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.
Study improves estimation of rare language model outputs.
Estimates joint probability distribution from 1-way marginals using low-rank tensors and random projections.
Method estimates joint probability density from samples using low-rank decomposition and random projections.
Matrix completion is a modern missing data problem where both the missing structure and the underlying parameter are high dimensional. Although missing structure is a key component to any missing data problems, existing matrix completion methods often assume a simple uniform missing mechanism. In this work, we study ma…
New method estimates and samples high-dimensional probability distributions avoiding optimization and approximation curse.
Improved matrix completion for non-uniformly sampled data.
Meta-analysis finds people value insurance for low-probability risks more than expected.
Matrix completion is often applied to data with entries missing not at random (MNAR). For example, consider a recommendation system where users tend to only reveal ratings for items they like. In this case, a matrix completion method that relies on entries being revealed at uniformly sampled row and column indices can …
This paper presents a method to efficiently estimate rare event probabilities using a combination of high and low-fidelity models.
Dynamic Vocabulary Pruning stabilizes LLM training by removing low-probability tokens.
Develops a new framework for estimating joint probability distributions.
A low-rank tensor model simplifies multi-dimensional Markov chains.
The estimation of probabilities of default (PDs) for low default portfolios by means of upper confidence bounds is a well established procedure in many financial institutions. However, there are often discussions within the institutions or between institutions and supervisors about which confidence level to use for the…
This paper presents a Bayesian method for estimating the rank of a low-rank tensor model of joint PMF.
NOFIS uses normalizing flows to estimate rare event probabilities more efficiently.
Convex optimization method recovers low-rank matrices from rank-one projections efficiently.
DoSE improves OOD detection by estimating model probability density.
Observational cohort studies with oversampled exposed subjects are typically implemented to understand the causal effect of a rare exposure. Because the distribution of exposed subjects in the sample differs from the source population, estimation of a propensity score function (i.e., probability of exposure given basel…
This paper aims at achieving a simultaneously sparse and low-rank estimator from the semidefinite population covariance matrices. We first benefit from a convex optimization which develops -norm penalty to encourage the sparsity and nuclear norm to favor the low-rank property. For the proposed estimator, we then p…
There has recently been considerable interest in completing a low-rank matrix or tensor given only a small fraction (or few linear combinations) of its entries. Related approaches have found considerable success in the area of recommender systems, under machine learning. From a statistical estimation point of view, the…
Develops methods to estimate high rank tensors from noisy data.
New method combines neural networks with Monte Carlo for complex system reliability.
Study shows targeting students with intermediate predicted outcomes is most effective for financial aid renewal.
The article explains the probabilistic method of default probability estimation by Pluto and Tasche.
The incredible variety of galaxy shapes cannot be summarized by human defined discrete classes of shapes without causing a possibly large loss of information. Dictionary learning and sparse coding allow us to reduce the high dimensional space of shapes into a manageable low dimensional continuous vector space. Statisti…
Deepfake detection is formulated as a hypothesis testing problem to classify an image as genuine or GAN-generated. A robust statistics view of GANs is considered to bound the error probability for various GAN implementations in terms of their performance. The bounds are further simplified using a Euclidean approximatio…
This study analyzes how well GANs approximate distributions from small samples.
The paper improves the probability flow ODE sampler for faster sampling of natural images.
We solve robust regression and matrix completion problems with sparse and low-rank models.
Novel approach for estimating joint probability densities using tensor decompositions and dictionaries.
This work studies applications and generalizations of a simple estimation technique that provides exponential concentration under heavy-tailed distributions, assuming only bounded low-order moments. We show that the technique can be used for approximate minimization of smooth and strongly convex losses, and specificall…
Optimizes optimal transport distances using low-dimensional embeddings.
The study shows how to accurately estimate embedding vectors in high dimensions.
This paper considers the problem of estimating a low-rank matrix from the observation of all or a subset of its entries in the presence of Poisson noise. When we observe all entries, this is a problem of matrix denoising; when we observe only a subset of the entries, this is a problem of matrix completion. In both case…
Estimates low-rank distributional matrices from incomplete samples.
Select-DC reduces GFLOPS for uncertainty estimation in neural networks.
Classification is an important statistical learning tool. In real application, besides high prediction accuracy, it is often desirable to estimate class conditional probabilities for new observations. For traditional problems where the number of observations is large, there exist many well developed approaches. Recentl…
We present power low rank ensembles (PLRE), a flexible framework for n-gram language modeling where ensembles of low rank matrices and tensors are used to obtain smoothed probability estimates of words in context. Our method can be understood as a generalization of n-gram modeling to non-integer n, and includes standar…
The question of how best to estimate a continuous probability density from finite data is an intriguing open problem at the interface of statistics and physics. Previous work has argued that this problem can be addressed in a natural way using methods from statistical field theory. Here I describe new results that allo…
Low-dimensional embedding, manifold learning, clustering, classification, and anomaly detection are among the most important problems in machine learning. The existing methods usually consider the case when each instance has a fixed, finite-dimensional feature representation. Here we consider a different setting. We as…
Kernel Density Machines learn probability densities without structural assumptions.
This paper proposes a method for multi-class classification problems, where the number of classes K is large. The method, referred to as Candidates vs. Noises Estimation (CANE), selects a small subset of candidate classes and samples the remaining classes. We show that CANE is always consistent and computationally effi…
Method fuses low and high-resolution data for better health estimates.
This paper studies least-square regression penalized with partly smooth convex regularizers. This class of functions is very large and versatile allowing to promote solutions conforming to some notion of low-complexity. Indeed, they force solutions of variational problems to belong to a low-dimensional manifold (the so…
Develops a method to estimate rare-event probabilities under distributional uncertainty.
Approximate message passing (AMP) refers to a class of efficient algorithms for statistical estimation in high-dimensional problems such as compressed sensing and low-rank matrix estimation. This paper analyzes the performance of AMP in the regime where the problem dimension is large but finite. For concreteness, we co…
In-game win probability models, which provide a sports team's likelihood of winning at each point in a game based on historical observations, are becoming increasingly popular. In baseball, basketball and American football, they have become important tools to enhance fan experience, to evaluate in-game decision-making,…
In this paper, we propose a general framework for sparse and low-rank tensor estimation from cubic sketchings. A two-stage non-convex implementation is developed based on sparse tensor decomposition and thresholded gradient descent, which ensures exact recovery in the noiseless case and stable recovery in the noisy cas…