arXiv research

New approach for neural models to be transparent over structured data.

Geometric framework for signed multivariate tail-dependence compatibility at various thresholds.

Proximal Mediation Analysis with Hidden Recanting Witnesses

New graphs show hierarchical hyperbolic properties, extending previous work.

A new method detects hidden driving forces in systems with multiple observables.

USD algorithm transports distributions with or without mass conservation.

A new witness two-sample test improves data efficiency and power.

Loxodromic elements are pseudo-Anosov on specific graphs.

AutoML simplifies two-sample tests for detecting distribution shifts.

New RL algorithms show model-based methods are more efficient than model-free ones in complex decision processes.

Link's sphere number equals its bridge number.

In this paper we show that certain generalizations of the $C^r$-Whitney topology, which include the Hölder-Whitney and Sobolev-Whitney topologies on smooth manifolds, satisfy the Baire property, to wit, the countable intersection of open and dense sets is dense.

A new method for analyzing adaptive experiments using kernel treatment effects.

Example shows dense subgroup of SL5(Z) not finitely presented.

A new test detects differences between two distributions without flow.

This note shows how to transform high-probability to in-expectation guarantees in machine learning.

Unified approach for multicalibration in weakly supervised learning.

We present new, unified proofs for the cell-like, $\mathbb{Z}/p$-, and $\mathbb{Q}$-resolution theorems. Our arguments employ extensions that are much simpler then those used by our predecessors. The techniques allow us to solve problems involving cohomology groups by converting them into problems about homology groups…

We present new excess risk bounds for general unbounded loss functions including log loss and squared loss, where the distribution of the losses may be heavy-tailed. The bounds hold for general estimators, but they are optimized when applied to $η$-generalized Bayesian, MDL, and empirical risk minimization estimators. …

New methods estimate causal effects through mediators, handling confounding without strict assumptions.

Proposes DR-ME test for interpretable distributional treatment effects.

We study a continuous-time version of the intermediation model of Grossman and Miller (1988). To wit, we solve for the competitive equilibrium prices at which liquidity takers' demands are absorbed by dealers with quadratic inventory costs, who can in turn gradually transfer these positions to an exogenous open market …

This article constructs the moduli stack of torsionfree $G$-jet-structures in homotopy type theory with one monadic modality. This yields a construction of this moduli stack for any $\infty$-topos equipped with any stable factorization systems. In the intended applications of this theory, the factorization systems are …

Foundation for learning in changing conditions.

Given a closed simply connected manifold $M$ of dimension $2n\ge6$, we compare the ring of characteristic classes of smooth oriented bundles with fibre $M$ to the analogous ring resulting from replacing $M$ by the connected sum $M\sharpΣ$ with an exotic sphere $Σ$. We show that, after inverting the order of $Σ$ in the …

Proposes GFMMD for comparing signals on graphs.

A new method uses Hermite polynomials to improve machine learning models.

Sharp comparison for sub-Gaussian random variables in convex order.

Optimal bounds proven for ordinal embedding convergence rate.

Survey of DRO, a robust optimization framework.

Oleg Viro studied in arXiv:math/0204290 two interpretations of the (multivariable) Alexander polynomial as a quantum link invariant: either by considering the quasi triangular Hopf algebra associated to $U_q sl(2)$ at fourth roots of unity, or by considering the super Hopf algebra $U_q gl(1|1)$. In this paper, we show …

Survey on AI math foundations, focusing on neural networks.

We propose a new NFT price index to track the digital art market.

The study shows that several properties are not profinite invariants.

A framework for analyzing financial systems under scenario constraints.

In the recent years, we have witnessed the development of multi-label classification methods which utilize the structure of the label space in a divide and conquer approach to improve classification performance and allow large data sets to be classified efficiently. Yet most of the available data sets have been provide…

The ability to witness non-local correlations lies at the core of foundational aspects of quantum mechanics and its application in the processing of information. Commonly, this is achieved via the violation of Bell inequalities. Unfortunately, however, their systematic derivation quickly becomes unfeasible as the scena…

Gaussian processes (GPs) have been proven to be powerful tools in various areas of machine learning. However, there are very few applications of GPs in the scenario of multi-view learning. In this paper, we present a new GP model for multi-view learning. Unlike existing methods, it combines multiple views by regularizi…

Great successes of deep neural networks have been witnessed in various real applications. Many algorithmic and implementation techniques have been developed, however, theoretical understanding of many aspects of deep neural networks is far from clear. A particular interesting issue is the usefulness of dropout, which w…

OMLE combines optimism and MLE for efficient sequential decision making.

Graph neural networks struggle with proving unsatisfiability in complex logical formulas.

Article examines NFT market microstructure and trading risks.

XRAI improves saliency maps for deep neural networks.

The study of networks has witnessed an explosive growth over the past decades with several ground-breaking methods introduced. A particularly interesting -- and prevalent in several fields of study -- problem is that of inferring a function defined over the nodes of a network. This work presents a versatile kernel-base…

Survey on quantum computing and neural networks.

New proof shows equality in spacetime mass theorem.

One of the most fundamental problems in causal inference is the estimation of a causal effect when variables are confounded. This is difficult in an observational study, because one has no direct evidence that all confounders have been adjusted for. We introduce a novel approach for estimating causal effects that explo…

NVGD uses neural networks to infer distributions without kernel choices.