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arXiv research

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.

169,181 papers · 148 categories

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48 results for frequent closed rankings

Proposes a new method for finding frequent closed patterns in transaction bases.

problem Frequent closed patterns in transaction bases.
method Partitioning the search space into subcontexts and updating frequent closed patterns with their minimal generators.
result Proposed approach called UFCIGs-DAC for efficient search of frequent closed itemsets.

Paper improves tensor approximation for streaming data.

problem Challenges in finding accurate low-tubal-rank tensor approximations in streaming settings.
method Extends Frequent Directions for efficient low-tubal-rank tensor approximation.
result The new algorithm achieves arbitrarily small approximation error with linear sketch size growth.

A new algorithm reduces the time and space complexity for multinomial logistic bandits.

problem High-dimensional feedback in multinomial logistic bandits makes existing algorithms inefficient.
method Integrates frequent directions matrix sketching into OFUL-MLogB to reduce time and space complexity.
result Achieves a regret bound of ildeO(ΔT(KdlnΔT+m)T) ilde{\mathcal{O}}(Δ_T(Kd\lnΔ_T+m)\sqrt{T}).

Sketchy reduces memory and compute requirements for adaptive regularization in deep learning.

problem Prohibitive memory and running time for adaptive regularization methods in deep learning.
method Low-rank sketching approach using Frequent Directions (FD) to reduce memory and compute requirements.
result Efficient interpolation between resource requirements and degradation in regret guarantees with rank kk.

Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at directly optimizing ranking-based evaluation metrics such as Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We first o…

2017-05-23abs ↗pdf ↗

The paper tackles learning true rankings from noisy, incomplete data.

problem Learning true rankings from incomplete and noisy data.
method Introduces a selective Mallows model for noisy rankings and derives upper and lower bounds on sample complexity.
result Strong asymptotically tight bounds on sample complexity for learning complete rankings and top-k rankings.

CICLAD efficiently mines frequent closed itemsets from data streams with minimal memory usage.

problem Mining frequent closed itemsets from data streams is resource-intensive.
method CICLAD is an intersection-based sliding-window FCI miner that optimizes memory usage while maintaining performance.
result CICLAD achieves significantly lower memory footprint compared to existing methods.

Max-rank improves multiple testing in conformal prediction.

problem Simultaneous testing of multiple hypotheses in scientific inquiries.
method Introduces max-rank, a novel correction for positive dependencies in simultaneous testing.
result Max-rank efficiently controls family-wise error rate and improves predictive uncertainty estimates.

A framework for quantifying uncertainty in feature importance values.

problem Stable interpretation of feature importance values in machine learning models.
method A novel method based on pairwise comparisons of feature importance values to produce confidence intervals for feature ranks.
result The method produces simultaneous confidence intervals for feature ranks, enabling selection of top-k important features.

The paper proposes using low rank assumption to improve causal structure learning in DAGs.

problem Challenges in learning causal structures in high-dimensional, non-sparse DAGs.
method Exploits low rank assumption of DAG adjacency matrix to adapt causal structure learning methods.
result Maximum rank is highly related to hubs, suggesting low rank for scale-free networks.

Proposes a method to enhance multi-view learning by maximizing higher order correlations.

problem Losing intrinsic interconnections among multiple views in pairwise correlation maximization.
method Formulates multi-view data as a low rank approximation problem using higher order correlation tensor and solves it with the generating polynomial method.
result Consistently outperforms prior methods on real multi-view data.

We show that if a closed manifold M admits an F-structure (possibly of rank 0) then its minimal entropy vanishes. In particular, this is the case if M admits a non-trivial circle action. As a corollary we obtain that the simplicial volume of a colsed manifold admitting an F-structure is zero. We also show that if M adm…

2000-11-15abs ↗pdf ↗

Proposes a new loss function to avoid treating missing information as positive or negative feedback.

problem Handling missing information in implicit feedback datasets for recommender systems.
method Introduces the Missing Information Loss (MIL) function and applies it to Matrix Factorization and Denoising Autoencoder.
result MIL achieves competitive performance in ranking-aware metrics and reduces the recommendation of popular items.

New method estimates and completes tensors from ordinal data, improving accuracy and efficiency.

problem Estimating and completing tensors from incomplete, ordinal observations.
method Multi-linear cumulative link model with rank-constrained M-estimator.
result The proposed estimator achieves faster convergence and is minimax optimal.

In many applications that require matrix solutions of minimal rank, the underlying cost function is non-convex leading to an intractable, NP-hard optimization problem. Consequently, the convex nuclear norm is frequently used as a surrogate penalty term for matrix rank. The problem is that in many practical scenarios th…

2014-08-09abs ↗pdf ↗

In many applications that require matrix solutions of minimal rank, the underlying cost function is non-convex leading to an intractable, NP-hard optimization problem. Consequently, the convex nuclear norm is frequently used as a surrogate penalty term for matrix rank. The problem is that in many practical scenarios th…

2012-07-10abs ↗pdf ↗

FasTR efficiently solves sparse and unit-rank tensor regression problems.

problem Sparse and unit-rank tensor regression problems in tensor data analysis.
method FasTR decomposes tensor coefficients into component vectors and estimates each with 1\ell_1 regularized regression, solving in parallel.
result FasTR computes better solutions faster than baseline models.

Bayesian approach to portfolio selection reduces pessimism in frequent trading.

problem Tackling the challenge of estimating drift in Merton's portfolio selection model.
method Bayesian distributionally robust control with nonlinear Wasserstein projections.
result Reduced pessimism and improved performance in frequent rebalancing compared to existing methods.

New methods for faster ranking and link prediction using higher-order motifs.

problem Real-time ranking and link prediction in applications like web search.
method Higher-order ranking and link prediction methods based on closing higher-order network motifs.
result The methods are faster and more efficient than existing methods based on closing triangles.

Develops a statistical framework to measure uncertainty in model rankings based on human preferences.

problem Uncertainty in model rankings based on human preferences due to mismatch between human and model preferences.
method Statistical framework using pairwise comparisons by humans and models to provide rank-sets for each model.
result Rank-sets constructed using only pairwise comparisons by strong models often do not cover the true ranking of human preferences.

Article presents QR and LQ decomposition algorithms for various matrix sizes and ranks.

problem Solving least squares problems in machine learning and computer vision.
method Developed novel matrix backpropagation algorithms for QR and LQ decompositions of different matrix sizes and ranks.
result Numerical stability and computational efficiency of the proposed methods.

We show the set of faithful representations of a closed orientable hyperbolic surface group is dense in both irreducible components of the PSL(2,K) representation variety, where K is the field of real or complex numbers, answering a question of W. Goldman. We also prove the existence of faithful representations into PU…

2004-11-11abs ↗pdf ↗

The paper develops a method to estimate consumer preferences from observed rankings.

problem Estimating consumer preferences from partial ranking information.
method Interpreting observed rankings as pairwise comparisons, modeling latent utility, and correcting for selection bias.
result The method improves recommendation performance, especially for previously unconsumed products.

Nowadays, how to effectively evaluate visual properties has become a popular topic for fine-grained visual comprehension. In this paper we study the problem of how to estimate such visual properties from a ranking perspective with the help of the annotators from online crowdsourcing platforms. The main challenges of ou…

2014-08-15abs ↗pdf ↗

Ranking stock indices based on causal influence using directed information graphs.

problem Identifying which countries exert the most economic influence in a subset of the global economy.
method Representing indices as nodes in a directed graph, estimating causal influences using directed information functional, ranking indices based on net-flow.
result Indices representing smaller economies can exert significant influence on larger economies.

Unified framework for non-Euclidean CPD under scalable stochastic mirror descent.

problem Handling non-Euclidean losses in tensor decomposition.
method Tensor fiber sampling strategy-based stochastic mirror descent.
result Global convergence to a stationary point under reasonable conditions.

For a given lattice, we establish an equivalence involving a closed zone of the corresponding Voronoi polytope, a lamina hyperplane of the corresponding Delaunay partition and a quadratic form of rank 1 being an extreme ray of the corresponding L-type domain.

2000-04-01abs ↗pdf ↗

Estimates intrinsic dimensionality of biological datasets using Fisher separability.

problem High-dimensional biological datasets with complex structures.
method Fisher separability analysis to estimate intrinsic dimensionality.
result The method performs competitively with state-of-the-art measures and is robust to noise.

Study on Finsler spaces with unique prime geodesics.

problem Estimating the number of orbits of prime closed geodesics in Finsler manifolds.
method Generalizing works on two prime closed geodesics to equivariant situation, studying homogeneous Finsler geometry.
result Closed Finsler manifold with only one orbit of prime closed geodesic is a compact rank-one Riemannian symmetric space when dimension is even or metric is reversible.

Politicians world-wide frequently promise a better life for their citizens. We find that the probability that a country will increase its {\it per capita} GDP ({\it gdp}) rank within a decade follows an exponential distribution with decay constant λ=0.12λ= 0.12. We use the Corruption Perceptions Index (CPI) and the Global …

2012-09-13abs ↗pdf ↗

Study manifolds with positive intermediate Ricci curvature and large symmetry rank.

problem Closed, simply connected manifolds with positive 2nd-intermediate Ricci curvature and large symmetry rank.
method New tools for studying isometric actions on closed manifolds with positive kth-intermediate Ricci curvature, including isotropy rank lemma, symmetry rank bound, and connectedness principle.
result Even dimensional manifolds with large symmetry rank must have trivial odd degree integral cohomology and are either spheres or complex projective spaces.