On-device research index

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 “input ranking”

Gaussian Process Models with Low-Rank Correlation Matrices for Both Continuous and Categorical Inputsstat.ML

A new method for Gaussian Processes handles mixed continuous and categorical inputs.

problem Modeling cross-correlations between continuous and categorical data.

method Low-Rank Correlation (LRC) method for Gaussian Processes with flexible rank approximation.

result LRC outperforms existing methods in estimating cross-correlations and predicting response surfaces.

Gaussian Processes Low-Rank Correlation Mixed Data Continuous and Categorical Inputs Cross-Correlation

2020-10-06abs ↗pdf ↗

Stochastic Rank Aggregationcs.LG

This paper addresses the problem of rank aggregation, which aims to find a consensus ranking among multiple ranking inputs. Traditional rank aggregation methods are deterministic, and can be categorized into explicit and implicit methods depending on whether rank information is explicitly or implicitly utilized. Surpri…

2013-09-26abs ↗pdf ↗

Pairwise Ranking with Gaussian Kernelsstat.ML

Develops new oracle inequalities for Gaussian ranking estimators.

problem Lack of rigorous theoretical support for Gaussian ranking estimators.

method Novel oracle inequalities for regularized pairwise ranking.

result Derives fast learning rates under general dimension assumptions.

Gaussian ranking oracle inequalities pairwise ranking learning rates intrinsic dimension

2023-04-06abs ↗pdf ↗

Targeted matrix completioncs.LG

Matrix completion is a problem that arises in many data-analysis settings where the input consists of a partially-observed matrix (e.g., recommender systems, traffic matrix analysis etc.). Classical approaches to matrix completion assume that the input partially-observed matrix is low rank. The success of these methods…

2017-04-30abs ↗pdf ↗

Ranking and Selection with Simultaneous Input Data Collectionstat.ML

Proposes a method to optimize budget allocation for collecting and analyzing streaming data.

problem Optimizing resource allocation for collecting and analyzing streaming data.

method Formulates optimization problems to allocate budgets for collecting input data and running simulations, characterizes asymptotic behavior of performance estimators, and develops a multi-stage simultaneous budget allocation procedure.

result Demonstrates competitive performance of the proposed procedure through numerical studies.

ranking and selection streaming data budget allocation asymptotic behavior simultaneous collection

2025-03-14abs ↗pdf ↗

Upstreamness and downstreamness in input-output analysis from local and aggregate informationphysics.soc-ph

New method ranks sectors and countries using local and aggregate I-O data.

problem Ranking sectors and countries in global value chains using incomplete I-O tables.

method Rank-

1

approximation to I-O tables using local and aggregate information.

result Consistently good performance in reconstructing rankings of upstreamness and downstreamness.

Input-Output analysis Global value chains Ranking Spectral properties Rank-$1$ approximation

2020-09-14abs ↗pdf ↗

Optimizing Input Data Collection for Ranking and Selectionstat.ME

Optimizes data collection for ranking and selection problems.

problem Identifying the best system from multiple solutions with limited data.

method Sequential sampling algorithm with MPB estimator and kernel ridge regression.

result OSAR achieves optimal sampling ratios almost surely in the limit.

ranking and selection sequential sampling Bayesian input models kernel ridge regression optimal sampling ratios

2025-02-23abs ↗pdf ↗

Low Rank Gradients and Where to Find Themcs.LG

The paper reveals low-rank structure in neural network gradients, influenced by data and model parameters.

problem Investigating low-rank structure in gradients of neural networks under relaxed assumptions.

method Spiked data model, relaxation of isotropy assumptions, analysis of mean-field and neural-tangent-kernel scalings.

result Gradient of input weights is approximately low rank, dominated by two rank-one terms.

neural networks gradient structure low-rank spiked data neural tangent kernel

2025-10-01abs ↗pdf ↗

Ranking Feature-Block Importance in Artificial Multiblock Neural Networkscs.LG

This study ranks feature-block importance in multiblock neural networks.

problem Understanding feature contributions in multiblock neural networks.

method Three methods: composite, knock-in, and knock-out strategies.

result Each strategy has its merits for specific application scenarios.

feature importance multiblock neural networks mutual information artificial neural networks model explainability

2021-09-21abs ↗pdf ↗

Practical sketching algorithms for low-rank matrix approximationmath.NA

This paper describes a suite of algorithms for constructing low-rank approximations of an input matrix from a random linear image of the matrix, called a sketch. These methods can preserve structural properties of the input matrix, such as positive-semidefiniteness, and they can produce approximations with a user-speci…

2016-08-31abs ↗pdf ↗

On Rank-Dependent Generalisation Error Bounds for Transformersstat.ML

The paper improves transformer generalization bounds using rank-dependent covering number bounds.

problem Improving generalization bounds for transformers.

method Introducing rank-dependent covering number bounds for linear function classes and applying them to transformers.

result Generalization error bounds for transformers decay as

O(1/\sqrt{n})

and

O(\log r_w)

, improving existing bounds.

transformers generalization rank-dependent covering number linear function classes

2024-10-15abs ↗pdf ↗

Approximate Matrix Multiplication with Application to Linear Embeddingsmath.ST

In this paper, we study the problem of approximately computing the product of two real matrices. In particular, we analyze a dimensionality-reduction-based approximation algorithm due to Sarlos [1], introducing the notion of nuclear rank as the ratio of the nuclear norm over the spectral norm. The presented bound has i…

2014-03-30abs ↗pdf ↗

Top-

k

Ranking Bayesian Optimizationcs.LG

Paper introduces MPES for top-k ranking BO with preferential observations.

problem Handling top-k ranking and tie/indifference observations in Bayesian optimization.

method Designs a surrogate model and introduces MPES acquisition function.

result MPES outperforms existing acquisition functions in handling preferential observations.

Bayesian optimization top-k ranking preferential observations multinomial predictive entropy search

2020-12-19abs ↗pdf ↗

Sparse Hierarchical Regression with Polynomialsmath.OC

New method for sparse polynomial regression with fast input ranking and cutting plane optimization.

problem Sparse polynomial regression with controlled functional complexity.

method Two-step approach: input ranking followed by integer optimization.

result Empirical phase transition in identifying relevant inputs and monomials.

sparse regression polynomial models input ranking integer optimization phase transition

2017-09-28abs ↗pdf ↗

Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Modelscs.LG

LoRAs enable efficient adaptation of large models; this paper explores processing LoRA weights with machine learning.

problem Efficient processing of low-rank weight decompositions in large finetuned models.

method Developed symmetry-aware invariant and equivariant LoL models to process LoRA weights.

result LoL models can predict CLIP scores, finetuning data attributes, and accuracy on downstream tasks.

LoRAs Low-rank weights Machine learning Invariant models Equivariant models

2024-10-05abs ↗pdf ↗

Double Descent and Overfitting under Noisy Inputs and Distribution Shift for Linear Denoiserscs.LG

The study examines denoising and noisy-input regression under distribution shift, revealing double descent behavior and insights for data augmentation.

problem Understanding denoising in machine learning, especially under noisy inputs and distribution shift.

method Theoretical analysis of supervised denoising and noisy-input regression, considering low-rank data and proportional regime.

result The test error exhibits double descent under general distribution shift, indicating that overfitting the noise can be benign, tempered, or catastrophic.

denoising noisy-input regression distribution shift double descent overfitting

2023-05-26abs ↗pdf ↗

Rank Ordered Autoencoderscs.LG

A new method for the unsupervised learning of sparse representations using autoencoders is proposed and implemented by ordering the output of the hidden units by their activation value and progressively reconstructing the input in this order. This can be done efficiently in parallel with the use of cumulative sums and …

2016-05-05abs ↗pdf ↗

Geometry of rank 2 distributions with nonzero Wilczynski invariants and affine control systems with one inputmath.DG

We demonstrate how the novel approach to the local geometry of structures of nonholonomic nature, originated by Andrei Agrachev, works in the following two situations: rank 2 distributions of maximal class in R^n with non-zero generalized Wilczynski invariants and rank 2 distributions of maximal class in R^n with addit…

2013-01-13abs ↗pdf ↗

Additive Distributionally Robust Ranking and Selectionstat.ML

Proposes a new allocation method for distributionally robust ranking and selection.

problem Inaccurate simulation input modeling due to limited data.

method Introduces a simple additive allocation (AA) procedure and a general additive allocation (GAA) framework.

result Proves that the proposed AA procedure is consistent and achieves additivity in the strongest sense.

ranking and selection distributionally robust additive allocation boundary-crossing arguments consistency

2025-09-07abs ↗pdf ↗

Learning a Latent Simplex in Input-Sparsity Timecs.LG

Algorithm learns latent simplex from perturbed points in input-sparsity time.

problem Learning a latent

k

-vertex simplex from noisy data.

method Input-sparsity time algorithm using low-rank approximation and adaptive selection.

result Algorithm achieves

O( extrm{nnz}(A))

time complexity, avoiding

k\cdot extrm{nnz}(A)

latent simplex input-sparsity low-rank approximation adaptive selection spectral low rank approximation

2021-05-17abs ↗pdf ↗

Structural Incompatibility of Differentiable Sorting and Within-Vector Rank Normalizationcs.LG

Differentiable sorting and rank normalization are incompatible, with specific conditions for admissibility.

problem Incompatibility between differentiable sorting and rank normalization.

method Formalized admissibility through monotone invariance, batch independence, and rank-space stability conditions.

result Different gap-sensitive and batchwise relaxations of rank normalization violate the conditions for admissibility.

differentiable sorting rank normalization incompatibility monotone invariance batch independence

2025-12-27abs ↗pdf ↗

On the Initialisation of Wide Low-Rank Feedforward Neural Networksstat.ML

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

problem Reducing the number of learnable parameters in wide neural networks.

method Analyzed edge-of-chaos dynamics and derived formulae for optimal weight and bias variances.

result Optimal weight and bias variances for low-rank networks follow from multiplicative scaling.

neural networks low-rank initialization edge-of-chaos variance

2023-01-31abs ↗pdf ↗

Selecting the rank of truncated SVD by Maximum Approximation Capacitycs.IT

Truncated Singular Value Decomposition (SVD) calculates the closest rank- $k$ approximation of a given input matrix. Selecting the appropriate rank $k$ defines a critical model order choice in most applications of SVD. To obtain a principled cut-off criterion for the spectrum, we convert the underlying optimization prob…

2011-02-15abs ↗pdf ↗

New Additive OCBA Procedures for Robust Ranking and Selectionstat.ME

New OCBA procedures minimize PICS in robust R&S.

problem Selecting the best alternative under input uncertainty.

method Developed new asymptotically optimal OCBA procedures.

result Procedures minimize probability of incorrect selection.

robust ranking selection input uncertainty budget allocation PICS

2024-12-08abs ↗pdf ↗

Queryable LoRA: Instruction-Regularized Routing Over Shared Low-Rank Update Atomscs.LG

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

problem Rigidity of static low-rank adaptation methods when input and depth-wise computation vary.

method A shared queryable memory of low-rank update atoms, allowing dynamic and context-sensitive adaptation.

result Improves final test performance and training stability compared to standard low-rank adaptation.

neural network fine-tuning low-rank adaptation instruction-regularization queryable memory attention mechanism

2026-05-08abs ↗pdf ↗

Robustness via Deep Low-Rank Representationscs.LG

Low-rank regularizer improves DNN robustness.

problem Improving DNN robustness to input perturbations.

method Low-rank regularizer applied to DNN intermediate layers.

result LR-trained models are more robust to Gaussian and adversarial noise.

Deep Learning Robustness Low-Rank Feature Compression Regularization

2018-04-19abs ↗pdf ↗

A comparative analysis of rank aggregation methods for the partial label ranking problemcs.LG

This paper compares rank aggregation methods for partial label ranking.

problem Handling partial label ranking with ties.

method Scoring-based and non-parametric probabilistic-based rank aggregation methods.

result Scoring-based variants consistently outperform the state-of-the-art method.

rank aggregation partial label ranking label ranking tie handling supervised learning

2025-02-24abs ↗pdf ↗

Pseudorandom Streams within Diffusion Models Act as Learnable Inputs That Affect Generation Qualitycs.LG

Pseudorandom inputs in diffusion models affect generation quality.

problem Pseudorandom inputs in diffusion models can be learned and affect model performance.

method Used a small multilayer perceptron to predict next values in pseudorandom orbits and a diffusion probe to replace real images with random tensors.

result Pseudorandom inputs can produce markedly different diffusion losses and generation quality.

Diffusion models Pseudorandom inputs Generation quality Sequence predictability MNIST

2026-08-03abs ↗pdf ↗

Deep Attentive Ranking Networks for Learning to Order Sentencescs.CL

Framework learns sentence order from paragraphs using attention and transformer networks.

problem Learning to order sentences from a paragraph.

method Bidirectional sentence encoder and self-attention transformer network for ranking.

result Framework outperforms state-of-the-art methods on sentence ordering and discrimination tasks.

sentence ordering paragraph ranking attention networks transformer ranking losses

2019-12-31abs ↗pdf ↗

Patch Reordering: a Novel Way to Achieve Rotation and Translation Invariance in Convolutional Neural Networkscs.CV

Patch ranking improves CNN performance by focusing on object content, not location.

problem CNNs lack rotation and translation invariance, limiting model capacity.

method Patch ranking before convolution and pooling to encode invariance.

result Patch ranking module improves CNN performance on various tasks.

CNNs patch ranking invariance rotation translation

2019-11-28abs ↗pdf ↗

Detecting Adversarial Samples Using Influence Functions and Nearest Neighborscs.LG

Method detects adversarial samples using influence functions and nearest neighbors.

problem Detecting adversarial attacks on deep neural networks.

method Influence functions and k-NN model on activation layers.

result Successfully distinguishes adversarial examples with state-of-the-art results.

adversarial attacks deep neural networks influence functions nearest neighbors robust classification

2019-09-15abs ↗pdf ↗

Functional Bipartite Ranking: a Wavelet-Based Filtering Approachstat.ML

It is the main goal of this article to address the bipartite ranking issue from the perspective of functional data analysis (FDA). Given a training set of independent realizations of a (possibly sampled) second-order random function with a (locally) smooth autocorrelation structure and to which a binary label is random…

2013-12-18abs ↗pdf ↗

Estimating leverage scores via rank revealing methods and randomizationcs.DS

New algorithms estimate matrix leverage scores using rank revealing and randomization.

problem Estimating leverage scores for matrices of arbitrary rank.

method Combining rank revealing methods with randomized dimensionality reduction.

result Effective estimators for leverage scores, even in rank deficient cases.

leverage scores matrix rank randomization dimensionality reduction statistical estimation

2021-05-23abs ↗pdf ↗

Efficient Preconditioning for Noisy Separable NMFs by Successive Projection Based Low-Rank Approximationsmath.NA

A modified SPA preconditioner enhances noise robustness in separable NMFs.

problem Noisy separable NMFs are challenging to solve efficiently.

method Proposes a modified SPA preconditioner to enhance noise robustness.

result The modified SPA preconditioner improves noise robustness without significantly increasing computational cost.

NMF Noisy Preconditioning Successive Projection Robustness

2017-10-01abs ↗pdf ↗

Support vector comparison machinesstat.ML

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 …

2014-01-30abs ↗pdf ↗

Mildly Overparameterized ReLU Networks Have a Favorable Loss Landscapecs.LG

Deep ReLU networks with extra parameters have mostly good loss landscapes.

problem Finding good local minima in the loss landscape of deep neural networks.

method Analyzing shallow and deep ReLU networks with extra parameters on a generic dataset.

result Most activation patterns correspond to regions with no bad local minima.

ReLU networks loss landscape overparameterization local minima global minima

2023-05-31abs ↗pdf ↗

LOCUS: A Distribution-Free Loss-Quantile Score for Risk-Aware Predictionsstat.ML

Locus scores predictions for risk, reducing large-loss events.

problem Deployment cost from inaccurate predictions, especially large losses.

method Distribution-free loss-scale reliability score using any predictive distribution.

result Reduces large-loss frequency compared to standard heuristics.

machine learning risk-aware prediction loss quantile distribution-free

2026-03-02abs ↗pdf ↗

Ranking with Adaptive Neighborscs.LG

A new ranking algorithm learns data affinity and ranking scores simultaneously.

problem Retrieving similar objects in large databases is challenging.

method Proposes a ranking algorithm that learns data affinity and ranking scores simultaneously, using adaptive neighbors and smoothness constraints.

result The proposed algorithm outperforms existing methods in synthetic and real datasets.

ranking adaptive neighbors data affinity smoothness constraint optimization

2018-03-14abs ↗pdf ↗

LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attackcs.CV

LSDAT reduces query efficiency for decision-based adversarial attacks.

problem Improving query efficiency for decision-based adversarial attacks.

method Low-rank and sparse decomposition (LSD) to craft perturbations.

result LSDAT achieves superior fooling rates with fewer queries.

adversarial attacks decision-based query efficiency sparse decomposition low-rank

2021-03-19abs ↗pdf ↗

Valid Explanations for Learning to Rank Modelscs.LG

Proposes a method for explaining ranking decisions in learning systems.

problem Limited work on interpreting ranking decisions from learning systems.

method Model agnostic local explanation method using optimization to maximize validity.

result Approach outperforms other methods in validity across different LTR models.

learning to rank explanation methods ranking validity model agnostic

2020-04-29abs ↗pdf ↗

Tensorized Random Projectionscs.LG

Tensorized random projections reduce high-dimensional tensor size efficiently.

problem Efficiently reducing the dimension of very high-dimensional tensors.

method Proposes two tensorized random projection maps using TT and CP decompositions.

result TT format offers superior performance in terms of required random projection size.

tensor decomposition random projections high-dimensional data low-rank tensors Euclidean distance

2020-03-11abs ↗pdf ↗

Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRankcs.CR

Differentially private graph learning via bounded sensitivity PPR.

problem Protecting user data in graph learning algorithms.

method Proposes a sensitivity-bounded personalized PageRank (PPR) algorithm.

result Achieves similar accuracy to non-private algorithms with large degrees.

differential privacy personalized PageRank graph learning sensitivity bound

2022-07-14abs ↗pdf ↗

On the Power of Adaptivity in Matrix Completion and Approximationstat.ML

We consider the related tasks of matrix completion and matrix approximation from missing data and propose adaptive sampling procedures for both problems. We show that adaptive sampling allows one to eliminate standard incoherence assumptions on the matrix row space that are necessary for passive sampling procedures. Fo…

2014-07-14abs ↗pdf ↗

Fast Rank-1 NMF for Missing Data with KL Divergencestat.ML

A1GM method improves efficiency in reconstructing missing data using KL divergence.

problem Efficiently reconstructing missing data in matrices.

method Fast non-gradient-based rank-1 NMF using KL divergence.

result A1GM outperforms gradient methods in efficiency with competitive reconstruction errors.

matrix factorization non-negative matrix factorization KL divergence missing data rank-1

2021-10-25abs ↗pdf ↗

FRAPPE:

\underline{\text{F}}

ast

\underline{\text{Ra}}

\underline{\text{App}}

roximation with

\underline{\text{E}}

xplainable Features for Tensorscs.LG

FRAPPE estimates tensor canonical rank without CPD computation.

problem Estimating the canonical rank of tensors efficiently.

method Generates synthetic data matching input tensor's size and sparsity, trains a regression model to estimate rank.

result 24 times faster than best baseline, 10% improvement in MAPE on synthetic dataset.

tensor decomposition canonical rank synthetic data regression model fast computation

2022-06-19abs ↗pdf ↗

GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuningcs.LG

GeLoRA optimizes LoRA fine-tuning by dynamically adjusting ranks based on intrinsic dimensionality.

problem Efficient fine-tuning of large language models with limited computational resources.

method GeLoRA computes intrinsic dimensionality to adaptively select LoRA ranks, balancing expressivity and efficiency.

result GeLoRA consistently outperforms recent baselines within the same parameter budget on multiple tasks.

LoRA fine-tuning intrinsic dimensionality adaptive ranks computational efficiency

2024-12-12abs ↗pdf ↗

Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Datamath.NA

Several important applications, such as streaming PCA and semidefinite programming, involve a large-scale positive-semidefinite (psd) matrix that is presented as a sequence of linear updates. Because of storage limitations, it may only be possible to retain a sketch of the psd matrix. This paper develops a new algorith…

2017-06-18abs ↗pdf ↗

Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimizationstat.ML

Nonnegative matrix factorization (NMF) has been shown recently to be tractable under the separability assumption, under which all the columns of the input data matrix belong to the convex cone generated by only a few of these columns. Bittorf, Recht, Ré and Tropp (`Factoring nonnegative matrices with linear programs', …

2013-02-18abs ↗pdf ↗