40,549 research outputs found
{iFair}: {L}earning 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: ensuring that each ethnic or social group receives its fair share in the outcome of classifiers and rankings. In contrast, the alternative paradigm of individual fairness has received relatively little attention. This paper introduces a method for probabilistically clustering user records into a low-rank representation that captures individual fairness yet also achieves high accuracy in classification and regression models. 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. Since the case for fairness is ubiquitous across many tasks, we aim to learn general representations that can be applied to arbitrary downstream use-cases. We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on two real-world datasets. Our experiments show substantial improvements over the best prior work for this setting
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
Dynamic Control of Explore/Exploit Trade-Off In Bayesian Optimization
Bayesian optimization offers the possibility of optimizing black-box
operations not accessible through traditional techniques. The success of
Bayesian optimization methods such as Expected Improvement (EI) are
significantly affected by the degree of trade-off between exploration and
exploitation. Too much exploration can lead to inefficient optimization
protocols, whilst too much exploitation leaves the protocol open to strong
initial biases, and a high chance of getting stuck in a local minimum.
Typically, a constant margin is used to control this trade-off, which results
in yet another hyper-parameter to be optimized. We propose contextual
improvement as a simple, yet effective heuristic to counter this - achieving a
one-shot optimization strategy. Our proposed heuristic can be swiftly
calculated and improves both the speed and robustness of discovery of optimal
solutions. We demonstrate its effectiveness on both synthetic and real world
problems and explore the unaccounted for uncertainty in the pre-determination
of search hyperparameters controlling explore-exploit trade-off.Comment: Accepted for publication in the proceedings of 2018 Computing
Conferenc
Gaussian Processes with Context-Supported Priors for Active Object Localization
We devise an algorithm using a Bayesian optimization framework in conjunction
with contextual visual data for the efficient localization of objects in still
images. Recent research has demonstrated substantial progress in object
localization and related tasks for computer vision. However, many current
state-of-the-art object localization procedures still suffer from inaccuracy
and inefficiency, in addition to failing to provide a principled and
interpretable system amenable to high-level vision tasks. We address these
issues with the current research.
Our method encompasses an active search procedure that uses contextual data
to generate initial bounding-box proposals for a target object. We train a
convolutional neural network to approximate an offset distance from the target
object. Next, we use a Gaussian Process to model this offset response signal
over the search space of the target. We then employ a Bayesian active search
for accurate localization of the target.
In experiments, we compare our approach to a state-of-theart bounding-box
regression method for a challenging pedestrian localization task. Our method
exhibits a substantial improvement over this baseline regression method.Comment: 10 pages, 4 figure
Supervised Learning with Similarity Functions
We address the problem of general supervised learning when data can only be
accessed through an (indefinite) similarity function between data points.
Existing work on learning with indefinite kernels has concentrated solely on
binary/multi-class classification problems. We propose a model that is generic
enough to handle any supervised learning task and also subsumes the model
previously proposed for classification. We give a "goodness" criterion for
similarity functions w.r.t. a given supervised learning task and then adapt a
well-known landmarking technique to provide efficient algorithms for supervised
learning using "good" similarity functions. We demonstrate the effectiveness of
our model on three important super-vised learning problems: a) real-valued
regression, b) ordinal regression and c) ranking where we show that our method
guarantees bounded generalization error. Furthermore, for the case of
real-valued regression, we give a natural goodness definition that, when used
in conjunction with a recent result in sparse vector recovery, guarantees a
sparse predictor with bounded generalization error. Finally, we report results
of our learning algorithms on regression and ordinal regression tasks using
non-PSD similarity functions and demonstrate the effectiveness of our
algorithms, especially that of the sparse landmark selection algorithm that
achieves significantly higher accuracies than the baseline methods while
offering reduced computational costs.Comment: To appear in the proceedings of NIPS 2012, 30 page
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
We present a tutorial on Bayesian optimization, a method of finding the
maximum of expensive cost functions. Bayesian optimization employs the Bayesian
technique of setting a prior over the objective function and combining it with
evidence to get a posterior function. This permits a utility-based selection of
the next observation to make on the objective function, which must take into
account both exploration (sampling from areas of high uncertainty) and
exploitation (sampling areas likely to offer improvement over the current best
observation). We also present two detailed extensions of Bayesian optimization,
with experiments---active user modelling with preferences, and hierarchical
reinforcement learning---and a discussion of the pros and cons of Bayesian
optimization based on our experiences
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