74,825 research outputs found
COCO: Performance Assessment
We present an any-time performance assessment for benchmarking numerical
optimization algorithms in a black-box scenario, applied within the COCO
benchmarking platform. The performance assessment is based on runtimes measured
in number of objective function evaluations to reach one or several quality
indicator target values. We argue that runtime is the only available measure
with a generic, meaningful, and quantitative interpretation. We discuss the
choice of the target values, runlength-based targets, and the aggregation of
results by using simulated restarts, averages, and empirical distribution
functions
Approximation of empowerment in the continuous domain
The empowerment formalism offers a goal-independent utility function fully derived from an agent's embodiment. It produces intrinsic motivations which can be used to generate self-organizing behaviours in agents. One obstacle to the application of empowerment in more demanding (esp. continuous) domains is that previous ways of calculating empowerment have been very time consuming and only provided a proof-of-concept. In this paper we present a new approach to efficiently approximate empowerment as a parallel, linear, Gaussian channel capacity problem. We use pendulum balancing to demonstrate this new method, and compare it to earlier approximation methods.Peer reviewe
Learning with Clustering Structure
We study supervised learning problems using clustering constraints to impose
structure on either features or samples, seeking to help both prediction and
interpretation. The problem of clustering features arises naturally in text
classification for instance, to reduce dimensionality by grouping words
together and identify synonyms. The sample clustering problem on the other
hand, applies to multiclass problems where we are allowed to make multiple
predictions and the performance of the best answer is recorded. We derive a
unified optimization formulation highlighting the common structure of these
problems and produce algorithms whose core iteration complexity amounts to a
k-means clustering step, which can be approximated efficiently. We extend these
results to combine sparsity and clustering constraints, and develop a new
projection algorithm on the set of clustered sparse vectors. We prove
convergence of our algorithms on random instances, based on a union of
subspaces interpretation of the clustering structure. Finally, we test the
robustness of our methods on artificial data sets as well as real data
extracted from movie reviews.Comment: Completely rewritten. New convergence proofs in the clustered and
sparse clustered case. New projection algorithm on sparse clustered vector
For whom will the Bayesian agents vote?
Within an agent-based model where moral classifications are socially learned,
we ask if a population of agents behaves in a way that may be compared with
conservative or liberal positions in the real political spectrum. We assume
that agents first experience a formative period, in which they adjust their
learning style acting as supervised Bayesian adaptive learners. The formative
phase is followed by a period of social influence by reinforcement learning. By
comparing data generated by the agents with data from a sample of 15000 Moral
Foundation questionnaires we found the following. 1. The number of information
exchanges in the formative phase correlates positively with statistics
identifying liberals in the social influence phase. This is consistent with
recent evidence that connects the dopamine receptor D4-7R gene, political
orientation and early age social clique size. 2. The learning algorithms that
result from the formative phase vary in the way they treat novelty and
corroborative information with more conservative-like agents treating it more
equally than liberal-like agents. This is consistent with the correlation
between political affiliation and the Openness personality trait reported in
the literature. 3. Under the increase of a model parameter interpreted as an
external pressure, the statistics of liberal agents resemble more those of
conservative agents, consistent with reports on the consequences of external
threats on measures of conservatism. We also show that in the social influence
phase liberal-like agents readapt much faster than conservative-like agents
when subjected to changes on the relevant set of moral issues. This suggests a
verifiable dynamical criterium for attaching liberal or conservative labels to
groups.Comment: 31 pages, 5 figure
High-Multiplicity Fair Allocation Using Parametric Integer Linear Programming
Using insights from parametric integer linear programming, we significantly
improve on our previous work [Proc. ACM EC 2019] on high-multiplicity fair
allocation. Therein, answering an open question from previous work, we proved
that the problem of finding envy-free Pareto-efficient allocations of
indivisible items is fixed-parameter tractable with respect to the combined
parameter "number of agents" plus "number of item types." Our central
improvement, compared to this result, is to break the condition that the
corresponding utility and multiplicity values have to be encoded in unary
required there. Concretely, we show that, while preserving fixed-parameter
tractability, these values can be encoded in binary, thus greatly expanding the
range of feasible values.Comment: 15 pages; Published in the Proceedings of ECAI-202
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