ICML 2011
TechTalks from event: ICML 2011
Ranking and Information Retrieval

Learning Mallows Models with Pairwise PreferencesLearning preference distributions is a key problem in many areas (e.g., recommender systems, IR, social choice). However, existing methods require restrictive data models for evidence about user preferences. We relax these restrictions by considering as data arbitrary pairwise comparisonsthe fundamental building blocks of ordinal rankings. We develop the first algorithms for learning Mallows models (and mixtures) with pairwise comparisons. At the heart is a new algorithm, the generalized repeated insertion model (GRIM), for sampling from arbitrary ranking distributions. We develop approximate samplers that are exact for many important special casesand have provable bounds with pairwise evidenceand derive algorithms for evaluating loglikelihood, learning Mallows mixtures, and nonparametric estimation. Experiments on large, realworld datasets show the effectiveness of our approach.

Preserving Personalized Pagerank in SubgraphsChoosing a subgraph that can concisely represent a large realworld graph is useful in many scenarios. The usual strategy employed is to sample nodes so that the induced subgraph matches the original graph’s degree distribution, clustering coefficient, etc., but no attempt is made to preserve finegrained relationships between nodes, which are vital for applications like clustering, classification, and ranking. In this work, we model such relationships via the notion of Personalized PageRank Value (PPV). We show that induced subgraphs output by current sampling methods do not preserve PPVs, and propose algorithms for creating PPVpreserving subgraphs on any given subset of graph nodes. Experiments on three large realworld graphs show that the subgraphs created by our method improve upon induced subgraphs in terms of preserving PPVs, clustering accuracy, and maintaining basic graph properties.

Learning Scoring Functions with OrderPreserving Losses and Standardized SupervisionWe address the problem of designing surrogate losses for learning scoring functions in the context of label ranking. We extend to ranking problems a notion of order preserving losses previously introduced for multiclass classi?cation, and show that these losses lead to consistent formulations with respect to a family of ranking evaluation metrics. An orderpreserving loss can be tailored for a given evaluation metric by appropriately setting some weights depending on this metric and the observed supervision. These weights, called the standard form of the supervision, do not always exist, but we show that previous consistency results for ranking were proved in special cases where they do. We then evaluate a new pairwise loss consistent with the (Normalized) Discounted Cumulative Gain on benchmark datasets.

Bipartite Ranking through Minimization of Univariate LossMinimization of the rank loss or, equivalently, maximization of the AUC in bipartite ranking calls for minimizing the number of disagreements between pairs of instances. Since the complexity of this problem is inherently quadratic in the number of training examples, it is tempting to ask how much is actually lost by minimizing a simple univariate loss function, as done by standard classification methods, as a surrogate. In this paper, we first note that minimization of 0/1 loss is not an option, as it may yield an arbitrarily high rank loss. We show, however, that better results can be achieved by means of a weighted (costsensitive) version of 0/1 loss. Yet, the real gain is obtained through marginbased loss functions, for which we are able to derive proper bounds, not only for rank risk but, more importantly, also for rank regret. The paper is completed with an experimental study in which we address specific questions raised by our theoretical analysis.

kDPPs: FixedSize Determinantal Point ProcessesDeterminantal point processes (DPPs) have recently been proposed as models for set selection problems where diversity is preferred. For example, they can be used to select diverse sets of sentences to form document summaries, or to find multiple nonoverlapping human poses in an image. However, DPPs conflate the modeling of two distinct characteristics: the size of the set, and its content. For many realistic tasks, the size of the desired set is known up front; e.g., in search we may want to show the user exactly ten results. In these situations the effort spent by DPPs modeling set size is not only wasteful, but actually introduces unwanted bias into the modeling of content. Instead, we propose the kDPP, a conditional DPP that models only sets of cardinality k. In exchange for this restriction, kDPPs offer greater expressiveness and control over content, and simplified integration into applications like search. We derive algorithms for efficiently normalizing, sampling, and marginalizing kDPPs, and propose an expertsstyle algorithm for learning combinations of kDPPs. We demonstrate the usefulness of the model on an image search task, where kDPPs significantly outperform MMR as judged by human annotators.
 All Sessions
 Keynotes
 Bandits and Online Learning
 Structured Output
 Reinforcement Learning
 Graphical Models and Optimization
 Recommendation and Matrix Factorization
 Neural Networks and Statistical Methods
 LargeScale Learning
 Learning Theory
 Feature Selection, Dimensionality Reduction
 Invited CrossConference Track
 LatentVariable Models
 Neural Networks and Deep Learning
 LatentVariable Models
 Active and Online Learning
 Tutorial : Collective Intelligence and Machine Learning
 Tutorial: Machine Learning in Ecological Science and Environmental Policy
 Tutorial: Machine Learning and Robotics
 Ensemble Methods
 Tutorial: Introduction to Bandits: Algorithms and Theory
 Tutorial: Machine Learning for Large Scale Recommender Systems
 Tutorial: Learning Kernels
 TestofTime
 Best Paper
 Robotics and Reinforcement Learning
 Transfer Learning
 Kernel Methods
 Optimization
 Learning Theory
 Invited CrossConference Session
 Neural Networks and Deep Learning
 Reinforcement Learning
 Bayesian Inference and Probabilistic Models
 Supervised Learning
 Social Networks
 Evaluation Metrics
 statistical relational learning
 Outlier Detection
 Time Series
 Graphical Models and Bayesian Inference
 Sparsity and Compressed Sensing
 Clustering
 Game Theory and Planning and Control
 SemiSupervised Learning
 Kernel Methods and Optimization
 Neural Networks and NLP
 Probabilistic Models & MCMC
 Online Learning
 Ranking and Information Retrieval