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.
Memory-efficient learning for large-scale imaging systems.
Efficiently solves large-scale robust portfolio optimization problems.
PALMS reconstructs large-scale networks efficiently with parallel computing.
This paper surveys large-scale machine learning methods for efficient data analysis.
We propose a practical and scalable Gaussian process model for large-scale nonlinear probabilistic regression. Our mixture-of-experts model is conceptually simple and hierarchically recombines computations for an overall approximation of a full Gaussian process. Closed-form and distributed computations allow for effici…
Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training.…
Representations of probability measures in reproducing kernel Hilbert spaces provide a flexible framework for fully nonparametric hypothesis tests of independence, which can capture any type of departure from independence, including nonlinear associations and multivariate interactions. However, these approaches come wi…
Paper proposes data quality measures for large-scale high-dimensional data.
A new TwinGP framework for efficient large-scale GP modeling.
We propose a new two stage algorithm LING for large scale regression problems. LING has the same risk as the well known Ridge Regression under the fixed design setting and can be computed much faster. Our experiments have shown that LING performs well in terms of both prediction accuracy and computational efficiency co…
The paper simplifies influence computations for large-scale machine learning models.
Knowledge distillation is an effective technique that transfers knowledge from a large teacher model to a shallow student. However, just like massive classification, large scale knowledge distillation also imposes heavy computational costs on training models of deep neural networks, as the softmax activations at the la…
New method speeds up learning of complex dynamical systems.
We study Nyström type subsampling approaches to large scale kernel methods, and prove learning bounds in the statistical learning setting, where random sampling and high probability estimates are considered. In particular, we prove that these approaches can achieve optimal learning bounds, provided the subsampling leve…
Local GP approach improves simulation efficiency for large datasets.
Benefitting from large-scale training datasets and the complex training network, Convolutional Neural Networks (CNNs) are widely applied in various fields with high accuracy. However, the training process of CNNs is very time-consuming, where large amounts of training samples and iterative operations are required to ob…
DistPre predicts traffic speeds efficiently for large networks.
New method estimates bidirectional causal effects in large-scale systems.
In recent years, ideas from statistics and scientific computing have begun to interact in increasingly sophisticated and fruitful ways with ideas from computer science and the theory of algorithms to aid in the development of improved worst-case algorithms that are useful for large-scale scientific and Internet data an…
This work improves Gaussian process model selection for large datasets.
New method approximates CV efficiently for large-scale problems.
Many real-world regression problems demand a measure of the uncertainty associated with each prediction. Standard decision forests deliver efficient state-of-the-art predictive performance, but high-quality uncertainty estimates are lacking. Gaussian processes (GPs) deliver uncertainty estimates, but scaling GPs to lar…
Estimates model performance from compute budget for distillation.
Researchers parallelize neural kernels for large-scale data, achieving state-of-the-art accuracy.
Quantum computing offers energy savings over classical computing.
New algorithms estimate Jacobian matrices for large-scale machine learning.
Scalable and robust TR decomposition for large-scale data with missing entries and outliers.
We study large-scale classification problems in changing environments where a small part of the dataset is modified, and the effect of the data modification must be quickly incorporated into the classifier. When the entire dataset is large, even if the amount of the data modification is fairly small, the computational …
Study evaluates machine learning methods for large-scale network reliability, revealing ANN's and PR's performance.
This paper proposes a new Nystrom-based clustering algorithm for large-scale data.
Efficiently maps indoor magnetic fields with SKI and D-SKI.
In this paper we address the problem of performing statistical inference for large scale data sets i.e., Big Data. The volume and dimensionality of the data may be so high that it cannot be processed or stored in a single computing node. We propose a scalable, statistically robust and computationally efficient bootstra…
We propose a method to visualize class similarity in large-scale classifiers.
New method adds interactions to interpretable models for large-scale data.
A new algorithm speeds up CP decomposition for large tensors.
This paper introduces Sigma, a domain-specific computational representation for collaboration in large-scale for the field of economics. A computational representation is not a programming language or a software platform. A computational representation is a domain-specific representation system based on three specific …
New algorithm tackles big data Bayesian problems with latent variables.
We study linear models under heavy-tailed priors from a probabilistic viewpoint. Instead of computing a single sparse most probable (MAP) solution as in standard deterministic approaches, the focus in the Bayesian compressed sensing framework shifts towards capturing the full posterior distribution on the latent variab…
Algorithm tackles large-scale portfolio optimization with higher moments, improving computational efficiency.
The nowadays massive amounts of generated and communicated data present major challenges in their processing. While capable of successfully classifying nonlinearly separable objects in various settings, subspace clustering (SC) methods incur prohibitively high computational complexity when processing large-scale data. …
Paper tackles non-convex constrained DRO with a stochastic algorithm for large-scale applications.
Proposes GBBHE for efficient large-scale regression.
New algorithms solve large-scale convex regression problems.
Video applications and analytics are routinely projected as a stressing and significant service of the Nationwide Public Safety Broadband Network. As part of a NIST PSCR funded effort, the New Jersey Office of Homeland Security and Preparedness and MIT Lincoln Laboratory have been developing a computer vision dataset o…
New quasi-geodesics for Stiefel manifold simplify complex computations.
Drawing a sample from a discrete distribution is one of the building components for Monte Carlo methods. Like other sampling algorithms, discrete sampling suffers from the high computational burden in large-scale inference problems. We study the problem of sampling a discrete random variable with a high degree of depen…
We focus on developing a novel scalable graph-based semi-supervised learning (SSL) method for a small number of labeled data and a large amount of unlabeled data. Due to the lack of labeled data and the availability of large-scale unlabeled data, existing SSL methods usually encounter either suboptimal performance beca…
We study in this paper a variant of Wasserstein barycenter problem, which we refer to as tree-Wasserstein barycenter, by leveraging a specific class of ground metrics, namely tree metrics, for Wasserstein distance. Drawing on the tree structure, we propose an efficient algorithmic approach to solve the tree-Wasserstein…