arXiv research

Extends conformal prediction for controlling expected risk of monotone loss functions.

Paper proposes a probabilistic method to handle missing data in decision trees.

New decision-theoretic calibration error metric improves prediction reliability.

Generalized algorithm for translation and scale-invariant prediction.

Paper establishes a formula linking model performance to insurance loss ratio.

Flexible framework for bounding high-loss predictions using quantiles.

The goal of regression and classification methods in supervised learning is to minimize the empirical risk, that is, the expectation of some loss function quantifying the prediction error under the empirical distribution. When facing scarce training data, overfitting is typically mitigated by adding regularization term…

Paper tackles regression with cost-based rejection, balancing prediction and rejection costs.

New risk control method for non-monotonic losses in complex parameters.

SEMF predicts prediction intervals for ML models using latent variables.

AEW estimator achieves optimal risk in expectation for large enough temperatures.

Under covariate shift, training (source) data and testing (target) data differ in input space distribution, but share the same conditional label distribution. This poses a challenging machine learning task. Robust Bias-Aware (RBA) prediction provides the conditional label distribution that is robust to the worstcase lo…

Enhances crypto-asset AMM with deep learning for better liquidity and efficiency.

Given two networks with the same training loss on a dataset, when would they have drastically different test losses and errors? Better understanding of this question of generalization may improve practical applications of deep networks. In this paper we show that with cross-entropy loss it is surprisingly simple to ind…

The predictive quality of machine learning models is typically measured in terms of their (approximate) expected prediction accuracy or the so-called Area Under the Curve (AUC). Minimizing the reciprocals of these measures are the goals of supervised learning. However, when the models are constructed by the means of em…

Optimal decision-making using prediction sets to minimize risk.

The paper improves sparse Gaussian processes by optimizing predictive loss.

Forecastability measures predictive information across horizons.

Transformers approximate Bayesian posteriors but not exactly.

Develops a framework for consistent loss functions with variable transformations.

Theory integrates loss aversion into expected utility for monetary returns.

Study asset pricing with reference-dependent preferences, finding matching equity premia.

New algorithm handles bandit problems under translations and scales.

A new method approximates expected empirical loss for stochastic deep learning tasks.

Transformer-based models overfit financial time series data, leading to increased prediction variance.

Uniform AMMs control loss in prediction markets.

Loss-calibrated EP improves Bayesian decision-making by focusing on utility-sensitive posterior approximations.

Investigates methods to regularize quantile regression for accurate predictions.

Investigates conditions for risk or utility functionals to be sensitive to large losses.

Model predicts operational risk using HMMs with economic covariates.

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

MRCs minimize worst-case expected 0-1 loss and provide performance guarantees.

The dependency structure of credit risk parameters is a key driver for capital consumption and receives regulatory and scientific attention. The impact of parameter imperfections on the quality of expected loss (EL) in the sense of a fair, unbiased estimate of risk expenses however is barely covered. So far there are n…

New PG losses improve decision optimization in misspecified models.

This paper rethinks confidence calibration under covariate shifts.

Max-margin learning is a powerful approach to building classifiers and structured output predictors. Recent work on max-margin supervised topic models has successfully integrated it with Bayesian topic models to discover discriminative latent semantic structures and make accurate predictions for unseen testing data. Ho…

Algorithm provides online learning guarantees against general comparators in full and bandit feedback.

The paper tackles performative risk optimization under weak convexity assumptions.

The paper optimizes forecasting for risk-adjusted decisions under trading frictions.

GOIMDA selects inputs to maximize expected influence on a goal functional, reducing data acquisition needs.

Simplified plug-in loss approximates EDL for reliable uncertainty estimation.

Gaptron algorithm reduces mistakes in online multiclass classification.

WES improves neural network regression by stretching distribution error.

MAP perturbation models have emerged as a powerful framework for inference in structured prediction. Such models provide a way to efficiently sample from the Gibbs distribution and facilitate predictions that are robust to random noise. In this paper, we propose a provably polynomial time randomized algorithm for learn…

Proposes non-exchangeable conformal risk control for better uncertainty bounds.

New algorithm identifies best arm in rested bandit setting.

A new framework for time series forecasting that adapts to varying patterns.

Given a task of predicting $Y$ from $X$, a loss function $L$, and a set of probability distributions $Γ$ on $(X,Y)$, what is the optimal decision rule minimizing the worst-case expected loss over $Γ$? In this paper, we address this question by introducing a generalization of the principle of maximum entropy. Applying t…