arXiv research

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 …

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

Robustifies elicitable functionals to handle small distribution misspecifications.

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

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

Generative Adversarial Regression (GAR) learns risk scenarios robustly across policies.

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

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

Constructs new elicitable risk measures with multiplicative scoring functions.

Conditional forecasts improve performative prediction accuracy.

New algorithm achieves faster multicalibration in online settings.

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. …

Proposes a new framework for risk-sensitive RL using deep nets.

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…

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

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

This note corrects conditions in Proposition 3.4 and Theorem 5.2(ii) and comments on imprecisions in Propositions 4.2 and 4.4 in Fissler and Ziegel (2016).

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)…

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,…

Recently, financial industry and regulators have enhanced the debate on the good properties of a risk measure. A fundamental issue is the evaluation of the quality of a risk estimation. On the one hand, a backtesting procedure is desirable for assessing the accuracy of such an estimation and this can be naturally achie…

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

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…

New method simplifies checking consistency of differentiable loss functions.

CAESar improves risk forecasting by combining VaR and ES estimates.

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…

Formulates a Dueling Bandits problem for eliciting Kemeny rankings.

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

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…

New model for recovering unverifiable signals from observers in decentralized networks.

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…

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…

Crowdsourced wisdom improves causal learning.

The concept of refinement from probability elicitation is considered for proper scoring rules. Taking directions from the axioms of probability, refinement is further clarified using a Hilbert space interpretation and reformulated into the underlying data distribution setting where connections to maximal marginal diver…

Platform uses queries to elicit investor preferences for portfolio trades, improving allocation efficiency.

Requirements elicitation requires extensive knowledge and deep understanding of the problem domain where the final system will be situated. However, in many software development projects, analysts are required to elicit the requirements from an unfamiliar domain, which often causes communication barriers between analys…

ContextBench benchmarks methods for generating linguistically fluent inputs that activate specific latent features in language models.