226 research outputs found
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback
We develop a learning principle and an efficient algorithm for batch learning
from logged bandit feedback. This learning setting is ubiquitous in online
systems (e.g., ad placement, web search, recommendation), where an algorithm
makes a prediction (e.g., ad ranking) for a given input (e.g., query) and
observes bandit feedback (e.g., user clicks on presented ads). We first address
the counterfactual nature of the learning problem through propensity scoring.
Next, we prove generalization error bounds that account for the variance of the
propensity-weighted empirical risk estimator. These constructive bounds give
rise to the Counterfactual Risk Minimization (CRM) principle. We show how CRM
can be used to derive a new learning method -- called Policy Optimizer for
Exponential Models (POEM) -- for learning stochastic linear rules for
structured output prediction. We present a decomposition of the POEM objective
that enables efficient stochastic gradient optimization. POEM is evaluated on
several multi-label classification problems showing substantially improved
robustness and generalization performance compared to the state-of-the-art.Comment: 10 page
Fairness of Exposure in Rankings
Rankings are ubiquitous in the online world today. As we have transitioned
from finding books in libraries to ranking products, jobs, job applicants,
opinions and potential romantic partners, there is a substantial precedent that
ranking systems have a responsibility not only to their users but also to the
items being ranked. To address these often conflicting responsibilities, we
propose a conceptual and computational framework that allows the formulation of
fairness constraints on rankings in terms of exposure allocation. As part of
this framework, we develop efficient algorithms for finding rankings that
maximize the utility for the user while provably satisfying a specifiable
notion of fairness. Since fairness goals can be application specific, we show
how a broad range of fairness constraints can be implemented using our
framework, including forms of demographic parity, disparate treatment, and
disparate impact constraints. We illustrate the effect of these constraints by
providing empirical results on two ranking problems.Comment: In Proceedings of the 24th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, London, UK, 201
Query Chains: Learning to Rank from Implicit Feedback
This paper presents a novel approach for using clickthrough data to learn
ranked retrieval functions for web search results. We observe that users
searching the web often perform a sequence, or chain, of queries with a similar
information need. Using query chains, we generate new types of preference
judgments from search engine logs, thus taking advantage of user intelligence
in reformulating queries. To validate our method we perform a controlled user
study comparing generated preference judgments to explicit relevance judgments.
We also implemented a real-world search engine to test our approach, using a
modified ranking SVM to learn an improved ranking function from preference
data. Our results demonstrate significant improvements in the ranking given by
the search engine. The learned rankings outperform both a static ranking
function, as well as one trained without considering query chains.Comment: 10 page
Methods for Ordinal Peer Grading
MOOCs have the potential to revolutionize higher education with their wide
outreach and accessibility, but they require instructors to come up with
scalable alternates to traditional student evaluation. Peer grading -- having
students assess each other -- is a promising approach to tackling the problem
of evaluation at scale, since the number of "graders" naturally scales with the
number of students. However, students are not trained in grading, which means
that one cannot expect the same level of grading skills as in traditional
settings. Drawing on broad evidence that ordinal feedback is easier to provide
and more reliable than cardinal feedback, it is therefore desirable to allow
peer graders to make ordinal statements (e.g. "project X is better than project
Y") and not require them to make cardinal statements (e.g. "project X is a
B-"). Thus, in this paper we study the problem of automatically inferring
student grades from ordinal peer feedback, as opposed to existing methods that
require cardinal peer feedback. We formulate the ordinal peer grading problem
as a type of rank aggregation problem, and explore several probabilistic models
under which to estimate student grades and grader reliability. We study the
applicability of these methods using peer grading data collected from a real
class -- with instructor and TA grades as a baseline -- and demonstrate the
efficacy of ordinal feedback techniques in comparison to existing cardinal peer
grading methods. Finally, we compare these peer-grading techniques to
traditional evaluation techniques.Comment: Submitted to KDD 201
Making large scale SVM learning practical
Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large learning tasks with many training examples, off-the-shelf optimization techniques for general quadratic programs quickly become intractable in their memory and time requirements. SVMLight is an implementation of an SVM learner which addresses the problem of large tasks. This chapter presents algorithmic and computational results developed for SVMlight V2.0, which make large-scale SVM training more practical. The results give guidelines for the application of SVMs to large domains. Also published in: 'Advances in Kernel Methods - Support Vector Learning', Bernhard Schölkopf, Christopher J. C. Burges, and Alexander J. Smola (eds.), MIT Press, Cambridge, USA, 1998. The paper is written in English
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