The behaviour of algorithms for very robust regression depends on the distance between the regression data and the outliers. We introduce a parameter λ that defines a parametric path in the space of models and enables us to study, in a systematic way, the properties of estimators as the groups of data move from being far apart to close together. We examine, as a function of λ, the variance and ...

Classi cation is an important problem in data mining. Given an example x and a class c, a classi er usually works by estimating the probability of x being member of c (i.e., membership probability). Well calibrated classi ers are those able to provide accurate estimates of class membership probabilities, that is, the estimated probability p̂(c|x) is close to p(c|p̂(c|x)), which is the true, em...

The construction of martingales with given marginal distributions at given times is a recurrent problem in financial mathematics. From a theoretical point of view, this problem is well-known as necessary and sufficient conditions for the existence of such martingales have been described. Moreover several explicit constructions can even be derived from solutions to the Skorokhod embedding proble...

This paper studies a dynamic agency problem which includes limited liability, moral hazard and adverse selection. The paper develops a robust approach to dynamic contracting based on calibrating the payoffs that would have been delivered by simple benchmark contracts that are attractive but infeasible, due to limited liability constraints. The resulting dynamic contracts are detail-free and sat...

Peer-grading is widely believed to be an inexpensive and scalable way to assess students in large classroom settings. In this paper, we propose calibrated self-grading as a more efficient alternative to peer grading. For self-grading, students assign themselves a grade that they think they deserve via an incentive-compatible mechanism that elicits maximally truthful judgements of performance. W...

We study fairness within the stochastic,multi-armed bandit (MAB) decision making framework. We adapt the fairness framework of “treating similar individuals similarly” [5] to this seŠing. Here, an ‘individual’ corresponds to an arm and two arms are ‘similar’ if they have a similar quality distribution. First, we adopt a smoothness constraint that if two arms have a similar quality distribution ...

The concept of simulating chosen information channels of a linear incoherent imaging system by projecting reference signals is proposed and the system's ability to restore severely distorted but stationary images is experimentally demonstrated. A particular instrument's object field is divided into N discrete 'points' whose individual simulated outputs is measured at N particular sampling point...

Excellent ranking power along with well calibrated probability estimates are needed in many classification tasks. In this paper, we introduce a technique, Calibrated Boosting-Forest1 that captures both. This novel technique is an ensemble of gradient boosting machines that can support both continuous and binary labels. While offering superior ranking power over any individual regression or clas...

Calibration is a common practice in image steganalysis for extracting prominent features. Based on the idea of reembedding, a new set of calibrated features for audio steganalysis applications are proposed. These features are extracted from a model that has maximum deviation from human auditory system and had been specifically designed for audio steganalysis. Ability of the proposed system is t...