arXiv research

This paper rethinks confidence calibration under covariate shifts.

Develops a framework for consistent loss functions with variable transformations.

We provide an economic interpretation of the practice consisting in incorporating risk measures as constraints in a classic expected return maximization problem. For what we call the infimum of expectations class of risk measures, we show that if the decision maker (DM) maximizes the expectation of a random return unde…

Paper introduces MRCs that minimize worst-case 0-1 loss, providing tight performance guarantees.

We consider stochastic gradient descent and its averaging variant for binary classification problems in a reproducing kernel Hilbert space. In the traditional analysis using a consistency property of loss functions, it is known that the expected classification error converges more slowly than the expected risk even whe…

The authors examine the concept of probability of default for asset-backed loans. In contrast to unsecured loans it is shown that probability of default can be defined as either a measure of the likelihood of the borrower failing to make required payments, or as the likelihood of an insufficiency of collateral value on…

A new tail-shape index based on Value at Risk and Expected Shortfall.

We introduce a novel regression framework which simultaneously models the quantile and the Expected Shortfall (ES) of a response variable given a set of covariates. This regression is based on a strictly consistent loss function for the pair quantile and ES, which allows for M- and Z-estimation of the joint regression …

In this paper we develop a statistical arbitrage trading strategy with two key elements in hi-frequency trading: stop-loss and leverage. We consider, as in Bertram (2009), a mean-reverting process for the security price with proportional transaction costs; we show how to introduce stop-loss and leverage in an optimal t…

This work interprets GELU and related activations via a first-order loss function.

Bayesian approach uses Gaussian process for reinforcement learning.

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

Study excess risk in statistical inference with transformations.

The paper verifies the robustness of classifier ensembles against randomized attacks.

Theory integrates loss aversion into expected utility for monetary returns.

Spectral images captured by satellites and radio-telescopes are analyzed to obtain information about geological compositions distributions, distant asters as well as undersea terrain. Spectral images usually contain tens to hundreds of continuous narrow spectral bands and are widely used in various fields. But the vast…

The paper explores risk measures and arbitrage in financial markets.

Regression, unlike classification, has lacked a comprehensive and effective approach to deal with cost-sensitive problems by the reuse (and not a re-training) of general regression models. In this paper, a wide variety of cost-sensitive problems in regression (such as bids, asymmetric losses and rejection rules) can be…

New algorithm learns mappings between metric spaces, achieving strong consistency.

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

Unified surrogate loss framework for multi-label learning with strong consistency guarantees.

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

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

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

Study of loss functions for learning to defer, proving consistency.

Loss assigns examples to classes and superclasses in hierarchical data.

The debate of what quantitative risk measure to choose in practice has mainly focused on the dichotomy between Value at Risk (VaR) -- a quantile -- and Expected Shortfall (ES) -- a tail expectation. Range Value at Risk (RVaR) is a natural interpolation between these two prominent risk measures, which constitutes a trad…

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

Study improves $H$-consistency bounds for regression analysis.

AUC (area under ROC curve) is an important evaluation criterion, which has been popularly used in many learning tasks such as class-imbalance learning, cost-sensitive learning, learning to rank, etc. Many learning approaches try to optimize AUC, while owing to the non-convexity and discontinuousness of AUC, almost all …

SEMF predicts prediction intervals for ML models using latent variables.

Gaptron algorithm reduces mistakes in online multiclass classification.

The paper introduces risk consistency properties for credit ratings.

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…

Dropout, a simple and effective way to train deep neural networks, has led to a number of impressive empirical successes and spawned many recent theoretical investigations. However, the gap between dropout's training and inference phases, introduced due to tractability considerations, has largely remained under-appreci…

Study combines VaR and ES forecasts using MCS to improve risk predictions.

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

This paper improves multi-label ranking by reweighting univariate losses, enhancing consistency and performance.

OEUVRE estimates online loss with constant time and memory, outperforming other methods.

New algorithm identifies best arm in rested bandit setting.

Study on calibration and consistency of adversarial surrogate losses.

Approximations of loopy belief propagation, including expectation propagation and approximate message passing, have attracted considerable attention for probabilistic inference problems. This paper proposes and analyzes a generalization of Opper and Winther's expectation consistent (EC) approximate inference method. Th…

Just as semantic hashing can accelerate information retrieval, binary valued embeddings can significantly reduce latency in the retrieval of graphical data. We introduce a simple but effective model for learning such binary vectors for nodes in a graph. By imagining the embeddings as independent coin flips of varying b…

Study on $H$-consistency bounds for machine learning surrogates.

Paper corrects Max-Margin loss for multi-label tasks.

This paper introduces new loss functions for balanced multi-class classification.

The paper studies consistency of surrogate loss procedures under constrained classifiers.

In this study we prove the existence of statistical arbitrage opportunities in the Black-Scholes framework by considering trading strategies that consists of borrowing from the risk free rate and taking a long position in the stock until it hits a deterministic barrier level. We derive analytical formulas for the expec…