Paper Citation Record · LEDGER

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL

As of 16 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 2 inbound Pith citation observations for arXiv:2505.21974.

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

pith.paper-citation-record.v1
2505.21974 v2

Coverage vector

measured 77 of 77 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:30:31.380992Z

measured 79 of 79 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:28:54.642701Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T18:51:07.377125Z

Reference resolution

77 of 77 outbound references displayed

verified exact1
verified fuzzy68
unresolved8
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 184d0a75-344f-4d73-9059-f87cdc58178a · outbound

This paper cites Deep reinforcement learning at the edge of the statistical precipice.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Deep reinforcement learning at the edge of the statistical precipice

Reference 1

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

Observation 5c1b145c-9b37-4746-978e-e1e1b13e93e8 · outbound

This paper cites BoTorch: A framework for efficient Monte-Carlo Bayesian optimization.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL BoTorch: A framework for efficient Monte-Carlo Bayesian optimization

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:45.383002Z

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

Observation fb236295-5e78-4241-8008-02293b45896a · outbound

This paper cites Max-value entropy search for multi-objective Bayesian optimization.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Max-value entropy search for multi-objective Bayesian optimization

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:45.228083Z

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Observation e3ae6943-2b13-4082-9ff5-0107536238ae · outbound

This paper cites Uncertainty-aware search framework for multi-objective Bayesian optimization.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Uncertainty-aware search framework for multi-objective Bayesian optimization

Reference 4

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Observation 79a422fa-6a02-4f51-ab57-77abdc10e1cb · outbound

This paper cites Output space entropy search framework for multi-objective Bayesian optimization.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Output space entropy search framework for multi-objective Bayesian optimization

Reference 5

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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 e1df2f10-fb65-4984-a7a4-f33d30d60a76 · outbound

This paper cites SMS-EMOA: Multiobjective selection based on dominated hypervolume.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL SMS-EMOA: Multiobjective selection based on dominated hypervolume

Reference 6

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Source-reported events for the cited work

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Observation d8702222-271e-40b2-907a-ed195a07825b · outbound

This paper cites Settling the reward hypothesis.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Settling the reward hypothesis

Reference 7

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Source-reported events for the cited work

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Observation 41c7bda5-abfb-4414-8dc1-554980736d39 · outbound

This paper cites Q-Transformer: Scalable offline reinforcement learning via autoregressive q-functions.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Q-Transformer: Scalable offline reinforcement learning via autoregressive q-functions

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:44.345713Z

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Observation 98c52eda-4f01-41a6-b823-868cd5c795e2 · outbound

This paper cites Decision Transformer: Reinforcement learning via sequence modeling.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Decision Transformer: Reinforcement learning via sequence modeling

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:44.164337Z

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 2f859c5f-16bc-4002-9d0c-ebf17b5f7c6c · outbound

This paper cites Towards learning universal hyperparameter optimizers with transformers.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Towards learning universal hyperparameter optimizers with transformers

Reference 10

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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 2b241037-7d07-4db7-a94b-41cb4125d35d · outbound

This paper cites A reference vector guided evolutionary algorithm for many-objective optimization.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL A reference vector guided evolutionary algorithm for many-objective optimization

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:43.777610Z

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Observation 97f24142-45ba-4bae-9206-7747ccdec0a3 · outbound

This paper cites Unifying PAC and regret: Uniform PAC bounds for episodic reinforcement learning.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Unifying PAC and regret: Uniform PAC bounds for episodic reinforcement learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:43.483459Z

Source-reported events for the cited work

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Observation c9b4b1fe-ad8b-4350-997b-419e6a2b80fc · outbound

This paper cites Hypervolume knowledge gradient: A lookahead approach for multi-objective Bayesian optimization with partial information.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Hypervolume knowledge gradient: A lookahead approach for multi-objective Bayesian optimization with partial information

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:43.258818Z

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 2b320dc8-c370-4efe-afad-fe6c3bd0ba85 · outbound

This paper cites Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:43.011258Z

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 3b55293f-c719-4226-a0ba-914593d6f1d7 · outbound

This paper cites Parallel Bayesian optimization of multiple noisy objectives with expected hypervolume improvement.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Parallel Bayesian optimization of multiple noisy objectives with expected hypervolume improvement

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:42.782585Z

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 675225ee-a173-4afd-ac58-5b15dc4a06b2 · outbound

This paper cites Multi-objective Bayesian optimization over high-dimensional search spaces.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Multi-objective Bayesian optimization over high-dimensional search spaces

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:42.579088Z

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 f8abe92d-f2db-4eb2-8b22-14917fe6e27f · outbound

This paper cites A fast and elitist multiobjective genetic algorithm: NSGA-II.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL A fast and elitist multiobjective genetic algorithm: NSGA-II

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:42.333884Z

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 4be64c8f-50a6-4fb3-8223-eab8e715d78a · outbound

This paper cites Simple agent, complex environment: Efficient reinforcement learning with agent states.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Simple agent, complex environment: Efficient reinforcement learning with agent states

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:42.087491Z

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 1cbd0453-2bf4-4238-b68c-f0350653b18a · outbound

This paper cites The computation of the expected improvement in dominated hypervolume of Pareto front approximations.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL The computation of the expected improvement in dominated hypervolume of Pareto front approximations

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:41.858727Z

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 2a3716bf-df21-49c1-8401-07ac2d7ea0ef · outbound

This paper cites Hypervolume-based expected improvement: Monotonicity properties and exact computation.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Hypervolume-based expected improvement: Monotonicity properties and exact computation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:41.623600Z

Source-reported events for the cited work

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Observation 80338c98-5be4-440a-b091-52c819a9973a · outbound

This paper cites Generalized decision transformer for offline hindsight information matching.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Generalized decision transformer for offline hindsight information matching

Reference 21

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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 23ba7fde-ce8f-4513-91a8-9e43703f83d1 · outbound

This paper cites Conditional neural processes.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Conditional neural processes

Reference 22

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Observation f70a229f-5b13-422f-b637-68a463c062df · outbound

This paper cites Predictive entropy search for multi-objective Bayesian optimization with constraints.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Predictive entropy search for multi-objective Bayesian optimization with constraints

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:40.859698Z

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

Observation 26eca941-6628-45ca-b813-47502b7762c8 · outbound

This paper cites Automatic chemical design using a data-driven continuous representation of molecules.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Automatic chemical design using a data-driven continuous representation of molecules

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:40.666256Z

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Observation 73e15291-ccd1-4800-9c68-90deaaa441de · outbound

This paper cites Deep recurrent Q-learning for partially observable MDPs.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Deep recurrent Q-learning for partially observable MDPs

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:40.441072Z

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

Observation 43a71b4e-c9b1-4a94-a0f5-0a6ea2adb3d5 · outbound

This paper cites Predictive entropy search for multi-objective Bayesian optimization.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Predictive entropy search for multi-objective Bayesian optimization

Reference 26

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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 7903d9dc-b9bb-4c0d-a990-91f3eef88dd4 · outbound

This paper cites Reinforced few-shot acquisition function learning for Bayesian optimization.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Reinforced few-shot acquisition function learning for Bayesian optimization

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:40.033122Z

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 6774012c-ed4d-427d-b533-65fbca157ce7 · outbound

This paper cites Faster exact algorithms for computing expected hypervolume improvement.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Faster exact algorithms for computing expected hypervolume improvement

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:39.842664Z

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

Observation 7a36ebb2-b5de-4f90-80d3-d631348d12b9 · outbound

This paper cites Joint entropy search for maximally-informed Bayesian optimization.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Joint entropy search for maximally-informed Bayesian optimization

Reference 29

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

Observation 2ced482c-e1c8-485b-a9b2-dc4ecc52b0c0 · outbound

This paper cites Reinforcement learning algorithm for partially observable Markov decision problems.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Reinforcement learning algorithm for partially observable Markov decision problems

Reference 30

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

Observation 5e6dec40-2e80-47c8-b452-e3122835805e · outbound

This paper cites Offline reinforcement learning as one big sequence modeling problem.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Offline reinforcement learning as one big sequence modeling problem

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:39.420007Z

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Observation 17ee03e7-7a88-444a-bfc7-ca719a9f002e · outbound

This paper cites 3d gaussian splatting for real-time radiance field rendering.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL 3d gaussian splatting for real-time radiance field rendering

Reference 32

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Observation 22ea4b3e-e934-4b7a-8882-87176a7a5800 · outbound

This paper cites Attentive neural processes.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Attentive neural processes

Reference 33

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Observation 79214c1d-4515-4ae6-8364-1f1bfff4adf4 · outbound

This paper cites Adam: A method for stochastic optimization.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Adam: A method for stochastic optimization

Reference 34

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Observation e9b95255-edf9-4470-9473-c25f68a98fa9 · outbound

This paper cites Fast Bayesian optimization of machine learning hyperparameters on large datasets.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Fast Bayesian optimization of machine learning hyperparameters on large datasets

Reference 35

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Observation 43d61b46-40b5-484e-b426-58669a7b8053 · outbound

This paper cites ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems

Reference 36

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Observation 2a023a53-3325-4e33-ab29-99a185231afc · outbound

This paper cites Stabilizing off-policy Q-learning via bootstrapping error reduction.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Stabilizing off-policy Q-learning via bootstrapping error reduction

Reference 37

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Observation ef164d55-a0e3-4ee5-b6de-706c8e1367c2 · outbound

This paper cites The sample-complexity of general reinforcement learning.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL The sample-complexity of general reinforcement learning

Reference 38

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Observation 03c45583-1e4a-49ab-be8e-f8ec10fa7ffe · outbound

This paper cites Multi-game decision transformers.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Multi-game decision transformers

Reference 39

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Source-reported events for the cited work

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Observation 501127b4-92d5-4854-b81c-d2334da45bd7 · outbound

This paper cites Nonparametric general reinforcement learning.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Nonparametric general reinforcement learning

Reference 40

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Source-reported events for the cited work

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Observation 191af7e7-ad28-4a8c-a062-4ef4b6cf9301 · outbound

This paper cites SMAC3: A versatile Bayesian optimization package for hyperparameter optimization.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL SMAC3: A versatile Bayesian optimization package for hyperparameter optimization

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:37.735567Z

Source-reported events for the cited work

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Observation e3bdb9b6-ef64-4990-96ad-f2b5e479b8cd · outbound

This paper cites Reinforcement learning, bit by bit.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Reinforcement learning, bit by bit

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:37.509265Z

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 a77c0d4d-7dca-41ee-a4f2-0f811a662866 · outbound

This paper cites Batch Bayesian optimization via multi-objective acquisition ensemble for automated analog circuit design.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Batch Bayesian optimization via multi-objective acquisition ensemble for automated analog circuit design

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:37.339683Z

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 dc2320ad-c487-4130-ba40-904eff3d7316 · outbound

This paper cites Abstractions of general reinforcement learning: An inquiry into the scalability of generally intelligent agents.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Abstractions of general reinforcement learning: An inquiry into the scalability of generally intelligent agents

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:37.056105Z

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 49b569c0-2316-432a-a821-33cd035b7534 · outbound

This paper cites End-to-end meta-Bayesian optimisation with Transformer neural processes.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL End-to-end meta-Bayesian optimisation with Transformer neural processes

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:36.831297Z

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 198ac772-0c49-4520-96d3-396bf86c5975 · outbound

This paper cites Nonlinear multiobjective optimization, volume 12.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Nonlinear multiobjective optimization, volume 12

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:36.669851Z

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 0349e2e2-9292-4d6c-ada6-b02489ab693b · outbound

This paper cites NeRF: Representing scenes as neural radiance fields for view synthesis.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL NeRF: Representing scenes as neural radiance fields for view synthesis

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:36.480726Z

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 b80995a4-8eb9-4f9d-8e7c-564522c9742e · outbound

This paper cites A survey of partially observable Markov decision processes: Theory, models, and algorithms.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL A survey of partially observable Markov decision processes: Theory, models, and algorithms

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:36.279988Z

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 c8faf81d-3bb2-4871-85f1-d37b75bbda64 · outbound

This paper cites A unifying view of optimism in episodic reinforcement learning.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL A unifying view of optimism in episodic reinforcement learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:36.031269Z

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

Observation 5a37213c-1b5e-4dca-9a83-f50801e3791b · outbound

This paper cites A flexible framework for multi-objective Bayesian optimization using random scalarizations.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL A flexible framework for multi-objective Bayesian optimization using random scalarizations

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:35.887133Z

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 9611fbcc-2b6c-40b4-ba03-a7b818d2810f · outbound

This paper cites Multiobjective optimization using Gaussian process emulators via stepwise uncertainty reduction.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Multiobjective optimization using Gaussian process emulators via stepwise uncertainty reduction

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:35.717825Z

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Observation 5a8e569d-634f-4e32-a5a6-8d12b6447aa6 · outbound

This paper cites Multiobjective optimization on a limited budget of evaluations using model-assisted-metric selection.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Multiobjective optimization on a limited budget of evaluations using model-assisted-metric selection

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:35.581837Z

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 b2a84f54-c880-4768-b4d2-2aa8c262e1f1 · outbound

This paper cites Prioritized experience replay.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Prioritized experience replay

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:35.328627Z

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 8788ce0a-e4a4-4fde-8217-1fade5dd987c · outbound

This paper cites Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:29.406729Z

Source-reported events for the cited work

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Observation 3073a293-11a8-4357-91af-5b05742dd327 · outbound

This paper cites RTDK-BO: High dimensional Bayesian optimization with reinforced transformer deep kernels.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL RTDK-BO: High dimensional Bayesian optimization with reinforced transformer deep kernels

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:35.141019Z

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Observation 3958e36e-8561-4dee-bd7d-77d7b3da2965 · outbound

This paper cites Practical Bayesian optimization of machine learning algorithms.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Practical Bayesian optimization of machine learning algorithms

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:34.953913Z

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Observation 81fd364b-e6a9-4958-958c-c3504a1635ea · outbound

This paper cites Multi-objective Bayesian optimization using Pareto-frontier entropy.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Multi-objective Bayesian optimization using Pareto-frontier entropy

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:34.762513Z

Source-reported events for the cited work

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Observation d12f8cc7-8d88-4d77-82d2-e7eb158412b1 · outbound

This paper cites MuJoCo: A physics engine for model-based control.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL MuJoCo: A physics engine for model-based control

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:34.536299Z

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 bdc7a6f9-29a3-46c8-8afa-81c203e1c890 · outbound

This paper cites Joint entropy search for multi-objective Bayesian optimization.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Joint entropy search for multi-objective Bayesian optimization

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:34.369351Z

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 c4032f58-cad2-4baf-86e4-32401bdce947 · outbound

This paper cites COMBO: An Efficient Bayesian Optimization Library for Materials Science.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL COMBO: An Efficient Bayesian Optimization Library for Materials Science

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:34.128572Z

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 76e2ee6a-b32d-4f87-ad9d-9c7a9dcadda5 · outbound

This paper cites Meta-learning acquisition functions for transfer learning in Bayesian optimization.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Meta-learning acquisition functions for transfer learning in Bayesian optimization

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:33.937545Z

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

Observation 140af291-0a5f-45f8-a250-806c295591c4 · outbound

This paper cites Bayesian Optimization for Multi-objective Optimization and Multi-point Search.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Bayesian Optimization for Multi-objective Optimization and Multi-point Search

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:30:31.584472Z

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Observation 182ba24e-4acd-4b0b-a97b-daf6b3ae2385 · outbound

This paper cites Max-value entropy search for efficient Bayesian optimization.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Max-value entropy search for efficient Bayesian optimization

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:33.788154Z

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 e4f9a323-1e81-44d8-8625-e460018dcd59 · outbound

This paper cites Gaussian processes for machine learning, volume 2.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Gaussian processes for machine learning, volume 2

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:33.618404Z

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 0eea353c-09c2-459e-936d-d1f5142e5775 · outbound

This paper cites Multi-objective Bayesian global optimization using expected hypervolume improvement gradient.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Multi-objective Bayesian global optimization using expected hypervolume improvement gradient

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:33.410899Z

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 451aaf90-dc61-4f55-a96b-bbce3299c0bd · outbound

This paper cites Bayesian model-agnostic meta-learning.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Bayesian model-agnostic meta-learning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:33.215969Z

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 5e646d7b-1a13-4518-a8fc-2c762455612d · outbound

This paper cites MVImgNet: A large-scale dataset of multi-view images.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL MVImgNet: A large-scale dataset of multi-view images

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:32.967306Z

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 ec2e4126-4041-4556-b22a-9d42864ce534 · outbound

This paper cites MOEA/D: A multiobjective evolutionary algorithm based on decomposition.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL MOEA/D: A multiobjective evolutionary algorithm based on decomposition

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:32.742329Z

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 093e63d3-f756-466f-8675-6ac1d3edf8ec · outbound

This paper cites Expensive multiobjective optimization by MOEA/D with Gaussian process model.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Expensive multiobjective optimization by MOEA/D with Gaussian process model

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:32.544639Z

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 5ffcba6d-4eb9-4ff1-8f26-b4d6915ea467 · outbound

This paper cites Online decision transformer.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Online decision transformer

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:32.298742Z

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 2a2190dc-e1dd-4560-a321-c301f670148b · outbound

This paper cites A trust-region parallel Bayesian optimization method for simulation-driven antenna design.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL A trust-region parallel Bayesian optimization method for simulation-driven antenna design

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:32.105500Z

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 78f4df41-0068-4281-88a6-c8d8412bf277 · outbound

This paper cites Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:31.868833Z

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 18bf8250-516e-42fc-9230-eed90e457f10 · outbound

This paper cites write newline.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL write newline

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:30.994508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cc6a4b4-c856-427e-8119-221b2af157ce · outbound

This paper cites write newline.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL write newline

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:31.128866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d755ff4e-479f-4f11-8db8-410ff898bec5 · outbound

This paper cites @esa (Ref.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL @esa (Ref

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:31.215778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99840b55-aae2-4354-909c-1a3b73765b19 · outbound

This paper cites an unresolved cited work.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Unresolved cited work

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:31.279197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aad54476-6a08-4448-96df-3a9e5bef8d1d · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Playing Atari with Deep Reinforcement Learning

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:31.380992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation e69dc31d-ede3-4223-93e5-78dfad687a2a · inbound

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents cites this paper.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-06T16:28:54.642701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c85eaee-7558-4729-a319-39fde2451e00 · inbound

In-Context Black-Box Optimization with Unreliable Feedback cites this paper.

In-Context Black-Box Optimization with Unreliable Feedback BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:51:07.379269Z

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.