arXiv research

Mixed-SCORE+ improves community detection in weak signal networks.

New proof for weak mixing in polygonal billiards.

Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mixing relationships. To address this weakness, we propose a new model, MixHop, that can learn these relationships, including difference operat…

Introduces a new framework for Riemannian diffeology.

Paper proves inequalities on Hermitian manifolds with applications to bounded solutions.

Mixed data comprises both numeric and categorical features, and mixed datasets occur frequently in many domains, such as health, finance, and marketing. Clustering is often applied to mixed datasets to find structures and to group similar objects for further analysis. However, clustering mixed data is challenging becau…

Develops a diagrammatic method for symplectic filling classifications.

We study the stochastic multi-armed bandit problem in the case when the arm samples are dependent over time and generated from so-called weak $\cC$-mixing processes. We establish a $\cC-$Mix Improved UCB agorithm and provide both problem-dependent and independent regret analysis in two different scenarios. In the first…

Sparse-penalized deep neural networks improve performance in weakly dependent processes.

Survey on recent developments in isometric immersions using PDE techniques.

uHMC achieves fast mixing in high dimensions with gradient evaluations.

The paper proposes a mixed-frequency quantile regression model for VaR and ES forecasting.

We consider a jump-type Cox--Ingersoll--Ross (CIR) process driven by a standard Wiener process and a subordinator, and we study asymptotic properties of the maximum likelihood estimator (MLE) for its growth rate. We distinguish three cases: subcritical, critical and supercritical. In the subcritical case we prove weak …

We create precise formulas for VIX option implied volatility.

Deep neural nets learn from weakly dependent processes.

This paper compares methods for handling mixed-attribute data in GFMM neural networks.

The paper bounds the excess risk of deep neural networks for weakly dependent processes.

Stochastic cutting planes improve data-driven optimization speed.

The paper proves positivity of third Chern form for certain vector bundles.

Orion-Bix combines biaxial attention and meta-learning for tabular few-shot learning.

Study equilibrium measures on manifolds without conjugate points with visibility covering.

MixML unifies analysis of weakly consistent parallel learning.

Regression problems assume every instance is annotated (labeled) with a real value, a form of annotation we call \emph{strong guidance}. In order for these annotations to be accurate, they must be the result of a precise experiment or measurement. However, in some cases additional \emph{weak guidance} might be given by…

We obtain a Bernstein-type inequality for sums of Banach-valued random variables satisfying a weak dependence assumption of general type and under certain smoothness assumptions of the underlying Banach norm. We use this inequality in order to investigate in the asymptotical regime the error upper bounds for the broad …

New theory explains consistency of kernel methods with non-i.i.d. data.

The study provides statistical theory for WGANs in time series forecasting.

Sharp rates found for learning with dependent data, avoiding sample size deflation.

New conic quadratic formulations improve outlier detection in regression models.

We show that the gradient norm $\|\nabla f(x)\|$ for $x \sim \exp(-f(x))$, where $f$ is strongly convex and smooth, concentrates tightly around its mean. This removes a barrier in the prior state-of-the-art analysis for the well-studied Metropolized Hamiltonian Monte Carlo (HMC) algorithm for sampling from a strongly l…

By generalizing the measurements on the game experiments of mixed strategy Nash equilibrium, we study the dynamical pattern in a representative dynamic stochastic general equilibrium (DSGE). The DSGE model describes the entanglements of the three variables (output gap [$y$], inflation [$π$] and nominal interest rate [$…

The Gaussian process (GP) is a popular way to specify dependencies between random variables in a probabilistic model. In the Bayesian framework the covariance structure can be specified using unknown hyperparameters. Integrating over these hyperparameters considers different possible explanations for the data when maki…

2D-PT improves sampling in constrained optimization problems.

Paper introduces MSA for weakly supervised covariance alignment in MEG signals.

New framework detects model weaknesses in decision tree ensembles.

We develop a new Monte Carlo variance reduction method to estimate the expectation of two commonly encountered path-dependent functionals: first-passage times and occupation times of sets. The method is based on a recursive approximation of the first-passage time probability and expected occupation time of sets of a Le…

We study some dynamical properties of the canonical Aut(F_n)-action on the space R_n(G) of redundant representations of the free group F_n in G, where G is the group of rational points of a simple algebraic group over a local field. We show that this action is always minimal and ergodic, confirming a conjecture of A. L…

We study the dependence of geometric quantization of the standard symplectic torus on the choice of invariant polarization. Real and mixed polarizations are interpreted as degenerate complex structures. Using a weak version of the equations of covariant constancy, and the Weil-Brezin expansion to describe distributiona…

We provide approximations for VIX futures and options in forward variance models.

We study asymptotic properties of maximum likelihood estimators of drift parameters for a jump-type Heston model based on continuous time observations, where the jump process can be any purely non-Gaussian Lévy process of not necessarily bounded variation with a Lévy measure concentrated on $(-1,\infty)$. We prove stro…

In the presence of weak overall correlation, it may be useful to investigate if the correlation is significantly and substantially more pronounced over a subpopulation. Two different testing procedures are compared. Both are based on the rankings of the values of two variables from a data set with a large number n of o…

Paper tackles robust deep learning from weakly dependent data with unbounded loss and input.

Purpose - This paper seeks to take a cautionary stance to the impact of the marketing mix on customer satisfaction, via a case study deriving consensus rankings for benchmarking on selected retail stores in Malaysia. Design/methodology/approach - The ELECTRE I model is used in deriving consensus rankings via multicrite…

We propose a faster and more accurate method for learning classification trees.

Class imbalance problem has been a challenging research problem in the fields of machine learning and data mining as most real life datasets are imbalanced. Several existing machine learning algorithms try to maximize the accuracy classification by correctly identifying majority class samples while ignoring the minorit…

New method identifies causal relationships without strong assumptions.

Many real world tasks require multiple agents to work together. Multi-agent reinforcement learning (RL) methods have been proposed in recent years to solve these tasks, but current methods often fail to efficiently learn policies. We thus investigate the presence of a common weakness in single-agent RL, namely value fu…

This study compares two methods for sampling with transport maps, finding flow-based proposals work better for multimodal distributions.

Paper addresses global convergence of MLR estimation under weak data conditions.