arXiv research

This paper explores how to choose scoring rules for estimating properties with parametric assumptions.

This study measures price risk aversion using indirect utility functions in a lab experiment.

New method simplifies checking consistency of differentiable loss functions.

This paper develops a new method for eliciting more flexible metrics, improving fairness and applicability.

Constructs new elicitable risk measures with multiplicative scoring functions.

A property, or statistical functional, is said to be elicitable if it minimizes expected loss for some loss function. The study of which properties are elicitable sheds light on the capabilities and limitations of point estimation and empirical risk minimization. While recent work asks which properties are elicitable, …

Proposes a method to select fair performance metrics through metric elicitation.

We discuss equivalent axiomatic characterizations of distortion risk measures, and give a novel and concise proof of the characterization of elicitable distortion risk measures. Elicitability has recently been discussed as a desirable criterion for risk measures, motivated by statistical considerations of forecasting. …

Robustifies elicitable functionals to handle small distribution misspecifications.

Study generalizes property elicitation to imprecise probabilities.

A statistical functional, such as the mean or the median, is called elicitable if there is a scoring function or loss function such that the correct forecast of the functional is the unique minimizer of the expected score. Such scoring functions are called strictly consistent for the functional. The elicitability of a …

Study creates web interface to elicit user-preferred metrics.

It is important to collect credible training samples $(x,y)$ for building data-intensive learning systems (e.g., a deep learning system). Asking people to report complex distribution $p(x)$, though theoretically viable, is challenging in practice. This is primarily due to the cognitive loads required for human agents t…

A method for eliciting expert beliefs using preferential questions and normalizing flows.

Given a binary prediction problem, which performance metric should the classifier optimize? We address this question by formalizing the problem of Metric Elicitation. The goal of metric elicitation is to discover the performance metric of a practitioner, which reflects her innate rewards (costs) for correct (incorrect)…

The paper analyzes elicitability of return risk measures and their scoring functions.

Proposes method for eliciting non-parametric joint priors using normalizing flows.

This thesis formalizes metric selection for machine learning applications.

The risk of a financial position is usually summarized by a risk measure. As this risk measure has to be estimated from historical data, it is important to be able to verify and compare competing estimation procedures. In statistical decision theory, risk measures for which such verification and comparison is possible,…

Method combines deep learning and elicitability for solving complex stochastic equations.

Develops a simulation-based method to translate expert knowledge into prior distributions for Bayesian models.

Study stability of trading strategy under market perturbations.

Paper establishes identifiability and elicitability of tail risk measures.

In this note, we comment on the relevance of elicitability for backtesting risk measure estimates. In particular, we propose the use of Diebold-Mariano tests, and show how they can be implemented for Expected Shortfall (ES), based on the recent result of Fissler and Ziegel (2015) that ES is jointly elicitable with Valu…

We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more complex and nuanced and thus require elicitation from individual or collective stakeholders. We introduce a framework in which pairs of individ…

We formalize and study the natural approach of designing convex surrogate loss functions via embeddings, for problems such as classification, ranking, or structured prediction. In this approach, one embeds each of the finitely many predictions (e.g.\ rankings) as a point in $\mathbb{R}^d$, assigns the original loss val…

New algorithm achieves faster multicalibration in online settings.

Permutation-valued features arise in a variety of applications, either in a direct way when preferences are elicited over a collection of items, or an indirect way in which numerical ratings are converted to a ranking. To date, there has been relatively limited study of regression, classification, and testing problems …

Framework uses IRL and RL to elicit and optimize risk preferences robustly to noise.

New method allows backtesting of systemic risk forecasts.

We propose a cost-effective framework for preference elicitation and aggregation under the Plackett-Luce model with features. Given a budget, our framework iteratively computes the most cost-effective elicitation questions in order to help the agents make a better group decision. We illustrate the viability of the fram…

Paper tackles RUL prediction with scarce data using indirect supervision.

Formulates a Dueling Bandits problem for eliciting Kemeny rankings.

Machine learning models trained on indirect data labels can fail on real-world examples.

Unified framework for learning with indirect supervision signals.

Paper proposes incentives for federated learning to ensure truthful contributions.

A framework for eliciting utility functions from investor preferences.

Learning predictive models from small high-dimensional data sets is a key problem in high-dimensional statistics. Expert knowledge elicitation can help, and a strong line of work focuses on directly eliciting informative prior distributions for parameters. This either requires considerable statistical expertise or is l…

PLRM synthesizes labels from mismatched sources for better training sets.

We address the problem of gauging the influence exerted by a given country on the global trade market from the viewpoint of complex networks. In particular, we apply the PWP method for computing indirect influences on the world trade network.

Reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks. In this paper, we classify RL into direct and indirect RL according to how they seek the optimal policy of the Markov decision process problem. The former solves the optimal poli…

Requirements elicitation can be very challenging in projects that require deep domain knowledge about the system at hand. As analysts have the full control over the elicitation process, their lack of knowledge about the system under study inhibits them from asking related questions and reduces the accuracy of requireme…

Providing accurate predictions is challenging for machine learning algorithms when the number of features is larger than the number of samples in the data. Prior knowledge can improve machine learning models by indicating relevant variables and parameter values. Yet, this prior knowledge is often tacit and only availab…

Study shows cooperation can improve everyone's market efficiency.

Study uses property elicitation to understand how fairness regularizers affect optimal decisions.

PPT optimizes transformer behavior by steering its latent posterior using prior samples.

Unified framework for estimating indirect effects in observational studies with unmeasured confounding.

In this paper we propose an approach to preference elicitation that is suitable to large configuration spaces beyond the reach of existing state-of-the-art approaches. Our setwise max-margin method can be viewed as a generalization of max-margin learning to sets, and can produce a set of "diverse" items that can be use…