arXiv research

A new method learns interpretable decision rules using submodular optimization.

Screening rules help identify active sets in optimization problems.

This paper improves fraud prevention rule sets in fintech by generating diverse rules and finding Pareto-optimal subsets.

A new screening rule improves lasso solving speed.

MOSS optimizes decision rules for accuracy and stability.

The paper offers guidelines for validating data-driven models.

FedRule uses graph neural networks to recommend rules for smart homes without centralizing data.

TransINT embeds KGs by preserving implication rules, outperforming existing methods.

Abstraction and realization are bilateral processes that are key in deriving intelligence and creativity. In many domains, the two processes are approached through rules: high-level principles that reveal invariances within similar yet diverse examples. Under a probabilistic setting for discrete input spaces, we focus …

Basic aspects of the equiaffine geometry of level sets are developed systematically. As an application there are constructed families of $2n$-dimensional nondegenerate hypersurfaces ruled by $n$-planes, having equiaffine mean curvature zero, and solving the affine normal flow. Each carries a symplectic structure with r…

This work proposes optimal decision rules for hierarchical classifiers to better align with evaluation metrics.

In this paper, we proved the quantum layer over a surface which is ruled outside a compact set, asymptotically flat but not totally geodesic admits ground states.

New method improves model explainability and accuracy with low computational cost.

Study ruled surfaces in 3D Riemannian manifolds, determining curvature and striction curves.

The Kelly rule fails to maximize growth in a time-changed return setting.

A new screening rule improves SLOPE efficiency for high-dimensional data.

In this paper, we propose an efficient algorithm for mining novel `Set of Contrasting Rules'-pattern (SCR-pattern), which consists of several association rules. This pattern is of high interest due to the guaranteed quality of the rules forming it and its ability to discover useful knowledge. However, SCR-pattern has n…

Two algorithms for interpreting and boosting tree-based models using rule covering.

This paper considers generalized linear models using rule-based features, also referred to as rule ensembles, for regression and probabilistic classification. Rules facilitate model interpretation while also capturing nonlinear dependences and interactions. Our problem formulation accordingly trades off rule set comple…

The paper classifies ruled surfaces in a specific space that move in a special way.

R2N learns interpretable rules and literals from numerical features.

Interpretable classifiers have recently witnessed an increase in attention from the data mining community because they are inherently easier to understand and explain than their more complex counterparts. Examples of interpretable classification models include decision trees, rule sets, and rule lists. Learning such mo…

Lasso is a widely used regression technique to find sparse representations. When the dimension of the feature space and the number of samples are extremely large, solving the Lasso problem remains challenging. To improve the efficiency of solving large-scale Lasso problems, El Ghaoui and his colleagues have proposed th…

In the artificial intelligence field, learning often corresponds to changing the parameters of a parameterized function. A learning rule is an algorithm or mathematical expression that specifies precisely how the parameters should be changed. When creating an artificial intelligence system, we must make two decisions: …

It is proved that if a Paley-Wiener family of eigenfunctions of the Laplace operator in $\mathbb R^3$ vanishes on a real analytically ruled two-dimensional surface $S \subset \mathbb R^3$ then $S$ is a union of cones, each of which is contained in a translate of the zero set of a nonzero harmonic homogeneous polynomial…

New methods prune unpromising rules from KGs, improving scalability and runtime.

We define ruling invariants for even-valence Legendrian graphs in standard contact three-space. We prove that rulings exist if and only if the DGA of the graph, introduced by the first two authors, has an augmentation. We set up the usual ruling polynomials for various notions of gradedness and prove that if the graph …

In this paper we use fuzzy systems theory to convert the technical trading rules commonly used by stock practitioners into excess demand functions which are then used to drive the price dynamics. The technical trading rules are recorded in natural languages where fuzzy words and vague expressions abound. In Part I of t…

In this paper, we confront the problem of deep learning's big labeled data requirements, offer a rule based strategy for extreme augmentation of small data sets and apply that strategy with the image to image translation model by Isola et al. (2016) to automate cel style cartoon coloring with very limited training data…

Paper presents unsupervised calibration for split conformal classification.

The nearest neighbor rule is proven consistent in a broad setting.

Study shows LLMs can extrapolate rules from out-of-distribution prompts.

FIRE extracts interpretable rules from tree ensembles.

We consider the setting of sequential prediction of arbitrary sequences based on specialized experts. We first provide a review of the relevant literature and present two theoretical contributions: a general analysis of the specialist aggregation rule of Freund et al. (1997) and an adaptation of fixed-share rules of He…

In high dimensional regression settings, sparsity enforcing penalties have proved useful to regularize the data-fitting term. A recently introduced technique called screening rules propose to ignore some variables in the optimization leveraging the expected sparsity of the solutions and consequently leading to faster s…

CRL approach improves understanding of heterogeneous treatment effects in complex diseases.

Proposes a score to compare rule-based algorithms' interpretability.

Invariant Causal Set Covering Machines avoid spurious associations.

Fed-FEARE model extracts rules from multiple agencies' data securely.

In this paper we investigate two variants of association rules for preference data, Label Ranking Association Rules and Pairwise Association Rules. Label Ranking Association Rules (LRAR) are the equivalent of Class Association Rules (CAR) for the Label Ranking task. In CAR, the consequent is a single class, to which th…

Study compares multivariate scoring rules for distribution forecasts.

Proposes PEMI for online selective conformal prediction with asymmetric rules.

From doctors diagnosing patients to judges setting bail, experts often base their decisions on experience and intuition rather than on statistical models. While understandable, relying on intuition over models has often been found to result in inferior outcomes. Here we present a new method, select-regress-and-round, f…

Forecasts of multivariate probability distributions are required for a variety of applications. Scoring rules enable the evaluation of forecast accuracy, and comparison between forecasting methods. We propose a theoretical framework for scoring rules for multivariate distributions, which encompasses the existing quadra…

New STDP rule for spiking neurons solves discrete action reinforcement learning tasks.

The lasso model has been widely used for model selection in data mining, machine learning, and high-dimensional statistical analysis. However, with the ultrahigh-dimensional, large-scale data sets now collected in many real-world applications, it is important to develop algorithms to solve the lasso that efficiently sc…

New learning rules achieve optimal sample complexity for weakly supervised classification.

New screening rules improve lasso model fitting efficiency.