arXiv research

Study on coskewness under varying dependence uncertainty.

The paper examines higher moments in insurance, focusing on coskewness and its impact on actuarial quantities.

A new tree-based model for multivariate responses interprets piecewise linear regimes.

Algorithm tackles large-scale portfolio optimization with higher moments, improving computational efficiency.

Standardizes weighted ranking correlation coefficients to maintain zero expected value.

Goldman and Turaev constructed a Lie bialgebra structure on the free $\mathbb{Z}$-module generated by free homotopy classes of loops on a surface. Turaev conjectured that his cobracket $Δ(α)$ is zero if and only if $α$ is a power of a simple class. Chas constructed examples that show Turaev's conjecture is, unfortunate…

The Nystrom method is a popular technique that uses a small number of landmark points to compute a fixed-rank approximation of large kernel matrices that arise in machine learning problems. In practice, to ensure high quality approximations, the number of landmark points is chosen to be greater than the target rank. Ho…

GANs improve missing data imputation for ranking tasks.

A new method for fair PCA ensures balanced error across groups.

Learning the true ordering between objects by aggregating a set of expert opinion rank order lists is an important and ubiquitous problem in many applications ranging from social choice theory to natural language processing and search aggregation. We study the problem of unsupervised rank aggregation where no ground tr…

Geometric families of low-rank covariances improve flexibility and tractability in high dimensions.

A new Riemannian framework optimizes LoRA for faster convergence and better performance.

We present an algorithm, AROFAC2, which detects the (CP-)rank of a degree 3 tensor and calculates its factorization into rank-one components. We provide generative conditions for the algorithm to work and demonstrate on both synthetic and real world data that AROFAC2 is a potentially outperforming alternative to the go…

QS-BO optimizes functions using only rank-based feedback.

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 …

TensorGuide improves LoRA efficiency and expressivity through joint tensor-train optimization.

CAIRO separates ranking from scaling to improve robustness.

Develops new oracle inequalities for Gaussian ranking estimators.

Reduced-rank method improves least-squares regression under output regularity.

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…

New algorithm improves asset ranking for better cross-sectional portfolios.

Time-aware fact-checking improves veracity predictions for time-sensitive claims.

This paper compares rank aggregation methods for partial label ranking.

A new method for efficient neural network fine-tuning using queryable low-rank update atoms.

New tensor recovery method improves efficiency under strict complementarity.

Extended dual Coxeter and Artin groups theory to rank-three systems.

Classifies rank-one submanifolds in Euclidean space.

An algorithm tackles low-rank linear bandit problems with improved regret bounds.

Researchers show mixtures of ranking models are generally identifiable.

A new method for uplift modeling using learning-to-rank techniques.

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 examines the problem of ranking a collection of objects using pairwise comparisons (rankings of two objects). In general, the ranking of $n$ objects can be identified by standard sorting methods using $n log_2 n$ pairwise comparisons. We are interested in natural situations in which relationships among the o…

This paper concerns a fundamental class of convex matrix optimization problems. It presents the first algorithm that uses optimal storage and provably computes a low-rank approximation of a solution. In particular, when all solutions have low rank, the algorithm converges to a solution. This algorithm, SketchyCGM, modi…

The study assesses low-rank approximations in Gaussian Process regression.

In ranking problems, the goal is to learn a ranking function from labeled pairs of input points. In this paper, we consider the related comparison problem, where the label indicates which element of the pair is better, or if there is no significant difference. We cast the learning problem as a margin maximization, and …

A new method for 1-bit matrix completion that is faster and more accurate.

The study assesses low-rank approximations in Gaussian Process regression.

Paper tackles fair low-rank approximation and column subset selection.

The paper proves concentration inequalities for two-sample rank processes and applies them to ranking performance criteria.

New method solves nonsmooth low-rank matrix optimization problems efficiently.

A 2-torus manifold is a closed smooth manifold of dimension $n$ with an effective action of a 2-torus group $(\Z_2)^n$ of rank $n$, and it is said to be locally standard if it is locally isomorphic to a faithful representation of $(\Z_2)^n$ on $\R^n$. This paper studies the equivariant classification of locally standar…

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…

Improves CRRR for better mobility analysis with DCTM.

Paper tackles underranking in group-fair ranking systems, proving a trade-off and presenting an algorithm.

New approach to handle ranking function variation in zero-shot NAS.

We optimize rank-based metrics using blackbox differentiation.

In recent years rank aggregation has received significant attention from the machine learning community. The goal of such a problem is to combine the (partially revealed) preferences over objects of a large population into a single, relatively consistent ordering of those objects. However, in many cases, we might not w…

Let X be quasi-isometric to either the mapping class group equipped with the word metric, or to Teichmuller space equipped with either the Teichmuller metric or the Weil-Petersson metric. We introduce a unified approach to study the coarse geometry of these spaces. We show that the quasi-Lipschitz image in X of a box i…