links between
tensors and information processing. We expect that many problems in, for
example, machine learning and kernel methods can benefit from being
expressing as tensor problems; conversely, the tensor community may
learn from the estimation techniques commonly used in information
processing and from some of the kernel extensions to nonlinear models.

On the other hand, standard tensor-based techniques can only deliver
multi-linear models. As a consequence, they may suffer from limited
discriminative power. A properly defined kernel-based extension might
overcome this limitation. The statistical machine learning community has
much to offer on different aspects such as learning (supervised,
unsupervised and semi-supervised) and generalization, regularization
techniques, loss functions and model selection.\[1ex]

The goal of this workshop is to promote the cross-fertilization between
machine learning and tensor-based techniques. \[1ex]

This workshop is appropriate for anyone who wishes to learn more about
tensor methods and/or share their machine learning or kernel techniques
with the tensor community; conversely, we invite contributions from
tensor experts seeking to use tensors for problems in machine learning
and information processing. \[1ex]

We hope to discuss the following …

Sparse modeling is a rapidly developing area at the intersection of statistics, machine-learning and signal processing, focused on the problem of variable selection in high-dimensional datasets. Selection (and, moreover, construction) of a small set of highly predictive variables is central to many applications where the ultimate objective is to enhance our understanding of underlying physical, biological and other natural processes, beyond just building accurate `black-box' predictors.

\par Recent years have witnessed a flurry of research on algorithms and theory for sparse modeling, mainly focused on l1-regularized optimization, a convex relaxation of the (NP-hard) smallest subset selection problem. Examples include sparse regression, such as Lasso and its various extensions, such as Elastic Net, fused Lasso, group Lasso, simultaneous (multi-task) Lasso, adaptive Lasso, bootstrap Lasso, etc.), sparse graphical model selection, sparse dimensionality reduction (sparse PCA, CCA, NMF, etc.) and learning dictionaries that allow sparse representations. Applications of these methods are wide-ranging, including computational biology, neuroscience, image processing, stock market prediction and social network analysis, as well as compressed sensing, an extremely fast-growing area of signal processing.

\par However, is the promise of sparse modeling realized in practice? It turns out that, despite the significant advances in the field, a number of open …

In recent years, there has been a lot of interest in algorithms that learn feature hierarchies from unlabeled data. Deep learning methods such as deep belief networks, sparse coding-based methods, convolutional networks, and deep Boltzmann machines, have shown promise and have already been successfully applied to a variety of tasks in computer vision, audio processing, natural language processing, information retrieval, and robotics.


In this workshop, we will bring together researchers who are interested in deep learning and unsupervised feature learning, review the recent technical progress, discuss the challenges, and identify promising future research directions. Through invited talks, panel discussions and presentations by attendees we will attempt to address some of the most important topics in deep learning today. We will discuss whether and why hierarchical systems are beneficial, what principles should guide the design of objective functions used to train these models, what are the advantages and disadvantages of bottom-up versus top-down approaches, how to design scalable systems, and how deep models can relate to biological systems. Finally, we will try to identify some of the major milestones and goals we would like to achieve during the next 5 or 10 years of research in deep learning.

At the core of statistical machine learning is to infer conclusions from data, typically using statistical models that describe probabilistic relationships among the underlying variables. Such modeling allows us to make strong predictions even from limited data by leveraging specific problem structure. However on the flip side, when the specific model assumptions do not exactly hold, the resulting methods may deteriorate severely. A simple example: even a few corrupted points, or points with a few corrupted entries, can severely throw off standard SVD-based PCA.

The goal of this workshop is to investigate this robust learning'' setting where the data deviate from the model assumptions in a variety of different ways. Depending on what is known about the deviations, we can have a spectrum of approaches: <br> <br>(a) Dirty Models: Statistical models that imposeclean'' structural assumptions such as sparsity, low-rank etc. have proven very effective at imposing bias without being overly restrictive. A superposition of two (or more) such clean models can provide a method that is also robust. For example, approximating data by the sum of a sparse matrix and a low-rank one leads to PCA that is robust to corrupted entries.

(b) Robust Optimization: Most statistical learning methods …

The bottleneck in many complex prediction problems is the prohibitive cost of inference or search at test time. Examples include structured problems such as object detection and segmentation, natural language parsing and translation, as well as standard classification with kernelized or costly features or a very large number of classes. These problems present a fundamental trade-off between approximation error (bias) and inference or search error due to computational constraints as we consider models of increasing complexity. This trade-off is much less understood than the traditional approximation/estimation (bias/variance) trade-off but is constantly encountered in machine learning applications. The primary aim of this workshop is to formally explore this trade-off and to unify a variety of recent approaches, which can be broadly described as coarse-to-fine'' methods, that explicitly learn to control this trade-off. Unlike approximate inference algorithms, coarse-to-fine methods typically involve exact inference in a coarsened or reduced output space that is then iteratively refined. They have been used with great success in specific applications in computer vision (e.g., face detection) and natural language processing (e.g., parsing, machine translation). However, coarse-to-fine methods have not been studied and formalized as a general machine learning problem. Thus many natural theoretical and empirical questions have …

Prescriptive Bayesian decision making has reached a high level of maturity supported by efficient, theoretically well-founded algorithms. While the long-standing problem of participant's rationality is addressed repeatedly, limited cognitive, acting and evaluative abilities/resources of participants involved have not been considered systematically. This problem of so-called imperfect decision makers emerges repeatedly, for instance, i) consistent theory of incomplete Bayesian games cannot be applied by them; ii) a desirable incorporation of deliberation effort into the design of decision strategies remains unsolved.

Societal, biological, engineered systems exhibit paradigms that can extend the scope of existing knowledge in prescriptive decision making. Societal and natural sciences and partially technology have considered imperfection aspects at the descriptive level. In particular, a broadly studied emerging behaviour resulting from descriptive properties of interacting imperfect decision makers can be exploited at prescriptive decision making. The goal of this workshop is to explore such connections between descriptive and prescriptive decision making and stimulate an exchange the results and ideas. The workshop will foster discussion of bounded-rationality and imperfection of decision-makers in light of Nature. We believe that in long-term perspective, the workshop will contribute to solution of the problems:

A. How to formalise rational decision making of an imperfect participant? …

Social computing aims to support the online social behavior through computational methods. The explosion of the Web has created and been creating social interactions and social contexts through the use of software, services and technologies, such as blogs, microblogs (Tweets), wikis, social network services, social bookmarking, social news, multimedia sharing sites, online auctions, reputation systems, and so on. Analyzing the information underneath the social interactions and social context, e.g., community detection, opinion mining, trend prediction, anomaly detection, product recommendation, expert finding, social ranking, information visualization, will benefit both of information providers and information consumers in the application areas of social sciences, economics, psychologies and computer sciences. However, the large volumes of user-generated contents and the complex structures among users and related entities require effective modeling methods and efficient solving algorithms, which therefore bring challenges to advanced techniques in machine learning. There are three major concerns:

1. How to effectively and accurately model the related task as a learning problem?
2. How to construct efficient and scalable algorithm to solve the learning task?
3. How to fully explore and exploit human computation?


This workshop aims to bring together researchers and practitioners interested in this area to share their perspectives, identify the …

The increasing amount and complexity of electronic data sets turns visualization into a key technology to provide an intuitive interface to the information. Unsupervised learning has developed powerful techniques for, e.g., manifold learning, dimensionality reduction, collaborative filtering, and topic modeling. However, the field has so far not fully appreciated the problems that data analysts seeking to apply unsupervised learning to information visualization are facing such as heterogeneous and context dependent objectives or
streaming and distributed data with different credibility. Moreover, the unsupervised learning field has hitherto failed to develop human-in-the-loop approaches to data visualization, even though such approaches including e.g. user relevance feedback are necessary to arrive at valid and interesting results.\par As a consequence, a number of challenges arise in the context of data visualization which cannot be solved by classical methods in the field:
\begin{itemize}
\item \emph{Methods have to deal with modern data formats and data sets:}\par\noindent How can the technologies be adapted to deal with streaming and probably non i.i.d. data sets? How can specific data formats be visualized appropriately such as spatio-temporal data, spectral data, data characterized by a general probably non-metric dissimilarity measure, etc.? How can we deal with heterogeneous data and different credibility? How …

In spite of its central role and position between physics and biology, chemistry has remained in a somewhat backward state of informatics development compared to its two close relatives, primarily for historical reasons. Computers, open public databases, and large collaborative projects have become the pervasive hallmark of research in physics and biology, but are still at an early stage of development in chemistry. Recently, however, large repositories with millions of small molecules have become freely available, and equally large repositories of chemical reactions have also become available, albeit not freely. These data create a wealth of interesting informatics and machine learning challenges to efficiently store, search, and predict the physical, chemical, and biological properties of small molecules and reactions and chart chemical space'', with significant scientific and technological impacts.

Small organic molecules, in particular, with at most a few dozen atoms play a fundamental role in chemistry, biology, biotechnology, and pharmacology. They can be used, for instance, as combinatorial building blocks for chemical synthesis, as molecular probes for perturbing and analyzing biological systems in chemical genomics and systems biology, and for the screening, design, and discovery of new drugs and other useful compounds. Huge arrays of new small molecules can …

Most machine learning (ML) methods are based on numerical mathematics (NM)
concepts, from differential equation solvers over dense matrix factorizations
to iterative linear system and eigen-solvers. For problems of moderate size,
NM routines can be invoked in a black-box fashion. However, for a growing
number of real-world ML applications, this separation is insufficient and
turns out to be a limit on further progress.\par

The increasing complexity of real-world ML problems must be met with layered
approaches, where algorithms are long-running and reliable components rather
than stand-alone tools tuned individually to each task at hand. Constructing
and justifying dependable reductions requires at least some awareness about NM
issues. With more and more basic learning problems being solved sufficiently
well on the level of prototypes, to advance towards real-world practice the
following key properties must be ensured: scalability, reliability, and
numerical robustness. \par

By inviting numerical mathematics researchers with interest in both numerical
methodology and real problems in applications close to machine learning, we
will probe realistic routes out of the prototyping sandbox. Our aim is to
strengthen dialog between NM, signal processing, and ML. Speakers are briefed
to provide specific high-level examples of interest to ML and to point out
accessible …

Intelligent systems must be capable of transferring previously-learned abstract knowledge to new concepts, given only a small or noisy set of examples. This transfer of higher order information to new learning tasks lies at the core of many problems in the fields of computer vision, cognitive science, machine learning, speech perception and natural language processing.

\par Over the last decade, there has been considerable progress in
developing cross-task transfer (e.g., multi-task learning and
semi-supervised learning) using both discriminative and generative approaches. However, many existing learning systems today can not cope with new tasks for which they have not been specifically trained. Even when applied to related tasks, trained systems often display unstable behavior. More recently, researchers have begun developing new approaches to building rich generative models that are capable of extracting useful, high-level structured representations from high-dimensional sensory input. The learned representations have been shown to give promising results for solving a multitude of novel learning tasks, even though these tasks may be unknown when the generative model is being trained. A few notable examples include learning of Deep Belief Networks, Deep Boltzmann Machines, deep nonparametric Bayesian models, as well as Bayesian models inspired by human learning. \

\parLearning to …

Today's data-driven society is full of large-scale datasets. In the context of machine learning, these datasets are often represented by large matrices representing either a set of real-valued features for each point or pairwise similarities between points. Hence, modern learning problems in computer vision, natural language processing, computational biology, and other areas often face the daunting task of storing and operating on matrices with thousands to millions of entries. An attractive solution to this problem involves working with low-rank approximations of the original matrix. Low-rank approximation is at the core of widely used algorithms such as Principle Component Analysis, Multidimensional Scaling, Latent Semantic Indexing, and manifold learning. Furthermore, low-rank matrices appear in a wide variety of applications including lossy data compression, collaborative filtering, image processing, text analysis, matrix completion and metric learning. In this workshop, we aim to survey recent work on matrix approximation with an emphasis on usefulness for practical large-scale machine learning problems. We aim to provide a forum for researchers to discuss several important questions associated with low-rank approximation techniques.

In the current era of web-scale datasets, high throughput biology and astrophysics, and multilanguage machine translation, modern datasets no longer fit on a single computer and traditional machine learning algorithms often have prohibitively long running times. Parallelized and distributed machine learning is no longer a luxury; it has become a necessity. Moreover, industry leaders have already declared that clouds are the future of computing, and new computing platforms such as Microsoft's Azure and Amazon's EC2 are bringing distributed computing to the masses. The machine learning community has been slow to react to these important trends in computing, and it is time for us to step up to the challenge.

While some parallel and distributed machine learning algorithms already exist, many relevant issues are yet to be addressed. Distributed learning algorithms should be robust to node failures and network latencies, and they should be able to exploit the power of asynchronous updates. Some of these issues have been tackled in other fields where distributed computation is more mature, such as convex optimization and numerical linear algebra, and we can learn from their successes and their failures.

The workshop aims to draw the attention of machine learning researchers to this rich and …

Since Hebb articulated his Neurophysiological Postulate in 1949 up to the present day, the relationship between synapse modification and neuronal activity has been the subject of enormous interest. Laboratory studies have revealed phenomena such as LTP, LTD, and STDP. Theoretical developments have both inspired studies and been inspired by them. The intent of the proposed workshop is to foster communication among researchers in this field. The workshop is intended to be of interest to experimentalists and modelers studying plasticity from the neurobiological level to the cognitive level. The workshop is targeted toward researchers in this area, hopefully drawing a 50/50 mix of experimental results and theoretical ideas. Another goal is to bring together established researchers with grad students and postdocs.

Networks are used across a wide variety of disciplines to describe interactions between entities --- in sociology these are relations between people, such as friendships (Facebook); in biology --- physical interactions between genes; and many others: the Internet, sensor networks, transport networks, ecological networks just to name a few. Computer scientists, physicists and mathematicians search for mechanisms and models that could explain observed networks and analyze their properties. The research into theoretical underpinnings of networks is very heterogeneous and the breadth of existing and possible applications is vast. Yet, many of such works are only known within their specific areas. Many books and articles are written on the subject making it hard to tease out the important unsolved questions especially as richer data becomes available. These issues call for collaborative environment, where scientists from a wide variety of fields could exchange their ideas: theoreticians could learn about new questions in network applications, whereas applied researchers could learn about potential new solutions for their problems. Researchers in different disciplines approach network modeling from complementary angles. For example, in physics, scientists create generative models with the fewest number of parameters and are able to study average behavior of very large networks, whereas …

Intended Audience: Researchers interested in models and algorithms for learning and planning from batches of time series, including those interested in batch reinforcement learning, dynamic Bayes nets, dynamical systems, and similar topics. Also, researchers interested in any applications where such algorithms and models can be of use, for example in medicine and robotics.

Overview: Consider the problem of learning a model or control policy from a batch of trajectories collected a priori that record observations over time. This scenario presents an array of practical challenges. For example, batch data are often noisy and/or partially missing. The data may be high-dimensional because the data collector may not know a priori which observations are useful for decision making. In fact, a data collector may not even have a clear idea of which observations should be used to measure the quality of a policy. Finally, even given low-noise data with a few useful state features and a well-defined objective, the performance of the learner can only be evaluated using the same batch of data that was available for learning.

The above challenges encountered in batch learning and planning from time series data are beginning to be addressed by adapting techniques that have proven …

The purpose of this cross-discipline workshop is to bring together machine learning and healthcare researchers interested in problems and applications of predictive models in the field of personalized medicine. The goal of the workshop will be to bridge the gap between the theory of predictive models and the applications and needs of the healthcare community. There will be exchange of ideas, identification of important and challenging applications and discovery of possible synergies. Ideally this will spur discussion and collaboration between the two disciplines and result in collaborative grant submissions. The emphasis will be on the mathematical and engineering aspects of predictive models and how it relates to practical medical problems.

Although related in a broad sense, the workshop does not directly overlap with the fields of Bioinformatics and Biostatistics. Although, predictive modeling for healthcare has been explored by biostatisticians for several decades, this workshop focuses on substantially different needs and problems that are better addressed by modern machine learning technologies. For example, how should we organize clinical trials to validate the clinical utility of predictive models for personalized therapy selection? The traditional bio-statistical approach for running trials on a large cohort of homogeneous patients would not suffice for the new …

Computational photography (CP) is a new field that explores and is about to redefine how we take photographs and videos. Applications of CP are not only everyday'' photography but also new methods for scientific imaging, such as microscopy, biomedical imaging, and astronomical imaging, and can thus be expected to have a significant impact in many areas. <br> <br>There is an apparent convergence of methods, what we have traditionally calledimage processing'', and recently many works in machine vision, all of which seem to be addressing very much the same, if not tightly related problems. These include deblurring, denoising, and enhancement algorithms of various kinds. What do we learn from this convergence and its application to CP? Can we create more contact between the practitioners of these fields, who often do not interact? Does this convergence mean that the fields are intellectually shrinking to the same point, or expanding and hence overlapping with each other more?

Besides discussing such questions, the goal of this workshop is two-fold: (i) to present the current approaches, their possible limitations, and open problems of CP to the NIPS community, and (ii) to foster interaction between researchers from machine learning, neuro science and CP to advance …

Solving optimization problems with ultimately discrete solutions is becoming increasingly important in machine learning: At the core of statistical machine learning is to infer conclusions from data, and when the variables underlying the data are discrete, both the tasks of inferring the model from data, as well as performing predictions using the estimated model are discrete optimization problems. Many of the resulting optimization problems are NP-hard, and typically, as the problem size increases, standard off-the-shelf optimization procedures become intractable.

Fortunately, most discrete optimization problems that arise in machine learning have specific structure, which can be leveraged in order to develop tractable exact or approximate optimization procedures. For example, consider the case of a discrete graphical model over a set of random variables. For the task of prediction, a key structural object is the marginal polytope,'' a convex bounded set characterized by the underlying graph of the graphical model. Properties of this polytope, as well as its approximations, have been successfully used to develop efficient algorithms for inference. For the task of model selection, a key structural object is the discrete graph itself. Another problem structure is sparsity: While estimating a high-dimensional model for regression from a limited amount of data …

Research on Multiple Kernel Learning (MKL) has matured to the point where efficient systems can be applied out of the box to various application domains. In contrast to last year's workshop, which evaluated the achievements of MKL in the past decade, this workshop looks beyond the standard setting and investigates new directions for MKL.

In particular, we focus on two topics:
1. There are three research areas, which are closely related, but have traditionally been treated separately: learning the kernel, learning distance metrics, and learning the covariance function of a Gaussian process. We therefore would like to bring together researchers from these areas to find a unifying view, explore connections, and exchange ideas.
2. We ask for novel contributions that take new directions, propose innovative approaches, and take unconventional views. This includes research, which goes beyond the limited classical sum-of-kernels setup, finds new ways of combining kernels, or applies MKL in more complex settings.

Taking advantage of the broad variety of research topics at NIPS, the workshop aims to foster collaboration across the borders of the traditional multiple kernel learning community.