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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.

168,932 papers · 148 categories

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0111 · Nov 201119922001200920172026
5 results for “pair-matching”
Pair-Matching: Links Prediction with Adaptive Queriesstat.ML

Algorithm discovers edges between pairs of nodes with limited queries.

problem Discovering good matches between pairs of entities with limited queries.
method Pair-matching problem as a multi-armed bandit with constraints, focusing on Stochastic Block Model.
result Sublinear regret achievable in Stochastic Block Model with two communities, with phase transition related to community detection.
pair-matchingmulti-armed banditStochastic Block Modelregretcommunity detection
2019-05-17abs ↗pdf ↗
Task-Driven Common Representation Learning via Bridge Neural Networkcs.LG

BNN learns shared features between two data sources for specific tasks.

problem Learning shared features between two data sources for specific tasks.
method BNN uses two CNNs to project data sources into a feature space and learns a common representation for each task.
result BNN achieves state-of-the-art performance on various tasks.
common representation learningbridge neural networkconvolutional neural networksmini-batch trainingtotal correlation
2019-06-26abs ↗pdf ↗
Implicit Finite-Horizon Approximation and Efficient Optimal Algorithms for Stochastic Shortest Pathcs.LG

New algorithms minimize regret in SSP with optimal sparse updates.

problem Minimizing regret in Stochastic Shortest Path models.
method Implicit finite-horizon approximation for analysis, model-free and model-based algorithms developed.
result Minimax optimal regret for both model-free and model-based algorithms.
Stochastic Shortest PathRegret minimizationFinite-horizon approximationModel-free algorithmsModel-based algorithms
2021-06-15abs ↗pdf ↗
The Matching Principle: A Geometric Theory of Loss Functions for Nuisance-Robust Representation Learningcs.LG

A geometric theory explains loss functions for robust representation learning.

problem Treats robustness, domain adaptation, and sensor drift as separate literatures.
method Estimates covariance Sigma_task and uses it to pin Jacobian penalties.
result Proves optimality and necessity of range coverage for penalty matrices.
robustnessdomain adaptationgeometric theoryloss functionsrepresentation learning
2026-05-21abs ↗pdf ↗