6,842 research outputs found
Bandit Online Optimization Over the Permutahedron
The permutahedron is the convex polytope with vertex set consisting of the
vectors for all permutations (bijections) over
. We study a bandit game in which, at each step , an
adversary chooses a hidden weight weight vector , a player chooses a
vertex of the permutahedron and suffers an observed loss of
.
A previous algorithm CombBand of Cesa-Bianchi et al (2009) guarantees a
regret of for a time horizon of . Unfortunately,
CombBand requires at each step an -by- matrix permanent approximation to
within improved accuracy as grows, resulting in a total running time that
is super linear in , making it impractical for large time horizons.
We provide an algorithm of regret with total time
complexity . The ideas are a combination of CombBand and a recent
algorithm by Ailon (2013) for online optimization over the permutahedron in the
full information setting. The technical core is a bound on the variance of the
Plackett-Luce noisy sorting process's "pseudo loss". The bound is obtained by
establishing positive semi-definiteness of a family of 3-by-3 matrices
generated from rational functions of exponentials of 3 parameters
Online Optimization Methods for the Quantification Problem
The estimation of class prevalence, i.e., the fraction of a population that
belongs to a certain class, is a very useful tool in data analytics and
learning, and finds applications in many domains such as sentiment analysis,
epidemiology, etc. For example, in sentiment analysis, the objective is often
not to estimate whether a specific text conveys a positive or a negative
sentiment, but rather estimate the overall distribution of positive and
negative sentiments during an event window. A popular way of performing the
above task, often dubbed quantification, is to use supervised learning to train
a prevalence estimator from labeled data.
Contemporary literature cites several performance measures used to measure
the success of such prevalence estimators. In this paper we propose the first
online stochastic algorithms for directly optimizing these
quantification-specific performance measures. We also provide algorithms that
optimize hybrid performance measures that seek to balance quantification and
classification performance. Our algorithms present a significant advancement in
the theory of multivariate optimization and we show, by a rigorous theoretical
analysis, that they exhibit optimal convergence. We also report extensive
experiments on benchmark and real data sets which demonstrate that our methods
significantly outperform existing optimization techniques used for these
performance measures.Comment: 26 pages, 6 figures. A short version of this manuscript will appear
in the proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery
and Data Mining, KDD 201
Online Optimization with Memory and Competitive Control
This paper presents competitive algorithms for a novel class of online optimization problems with memory. We consider a setting where the learner seeks to minimize the sum of a hitting cost and a switching cost that depends on the previous p decisions. This setting generalizes Smoothed Online Convex Optimization. The proposed approach, Optimistic Regularized Online Balanced Descent, achieves a constant, dimension-free competitive ratio. Further, we show a connection between online optimization with memory and online control with adversarial disturbances. This connection, in turn, leads to a new constant-competitive policy for a rich class of online control problems
Online optimization of storage ring nonlinear beam dynamics
We propose to optimize the nonlinear beam dynamics of existing and future
storage rings with direct online optimization techniques. This approach may
have crucial importance for the implementation of diffraction limited storage
rings. In this paper considerations and algorithms for the online optimization
approach are discussed. We have applied this approach to experimentally improve
the dynamic aperture of the SPEAR3 storage ring with the robust conjugate
direction search method and the particle swarm optimization method. The dynamic
aperture was improved by more than 5 mm within a short period of time.
Experimental setup and results are presented
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