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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.

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5 results for “EPIG”

Batch Bayesian Active Learning with Partial Batch Label Samplingcs.LG

A new method improves active learning for large batch sizes.

problem Challenges in scaling Bayesian active learning to large batch sizes.

method Derives Partial Batch Label Sampling (ParBaLS) for EPIG algorithm.

result ParBaLS EPIG outperforms top-

B

selection and BatchBALD.

active learning Bayesian decision theory EPIG Partial batch sampling Bayesian logistic regression

2025-10-10abs ↗pdf ↗

Prediction-Oriented Bayesian Active Learningcs.LG

Proposes EPIG for active learning to improve predictive performance.

problem Suboptimal predictive performance of traditional active learning methods.

method Introduces EPIG, a new acquisition function measuring information gain in the space of predictions.

result EPIG leads to stronger predictive performance compared to BALD across various datasets and models.

active learning information gain predictive performance Bayesian methods

2023-04-17abs ↗pdf ↗

Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outlierscs.LG

New method corrects active learning for distribution shifts and outliers.

problem Conventional active learning methods fail to account for test-time distribution.

method JEPIG, a hybrid of BALD and EPIG, maximizes expected predictive information gain.

result JEPIG outperforms conventional methods in active learning with distribution shifts.

active learning distribution shift outliers Bayesian neural networks expected predictive information gain

2021-06-22abs ↗pdf ↗

Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimationstat.ML

Framework improves CATE estimation by aligning active learning with causal objectives.

problem High cost of outcome measurements limits CATE estimation.

method Causal-EPIG framework, targeting unobservable causal quantities.

result Strategies outperform standard baselines, revealing context-dependent optimal approaches.

CATE estimation active learning causal inference information gain potential outcomes

2025-09-26abs ↗pdf ↗

Observationally Informed Adaptive Causal Experimental Designstat.ML

New method uses observational data to improve trial design efficiency.

problem Scarce randomized controlled trials; inefficiency of using observational data.

method Active Residual Learning, R-Design framework, R-EPIG criterion.

result Efficiently estimating residuals to correct observational bias improves trial design.

observational data cognitive bias randomized controlled trials residual learning information efficiency

2026-03-04abs ↗pdf ↗