Paper Citation Record · LEDGER

Big Batch Bayesian Active Learning by Considering Predictive Probabilities

As of 15 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 1 inbound Pith citation observation for arXiv:2501.08223.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.08223 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:34:11.611291Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:09:10.621820Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T20:09:11.405327Z

Reference resolution

17 of 17 outbound references displayed

verified exact0
verified fuzzy15
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f01012ab-205e-4581-936b-97a57ff6406a · outbound

This paper cites Jiang, Samuel Daulton, Benjamin Letham, An- drew Gordon Wilson, and Eytan Bakshy.

Big Batch Bayesian Active Learning by Considering Predictive Probabilities Jiang, Samuel Daulton, Benjamin Letham, An- drew Gordon Wilson, and Eytan Bakshy

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:34:11.845257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ec1e31a8-f56d-4075-b381-223a2a12e323 · outbound

This paper cites Fast greedy MAP inference for determinan- tal point process to improve recommendation diversity.

Big Batch Bayesian Active Learning by Considering Predictive Probabilities Fast greedy MAP inference for determinan- tal point process to improve recommendation diversity

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:34:11.831496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 49d5830b-4842-494f-999d-a6e4206e2ac1 · outbound

This paper cites Numerical computation of multivariate normal probabilities using bivariate conditioning.

Big Batch Bayesian Active Learning by Considering Predictive Probabilities Numerical computation of multivariate normal probabilities using bivariate conditioning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:34:11.820918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 11906ddf-8780-49cc-89ae-7b37bf4c42f3 · outbound

This paper cites Scalable variational Gaus- sian process classification.

Big Batch Bayesian Active Learning by Considering Predictive Probabilities Scalable variational Gaus- sian process classification

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:34:11.809387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5e896fb8-e664-4af3-ae7b-38d5e190fa2d · outbound

This paper cites Bayesian Active Learning for Classification and Preference Learning.

Big Batch Bayesian Active Learning by Considering Predictive Probabilities Bayesian Active Learning for Classification and Preference Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T20:34:11.563420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6bb4803c-3d48-4f26-a062-f116166bddb2 · outbound

This paper cites What uncertainties do we need in Bayesian deep learning for computer vision? Advances in Neural Information Processing Systems (NeurIPS), 2017.

Big Batch Bayesian Active Learning by Considering Predictive Probabilities What uncertainties do we need in Bayesian deep learning for computer vision? Advances in Neural Information Processing Systems (NeurIPS), 2017

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:34:11.796605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 377d2cb0-a7ef-4827-981a-bd5f757edb91 · outbound

This paper cites BatchBALD: Efficient and diverse batch acquisition for deep Bayesian active learning.

Big Batch Bayesian Active Learning by Considering Predictive Probabilities BatchBALD: Efficient and diverse batch acquisition for deep Bayesian active learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:34:11.783477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 03be9637-e952-405b-b755-e0005263a62e · outbound

This paper cites Learning multiple layers of features from tiny images, 2009.

Big Batch Bayesian Active Learning by Considering Predictive Probabilities Learning multiple layers of features from tiny images, 2009

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:34:11.772878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f1d8570f-1f0b-48f6-a6e4-07e3f2b5fb06 · outbound

This paper cites A well-conditioned estimator for large-dimensional covari- ance matrices.

Big Batch Bayesian Active Learning by Considering Predictive Probabilities A well-conditioned estimator for large-dimensional covari- ance matrices

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:34:11.760632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 48515bd6-2285-470e-9fea-a36e62f34cea · outbound

This paper cites GIBBON: General-purpose Information-Based Bayesian OptimisatioN.

Big Batch Bayesian Active Learning by Considering Predictive Probabilities GIBBON: General-purpose Information-Based Bayesian OptimisatioN

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:34:11.748982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ac326d57-d813-40f2-8405-6ade6b65d18c · outbound

This paper cites MUMBO: MUlti-task Max-value Bayesian Optimization.

Big Batch Bayesian Active Learning by Considering Predictive Probabilities MUMBO: MUlti-task Max-value Bayesian Optimization

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:34:11.735389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 79989121-3d48-4f47-87c3-d3029504c87c · outbound

This paper cites Inducing point allocation for sparse Gaussian processes in high-throughput Bayesian optimisation.

Big Batch Bayesian Active Learning by Considering Predictive Probabilities Inducing point allocation for sparse Gaussian processes in high-throughput Bayesian optimisation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:34:11.723336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4fa42fa0-8143-4ecd-a72a-fac4fd8def8f · outbound

This paper cites Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes.

Big Batch Bayesian Active Learning by Considering Predictive Probabilities Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:34:11.711377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 23c43c1b-3650-4bc8-888d-a0cefe9c53c1 · outbound

This paper cites Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow.

Big Batch Bayesian Active Learning by Considering Predictive Probabilities Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T20:34:11.599086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1073c25e-402b-4425-ab35-9a378c68ffca · outbound

This paper cites Bayesian batch active learning as sparse subset approximation.

Big Batch Bayesian Active Learning by Considering Predictive Probabilities Bayesian batch active learning as sparse subset approximation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:34:11.698276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ea122846-7e70-4db5-a1a2-f143dabc7053 · outbound

This paper cites Gaussian Processes for Machine Learn- ing.

Big Batch Bayesian Active Learning by Considering Predictive Probabilities Gaussian Processes for Machine Learn- ing

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:34:11.683381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f8621351-9a77-4a7c-b2a5-2aff84ade9ed · outbound

This paper cites Active learning in Bayesian neural networks with balanced entropy learning principle.

Big Batch Bayesian Active Learning by Considering Predictive Probabilities Active learning in Bayesian neural networks with balanced entropy learning principle

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:34:11.669724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation a053561b-4897-4e9e-b67d-86425bd12338 · inbound

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces cites this paper.

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces Big Batch Bayesian Active Learning by Considering Predictive Probabilities

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T20:09:11.561364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.