19,205 research outputs found
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making
In multi-objective decision planning and learning, much attention is paid to
producing optimal solution sets that contain an optimal policy for every
possible user preference profile. We argue that the step that follows, i.e,
determining which policy to execute by maximising the user's intrinsic utility
function over this (possibly infinite) set, is under-studied. This paper aims
to fill this gap. We build on previous work on Gaussian processes and pairwise
comparisons for preference modelling, extend it to the multi-objective decision
support scenario, and propose new ordered preference elicitation strategies
based on ranking and clustering. Our main contribution is an in-depth
evaluation of these strategies using computer and human-based experiments. We
show that our proposed elicitation strategies outperform the currently used
pairwise methods, and found that users prefer ranking most. Our experiments
further show that utilising monotonicity information in GPs by using a linear
prior mean at the start and virtual comparisons to the nadir and ideal points,
increases performance. We demonstrate our decision support framework in a
real-world study on traffic regulation, conducted with the city of Amsterdam.Comment: AAMAS 2018, Source code at
https://github.com/lmzintgraf/gp_pref_elici
Learning to Approximate a Bregman Divergence
Bregman divergences generalize measures such as the squared Euclidean
distance and the KL divergence, and arise throughout many areas of machine
learning. In this paper, we focus on the problem of approximating an arbitrary
Bregman divergence from supervision, and we provide a well-principled approach
to analyzing such approximations. We develop a formulation and algorithm for
learning arbitrary Bregman divergences based on approximating their underlying
convex generating function via a piecewise linear function. We provide
theoretical approximation bounds using our parameterization and show that the
generalization error for metric learning using our framework
matches the known generalization error in the strictly less general Mahalanobis
metric learning setting. We further demonstrate empirically that our method
performs well in comparison to existing metric learning methods, particularly
for clustering and ranking problems.Comment: 19 pages, 4 figure
Unbiased Comparative Evaluation of Ranking Functions
Eliciting relevance judgments for ranking evaluation is labor-intensive and
costly, motivating careful selection of which documents to judge. Unlike
traditional approaches that make this selection deterministically,
probabilistic sampling has shown intriguing promise since it enables the design
of estimators that are provably unbiased even when reusing data with missing
judgments. In this paper, we first unify and extend these sampling approaches
by viewing the evaluation problem as a Monte Carlo estimation task that applies
to a large number of common IR metrics. Drawing on the theoretical clarity that
this view offers, we tackle three practical evaluation scenarios: comparing two
systems, comparing systems against a baseline, and ranking systems. For
each scenario, we derive an estimator and a variance-optimizing sampling
distribution while retaining the strengths of sampling-based evaluation,
including unbiasedness, reusability despite missing data, and ease of use in
practice. In addition to the theoretical contribution, we empirically evaluate
our methods against previously used sampling heuristics and find that they
generally cut the number of required relevance judgments at least in half.Comment: Under review; 10 page
On the geometric mean method for incomplete pairwise comparisons
When creating the ranking based on the pairwise comparisons very often, we
face difficulties in completing all the results of direct comparisons. In this
case, the solution is to use the ranking method based on the incomplete PC
matrix. The article presents the extension of the well known geometric mean
method for incomplete PC matrices. The description of the methods is
accompanied by theoretical considerations showing the existence of the solution
and the optimality of the proposed approach.Comment: 15 page
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