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.
Convex optimization method recovers low-rank matrices from rank-one projections efficiently.
Develops methods to estimate high rank tensors from noisy data.
New algorithms estimate matrix leverage scores using rank revealing and randomization.
Novel method for efficient low-rank matrix estimation and bandit algorithms.
This paper presents a Bayesian method for estimating the rank of a low-rank tensor model of joint PMF.
New spectral methods improve matrix estimation in RL with low-rank structure.
Develops new oracle inequalities for Gaussian ranking estimators.
Ranked data appear in many different applications, including voting and consumer surveys. There often exhibits a situation in which data are partially ranked. Partially ranked data is thought of as missing data. This paper addresses parameter estimation for partially ranked data under a (possibly) non-ignorable missing…
A new estimator reduces bias and variance in ranking policy evaluation.
We present a unified framework for low-rank matrix estimation with nonconvex penalties. We first prove that the proposed estimator attains a faster statistical rate than the traditional low-rank matrix estimator with nuclear norm penalty. Moreover, we rigorously show that under a certain condition on the magnitude of t…
The paper addresses privacy in rank aggregation using randomized responses.
In the modern age, rankings data is ubiquitous and it is useful for a variety of applications such as recommender systems, multi-object tracking and preference learning. However, most rankings data encountered in the real world is incomplete, which prevents the direct application of existing modelling tools for complet…
New Hermite series estimator for Spearman rank correlation in non-stationary data.
This paper studies the estimation of low-rank Markov chains from empirical trajectories. We propose a non-convex estimator based on rank-constrained likelihood maximization. Statistical upper bounds are provided for the Kullback-Leiber divergence and the risk between the estimator and the true transition matri…
SON-NMF estimates nonnegative rank on-the-fly for NMF.
Paper develops RGN method for estimating low-rank tensors from noisy measurements.
Proposes a model for identifying edges in low-rank dynamical networks.
New framework explains why nonconvex methods work well in low-rank matrix estimation.
Estimation of low-rank matrices is of significant interest in a range of contemporary applications. In this paper, we introduce a rank-one projection model for low-rank matrix recovery and propose a constrained nuclear norm minimization method for stable recovery of low-rank matrices in the noisy case. The procedure is…
Optimal rank-adaptive matrix estimation from linear measurements.
We consider the problem of constructing a reduced-rank regression model whose coefficient parameter is represented as a singular value decomposition with sparse singular vectors. The traditional estimation procedure for the coefficient parameter often fails when the true rank of the parameter is high. To overcome this …
The problem of low-rank matrix estimation recently received a lot of attention due to challenging applications. A lot of work has been done on rank-penalized methods and convex relaxation, both on the theoretical and applied sides. However, only a few papers considered Bayesian estimation. In this paper, we review the …
Rank-statistic method approximates -divergences without density-ratio estimation.
Rank aggregation systems collect ordinal preferences from individuals to produce a global ranking that represents the social preference. Rank-breaking is a common practice to reduce the computational complexity of learning the global ranking. The individual preferences are broken into pairwise comparisons and applied t…
PLUMAGE improves large model training efficiency and stability.
New estimator GMIPS reduces variance in ranking policy evaluation.
The paper analyzes deflation for estimating a low-rank spike in large tensors with noise.
CRS model improves ranking data modeling with theoretical guarantees.
We consider the problem of noisy matrix completion, in which the goal is to reconstruct a structured matrix whose entries are partially observed in noise. Standard approaches to this underdetermined inverse problem are based on assuming that the underlying matrix has low rank, or is well-approximated by a low rank matr…
This paper protects rankings from differential privacy breaches.
We propose a unified framework for estimating low-rank matrices through nonconvex optimization based on gradient descent algorithm. Our framework is quite general and can be applied to both noisy and noiseless observations. In the general case with noisy observations, we show that our algorithm is guaranteed to linearl…
We develop a flexible framework for low-rank matrix estimation that allows us to transform noise models into regularization schemes via a simple bootstrap algorithm. Effectively, our procedure seeks an autoencoding basis for the observed matrix that is stable with respect to the specified noise model; we call the resul…
This paper presents a new method for estimating high dimensional covariance matrices. The method, permuted rank-penalized least-squares (PRLS), is based on a Kronecker product series expansion of the true covariance matrix. Assuming an i.i.d. Gaussian random sample, we establish high dimensional rates of convergence to…
Most recent results in matrix completion assume that the matrix under consideration is low-rank or that the columns are in a union of low-rank subspaces. In real-world settings, however, the linear structure underlying these models is distorted by a (typically unknown) nonlinear transformation. This paper addresses the…
ScaledGD accelerates ill-conditioned low-rank estimation.
Paper develops inference methods for low-rank tensors without debiasing.
We propose a novel and efficient algorithm for the collaborative preference completion problem, which involves jointly estimating individualized rankings for a set of entities over a shared set of items, based on a limited number of observed affinity values. Our approach exploits the observation that while preferences …
Consider the problem of estimating a low-rank matrix when its entries are perturbed by Gaussian noise. If the empirical distribution of the entries of the spikes is known, optimal estimators that exploit this knowledge can substantially outperform simple spectral approaches. Recent work characterizes the asymptotic acc…
Estimates joint probability distribution from 1-way marginals using low-rank tensors and random projections.
We provide new approximation guarantees for greedy low rank matrix estimation under standard assumptions of restricted strong convexity and smoothness. Our novel analysis also uncovers previously unknown connections between the low rank estimation and combinatorial optimization, so much so that our bounds are reminisce…
This paper sets fundamental limits for rank-one matrix estimation with varying noise levels.
New ranking models for time series data using GARCH-type approach.
The paper introduces metrics to rank potential outcomes for better decision-making.
Low-rank framework for task-specific LLM ranking from sparse comparisons.
UA-LQE improves value function learning by selectively erasing uncertain entries in Q-matrix.
Estimates low-rank distributional matrices from incomplete samples.
AIPS improves ranking policy evaluation by adapting to diverse user behavior.
Recovery of low-rank matrices has recently seen significant activity in many areas of science and engineering, motivated by recent theoretical results for exact reconstruction guarantees and interesting practical applications. A number of methods have been developed for this recovery problem. However, a principled meth…