# Minimax Learning for Remote Prediction

@article{Li2018MinimaxLF, title={Minimax Learning for Remote Prediction}, author={Cheuk Ting Li and Xiugang Wu and Ayfer {\"O}zg{\"u}r and Abbas El Gamal}, journal={2018 IEEE International Symposium on Information Theory (ISIT)}, year={2018}, pages={541-545} }

The classical problem of supervised learning is to infer an accurate predictor of a target variable $Y$ from a measured variable $X$ by using a finite number of labeled training samples. Motivated by the increasingly distributed nature of data and decision making, in this paper we consider a variation of this classical problem in which the prediction is performed remotely based on a rate-constrained description $M$ of X. Upon receiving M, the remote node computes an estimate Y of Y. We follow… Expand

#### 6 Citations

Minimax Learning for Distributed Inference

- Computer Science
- IEEE Transactions on Information Theory
- 2020

The recent minimax learning approach is followed to study this inference problem and it is shown that it corresponds to a one-shot minimax noisy lossy source coding problem, leading to a general method for designing a near-optimal descriptor-estimator pair. Expand

Vector Gaussian CEO Problem Under Logarithmic Loss and Applications

- Mathematics, Computer Science
- IEEE Transactions on Information Theory
- 2020

This paper finds an explicit characterization of the rate-distortion region of the vector Gaussian CEO problem under logarithmic loss distortion measure and develops Blahut-Arimoto type algorithms that allow to compute numerically the regions provided in this paper, for both discrete and Gaussian models. Expand

A Unified Framework for One-Shot Achievability via the Poisson Matching Lemma

- Computer Science, Mathematics
- IEEE Transactions on Information Theory
- 2021

This paper studies fixed-length settings, and is not limited to source coding, showing that the Poisson functional representation is a viable alternative to typicality for most problems in network information theory. Expand

A Unified Framework for One-shot Achievability via the Poisson Matching Lemma

- Computer Science
- 2019 IEEE International Symposium on Information Theory (ISIT)
- 2019

We introduce the Poisson matching lemma and apply it to prove one-shot achievability results for channels with state information at the encoder, lossy source coding with side information at the… Expand

An information-theoretic approach to distributed learning : distributed source coding under logarithmic loss

- Philosophy
- 2019

Une question de fond, souvent discutee dans l’apprentissage de la theorie, est de savoir comment choisir une fonction de `bonne' perte qui mesure la fidelite de la reconstruction a l’originale. La… Expand

Efficient Approximate Minimum Entropy Coupling of Multiple Probability Distributions

- Computer Science, Mathematics
- IEEE Transactions on Information Theory
- 2021

An efficient algorithm is presented for computing a coupling with entropy within 2 bits from the entropy of the greatest lower bound of <inline-formula> <tex-math notation="LaTeX">$p_{1},\ldots,p_{m}$ </tex- Math> with respect to majorization. Expand

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