A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Efficiently discovers multi-label rules with relaxed pruning.
R2N learns interpretable rules and literals from numerical features.
New framework learns interpretable rule ensembles without sacrificing accuracy.
Simplified holonomy map for ruled submanifolds in graded manifolds.
Symbolic regression is a type of discrete optimization problem that involves searching expressions that fit given data points. In many cases, other mathematical constraints about the unknown expression not only provide more information beyond just values at some inputs, but also effectively constrain the search space. …
Proposes a neural network for efficient imbalance electricity price forecasting.
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…
Study ruled surfaces in 3D Riemannian manifolds, determining curvature and striction curves.
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: …
New methods prune unpromising rules from KGs, improving scalability and runtime.
A new method learns interpretable decision rules using submodular optimization.
In this work, we consider the hedging error due to discrete trading in models with jumps. Extending an approach developed by Fukasawa [In Stochastic Analysis with Financial Applications (2011) 331-346 Birkhäuser/Springer Basel AG] for continuous processes, we propose a framework enabling us to (asymptotically) optimize…
Kernel-based quadrature rules are becoming important in machine learning and statistics, as they achieve super- convergence rates in numerical integration, and thus provide alternatives to Monte Carlo integration in challenging settings where integrands are expensive to evaluate or where integrands are high d…
It is "well known" that there is no explicit expression for the Black-Scholes implied volatility. We prove that, as a function of underlying, strike, and call price, implied volatility does not belong to the class of D-finite functions. This does not rule out all explicit expressions, but shows that implied volatility …
A nonparametric kernel-based method for realizing Bayes' rule is proposed, based on representations of probabilities in reproducing kernel Hilbert spaces. Probabilities are uniquely characterized by the mean of the canonical map to the RKHS. The prior and conditional probabilities are expressed in terms of RKHS functio…
We analyze families of non-autonomous systems of first-order ordinary differential equations admitting a common time-dependent superposition rule, i.e., a time-dependent map expressing any solution of each of these systems in terms of a generic set of particular solutions of the system and some constants. We next study…
The paper connects knot homology, quantum 6j-symbols, and complements of knots.
This paper deals with the binary classification task when the target class has the lower probability of occurrence. In such situation, it is not possible to build a powerful classifier by using standard methods such as logistic regression, classification tree, discriminant analysis, etc. To overcome this short-coming o…
Coregulation of the expression of groups of genes has been extensively demonstrated empirically in bacterial and eukaryotic systems. Such coregulation can arise through the use of shared regulatory motifs, which allow the coordinated expression of modules (and module groups) of functionally related genes across the gen…
Study of flat ribbons constructed along curves in 3D space.
We present a framework for autonomously learning a portable representation that describes a collection of low-level continuous environments. We show that these abstract representations can be learned in a task-independent egocentric space specific to the agent that, when grounded with problem-specific information, are …
Wittgenstein's Rule Following evolves datasets by extrapolating structural descriptors.
Novel approach for creating interpretable classifiers using bilevel optimization of split-rules in NLDTs.
New scoring rules for multivariate distributions and level sets.
In this paper, we describe the "implicit autoencoder" (IAE), a generative autoencoder in which both the generative path and the recognition path are parametrized by implicit distributions. We use two generative adversarial networks to define the reconstruction and the regularization cost functions of the implicit autoe…
New model captures complex relationships from experimental data.
GCNs struggle with learning graph moments, but modular designs improve their performance.
System uses neural networks to prove program equivalence via rewrite rules.
Geometric pruning rules improve change point detection in multiple time series.
We propose a new Bayesian model for flexible nonlinear regression and classification using tree ensembles. The model is based on the RuleFit approach in Friedman and Popescu (2008) where rules from decision trees and linear terms are used in a L1-regularized regression. We modify RuleFit by replacing the L1-regularizat…
The article derives a novel Gram-Charlier A (GCA) Series based Extended Rule-of-Thumb (ExROT) for bandwidth selection in Kernel Density Estimation (KDE). There are existing various bandwidth selection rules achieving minimization of the Asymptotic Mean Integrated Square Error (AMISE) between the estimated probability d…
The paper introduces new portfolio rules beyond mean-variance, addressing asymmetry and uncertainty.
Derives exact gradients for linear optics with single photons.
Characterizes surfaces enveloped by rotating cones for CNC machining.
Study curvature and torsion from cross-ratios in discrete curves.
We consider the problem of high-dimensional classification between the two groups with unequal covariance matrices. Rather than estimating the full quadratic discriminant rule, we propose to perform simultaneous variable selection and linear dimension reduction on original data, with the subsequent application of quadr…
Paper introduces a statistical framework for watermarking LLM-generated text.
Transform ANNs into interpretable decision trees.
In this paper we study a class of insurance products where the policy holder has the option to insure of its annual Operational Risk losses in a horizon of years. This involves a choice of out of years in which to apply the insurance policy coverage by making claims against losses in the given year. The…
We study power expansions of the characteristic function of a linear operator in a -dimensional superspace . We show that traces of exterior powers of satisfy universal recurrence relations of period . `Underlying' recurrence relations hold in the Grothendieck ring of representations of $\GL(V)$. The…
Transformers model contextual relations using probabilistic measures, revealing their expressive power.
The correlation functions of supersymmetric gauge theories on a four-manifold X can sometimes be expressed in terms of topological invariants of X. We show how the existence of superconformal fixed points in the gauge theory can provide nontrivial information about four-manifold topology. In particular, in the example …
Search-based methods for hard combinatorial optimization are often guided by heuristics. Tuning heuristics in various conditions and situations is often time-consuming. In this paper, we propose NeuRewriter that learns a policy to pick heuristics and rewrite the local components of the current solution to iteratively i…
Most work in machine reading focuses on question answering problems where the answer is directly expressed in the text to read. However, many real-world question answering problems require the reading of text not because it contains the literal answer, but because it contains a recipe to derive an answer together with …
A conformal procedure improves CoT reasoning by aggregating reasoning paths and calibrating abstention rules.
Boosting combines weak hypotheses to create accurate predictions under bounded VC dimension.
We propose a probabilistic framework to directly insert prior knowledge in reinforcement learning (RL) algorithms by defining the behaviour policy as a Bayesian posterior distribution. Such a posterior combines task specific information with prior knowledge, thus allowing to achieve transfer learning across tasks. The …
New bounds prevent degradation in high-dimensional signal estimation.