Paper Citation Record · LEDGER

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

As of 8 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 1 inbound Pith citation observation for arXiv:2507.03910.

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

pith.paper-citation-record.v1
2507.03910 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:09:11.186562Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T10:21:46.912554Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T10:23:11.993809Z

Reference resolution

15 of 15 outbound references displayed

verified exact1
verified fuzzy5
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 41e577bd-5e99-4129-8bc5-a06e081b1955 · outbound

This paper cites We stress best average scores achieved after 300 evaluations (dark) and scores within a single standard deviation of best (light).

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces We stress best average scores achieved after 300 evaluations (dark) and scores within a single standard deviation of best (light)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:12.790047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f65ef600-e5f6-4be4-b7d7-9f5b69d26be6 · outbound

This paper cites Gaussian Process Molecule Property Prediction with FlowMO.

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces Gaussian Process Molecule Property Prediction with FlowMO

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T20:09:12.126478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

This paper cites Big Batch Bayesian Active Learning by Considering Predictive Probabilities.

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 2026-08-07T06:34:17.273281+00:00.

Observation d9f5a2fc-a21b-4f7b-a245-c62c791fef70 · outbound

This paper cites Sampling Generative Networks.

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces Sampling Generative Networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T20:09:10.882794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea3c629b-db83-4dc4-b95e-75a922870505 · outbound

This paper cites Note that we keep track of the latent value that decoded to give the best structure so far zbest and use this to start our chains.

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces Note that we keep track of the latent value that decoded to give the best structure so far zbest and use this to start our chains

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:13.114533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7a7d5ee7-6da7-4669-9d03-3291bb023e7a · outbound

This paper cites Denoising Diffusion Implicit Models.

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces Denoising Diffusion Implicit Models

Reference 1985

Resolution: unresolved; no resolver link, observed 2026-08-06T20:09:10.754645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d19f5ff8-babf-457e-a3a2-2acf09cbf990 · outbound

This paper cites The Bayesian approach to global optimization.

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces The Bayesian approach to global optimization

Reference 2011

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:13.354837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8304e77b-b42a-4d93-ace1-4eaecf6c17b5 · outbound

This paper cites πbo: Augmenting acquisition functions with user beliefs for bayesian optimization.

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces πbo: Augmenting acquisition functions with user beliefs for bayesian optimization

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:13.972193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8322e127-2995-4028-aec7-8a9cd1b89bde · outbound

This paper cites A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences.

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-06T20:09:09.409547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2c71db9-9f0b-45b3-8c51-8caaf0138032 · outbound

This paper cites Fantasizing with Dual GPs in Bayesian Optimization and Active Learning.

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces Fantasizing with Dual GPs in Bayesian Optimization and Active Learning

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T20:09:09.297702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f91e038a-dc4c-44d1-8199-8dc478fad7fd · outbound

This paper cites J., Paige, B., and Hern ´andez-Lobato, J.

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces J., Paige, B., and Hern ´andez-Lobato, J

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:13.658074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ff5f5932-c4a8-4b97-a2ef-d67a72b45bfa · outbound

This paper cites Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation.

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation

Reference 2021

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:09:11.885053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cd98e85a-2db2-4b29-809e-c902d04db9b9 · outbound

This paper cites Vanilla Bayesian Optimization Performs Great in High Dimensions.

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces Vanilla Bayesian Optimization Performs Great in High Dimensions

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T20:09:09.788621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b8e8280-6e82-4143-8bcd-c8fd07ac6817 · outbound

This paper cites High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning.

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T20:09:09.551163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5233bcf2-29c7-466d-acb0-5ca54fc678ed · outbound

This paper cites Discovering Many Diverse Solutions with Bayesian Optimization.

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces Discovering Many Diverse Solutions with Bayesian Optimization

Reference 2025

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T20:09:12.427018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation 7166e723-2f15-44f1-b833-60ee965aeca0 · inbound

Embedding by Elicitation: Dynamic Representations for Bayesian Optimization of System Prompts cites this paper.

Embedding by Elicitation: Dynamic Representations for Bayesian Optimization of System Prompts Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-20T10:23:11.995301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.