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.
Paper proves KRR saturation effect for smooth functions.
New method combines CATE and CQTE to estimate treatment effects across different quantiles.
According to the work of Laitinen, Morimoto, Oliver and Pawałowski, a finite group has a smooth effective one fixed point action on some sphere if and only if is an Oliver group. For some finite Oliver groups of order up to , and for for , we present a strategy of excluding o…
We show that the driving force behind the regularizing effect of Laplacian smoothing on surface elements is the popular mean ratio quality measure. We use these insights to provide natural generalizations to polygons and polyhedra. The corresponding functions measuring the quality of meshes are easily seen to be convex…
Surrogate-based analysis of interactions via local effect smooths
New method improves counterfactual distribution learning for high-dimensional outcomes.
Label smoothing improves generalization by controlling generalization loss.
New method uses curvature to improve graph neural networks.
Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals, and can show poor predictive performance in the presence of strong, smooth effects. Taking the perspective of random forests as an adaptive kernel method, we pair the forest kernel with a local li…
Paper proves structure for compact Kähler manifolds with pseudo-effective tangent bundles.
New method estimates treatment effects over time for survival data, improving accuracy and smoothness.
An arbitrary Lie groupoid gives rise to a groupoid of germs of local diffeomorphisms over its base manifold, known as its effect. The effect of any bundle of Lie groups is trivial. All quotients of a given Lie groupoid determine the same effect. It is natural to regard the effects of any two Morita equivalent Lie group…
We consider first order gradient methods for effectively optimizing a composite objective in the form of a sum of smooth and, potentially, non-smooth functions. We present accelerated and adaptive gradient methods, called FLAG and FLARE, which can offer the best of both worlds. They can achieve the optimal convergence …
Enhances random forests by smoothing predictions for better performance.
In this paper, we introduce a new machine learning (ML) model for nonlinear regression called the Boosted Smooth Transition Regression Trees (BooST), which is a combination of boosting algorithms with smooth transition regression trees. The main advantage of the BooST model is the estimation of the derivatives (partial…
Prediction of dynamical time series with additive noise using support vector machines or kernel based regression has been proved to be consistent for certain classes of discrete dynamical systems. Consistency implies that these methods are effective at computing the expected value of a point at a future time given the …
Large-batch stochastic gradient descent (SGD) is widely used for training in distributed deep learning because of its training-time efficiency, however, extremely large-batch SGD leads to poor generalization and easily converges to sharp minima, which prevents naive large-scale data-parallel SGD (DP-SGD) from convergin…
Estimates individual treatment effects using gradient interpolation and kernel smoothing.
New measure captures differences across entire distributions of counterfactual outcomes.
Graph pruning improves neural network performance by addressing squashing and smoothing issues.
Non-parametric estimation of a multivariate density estimation is tackled via a method which combines traditional local smoothing with a form of global smoothing but without imposing a rigid structure. Simulation work delivers encouraging indications on the effectiveness of the method. An application to density-based c…
Recently smoothing deep neural network based classifiers via isotropic Gaussian perturbation is shown to be an effective and scalable way to provide state-of-the-art probabilistic robustness guarantee against norm bounded adversarial perturbations. However, how to train a good base classifier that is accurate …
Graph neural networks over-smooth when layers increase, reducing discriminative power.
Despite spectacular advances in defining invariants for simply connected smooth and symplectic 4-dimensional manifolds and the discovery of effective surgical techniques, we still have been unable to classify simply connected smooth manifolds up to diffeomorphism. In these notes, adapted from six lectures given at the …
Paper introduces a new regularization method for kernel gradient descent learning.
It is well-known that reduced smooth orbifolds and proper effective foliation Lie groupoids form equivalent categories. However, for certain recent lines of research, equivalence of categories is not sufficient. We propose a notion of maps between reduced smooth orbifolds and a definition of a category in terms of mark…
Denoised smoothing defends pretrained classifiers against adversarial attacks.
Some methods based on simple regularizing geometric element transformations have heuristically been shown to give runtime efficient and quality effective smoothing algorithms for meshes. We describe the mathematical framework and a systematic approach to global optimization-based versions of such methods for mixed volu…
Regularization is an effective way to promote the generalization performance of machine learning models. In this paper, we focus on label smoothing, a form of output distribution regularization that prevents overfitting of a neural network by softening the ground-truth labels in the training data in an attempt to penal…
Study estimates heterogeneous principal causal effects with binary treatments and intermediate variables.
A comprehensive methodology is provided for smoothing noisy, irregularly sampled data with non-Gaussian noise using smoothing splines. We demonstrate how the spline order and tension parameter can be chosen a priori from physical reasoning. We also show how to allow for non-Gaussian noise and outliers which are typical…
A gap in the proof of the main result in reference [1] in our original submission propagated into the constructions presented in the first version of our manuscript. In this version we give an alternative proof for the existence of Riemannian metrics with positive Ricci curvature on an infinite subfamily of closed, sim…
Diffusion models improve creativity by smoothing the score function, leading to interpolated data.
We show that every effective smooth action of a Lie group G on a manifold M is a diffeomorphism from G onto its image in Diff(M), where the image is equipped with the subset diffeology of the functional diffeology.
We say that a topologically embedded 3-sphere in a smoothing of Euclidean 4-space is a barrier provided, roughly, no diffeomorphism of the 4-manifold moves the 3-sphere off itself. In this paper we construct infinitely many one parameter families of distinct smoothings of 4-space with barrier 3-spheres. \par The existe…
Paper improves learning efficiency by focusing on effective dimensionality.
Study bandit problem on smooth graph functions for recommender systems.
Deep GCNII tackles over-smoothing problem in graph convolutional networks.
This paper investigates the nonparametric regression problem using SVMs with anisotropic Gaussian RBF kernels. Under the assumption that the target functions are resided in certain anisotropic Besov spaces, we establish the almost optimal learning rates, more precisely, optimal up to some logarithmic factor, presented …
We study a transformation of metric measure spaces introduced by Gigli and Mantegazza consisting in replacing the original distance with the length distance induced by the transport distance between heat kernel measures. We study the smoothing effect of this procedure in two important examples. Firstly, we show that in…
This study improves scalability of randomized smoothing for certifying classifier robustness.
Label smoothing improves model performance even with noisy labels.
Method estimates treatment effects in dyadic data with unknown confounders.
The study examines label smoothing to improve confidence calibration in fine-tuned LLMs.
Global existence and smoothing effects for reaction-diffusion equations with blowup in infinite time.
Finite-time extinction and smoothing effects in fractional fast diffusion on manifolds.
Study analyzes label smoothing in deep learning optimization.
Paper explains how tree ensembles improve predictions by smoothing and regulating smoothness.