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.
Tall wheatgrass outperforms rye in energy and environmental metrics, marginally improving economic viability.
New method speeds up inference for tall data models.
Kernel improves Gaussian process scalability for wide datasets.
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…
Interference effects of tall buildings have attracted numerous studies due to the boom of clusters of tall buildings in megacities. To fully understand the interference effects of buildings, it often requires a substantial amount of wind tunnel tests. Limited wind tunnel tests that only cover part of interference scena…
Solves area-minimizing surface problem for finite curves in H^2xR.
Kähler complexity one Hamiltonian T-manifolds have trivial paintings.
This paper describes a distributed MapReduce implementation of the minimum Redundancy Maximum Relevance algorithm, a popular feature selection method in bioinformatics and network inference problems. The proposed approach handles both tall/narrow and wide/short datasets. We further provide an open source implementation…
Markov chain Monte Carlo methods are often deemed too computationally intensive to be of any practical use for big data applications, and in particular for inference on datasets containing a large number of individual data points, also known as tall datasets. In scenarios where data are assumed independent, various…
R package spca computes sparse principal components efficiently.
This note provides some new perspectives and calculations regarding an interesting known family of minimal surfaces in . The surfaces in this family are the catenoids, parabolic catenoids and tall rectangles. Each is foliated by either circles, horocycles or circular arcs in horizontal c…
ECD algorithm speeds up non-convex optimization, offering quantum and stochastic enhancements.
The paper proposes methods to estimate MCMC quality with couplings, bounding Wasserstein distance.
Nonnegative matrix factorization (NMF) has an established reputation as a useful data analysis technique in numerous applications. However, its usage in practical situations is undergoing challenges in recent years. The fundamental factor to this is the increasingly growing size of the datasets available and needed in …
SMI uses mixture models to improve SVGD's performance in Bayesian inference.
New MCMC methods use auxiliary variables to sample from intractable distributions.
Random sampling has become a critical tool in solving massive matrix problems. For linear regression, a small, manageable set of data rows can be randomly selected to approximate a tall, skinny data matrix, improving processing time significantly. For theoretical performance guarantees, each row must be sampled with pr…
Matrix completion, where we wish to recover a low rank matrix by observing a few entries from it, is a widely studied problem in both theory and practice with wide applications. Most of the provable algorithms so far on this problem have been restricted to the offline setting where they provide an estimate of the unkno…
Light pillars over rippled water appear parallel due to projection geometry.
The n-solvable filtration of the smooth knot concordance group (denoted by ), due to Cochran-Orr-Teichner, has been instrumental in the study of knot concordance in recent years. Part of its significance is due to the fact that certain geometric characterizations of a knot …
A new method reduces the bias in estimating inverse covariance matrices from sketches.