Non-Parametric Calibration of Probabilistic Regression

The task of calibration is to retrospectively adjust the outputs from a machine learning model to provide better probability estimates on the target variable. While calibration has been investigated thoroughly in classification, it has not yet been well-established for regression tasks. This paper considers the problem of calibrating a probabilistic regression model to improve the estimated probability densities over the real-valued targets. We propose to calibrate a regression model through the cumulative probability density, which can be derived from calibrating a multi-class classifier. We provide three non-parametric approaches to solve the problem, two of which provide empirical estimates and the third providing smooth density estimates. The proposed approaches are experimentally evaluated to show their ability to improve the performance of regression models on the predictive likelihood

Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation

In complicated, interacting auctions, a fundamental problem is the prediction of prices of goods in the auctions, and more broadly, the modeling of uncertainty regarding these prices. In this paper, we present a machine-learning approach to this problem. The technique is based on a new and general boosting-based algorithm for conditional density estimation problems of this kind

Techniques in Ordinal Classification and Image-to-Image Translation

Dans cette thèse, nous explorons deux thèmes de recherche dans le domaine de l’apprentissage en profondeur et de l’imagerie médicale. La première est dans la classification ordinale, dans laquelle les classes à prévoir sont discrètes mais ont une relation d’ordonnancement. Les distributions de probabilités sous les classes ordinales peuvent posséder des propriétés indésirables, comme la non-unimodalité. Nous proposons une technique simple pour contraindre les distributions de probabilités ordinales discrètes à être unimodales par l’utilisation des distributions de Poisson et des distributions de probabilités binomiales. Nous évaluons cette approche sur la base d’une estimation de l’âge et d’un ensemble de données Kaggle sur la rétinopathie diabétique et obtenons des résultats compétitifs. Nous supposons que la contrainte d’unimodalité – en plus de rendre les distributions de probabilité plus interprétables – agit comme un régularisateur qui peut atténuer le dépassement, surtout dans un régime de données faible. Dans le second thème, nous explorons la traduction d’image à image contradictoire et motivons leur utilité dans le cadre d’un apprentissage semi-supervisé. Nous évaluons une méthode existante et en proposons une nouvelle que nous évaluons sur plusieurs bases de données comme celles utilisées dans notre travail sur la classification ordinale. Dans ce dernier cas, nous voulons établir une correspondance entre le domaine des scanners de patients symptomatiques et celui des scanners de patients non symptomatiques. Cela forme effectivement un modèle qui peut démêler les facteurs de variation sous-jacents et apprendre à détecter et à supprimer les zones symptomatiques de l’image, ce qui pourrait être exploité de plusieurs façons, comme aider un réseau qui s’appuie sur des étiquettes riches, ou générer des exemples synthétiques. Nous présentons des résultats qualitatifs intéressants et motivons plusieurs pistes prometteuses pour l’avenir.----------ABSTRACT: In this thesis we explore two research topics within the realm of deep learning and medical imaging. The first is in ordinal classification, in which the classes to be predicted are discrete but have an ordering relation. Probability distributions under ordinal classes can possess undesired properties, such as non-unimodality. We propose a straightforward technique to constrain discrete ordinal probability distributions to be unimodal via the use of the Poisson and binomial probability distributions. We evaluate this approach on an age estimation and Kaggle diabetic retinopathy dataset and obtain competitive results. We conjecture that the unimodality constraint – in addition to making the probability distributions more interpretable – acts as a regulariser which can mitigate overfitting, especially in a low data regime. In the second topic, we explore adversarial image-to-image translation and motivate their utility within the framework of semi-supervised learning. We evaluate an existing method and propose a new one which we evaluate on several datasets such as the ones employed in our work on ordinal classification. In the case of the latter, we want to map from the domain of symptomatic patient scans to non-symptomatic patient scans. This effectively trains a model which can disentangle the underlying factors of variation and learn to detect and remove symptomatic regions in the image, which could be leveraged in several ways, such as aiding a network which relies on rich labels, or generating synthetic examples. We present some interesting qualitative results and motivate several promising avenues to take for the future

Probabilistic reframing for cost-sensitive regression

© ACM, 2014. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Knowledge Discovery from Data (TKDD), VOL. 8, ISS. 4, (October 2014) http://doi.acm.org/10.1145/2641758Common-day applications of predictive models usually involve the full use of the available contextual information. When the operating context changes, one may fine-tune the by-default (incontextual) prediction or may even abstain from predicting a value (a reject). Global reframing solutions, where the same function is applied to adapt the estimated outputs to a new cost context, are possible solutions here. An alternative approach, which has not been studied in a comprehensive way for regression in the knowledge discovery and data mining literature, is the use of a local (e.g., probabilistic) reframing approach, where decisions are made according to the estimated output and a reliability, confidence, or probability estimation. In this article, we advocate for a simple two-parameter (mean and variance) approach, working with a normal conditional probability density. Given the conditional mean produced by any regression technique, we develop lightweight “enrichment” methods that produce good estimates of the conditional variance, which are used by the probabilistic (local) reframing methods. We apply these methods to some very common families of costsensitive problems, such as optimal predictions in (auction) bids, asymmetric loss scenarios, and rejection rules.This work was supported by the MEC/MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, and TIN 2013-45732-C4-1-P and GVA projects PROMETEO/2008/051 and PROMETEO2011/052. Finally, part of this work was motivated by the REFRAME project (http://www.reframe-d2k.org) granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA) and funded by Ministerio de Economia y Competitividad in Spain (PCIN-2013-037).Hernández Orallo, J. (2014). Probabilistic reframing for cost-sensitive regression. ACM Transactions on Knowledge Discovery from Data. 8(4):1-55. https://doi.org/10.1145/2641758S15584G. Bansal, A. Sinha, and H. Zhao. 2008. 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