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The probability density function (PDF) optimized quantization has been shown to be more efficient than the conventional quantization methods. In practical application, the data with bounded support can be modelled better with bounded support distribution (e.g. beta distribution, Dirichlet distribution) and a better quantization performance could be achieved by a more reasonable modelling. In this...
We propose a nonsmooth bilevel programming method for training linear learning models with hyperparameters optimized via T-fold cross-validation (CV). This algorithm scales well in the sample size. The method handles loss functions with embedded maxima such as in support vector machines. Current practice constructs models over a predefined grid of hyperparameter combinations and selects the best one,...
It is now widely accepted that in many situations where classifiers are deployed, adversaries deliberately manipulate data in order to reduce the classifier's accuracy. The most prominent example is email spam, where spammers routinely modify emails to get past classifier-based spam filters. In this paper we model the interaction between the adversary and the data miner as a two-person sequential...
Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment. Constraints are usually unobservable and frequently change between contexts. In this paper, we present a novel approach for learning (unconstrained) control policies from movement data, where observations are recorded under different constraint settings. Our approach seamlessly...
Clustering of real-world data is often ill-posed. Because of noise and intrinsic ambiguity in data, optimization models attempting to maximize a fitness function can be misled by the assumption of uniqueness of the solution. In this work we present a methodology including classic and novel techniques to approach clustering in a systematic way, with two application examples to biological data sets...
There is a growing realization that uncertain information is a first-class citizen in modern database management. As such, we need techniques to correctly and efficiently process uncertain data in database systems. In particular, data reduction techniques that can produce concise, accurate synopses of large probabilistic relations are crucial. Similar to their deterministic relation counterparts,...
We propose an approach for inferring bounds on the finite-horizon return of a control policy from an off-policy sample of trajectories collecting state transitions, rewards, and control actions. In this paper, the dynamics, control policy, and reward function are supposed to be deterministic and Lipschitz continuous. Under these assumptions, a polynomial algorithm, in terms of the sample size and...
The use of PC cluster systems composed of many PCs is largely spread in these days. It is important to improve system usage keeping proper fair-share policy. This optimization problem is difficult to solve. In scheduler software, there are many parameters and the effectiveness of many parameters is related with each other complicatedly. Therefore it is more difficult to find optimal parameter configuration...
The assessment of a theory is the main objective of scientists. Theories are always introduced by models, and model selection is applied to many various fields of scientific studies in order to corroborate or verify the theory as the winning one among a set of competing hypotheses. Different criteria are taken as bases to select one model among several parallel models in both statistical and visual...
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