arXiv research

The paper studies how more data affects prediction risk in high-dimensional models.

In risk management, tail risks are of crucial importance. The assessment of risks should be carried out in accordance with the regulatory authority's requirement at high quantiles. In general, the underlying distribution function is unknown, the database is sparse, and therefore special tail models are used. Very often…

Method controls treatment risk in learning beneficial allocations.

The MDL two-part coding $\textit{index of resolvability}$ provides a finite-sample upper bound on the statistical risk of penalized likelihood estimators over countable models. However, the bound does not apply to unpenalized maximum likelihood estimation or procedures with exceedingly small penalties. In this paper,…

We introduce a simulation method for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the dynamic value process of a portfolio from a finite sample of its cumulative cash flow. The learned value process is given in closed form thanks to a suitable choice of the kernel.…

Causal invariance can improve finite-sample domain adaptation, but only when the target risk margins are large.

Study shows interpolating predictor's risk is optimal in low-dimensional factor regression models.

Study non-monotonic loss functions in CRC, achieving valid risk control with large calibration samples.

We formalize AURC and develop estimators for SC systems.

This work analyzes IRM and ERM from sample complexity perspective, revealing different behaviors under various distribution shifts.

This paper approaches the definition and properties of dynamic convex risk measures through the notion of a family of concave valuation operators satisfying certain simple and credible axioms. Exploring these in the simplest context of a finite time set and finite sample space, we find natural risk-transfer and time-co…

In this work we investigate to which extent one can recover class probabilities within the empirical risk minimization (ERM) paradigm. The main aim of our paper is to extend existing results and emphasize the tight relations between empirical risk minimization and class probability estimation. Based on existing literat…

New framework calibrates decision robustness using inverse conformal risk control.

Study shows DQN's performance degrades with temporal dependence in data.

New risk class defined based on loss location and deviation.

This study analyzes prediction risk for PCR method in latent factor regression models.

GD outperforms ridge regression and SGD in linear regression problems.

Novel AMP framework for multi-environment transfer learning.

Ensemble method for fast portfolio valuation and risk management.

The paper analyzes risk estimation methods and derives bounds for OCE risk.

Robo-advisors estimate clients' risk aversion using interactive questionnaires.

Project estimates risk-neutral dependence from option prices.

New method identifies parameters of wider shallow neural networks with biases.

The minimum description length (MDL) principle in supervised learning is studied. One of the most important theories for the MDL principle is Barron and Cover's theory (BC theory), which gives a mathematical justification of the MDL principle. The original BC theory, however, can be applied to supervised learning only …

A machine learning model manages portfolio risk in high dimensions.

We introduce new forecast encompassing tests for the risk measure Expected Shortfall (ES). The ES currently receives much attention through its introduction into the Basel III Accords, which stipulate its use as the primary market risk measure for the international banking regulation. We utilize joint loss functions fo…

The paper analyzes LOCV for high-dimensional risk estimation, proving error bounds.

We derive PAC-Bayesian learning guarantees for heavy-tailed losses, and obtain a novel optimal Gibbs posterior which enjoys finite-sample excess risk bounds at logarithmic confidence. Our core technique itself makes use of PAC-Bayesian inequalities in order to derive a robust risk estimator, which by design is easy to …

Study improves understanding of non-differentiable penalties in high-dimensional settings.

Split conformal prediction provides finite-sample guarantees for black-box models without distributional assumptions.

New method forecasts systemic risk with improved precision.

VIB balances empirical and Bayesian approaches in predictive models.

We study the task of learning from non-i.i.d. data. In particular, we aim at learning predictors that minimize the conditional risk for a stochastic process, i.e. the expected loss of the predictor on the next point conditioned on the set of training samples observed so far. For non-i.i.d. data, the training set contai…

This paper addresses the problem of estimating, in the presence of random censoring as well as competing risks, the extreme value index of the (sub)-distribution function associated to one particular cause, in the heavy-tail case. Asymptotic normality of the proposed estimator (which has the form of an Aalen-Johansen i…

This paper improves risk control for financial markets by calibrating VaR forecasts using conformal methods.

Paper introduces risk assessment for contextual bandits without experiments.

We propose a new active learning algorithm for parametric linear regression with random design. We provide finite sample convergence guarantees for general distributions in the misspecified model. This is the first active learner for this setting that provably can improve over passive learning. Unlike other learning se…

This manuscript studies statistical properties of linear classifiers obtained through minimization of an unregularized convex risk over a finite sample. Although the results are explicitly finite-dimensional, inputs may be passed through feature maps; in this way, in addition to treating the consistency of logistic reg…

Online surveillance detects systemic risk in financial markets.

Paper proposes robust method to detect risk heterogeneity across ethnic groups.

We develop an approach to risk minimization and stochastic optimization that provides a convex surrogate for variance, allowing near-optimal and computationally efficient trading between approximation and estimation error. Our approach builds off of techniques for distributionally robust optimization and Owen's empiric…

Unified framework for fair classification with group-blindness/awareness guarantees.

Dynamic models improve CoVaR forecasts for financial system risks.

New framework calibrates models to control risk under performativity.

We enhance conformal prediction for risk-averse decisions with action-conditional guarantees.

Framework calibrates ML models for risk control in various tasks.

New method improves model risk prediction using cross-audit projection.

Optimal decision-making using prediction sets to minimize risk.