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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.

168,694 papers · 148 categories

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7 results for “feature-partitioned”

Backdoor attacks and defenses in feature-partitioned collaborative learningcs.LG

Backdoor attacks are possible in feature-partitioned collaborative learning, even without labels.

problem Backdoor attacks in feature-partitioned collaborative learning.

method Demonstrated the feasibility of backdoor attacks and introduced defense techniques.

result Backdoor attacks can be blocked without significantly affecting main task accuracy.

backdoor attacks feature-partitioned collaborative learning defense techniques

2020-07-07abs ↗pdf ↗

Feature Partitioning for Robust Tree Ensembles and their Certification in Adversarial Scenarioscs.LG

This work proposes a robust ensemble method for decision trees that resists adversarial attacks.

problem Adversarial attacks on machine learning models, especially decision trees.

method Feature partitioning to train robust ensembles and approximate certification methods.

result The proposed ensemble method can resist evasion attacks by a majority of its models.

Adversarial Attacks Decision Trees Feature Partitioning Robust Ensembles Certification

2020-04-07abs ↗pdf ↗

Imputation-free Learning of Tabular Data with Missing Values using Incremental Feature Partitions in Transformercs.LG

Imputation-free method learns tabular data with missing values using transformer.

problem Machine learning on tabular data with missing values often leads to unreliable outcomes due to synthetic imputation.

method Incremental attention learning (IFIAL) using transformer with attention masks.

result IFIAL outperforms state-of-the-art methods in 17 diverse tabular data sets.

tabular data missing values imputation-free transformer attention learning

2025-04-20abs ↗pdf ↗

Feature Partitioning for Efficient Multi-Task Architecturescs.LG

Multi-task learning holds the promise of less data, parameters, and time than training of separate models. We propose a method to automatically search over multi-task architectures while taking resource constraints into consideration. We propose a search space that compactly represents different parameter sharing strat…

2019-08-12abs ↗pdf ↗

Handling Covariate Mismatch in Federated Linear Predictionmath.ST

Study addresses covariate mismatch in federated learning, improving model accuracy.

problem Learning from clients with different feature sets in federated learning.

method Developed two approaches for linear prediction under covariate mismatch: plug-in estimator and impute-then-regress strategy.

result Proposed methods provide asymptotic and finite-sample learning rates, improving model accuracy.

federated learning covariate mismatch linear prediction ridge regularization asymptotic analysis

2026-02-02abs ↗pdf ↗

Learning Unknown Interdependencies for Decentralized Root Cause Analysis in Nonlinear Dynamical Systemscs.LG

Paper tackles RCA in complex networks with unknown interdependencies.

problem Difficult RCA in networked systems due to unknown interdependencies.

method Federated learning for feature-partitioned, nonlinear data without modifying client models.

result Established theoretical convergence guarantees and validated on real-world data.

federated learning root cause analysis nonlinear dynamics privacy-preserving interdependency learning

2026-02-25abs ↗pdf ↗

Improvements to Supervised EM Learning of Shared Kernel Models by Feature Space Partitioningcs.LG

Improved supervised EM learning for shared kernel models with feature space partitioning.

problem Lack of rigour in EM derivation and high computational complexity.

method Detailed derivation of EM for Gaussian shared kernel model, feature space partitioning to reduce complexity.

result Improved performance at reduced complexity achieved.

EM learning shared kernel model feature space partitioning classification algorithms MNIST data set

2022-05-31abs ↗pdf ↗