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.
Researchers identify critical protein residues using advanced graph theory.
RFRBoost uses random features to boost deep residual neural networks, improving performance and computational efficiency.
New method improves matrix completion accuracy, especially in noisy data.
New approach quantifies overfitting in high-dimensional regression.
The paper improves random forest models for non-Gaussian responses.
Tractable yet expressive density estimators are a key building block of probabilistic machine learning. While sum-product networks (SPNs) offer attractive inference capabilities, obtaining structures large enough to fit complex, high-dimensional data has proven challenging. In this paper, we present random sum-product …
Method uses random forest with distance covariance for transfer learning in healthcare.
DIET tests conditional independence using marginal dependence measures of residual information.
Proposes a robust method for high-dimensional linear models.
We regard pre-trained residual networks (ResNets) as nonlinear systems and use linearization, a common method used in the qualitative analysis of nonlinear systems, to understand the behavior of the networks under small perturbations of the input images. We work with ResNet-56 and ResNet-110 trained on the CIFAR-10 dat…
New method uses observational data to improve trial design efficiency.
The skip-connections used in residual networks have become a standard architecture choice in deep learning due to the increased training stability and generalization performance with this architecture, although there has been limited theoretical understanding for this improvement. In this work, we analyze overparameter…
AAS optimizes neural network PDE approximations by adaptively sampling.
The study analyzes deep linear networks from random initialization, capturing dynamics and hyperparameter effects.
A new ensemble learning method called Residual Likelihood Forests improves performance and reduces model size.
We revisit the initialization of deep residual networks (ResNets) by introducing a novel analytical tool in free probability to the community of deep learning. This tool deals with non-Hermitian random matrices, rather than their conventional Hermitian counterparts in the literature. As a consequence, this new tool ena…
SCORE improves tree-based predictions with boosted residual extraTrees.
Fast nonparametric conditional independence testing via two-stage regression
Our main result is that for densities a random group in the square model has the Haagerup property and is residually finite. Moreover, we generalize the Isoperimetric Inequality, to some class of non-planar diagrams and, using this, we introduce a system of modified hypergraphs providing the structure o…
In dealing with high-dimensional data sets, factor models are often useful for dimension reduction. The estimation of factor models has been actively studied in various fields. In the first part of this paper, we present a new approach to estimate high-dimensional factor models, using the empirical spectral density of …
Study infinite-depth limits of neural networks with fixed width.
In this paper we propose using the principle of boosting to reduce the bias of a random forest prediction in the regression setting. From the original random forest fit we extract the residuals and then fit another random forest to these residuals. We call the sum of these two random forests a \textit{one-step boosted …
We derive finite width and depth corrections for the Neural Tangent Kernel (NTK) of ResNets and DenseNets. Our analysis reveals that finite size residual architectures are initialized much closer to the "kernel regime" than their vanilla counterparts: while in networks that do not use skip connections, convergence to t…
To have a superior generalization, a deep learning neural network often involves a large size of training sample. With increase of hidden layers in order to increase learning ability, neural network has potential degradation in accuracy. Both could seriously limit applicability of deep learning in some domains particul…
In this paper, we propose a novel perturbation-based exploration method in bandit algorithms with bounded or unbounded rewards, called residual bootstrap exploration (\texttt{ReBoot}). The \texttt{ReBoot} enforces exploration by injecting data-driven randomness through a residual-based perturbation mechanism. This nove…
Automates debiasing for large language model evaluations through Fisher random walk.
RSIC identifies multiple ranks of interest in NMF by analyzing residual sensitivity.
Transformers improve Alzheimer's disease progression prediction by accounting for irregular biomarker histories.
Unified algorithm solves convex optimization problems with optimal rates.
This paper extends geometric study of neural networks to non-differentiable layers and random walks.
We demonstrate that in residual neural networks (ResNets) dynamical isometry is achievable irrespectively of the activation function used. We do that by deriving, with the help of Free Probability and Random Matrix Theories, a universal formula for the spectral density of the input-output Jacobian at initialization, in…
Sources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of systematic components, originating from the measuring instrument, and random measurement errors. Several novel biological technologies, such as ma…
New algorithm trains deep neural networks without global optimization.
Alternative to likelihood-based LSNM model selection, residual independence testing is more robust to noise misspecification.
Abstract: Non-residually finite hyperbolic groups imply non-residually finite rigid hyperbolic groups.
Deep linear ResNets converge globally with certain transformations.
New method combines randomization tests and flexible models for valid inference without splitting data.
Coordinate descent methods employ random partial updates of decision variables in order to solve huge-scale convex optimization problems. In this work, we introduce new adaptive rules for the random selection of their updates. By adaptive, we mean that our selection rules are based on the dual residual or the primal-du…
New method solves stochastic optimization problems with random models.
A method to reduce bias in model-based policy evaluation by shifting operators.
We find a novel correlation structure in the residual noise of stock market returns that is remarkably linked to the composition and stability of the top few significant factors driving the returns, and moreover indicates that the noise band is composed of multiple subbands that do not fully mix. Our findings allow us …
Residual finiteness is known to be an important property of groups appearing in combinatorial group theory and low dimensional topology. In a recent work [2] residual finiteness of quandles was introduced, and it was proved that free quandles and knot quandles are residually finite. In this paper, we extend these resul…
In this note, residual finiteness of quandles is defined and investigated. It is proved that free quandles and knot quandles of tame knots are residually finite and Hopfian. Residual finiteness of quandles arising from residually finite groups (conjugation, core and Alexander quandles) is established. Further, residual…
Developing efficient numerical algorithms for the solution of high dimensional random Partial Differential Equations (PDEs) has been a challenging task due to the well-known curse of dimensionality. We present a new solution framework for these problems based on a deep learning approach. Specifically, the random PDE is…
Deep adaptive sampling improves surrogate modeling for complex systems.
Two methods improve Gaussian process predictive distributions' calibration.
Every non-trivial knot group is fully residually perfect.
We study the generalization properties of minimum-norm solutions for three over-parametrized machine learning models including the random feature model, the two-layer neural network model and the residual network model. We proved that for all three models, the generalization error for the minimum-norm solution is compa…