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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7 results for “Query-by-Committee”

Structural query-by-committeecs.LG

Unified framework for interactive learning tasks using query-by-committee.

problem Handling various interactive learning tasks.

method Generalization of query-by-committee algorithm for new setting.

result Theoretical and empirical consistency and convergence rates.

query-by-committee interactive learning active learning consistency convergence

2018-03-17abs ↗pdf ↗

Support Vector Machine Active Learning Algorithms with Query-by-Committee versus Closest-to-Hyperplane Selectioncs.LG

Study evaluates SVM active learning for imbalanced datasets.

problem Imbalanced datasets in text classification and relation extraction.

method Combines SVM with closest-to-hyperplane and query-by-committee selection methods for imbalance.

result ClosestPA algorithm consistently outperforms other methods.

Support Vector Machine Active Learning Imbalanced Data Query-by-Committee Closest-to-Hyperplane

2018-01-24abs ↗pdf ↗

Active Learning by Query by Committee with Robust Divergencesstat.ML

This paper improves active learning by using robust divergences for committee disagreement.

problem Active learning with high measurement costs.

method Query by committee with Bregman divergence (including Kullback-Leibler divergence as a special case).

result The proposed method is more robust and performs as well as or better than conventional methods.

active learning query by committee robust divergences Bregman divergence Kullback-Leibler divergence

2022-11-18abs ↗pdf ↗

Active Learning in Symbolic Regression with Physical Constraintscs.LG

Active learning improves SR by proposing experiments in data-limited settings.

problem Efficiently gathering data for symbolic regression with physical constraints.

method Query by committee using the Pareto frontier of equations, with physical constraints.

result Reduces data required for SR and achieves state-of-the-art results.

active learning symbolic regression query by committee Pareto frontier physical constraints

2023-05-17abs ↗pdf ↗

REALITrees: Rashomon Ensemble Active Learning for Interpretable Treesstat.ML

REALITrees uses a Rashomon ensemble approach for active learning in sparse decision trees.

problem Active learning reduces labeling costs by selecting informative samples, but current methods often sacrifice model diversity and direct characterization of the hypothesis space.

method REALITrees constructs a committee of all near-optimal sparse decision tree models using a Rashomon Set and a Gibbs posterior to weight them by empirical risk.

result REALITrees outperforms randomized ensembles, especially in noisy environments, by leveraging expanded model multiplicity.

active learning sparse decision trees Rashomon Set PAC-Bayesian sparse models

2026-03-24abs ↗pdf ↗

How to Purchase Labels? A Cost-Effective Approach Using Active Learning Marketscs.LG

A cost-effective approach to label acquisition using active learning markets.

problem Improving model fitting and training for predictive analytics.

method Formalizing market clearing as an optimisation problem, integrating budget constraints and improvement thresholds, using two active learning strategies with distinct pricing mechanisms.

result Superior performance with fewer labels acquired compared to conventional methods.

active learning markets label acquisition predictive analytics optimisation budget constraints

2025-11-25abs ↗pdf ↗

Less is more: sampling chemical space with active learningphysics.comp-ph

Automated AL improves ML potentials for organic molecules, reducing training data by 90%.

problem Developing accurate and transferable ML potentials for molecular energetics.

method Active learning via Query by Committee (QBC) to sample chemical space.

result AL-based potentials achieve similar accuracy with 10-25% of data, outperforming ANI-1.

active learning machine learning molecular energetics organic molecules universal potentials

2018-01-28abs ↗pdf ↗