35,673 research outputs found
Probabilistic n-Choose-k Models for Classification and Ranking
In categorical data there is often structure in the number of variables that take on each label. For example, the total number of objects in an image and the number of highly relevant documents per query in web search both tend to follow a structured distribution. In this paper, we study a probabilistic model that explicitly includes a prior distribution over such counts, along with a count-conditional likelihood that defines probabilities over all subsets of a given size. When labels are binary and the prior over counts is a Poisson-Binomial distribution, a standard logistic regression model is recovered, but for other count distributions, such priors induce global dependencies and combinatorics that appear to complicate learning and inference. However, we demonstrate that simple, efficient learning procedures can be derived for more general forms of this model. We illustrate the utility of the formulation by exploring applications to multi-object classification, learning to rank, and top-K classification.Engineering and Applied Science
Probabilistic Models over Ordered Partitions with Application in Learning to Rank
This paper addresses the general problem of modelling and learning rank data
with ties. We propose a probabilistic generative model, that models the process
as permutations over partitions. This results in super-exponential
combinatorial state space with unknown numbers of partitions and unknown
ordering among them. We approach the problem from the discrete choice theory,
where subsets are chosen in a stagewise manner, reducing the state space per
each stage significantly. Further, we show that with suitable parameterisation,
we can still learn the models in linear time. We evaluate the proposed models
on the problem of learning to rank with the data from the recently held Yahoo!
challenge, and demonstrate that the models are competitive against well-known
rivals.Comment: 19 pages, 2 figure
iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making
People are rated and ranked, towards algorithmic decision making in an
increasing number of applications, typically based on machine learning.
Research on how to incorporate fairness into such tasks has prevalently pursued
the paradigm of group fairness: giving adequate success rates to specifically
protected groups. In contrast, the alternative paradigm of individual fairness
has received relatively little attention, and this paper advances this less
explored direction. The paper introduces a method for probabilistically mapping
user records into a low-rank representation that reconciles individual fairness
and the utility of classifiers and rankings in downstream applications. Our
notion of individual fairness requires that users who are similar in all
task-relevant attributes such as job qualification, and disregarding all
potentially discriminating attributes such as gender, should have similar
outcomes. We demonstrate the versatility of our method by applying it to
classification and learning-to-rank tasks on a variety of real-world datasets.
Our experiments show substantial improvements over the best prior work for this
setting.Comment: Accepted at ICDE 2019. Please cite the ICDE 2019 proceedings versio
Preference Networks: Probabilistic Models for Recommendation Systems
Recommender systems are important to help users select relevant and
personalised information over massive amounts of data available. We propose an
unified framework called Preference Network (PN) that jointly models various
types of domain knowledge for the task of recommendation. The PN is a
probabilistic model that systematically combines both content-based filtering
and collaborative filtering into a single conditional Markov random field. Once
estimated, it serves as a probabilistic database that supports various useful
queries such as rating prediction and top- recommendation. To handle the
challenging problem of learning large networks of users and items, we employ a
simple but effective pseudo-likelihood with regularisation. Experiments on the
movie rating data demonstrate the merits of the PN.Comment: In Proc. of 6th Australasian Data Mining Conference (AusDM), Gold
Coast, Australia, pages 195--202, 200
Threshold Choice Methods: the Missing Link
Many performance metrics have been introduced for the evaluation of
classification performance, with different origins and niches of application:
accuracy, macro-accuracy, area under the ROC curve, the ROC convex hull, the
absolute error, and the Brier score (with its decomposition into refinement and
calibration). One way of understanding the relation among some of these metrics
is the use of variable operating conditions (either in the form of
misclassification costs or class proportions). Thus, a metric may correspond to
some expected loss over a range of operating conditions. One dimension for the
analysis has been precisely the distribution we take for this range of
operating conditions, leading to some important connections in the area of
proper scoring rules. However, we show that there is another dimension which
has not received attention in the analysis of performance metrics. This new
dimension is given by the decision rule, which is typically implemented as a
threshold choice method when using scoring models. In this paper, we explore
many old and new threshold choice methods: fixed, score-uniform, score-driven,
rate-driven and optimal, among others. By calculating the loss of these methods
for a uniform range of operating conditions we get the 0-1 loss, the absolute
error, the Brier score (mean squared error), the AUC and the refinement loss
respectively. This provides a comprehensive view of performance metrics as well
as a systematic approach to loss minimisation, namely: take a model, apply
several threshold choice methods consistent with the information which is (and
will be) available about the operating condition, and compare their expected
losses. In order to assist in this procedure we also derive several connections
between the aforementioned performance metrics, and we highlight the role of
calibration in choosing the threshold choice method
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