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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.

169,051 papers · 148 categories

Trend · papers per month

count% of corpus
0.3%0.5%0.8%0.1% · Sep 201819922001200920182026
4 results for “queue-based”
Queue-based Resampling for Online Class Imbalance Learningcs.LG

Queue-based resampling tackles online class imbalance learning with selective resampling of past examples.

problem Online class imbalance learning under class imbalance and concept drift.
method Queue-based resampling algorithm that selectively includes past examples in the training set.
result Queue-based resampling outperforms state-of-the-art methods in terms of learning speed and quality.
class imbalanceconcept driftonline learningresamplingqueue-based
2018-09-27abs ↗pdf ↗
Learning Treatment Effects during Resource Allocation via Priority-Queue Randomizationcs.LG

Designs for allocating resources to prioritize needy applicants while estimating treatment effects.

problem Resource allocation under uncertainty with prioritized queues.
method Priority-queue randomization for treatment assignment and estimation of treatment effects.
result Identification of causal effects under different arrival and treatment assignment scenarios.
resource allocationpriority queuestreatment effectsrandomizationhousing allocation
2026-05-24abs ↗pdf ↗
Federated Learning for Ultra-Reliable Low-Latency V2V Communicationscs.NI

In this paper, a novel joint transmit power and resource allocation approach for enabling ultra-reliable low-latency communication (URLLC) in vehicular networks is proposed. The objective is to minimize the network-wide power consumption of vehicular users (VUEs) while ensuring high reliability in terms of probabilisti…

2018-05-11abs ↗pdf ↗
Risk-Based Auto-Deleveragingq-fin.RM

Optimizes cryptocurrency exchanges' risk management by reducing positions based on leverage.

problem Managing risk in cryptocurrency futures exchanges during large price moves.
method Formulates ADL as an optimization problem to minimize risk of loss, using a water-filling rule to equalize leverage.
result The optimal ADL policy minimizes maximum leverage among participants, providing a transparent and implementable benchmark.
cryptocurrency futuresrisk managementauto-deleveragingwater-filling ruleexpected loss
2026-03-16abs ↗pdf ↗