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 proposes a new method for training diffusion models using Markov operators.
New EiV models correct bias in operator learning with noisy data.
Maximal concentration bounds for stochastic approximation with heavy-tailed noise.
This work analyzes nonexpansive stochastic approximations with Markovian noise, proving convergence in reinforcement learning.
Noise-robust Koopman operator framework for control with improved stability and performance.
A new data-adaptive prior stabilizes kernel learning in operators.
We solve image inverse problems using a flow-based noise model.
New method uses random features and Tikhonov regularization for operator learning from noisy data.
Ridge regression performs optimally in noisy environments with heavy-tailed distributions.
Stochastic differential equation approximation for linear TD(0) under Markovian noise
SC-Net learns interpretable filters for inverse problems, achieving optimal convergence and super-resolution.
We consider the dynamic linear regression problem, where the predictor vector may vary with time. This problem can be modeled as a linear dynamical system, with non-constant observation operator, where the parameters that need to be learned are the variance of both the process noise and the observation noise. While var…
A new method for learning function parameters in operators using data-adaptive RKHS.
Free lunch from noise reveals linear spectral features for RL.
WaveletGAN improves GANs by homogenizing noise through multi-channel wavelet filtering.
Operator-theoretic analysis of nonlinear dynamical systems has attracted much attention in a variety of engineering and scientific fields, endowed with practical estimation methods using data such as dynamic mode decomposition. In this paper, we address a lifted representation of nonlinear dynamical systems with random…
As all physical adaptive quantum-enhanced metrology schemes operate under noisy conditions with only partially understood noise characteristics, so a practical control policy must be robust even for unknown noise. We aim to devise a test to evaluate the robustness of AQEM policies and assess the resource used by the po…
The study establishes minimax bounds for estimating operators from noisy samples.
Recovering the support of sparse vectors in underdetermined linear regression models, \textit{aka}, compressive sensing is important in many signal processing applications. High SNR consistency (HSC), i.e., the ability of a support recovery technique to correctly identify the support with increasing signal to noise rat…
The paper analyzes how disturbances affect the convergence of algorithms in complex systems.
Paper explores using EEG for better speaker identification, even in noisy environments.
We consider a setting, where the output of a linear dynamical system (LDS) is, with an unknown but fixed probability, replaced by noise. There, we present a robust method for the prediction of the outputs of the LDS and identification of the samples of noise, and prove guarantees on its statistical performance. One app…
Develops efficient inference for noise heterogeneity in machine learning models.
Simultaneous orthogonal matching pursuit (SOMP) and block OMP (BOMP) are two widely used techniques for sparse support recovery in multiple measurement vector (MMV) and block sparse (BS) models respectively. For optimal performance, both SOMP and BOMP require \textit{a priori} knowledge of signal sparsity or noise vari…
AugBagg improves random forest accuracy with added noise variables.
In this work, we investigate a novel training procedure to learn a generative model as the transition operator of a Markov chain, such that, when applied repeatedly on an unstructured random noise sample, it will denoise it into a sample that matches the target distribution from the training set. The novel training pro…
Study on SA with heavy-tailed and LRD noise, establishing finite-time bounds.
Linear regression models contaminated by Gaussian noise (inlier) and possibly unbounded sparse outliers are common in many signal processing applications. Sparse recovery inspired robust regression (SRIRR) techniques are shown to deliver high quality estimation performance in such regression models. Unfortunately, most…
In this paper, we introduce various mechanisms to obtain accelerated first-order stochastic optimization algorithms when the objective function is convex or strongly convex. Specifically, we extend the Catalyst approach originally designed for deterministic objectives to the stochastic setting. Given an optimization me…
Neurons and networks in the cerebral cortex must operate reliably despite multiple sources of noise. To evaluate the impact of both input and output noise, we determine the robustness of single-neuron stimulus selective responses, as well as the robustness of attractor states of networks of neurons performing memory ta…
AdaGrad converges under heavy-tailed noise without extra operations.
In real-world applications of reinforcement learning (RL), noise from inherent stochasticity of environments is inevitable. However, current policy evaluation algorithms, which plays a key role in many RL algorithms, are either prone to noise or inefficient. To solve this issue, we introduce a novel policy evaluation a…
Geodesic distance is the shortest path between two points in a Riemannian manifold. Manifold learning algorithms, such as Isomap, seek to learn a manifold that preserves geodesic distances. However, such methods operate on the ambient dimensionality, and are therefore fragile to noise dimensions. We developed an unsupe…
We model how Lipschitz continuity changes during neural network training.
Orthogonal matching pursuit (OMP) is a widely used algorithm for recovering sparse high dimensional vectors in linear regression models. The optimal performance of OMP requires \textit{a priori} knowledge of either the sparsity of regression vector or noise statistics. Both these statistics are rarely known \textit{a p…
A novel dynamical model for the study of operational risk in banks and suitable for the calculation of the Value at Risk (VaR) is proposed. The equation of motion takes into account the interactions among different bank's processes, the spontaneous generation of losses via a noise term and the efforts made by the bank …
We prove that the empirical risk of most well-known loss functions factors into a linear term aggregating all labels with a term that is label free, and can further be expressed by sums of the loss. This holds true even for non-smooth, non-convex losses and in any RKHS. The first term is a (kernel) mean operator --the …
New neural operators model turbulence with memory and randomness.
New algorithm learns halfspaces with noise using Forster decomposition.
New insights into learning for blind inverse problems with theoretical guarantees.
Deep neural networks (DNNs) have achieved excellent performance on several tasks and have been widely applied in both academia and industry. However, DNNs are vulnerable to adversarial machine learning attacks, in which noise is added to the input to change the network output. We have devised an image-processing-based …
Neural networks parameterize time-varying Markov dynamics in financial time series.
New method uses SLL to create masks for PX in noisy optimization problems.
New method calibrates noise for attack risk, improving ML model accuracy.
Unified analysis of stochastic iterative algorithms using Lyapunov functions.
In this paper, we analyze PAC learnability from labels produced by crowdsourcing. In our setting, unlabeled examples are drawn from a distribution and labels are crowdsourced from workers who operate under classification noise, each with their own noise parameter. We develop an end-to-end crowdsourced PAC learning algo…
A method to reduce bias in model-based policy evaluation by shifting operators.
Transformer model removes noise from light curves efficiently.