arXiv research

Monotonic relationship found between in-distribution and out-of-distribution performance.

Study shows how adjusting for a binary proxy can bound causal effects.

This paper introduces a novel monotone curve estimation framework based on convex duality.

This paper explores the topology of monotone Lagrangian submanifolds $L$ inside a symplectic manifold $M$ by exploiting the relationships between the quantum homology of $M$ and various quantum structures associated to the Lagrangian $L$.

Study finds non-monotonic Value of Information in dynamic multi-market monopoly.

Study causal inference under specific sampling methods with monotonicity assumptions.

Develops methods to analyze feature-outcome associations in subpopulations.

SurvSurf predicts first hitting times for intermittent events without monotonic violations.

The aim of this paper is to provide a new method for learning the relationships between data that have been obtained independently. Unlike existing methods like matching, the proposed technique does not require any contextual information, provided that the dependency between the variables of interest is monotone. It ca…

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

MCNet improves uncertainty calibration in online advertising by modeling complex relations and balancing performance.

Study reveals how neural network smoothness affects their vulnerability to adversarial attacks.

Article addresses practical challenges in conformal prediction.

New work shows FP potential monotonicity equals low-degree polynomial estimators limits.

The paper explores arbitrage opportunities in derivative markets under specific conditions.

We investigate quotation and transaction activities in the foreign exchange market for every week during the period of June 2007 to December 2010. A scaling relationship between the mean values of number of quotations (or number of transactions) for various currency pairs and the corresponding standard deviations holds…

One of the central themes in the classification task is the estimation of class posterior probability at a new point $\bf{x}$. The vast majority of classifiers output a score for $\bf{x}$, which is monotonically related to the posterior probability via an unknown relationship. There are many attempts in the literature …

New method learns SIMs with arbitrary monotone activations without strong distributional assumptions.

Additive isotonic regression attempts to determine the relationship between a multi-dimensional observation variable and a response, under the constraint that the estimate is the additive sum of univariate component effects that are monotonically increasing. In this article, we present a new method for such regression …

Testing two potentially multivariate variables for statistical dependence on the basis finite samples is a fundamental statistical challenge. Here we explore a family of tests that adapt to the complexity of the relationship between the variables, promising robust power across scenarios. Building on the distance correl…

Self-referential meta learning avoids explicit optimization by modifying itself.

A new method for disentangled latent spaces in VAEs that can manipulate attributes.

This paper compares imputation and direct parameter estimation methods for missing data in correlation matrix visualization.

Develops a robust model for skewed and heavy-tailed data in periodontal studies.

The paper addresses monotonicity in machine learning models for fairness and accountability.

Final part of a series on nonlinear observers on Riemannian metrics, establishing conditions for convergence.

Probit Monotone BART estimates binary outcomes using monotonic functions.

In this paper we present a kinetic model with stochastic game-type interactions, analyzing the relationship between the level of political competition in a society and the degree of economic liberalization. The above issue regards the complex interactions between economy and institutional policies intended to introduce…

Monotone neural networks can approximate and interpolate functions efficiently.

In this paper we combine two important extensions of ordinary least squares regression: regularization and optimal scaling. Optimal scaling (sometimes also called optimal scoring) has originally been developed for categorical data, and the process finds quantifications for the categories that are optimal for the regres…

Improves k-NN for monotonic data with robustness against noise.

Functional BART adds shape priors to Bayesian tree regression for better curve fitting.

Study examines explainable machine learning for monotonic models, finding Integrated gradients better for strong monotonicity.

Random forests is a common non-parametric regression technique which performs well for mixed-type data and irrelevant covariates, while being robust to monotonic variable transformations. Existing random forest implementations target regression or classification. We introduce the RFCDE package for fitting random forest…

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

We provide evidence that cumulative distributions of absolute normalized returns for the $100$ American companies with the highest market capitalization, uncover a critical behavior for different time scales $Δt$. Such cumulative distributions, in accordance with a variety of complex --and financial-- systems, can be m…

Formula proves monotonicity for anisotropic minimal hypersurfaces.

Study identifies conditions for proxy adjustment in confounded binary treatment outcomes.

We demonstrate how easy it is for modern machine-learned systems to violate common deontological ethical principles and social norms such as "favor the less fortunate," and "do not penalize good attributes." We propose that in some cases such ethical principles can be incorporated into a machine-learned model by adding…

New example of manifolds with monotonic heat kernels found.

Learning performance can show non-monotonic behavior. That is, more data does not necessarily lead to better models, even on average. We propose three algorithms that take a supervised learning model and make it perform more monotone. We prove consistency and monotonicity with high probability, and evaluate the algorit…

The paper develops algorithms to restore monotonicity in non-monotone functions.

Nonnegative matrix factorization (NMF) factorizes a non-negative matrix into product of two non-negative matrices, namely a signal matrix and a mixing matrix. NMF suffers from the scale and ordering ambiguities. Often, the source signals can be monotonous in nature. For example, in source separation problem, the source…

In [S. Basu, A. Gabrielov, N. Vorobjov, Semi-monotone sets. arXiv:1004.5047v2 (2011)] we defined semi-monotone sets, as open bounded sets, definable in an o-minimal structure over the reals, and having connected intersections with all translated coordinate cones in R^n. In this paper we develop this theory further by d…

COMET learns monotonic neural networks by incorporating counterexamples.

Monotonic differentiable sorting networks improve upon previous methods.

We prove three new monotonicity formulas for manifolds with a lower Ricci curvature bound and show that they are connected to rate of convergence to tangent cones. In fact, we show that the derivative of each of these three monotone quantities is bounded from below in terms of the Gromov-Hausdorff distance to the neare…

Paper introduces algorithms for explaining monotonic classifiers.