When does Active Learning Work?

2014-08-06 · Lewis Evans, Niall M. Adams, Christoforos Anagnostopoulos

Active Learning (AL) methods seek to improve classifier performance when labels are expensive or scarce. We consider two central questions: Where does AL work? How much does it help? To address these questions, a comprehensive experimental simulation study of Active Learning is presented. We consider a variety of tasks, classifiers and other AL factors, to present a broad exploration of AL performance in various settings. A precise way to quantify performance is needed in order to know when AL works. Thus we also present a detailed methodology for tackling the complexities of assessing AL performance in the context of this experimental study.

📄 PDF Abstract BibTeX arXiv:1408.1319

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Active Learning

How Optimal is Greedy Decoding for Extractive Question Answering?

2021-08-12 · Or Castel, Ori Ram, Avia Efrat, Omer Levy

Fine-tuned language models use greedy decoding to answer reading comprehension questions with relative success. However, this approach does not ensure that the answer is a span in the given passage, nor does it guarantee…

Extractive Question-Answering Question Answering Reading Comprehension

Active Learning and Transfer Learning for Anomaly Detection in Time-Series Data

2025-08-05 · John D. Kelleher, Matthew Nicholson, Rahul Agrahari, Clare Conran arxiv

This paper examines the effectiveness of combining active learning and transfer learning for anomaly detection in cross-domain time-series data. Our results indicate that there is an interaction between clustering and ac…

Transfer Learning Anomaly Detection Active Learning

When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning

2022-10-19 · Annie Xie, Fahim Tajwar, Archit Sharma, Chelsea Finn

A long-term goal of reinforcement learning is to design agents that can autonomously interact and learn in the world. A critical challenge to such autonomy is the presence of irreversible states which require external as…

continuous-control Continuous Control reinforcement-learning Reinforcement Learning (RL)

ProactiveBench: Benchmarking Proactiveness in Multimodal Large Language Models

2026-03-19 · Thomas De Min, Subhankar Roy, Stéphane Lathuilière, Elisa Ricci 외 arxiv

Effective collaboration begins with knowing when to ask for help. For example, when trying to identify an occluded object, a human would ask someone to remove the obstruction. Can MLLMs exhibit a similar "proactive" beha…

Reinforcement Learning

Para-active learning

2013-10-30 · Alekh Agarwal, Leon Bottou, Miroslav Dudik, John Langford

Training examples are not all equally informative. Active learning strategies leverage this observation in order to massively reduce the number of examples that need to be labeled. We leverage the same observation to bui…