arXiv research

The paper tackles fair ranking in ranked data by addressing causal discrimination.

We find a rank effect in commodity prices that yields higher returns.

Heteroskedasticity biases uplift model rankings, leading to inefficient treatment allocation.

Proves effective Chen ranks conjecture for Koszul modules.

This work examines how low-rank weights improve adversarial robustness in neural networks.

LORIS model estimates main and interaction effects in large data frames.

Effective rank rigidity proved for cubulated groups with factor systems.

StealthRank subtly boosts LLM rankings without detectable anomalies.

Two-stage risk control for ranked retrieval systems.

New estimator GMIPS reduces variance in ranking policy evaluation.

Deep networks learn clean structure before memorizing corrupted labels, leaving a spectral signature in gradient centered scatter.

The paper extends a proposal for effective twisted superpotentials to higher rank.

Optimal rank-adaptive matrix estimation from linear measurements.

The paper examines how trading strategies lose value due to stock turnover.

Matrix rank minimization problem is in general NP-hard. The nuclear norm is used to substitute the rank function in many recent studies. Nevertheless, the nuclear norm approximation adds all singular values together and the approximation error may depend heavily on the magnitudes of singular values. This might restrict…

Proposes a method to identify subgroup structure and estimate covariate effects for multivariate response data.

A new method trains compact ranking models for recommender systems.

Symmetry rank bound for manifolds with positive intermediate Ricci curvature.

Low-rank approach to metric learning from data.

Research reveals deep networks often learn low-rank structures, leading to more efficient training and fine-tuning.

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…

EENMF improves e-commerce sponsored search efficiency and effectiveness.

New methods recover best rank-r approximations from few entries.

Boosting framework improves Factorization Machines for recommendation systems.

New method estimates heterogeneous treatment effects with improved guarantees.

Paper proposes unbiased learning for recommendation causal effects.

Paper tackles real-world e-commerce search efficiency and user experience.

Proposes a low-rank PGD attack for more efficient adversarial training.

The paper sorts big data by revealed preferences, improving consumer and policy decisions.

Proposes a method for ranking items across multiple aspects based on user feedback.

A framework for ranking with abstention, offering theoretical guarantees and practical effectiveness.

We consider the link prediction problem in a partially observed network, where the objective is to make predictions in the unobserved portion of the network. Many existing methods reduce link prediction to binary classification problem. However, the dominance of absent links in real world networks makes misclassificati…

RaSE screens variables via random subspaces, identifying joint effects.

Extends expected value framework for cost-sensitive causal decision-making.

Sparse reduced-rank regression selects variables and ranks via manifold optimization.

Proposes a new method to optimize treatment allocation with budget constraints.

Study robust recovery of low-rank matrices from corrupted measurements without rank prior.

We address the problem of learning a ranking by using adaptively chosen pairwise comparisons. Our goal is to recover the ranking accurately but to sample the comparisons sparingly. If all comparison outcomes are consistent with the ranking, the optimal solution is to use an efficient sorting algorithm, such as Quicksor…

Random forest method improves label ranking performance.

This paper protects rankings from differential privacy breaches.

Paper proposes a method for estimating complex low-rank matrices from phase-only measurements.

The aim of this paper is to give an upper bound for the dimension of a torus $T$ which acts on a GKM manifold $M$ effectively. In order to do that, we introduce a free abelian group of finite rank, denoted by $\mathcal{A}(Γ,α,\nabla)$, from an (abstract) $(m,n)$-type GKM graph $(Γ,α,\nabla)$. Here, an $(m,n)$-type GKM …

We show that any effective isometric torus action of maximal rank on a compact Riemannian manifold with positive (sectional) curvature and maximal symmetry rank, that is, on a positively curved sphere, lens space, complex or real projective space, is equivariantaly diffeomorphic to a linear action. We show that a compa…

Optimizes wide low-rank neural networks for reduced parameters and cost.

Efficient algorithm for Hadamard decomposition of matrices.

Decomposes returns of bottom-ranked assets, finds excess returns.

New method corrects Laplace/BIC errors in singular models, revealing effective dimension.

An upper bound is obtained on the rank of a torus which can act smoothly and effectively on a smooth, closed, simply connected, rationally elliptic manifold. In the maximal-rank case, the manifolds admitting such actions are classified up to equivariant rational homotopy type.