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.
New algorithm accelerates single-pass SGD for generalized linear prediction.
SPACR trains uncertainty-aware regressors directly within a single pass, improving efficiency and validity.
New framework models uncertainty in classification debates.
Proposes PSCs for UQ in deep nets without retraining.
A new single-pass algorithm improves sparse PCA under limited computational resources.
Single-pass method estimates neural network uncertainty.
Improved single-pass streaming MAB regret bound to O(K^1/3T^2/3).
In this paper we present a new algorithm for computing a low rank approximation of the product by taking only a single pass of the two matrices and . The straightforward way to do this is to (a) first sketch and individually, and then (b) find the top components using PCA on the sketch. Our algori…
A new classifier encodes local neighborhoods for each class using Fly Bloom Filters.
We consider stochastic gradient descent (SGD) for least-squares regression with potentially several passes over the data. While several passes have been widely reported to perform practically better in terms of predictive performance on unseen data, the existing theoretical analysis of SGD suggests that a single pass i…
PySAD offers a unified Python framework for efficient streaming anomaly detection.
Given a collection of categorical data, we want to find the parameters of a Dirichlet distribution which maximizes the likelihood of that data. Newton's method is typically used for this purpose but current implementations require reading through the entire dataset on each iteration. In this paper, we propose a modific…
We derive a single pass algorithm for computing the gradient and Fisher information of Vecchia's Gaussian process loglikelihood approximation, which provides a computationally efficient means for applying the Fisher scoring algorithm for maximizing the loglikelihood. The advantages of the optimization techniques are de…
Numerous algorithms are used for nonnegative matrix factorization under the assumption that the matrix is nearly separable. In this paper, we show how to make these algorithms efficient for data matrices that have many more rows than columns, so-called "tall-and-skinny matrices". One key component to these improved met…
FinReflectKG builds a comprehensive financial knowledge graph from SEC filings, improving extraction quality.
FinReflectKG - EvalBench benchmarks financial KG extraction from SEC 10-K filings.
We develop and analyze a procedure for gradient-based optimization that we refer to as stochastically controlled stochastic gradient (SCSG). As a member of the SVRG family of algorithms, SCSG makes use of gradient estimates at two scales, with the number of updates at the faster scale being governed by a geometric rand…
This note explores probabilistic sampling weighted by uncertainty in active learning. This method has been previously used and authors have tangentially remarked on its efficacy. The scheme has several benefits: (1) it is computationally cheap, (2) it can be implemented in a single-pass streaming fashion which is a ben…
In many estimation problems, e.g. linear and logistic regression, we wish to minimize an unknown objective given only unbiased samples of the objective function. Furthermore, we aim to achieve this using as few samples as possible. In the absence of computational constraints, the minimizer of a sample average of observ…
Two-layer networks learn faster with batch reuse, overcoming information and leap exponents.
We present a streaming model for large-scale classification (in the context of -SVM) by leveraging connections between learning and computational geometry. The streaming model imposes the constraint that only a single pass over the data is allowed. The -SVM is known to have an equivalent formulation in …
Improved uncertainty estimation in neural networks with VBLL.
New algorithms minimize regret in streaming MAB with memory constraints.
Generative networks implicitly approximate complex densities from their sampling with impressive accuracy. However, because of the enormous scale of modern datasets, this training process is often computationally expensive. We cast generative network training into the recent framework of compressive learning: we reduce…
The Denoising Autoencoder (DAE) enhances the flexibility of the data stream method in exploiting unlabeled samples. Nonetheless, the feasibility of DAE for data stream analytic deserves an in-depth study because it characterizes a fixed network capacity that cannot adapt to rapidly changing environments. Deep evolving …
Itô maps provide a method for any-step SDE integration.
This paper provides fast estimates for complex option types.
We present SDA-Bayes, a framework for (S)treaming, (D)istributed, (A)synchronous computation of a Bayesian posterior. The framework makes streaming updates to the estimated posterior according to a user-specified approximation batch primitive. We demonstrate the usefulness of our framework, with variational Bayes (VB) …
Oja's algorithm has been the cornerstone of streaming methods in Principal Component Analysis (PCA) since it was first proposed in 1982. However, Oja's algorithm does not have a standardized choice of learning rate (step size) that both performs well in practice and truly conforms to the online streaming setting. In th…
A scalable method for heavy-tailed gradients using cheap stochastic sub-processes.
GAPA method provides efficient uncertainty quantification for pretrained networks.
DVE uses GPs on DNN outputs to provide UQ without retraining.
Recent work has introduced a simple numerical method for solving partial differential equations (PDEs) with deep neural networks (DNNs). This paper reviews and extends the method while applying it to analyze one of the most fundamental features in numerical PDEs and nonlinear analysis: irregular solutions. First, the S…
Aux-Net model handles dynamic systems with inconsistent inputs.
We analyze a compression scheme for large data sets that randomly keeps a small percentage of the components of each data sample. The benefit is that the output is a sparse matrix and therefore subsequent processing, such as PCA or K-means, is significantly faster, especially in a distributed-data setting. Furthermore,…
Cramming method evaluates learned policies from contextual bandits efficiently.
SONAR improves outlier detection for streaming data with strong theoretical guarantees.
RCLA reduces noise in topological data analysis, preserving essential structure.
Paper proposes a new landmark selection method for kernel ridge regression.
Kernel approximation using randomized feature maps has recently gained a lot of interest. In this work, we identify that previous approaches for polynomial kernel approximation create maps that are rank deficient, and therefore do not utilize the capacity of the projected feature space effectively. To address this chal…
Continual learning, the setting where a learning agent is faced with a never ending stream of data, continues to be a great challenge for modern machine learning systems. In particular the online or "single-pass through the data" setting has gained attention recently as a natural setting that is difficult to tackle. Me…
AUC (Area under the ROC curve) is an important performance measure for applications where the data is highly imbalanced. Learning to maximize AUC performance is thus an important research problem. Using a max-margin based surrogate loss function, AUC optimization problem can be approximated as a pairwise rankSVM learni…
Improved DP optimization for nonconvex, nonsmooth objectives with reduced sample complexity.
Fast-vollib offers high-performance option pricing and IV computation.
One-pass private sketch supports various machine learning tasks.
New algorithms for online MAP inference and learning for NDPPs.
This work brings together two powerful concepts in Gaussian processes: the variational approach to sparse approximation and the spectral representation of Gaussian processes. This gives rise to an approximation that inherits the benefits of the variational approach but with the representational power and computational …
Improved algorithm reduces stochastic gradient complexity for large-scale learning problems.