arXiv research

The paper discusses building ETF risk models using a multilevel classification taxonomy.

In statistical learning theory, convex surrogates of the 0-1 loss are highly preferred because of the computational and theoretical virtues that convexity brings in. This is of more importance if we consider smooth surrogates as witnessed by the fact that the smoothness is further beneficial both computationally- by at…

Paper bounds convergence rate of adversarial surrogate risk.

Sensitivity analysis for individualized effects in OTRs with binary risk factors.

Random forest models predict CLABSI risk in hospital admissions, with static models performing similarly to dynamic ones.

New method separates model and non-model risks for more practical asset pricing.

In this article, we study the problem of pricing defaultable bond with discrete default intensity and barrier under constant risk free short rate using higher order binary options and their integrals. In our credit risk model, the risk free short rate is a constant and the default event occurs in an expected manner whe…

Paper ranks stocks by compression risk, not volatility.

The paper analyzes recalibration methods for binary classifiers under distribution shift.

Develops a binary tree model for option pricing with skew dynamics.

Paper analyzes impact of PRM on binary random variables and distribution shifts.

Optimal cutoff interval for risk scores improves binary classification accuracy.

We give a simple explicit algorithm for building multi-factor risk models. It dramatically reduces the number of or altogether eliminates the risk factors for which the factor covariance matrix needs to be computed. This is achieved via a nested "Russian-doll" embedding: the factor covariance matrix itself is modeled v…

Empirical risk minimization (ERM), with proper loss function and regularization, is the common practice of supervised classification. In this paper, we study training arbitrary (from linear to deep) binary classifier from only unlabeled (U) data by ERM. We prove that it is impossible to estimate the risk of an arbitrar…

New research shows that binary classification can be done with noisy data, but only if there are clean samples available.

Risk bounds for Classification and Regression Trees (CART, Breiman et. al. 1984) classifiers are obtained under a margin condition in the binary supervised classification framework. These risk bounds are obtained conditionally on the construction of the maximal deep binary tree and permit to prove that the linear penal…

New geometric insights reveal properties of adversarial training problems.

New method turns optimization algorithms into uniformly stable learning algorithms for non-Euclidean norms.

Study on double descent behavior in two-layer neural networks for binary classification.

Defines diversification as a binary relationship between financial portfolios.

Defines computable learning for binary classification over metric spaces.

We consider a standard binary classification problem. The performance of any binary classifier based on the training data is characterized by the excess risk. We study Bahadur's type exponential bounds on the minimax accuracy confidence function based on the excess risk. We study how this quantity depends on the comple…

We present $α$-loss, $α\in [1,\infty]$, a tunable loss function for binary classification that bridges log-loss ($α=1$) and $0$-$1$ loss ($α= \infty$). We prove that $α$-loss has an equivalent margin-based form and is classification-calibrated, two desirable properties for a good surrogate loss function for the ideal y…

Paper shows similarity learning can lead to strong binary classification performance.

New approach improves classification guarantees by focusing on direction rather than regression risk.

The paper explores how machine learning models can be learnable despite label shifts.

Paper improves DP-ERM for binary linear classification with large-margin subsets.

We consider the problem of binary classification where one can, for a particular cost, choose not to classify an observation. We present a simple proof for the oracle inequality for the excess risk of structural risk minimizers using a lasso type penalty.

The paper sets information-theoretic lower bounds for neural networks' parameter recovery and excess risk.

Paper analyzes adversarial training's performance in binary classification.

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

Paper tackles weakly supervised learning from similarity-confidence data.

This paper analyzes the training dynamics of binary neural networks using information bottleneck.

Early stopping improves neural networks' performance on binary classification tasks.

Where machine-learned predictive risk scores inform high-stakes decisions, such as bail and sentencing in criminal justice, fairness has been a serious concern. Recent work has characterized the disparate impact that such risk scores can have when used for a binary classification task. This may not account, however, fo…

A matrix completion problem, which aims to recover a complete matrix from its partial observations, is one of the important problems in the machine learning field and has been studied actively. However, there is a discrepancy between the mainstream problem setting, which assumes continuous-valued observations, and some…

Paper discusses extending Gini score for tied rankings and case weights.

ConvResNets approximate Besov functions and classify on low-dimensional manifolds.

Malware constitutes a major global risk affecting millions of users each year. Standard algorithms in detection systems perform insufficiently when dealing with malware passed through obfuscation tools. We illustrate this studying in detail an open source metamorphic software, making use of a hybrid framework to obtain…

In statistical inference problems, we wish to obtain lower bounds on the minimax risk, that is to bound the performance of any possible estimator. A standard technique to obtain risk lower bounds involves the use of Fano's inequality. In an information-theoretic setting, it is known that Fano's inequality typically doe…

Algorithmic insurance tackles financial risks from AI errors, proving CVaR-optimal thresholds reduce tail risk.

The paper provides theoretical guarantees for neural network-based anomaly detection.

This paper analyzes neural network classifiers' performance in binary classification.

Develops a new framework for perpetual futures on binary prediction markets.

The paper develops methods for constructing confidence regions for regression functions in binary classification.

We have developed a strategy for the analysis of newly available binary data to improve outcome predictions based on existing data (binary or non-binary). Our strategy involves two modeling approaches for the newly available data, one combining binary covariate selection via LASSO with logistic regression and one based…

Good predictors of ICU Mortality have the potential to identify high-risk patients earlier, improve ICU resource allocation, or create more accurate population-level risk models. Machine learning practitioners typically make choices about how to represent features in a particular model, but these choices are seldom eva…

New method estimates hidden binary mixture model centers efficiently.