Abstract
Christopher Dance, Tomi Silander |
Journal of Machine Learning Research (JMLR), 20(35), pp. 1−93 |
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@article{JMLR:v20:17-185, author = {Christopher R. Dance and Tomi Silander}, title = {Optimal Policies for Observing Time Series and Related Restless Bandit Problems}, journal = {Journal of Machine Learning Research}, year = {2019}, volume = {20}, number = {35}, pages = {1-93}, url = {http://jmlr.org/papers/v20/17-185.html} }
Abstract
The trade-off between the cost of acquiring and processing data, and uncertainty due to a lack of data is fundamental in machine learning. A basic instance of this trade-off is the problem of deciding when to make noisy and costly observations of a discrete-time Gaussian random walk, so as to minimise the posterior variance plus observation costs. We present the first proof that a simple policy, which observes when the posterior variance exceeds a threshold, is optimal for this problem. The proof generalises to a wide range of cost functions other than the posterior variance. It is based on a new verification theorem by Nino-Mora that guarantees threshold structure for Markov decision processes, and on the relation between binary sequences known as Christoffel words and the dynamics of discontinuous nonlinear maps, which frequently arise in physics, control and biology. This result implies that optimal policies for linear-quadratic-Gaussian control with costly observations have a threshold structure. It also implies that the restless bandit problem of observing multiple such time series, has a well-defined Whittle index policy. We discuss computation of that index, give closed-form formulae for it, and compare the performance of the associated index policy with heuristic policies.
1. Difference in female/male salary: 33/40 points
2. Difference in salary increases female/male: 35/35 points
3. Salary increases upon return from maternity leave: uncalculable
4. Number of employees in under-represented gender in 10 highest salaries: 0/10 points
NAVER France targets (with respect to the 2022 index) are as follows:
En 2022, NAVER France a obtenu les notes suivantes pour chacun des indicateurs :
1. Les écarts de salaire entre les femmes et les hommes: 33 sur 40 points
2. Les écarts des augmentations individuelles entre les femmes et les hommes : 35 sur 35 points
3. Toutes les salariées augmentées revenant de congé maternité : non calculable
4. Le nombre de salarié du sexe sous-représenté parmi les 10 plus hautes rémunérations : 0 sur 10 points
Les objectifs de progression pour l’index 2022 de NAVER France sont :
NAVER LABS Europe 6-8 chemin de Maupertuis 38240 Meylan France Contact
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