Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning | Paper Citation Record

Observation 5bb8459a-4a5d-4519-89ba-43e14bfeb1ce · outbound

This paper cites Mysqltuner,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Mysqltuner,

Reference 1

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Observation c04f640b-949e-4196-a7d4-dd697369c782 · outbound

This paper cites Model-based genetic algorithms for algorithm configuration,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Model-based genetic algorithms for algorithm configuration,

Reference 2

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Observation 92aa1064-b783-48e1-9844-ad3953312b0f · outbound

This paper cites A gender-based genetic algorithm for the automatic configuration of algorithms,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning A gender-based genetic algorithm for the automatic configuration of algorithms,

Reference 3

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Observation d0b28cf4-cead-4e6e-b432-7502ac478f5a · outbound

This paper cites ACTGAN: automatic configuration tuning for software systems with generative adversarial networks,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning ACTGAN: automatic configuration tuning for software systems with generative adversarial networks,

Reference 4

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Observation 9e17a7d4-a0b9-482e-af47-043dc77c092c · outbound

This paper cites Autoconfig: automatic configuration tuning for distributed message systems,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Autoconfig: automatic configuration tuning for distributed message systems,

Reference 5

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Observation ee44de3e-536e-422a-bc4a-791bcbc67523 · outbound

This paper cites Cm-casl: Comparison-based performance modeling of software systems via collaborative active and semisupervised learning,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Cm-casl: Comparison-based performance modeling of software systems via collaborative active and semisupervised learning,

Reference 6

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Observation 63bde329-5424-4b96-b444-835f18ffed05 · outbound

This paper cites CM- CASL: comparison-based performance modeling of software systems via collaborative active and semisupervised learning,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning CM- CASL: comparison-based performance modeling of software systems via collaborative active and semisupervised learning,

Reference 7

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Observation b84c5180-161b-44fe-8cca-1e5b4113cfec · outbound

This paper cites Ptssbench: a performance evaluation platform in support of automated parameter tuning of software systems,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Ptssbench: a performance evaluation platform in support of automated parameter tuning of software systems,

Reference 8

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Observation 19b0e780-8259-4a07-b046-35b9999bab1f · outbound

This paper cites Cgptuner: a contextual gaussian process bandit approach for the automatic tuning of IT configurations under varying workload conditions,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Cgptuner: a contextual gaussian process bandit approach for the automatic tuning of IT configurations under varying workload conditions,

Reference 9

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Observation 91b7d24a-8078-4817-a494-303a94dcce09 · outbound

This paper cites “sampling.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning “sampling

Reference 10

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Observation fed16c4d-5f80-4455-a645-c8755f445d0f · outbound

This paper cites Efficient compiler autotuning via bayesian optimization,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Efficient compiler autotuning via bayesian optimization,

Reference 11

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Observation 8838d3fd-4b57-419c-b20a-a627b186e4c0 · outbound

This paper cites MMO: meta multi-objectivization for software configuration tuning,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning MMO: meta multi-objectivization for software configuration tuning,

Reference 12

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Observation 1aac8e97-edfb-4c8b-88d4-06abfaacee74 · outbound

This paper cites All versus one: an empirical comparison on retrained and incremental machine learning for modeling performance of adaptable software,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning All versus one: an empirical comparison on retrained and incremental machine learning for modeling performance of adaptable software,

Reference 13

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Observation 7dd3d23b-bafe-4556-966e-7bee04a8a6ea · outbound

This paper cites Lifelong dynamic optimization for self-adaptive systems: Fact or fiction?.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Lifelong dynamic optimization for self-adaptive systems: Fact or fiction?

Reference 14

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Observation c64c30ad-bb60-43db-a05b-f2f65728c78f · outbound

This paper cites Planning landscape analysis for self-adaptive systems,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Planning landscape analysis for self-adaptive systems,

Reference 15

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Observation 8277c7e9-7511-47ca-9823-10ad8fad32bd · outbound

This paper cites Self-adaptive and online qos modeling for cloud-based software services,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Self-adaptive and online qos modeling for cloud-based software services,

Reference 16

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Observation d059a239-0a81-4142-afb5-48b63629a10e · outbound

This paper cites Self-adaptive trade-off decision making for autoscaling cloud-based services,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Self-adaptive trade-off decision making for autoscaling cloud-based services,

Reference 17

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Observation 1dc7dce0-d708-4137-837f-989e07627a50 · outbound

This paper cites FEMOSAA: feature-guided and knee-driven multi-objective optimization for self-adaptive software,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning FEMOSAA: feature-guided and knee-driven multi-objective optimization for self-adaptive software,

Reference 18

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Observation 2dbf17e6-d146-4e70-887b-de403f4dfc9c · outbound

This paper cites Multi-objectivizing software configuration tuning,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Multi-objectivizing software configuration tuning,

Reference 19

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Observation 8edc9920-f357-4044-bf17-41053a2aa6a2 · outbound

This paper cites Do performance aspirations matter for guiding software configuration tuning? an empirical investigation under dual performance objectives,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Do performance aspirations matter for guiding software configuration tuning? an empirical investigation under dual performance objectives,

Reference 20

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Observation 708c2d9a-48d4-4b31-aa39-5a63225ce38e · outbound

This paper cites The weights can be harmful: Pareto search versus weighted search in multi-objective search-based software engineering,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning The weights can be harmful: Pareto search versus weighted search in multi-objective search-based software engineering,

Reference 21

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Observation e75042f6-84a2-4929-9f58-094feb164c5c · outbound

This paper cites Adapting multi-objectivized software configuration tuning,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Adapting multi-objectivized software configuration tuning,

Reference 22

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Observation 9f4f6b6b-caeb-4b8d-bd42-b482c402a702 · outbound

This paper cites Tuning configuration of apache spark on public clouds by combining multi- objective optimization and performance prediction model,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Tuning configuration of apache spark on public clouds by combining multi- objective optimization and performance prediction model,

Reference 23

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Observation 86f5f4d5-d969-473a-9ddd-acca56cec36c · outbound

This paper cites Hinnperf: Hierarchical interac- tion neural network for performance prediction of configurable systems,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Hinnperf: Hierarchical interac- tion neural network for performance prediction of configurable systems,

Reference 24

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Observation 3a30a582-8fb9-4da3-afde-84bad46513db · outbound

This paper cites Dominance statistics: Ordinal analyses to answer ordi- nal questions.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Dominance statistics: Ordinal analyses to answer ordi- nal questions

Reference 25

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Observation 720a5daa-d590-4c86-b532-6c22eaa0ff87 · outbound

This paper cites Atconf: auto-tuning high dimensional configuration parameters for big data process- ing frameworks,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Atconf: auto-tuning high dimensional configuration parameters for big data process- ing frameworks,

Reference 26

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Observation c698e27e-f80b-4e13-af74-b5ab4eb4c455 · outbound

This paper cites Framework and benchmarks for combinatorial and mixed-variable bayesian optimization,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Framework and benchmarks for combinatorial and mixed-variable bayesian optimization,

Reference 27

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Observation 1d328dd8-9039-41c7-8449-3f654b8f424e · outbound

This paper cites BOHB: robust and efficient hyperparameter optimization at scale,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning BOHB: robust and efficient hyperparameter optimization at scale,

Reference 28

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Observation 27262e7f-4110-40eb-a37e-f6a6c246c99b · outbound

This paper cites To tune or not to tune? in search of optimal configurations for data analytics,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning To tune or not to tune? in search of optimal configurations for data analytics,

Reference 29

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Observation 041c155b-2a1a-44b3-9202-218507dbd235 · outbound

This paper cites Resource-guided configuration space reduction for deep learning models,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Resource-guided configuration space reduction for deep learning models,

Reference 30

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Observation 87ba8691-fb02-4ed5-8954-f2ff9930734f · outbound

This paper cites Garnett, Bayesian optimization.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Garnett, Bayesian optimization

Reference 31

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Observation ca3cc207-7e20-4637-8088-f68326aa4ae6 · outbound

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Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Synthesis of probabilistic models for quality-of-service software engineering,

Reference 32

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Observation 87cdc1cf-969e-4a64-bc8a-5fbb1708d919 · outbound

This paper cites Does configuration encoding matter in learning software performance? an empirical study on encoding schemes,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Does configuration encoding matter in learning software performance? an empirical study on encoding schemes,

Reference 33

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Observation 40da9325-4faa-4cc6-b828-5a816077e7f4 · outbound

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Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Predicting software performance with divide-and-learn,

Reference 34

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Observation b52d1913-f97f-4e2f-83e6-53c2a4b3142c · outbound

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Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Deep configuration performance learning: A systematic survey and taxonomy,

Reference 35

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Observation 63cc3e77-25a7-4e80-b4b5-4cf05b7b3646 · outbound

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Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Predicting configuration performance in multiple environments with sequential meta-learning,

Reference 36

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Observation 9502c8b0-9336-4525-a186-6f9af247ee06 · outbound

This paper cites Dividable configuration performance learning,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Dividable configuration performance learning,

Reference 37

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Observation aeb7ad2d-ac53-4f08-8e7f-17591547a676 · outbound

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Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Are we forgetting about compositional optimisers in bayesian optimisation?

Reference 38

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Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Data-efficient perfor- mance learning for configurable systems,

Reference 39

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Observation 669aac2d-dbbd-4ee1-9f1c-e18c4d44e1d7 · outbound

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Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Deepperf: Performance prediction for configurable software with deep sparse neural network,

Reference 40

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This paper cites Deepperf: performance prediction for configurable software with deep sparse neural network,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Deepperf: performance prediction for configurable software with deep sparse neural network,

Reference 41

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Observation 592070e6-1290-4757-9d73-93c97061389a · outbound

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Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning An empirical study on performance bugs for highly configurable software systems,

Reference 42

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Observation fa2f64f9-dc63-4e61-aa73-52d825b5aa88 · outbound

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Reference 43

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Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Paramils: an automatic algorithm configuration framework,

Reference 44

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Observation 0aaed6da-b24d-4da2-a5a5-b8a71e144fd9 · outbound

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Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning An uncertainty-aware approach to optimal configuration of stream processing systems,

Reference 45

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Observation de6c0ce7-e782-4164-9472-cbb2189fd82b · outbound

This paper cites Transfer learning for performance modeling of configurable systems: an exploratory analysis,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Transfer learning for performance modeling of configurable systems: an exploratory analysis,

Reference 46

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Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Learning to sample: exploiting similarities across environments to learn performance models for configurable systems,

Reference 47

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Observation 3ada7005-76e4-4d59-a8a9-63338e628a8e · outbound

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Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Learning to sample: Exploiting similarities across environments to learn performance models for configurable systems,

Reference 48

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This paper cites Fitness distance correlation as a measure of problem difficulty for genetic algorithms,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Fitness distance correlation as a measure of problem difficulty for genetic algorithms,

Reference 49

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Observation e7394cbf-87d7-4f40-a48c-9b319bcab438 · outbound

This paper cites LlamaTune: Sample-Efficient DBMS Configuration Tuning.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning LlamaTune: Sample-Efficient DBMS Configuration Tuning

Reference 50

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Observation 24ef171e-86d3-42a5-9a8c-0d21c0f2f993 · outbound

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Reference 51

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Observation 00e882ff-1805-4143-b1fb-629526c883a6 · outbound

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Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Robotune: high-dimensional configura- tion tuning for cluster-based data analytics,

Reference 52

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Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning SCOPE: Safe Exploration for Dynamic Computer Systems Optimization

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:23:55.146878Z

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Observation 03297ca5-6af6-48f7-bb58-7c074f372f85 · outbound

This paper cites Whence to learn? transferring knowledge in configurable systems using beetle,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Whence to learn? transferring knowledge in configurable systems using beetle,

Reference 54

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This paper cites Whence to learn? transferring knowledge in configurable systems using BEETLE,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Whence to learn? transferring knowledge in configurable systems using BEETLE,

Reference 55

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This paper cites Conex: Efficient exploration of big-data system configurations for better perfor- mance,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Conex: Efficient exploration of big-data system configurations for better perfor- mance,

Reference 56

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Observation b26bb121-88c0-4a8c-8b56-b4a4f7e0b8f0 · outbound

This paper cites K2vtune: A workload- aware configuration tuning for rocksdb,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning K2vtune: A workload- aware configuration tuning for rocksdb,

Reference 57

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Observation 31d09de2-3636-403d-8946-fa2da42a6584 · outbound

This paper cites Understanding the automated parameter optimization on transfer learning for cross-project defect prediction: an empirical study,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Understanding the automated parameter optimization on transfer learning for cross-project defect prediction: an empirical study,

Reference 58

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Observation 4690e29a-4542-4d58-92c4-6c3533fe212e · outbound

This paper cites Hyperband: A novel bandit-based approach to hyperparameter optimization,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Hyperband: A novel bandit-based approach to hyperparameter optimization,

Reference 59

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Observation 05b2bdc7-7918-499e-96eb-7f509dc4e251 · outbound

This paper cites Phronesis: Efficient performance modeling for high-dimensional configuration tuning,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Phronesis: Efficient performance modeling for high-dimensional configuration tuning,

Reference 60

Resolution: verified exact; doi, observed 2026-08-10T22:23:53.663516Z

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Observation 2d38b1f8-1ca6-4456-a8c7-c0abda4b69e7 · outbound

This paper cites An empirical study of the impact of hyperparameter tuning and model optimization on the performance properties of deep neural networks,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning An empirical study of the impact of hyperparameter tuning and model optimization on the performance properties of deep neural networks,

Reference 61

Resolution: verified exact; doi, observed 2026-08-10T22:23:53.646069Z

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Observation 6500d6e7-8241-4d1d-9510-77399e65d0d3 · outbound

This paper cites Classification and regression by randomforest,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Classification and regression by randomforest,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-10T22:23:53.332468Z

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Observation d1ddb03d-a5ac-4c1c-96b8-c8272fb62b06 · outbound

This paper cites The irace package: Iterated racing for automatic algorithm configuration,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning The irace package: Iterated racing for automatic algorithm configuration,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:57.027357Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1276f903-26a6-45fc-bd02-bd392a8b9d65 · outbound

This paper cites A comparison of performance specialization learning for configurable systems,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning A comparison of performance specialization learning for configurable systems,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-10T22:23:53.342632Z

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Observation d2067549-91ab-4a5e-8ea1-7c1faa3615b8 · outbound

This paper cites Kruskal-wallis test,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Kruskal-wallis test,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:57.010731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9cd1d6d0-3779-4bc6-a4df-f9a9fb45dd26 · outbound

This paper cites Montgomery, E.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Montgomery, E

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.994255Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5e01d45e-0090-4caa-a819-5b47e80d10ff · outbound

This paper cites Analyzing the im- pact of workloads on modeling the performance of configurable software systems,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Analyzing the im- pact of workloads on modeling the performance of configurable software systems,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.979747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3301b300-027d-4b0e-b7ce-ebb3322edda3 · outbound

This paper cites Spearman correlation coefficients, differences between,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Spearman correlation coefficients, differences between,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.964356Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9d526e02-ba2d-486b-a433-a800f8819601 · outbound

This paper cites Using bad learners to find good configurations,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Using bad learners to find good configurations,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-10T22:23:53.366359Z

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Observation a33f4161-6e9a-4cbc-9d76-a54a2d19c6c1 · outbound

This paper cites Finding faster configurations using flash,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Finding faster configurations using flash,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-10T22:23:53.371280Z

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Observation 87775b56-27bb-49e1-8811-854701e6e85f · outbound

This paper cites Finding faster configurations using FLASH,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Finding faster configurations using FLASH,

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-10T22:23:53.376181Z

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Observation 5d58981b-a9c9-4a53-9649-0898690ca79c · outbound

This paper cites Veer: Disagreement-free multi-objective configuration,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Veer: Disagreement-free multi-objective configuration,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.938244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c0fe9d86-9937-48f0-8bf4-e2ec1f7a6368 · outbound

This paper cites VEER: Enhancing the Interpretability of Model-based Optimizations.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning VEER: Enhancing the Interpretability of Model-based Optimizations

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:23:54.671739Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6abea599-985e-4644-b84d-87e5d79b3dc3 · outbound

This paper cites Learning software configuration spaces: A systematic literature review,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Learning software configuration spaces: A systematic literature review,

Reference 74

Resolution: malformed identifier; no resolver link, observed 2026-08-10T22:23:53.391199Z

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Observation d25ac4bf-5ec5-46b3-8c89-2c65cb4451c0 · outbound

This paper cites A comprehensive survey on fitness landscape analysis,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning A comprehensive survey on fitness landscape analysis,

Reference 75

Resolution: verified exact; doi, observed 2026-08-10T22:23:53.629645Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 00c4f638-6e35-4964-afef-5c09634cce46 · outbound

This paper cites an unresolved cited work.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:23:56.922803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fcd563a8-ea08-4da8-b111-928e4db1f89c · outbound

This paper cites Software configuration engineering in practice interviews, survey, and systematic literature review,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Software configuration engineering in practice interviews, survey, and systematic literature review,

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-10T22:23:53.405506Z

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Observation a3b65549-9cae-42a0-bf59-80c6755d68b5 · outbound

This paper cites On parameter tuning in search based software engineering: A replicated empirical study,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning On parameter tuning in search based software engineering: A replicated empirical study,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.907391Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c6a9731e-163f-46b2-a2e1-da990a5958e3 · outbound

This paper cites Greening large language models of code,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Greening large language models of code,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.890905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 96b60fb6-61e4-4ed3-99ee-0e5fd75eb86d · outbound

This paper cites Spl conqueror: Toward optimization of non-functional properties in software product lines,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Spl conqueror: Toward optimization of non-functional properties in software product lines,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.875306Z

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Observation c08a846d-02e9-4433-a2a4-10ec54e1f50a · outbound

This paper cites NAPEL: near- memory computing application performance prediction via ensemble learning,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning NAPEL: near- memory computing application performance prediction via ensemble learning,

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-10T22:23:53.424563Z

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Observation a9b8df47-2ff3-4911-97f6-eee10bb8af47 · outbound

This paper cites A tutorial on support vector regression,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning A tutorial on support vector regression,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.860075Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d1c1e6c7-bd96-419c-b237-7b425b53fecf · outbound

This paper cites Decision tree methods: applications for classification and prediction,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Decision tree methods: applications for classification and prediction,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.844861Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 41d528e8-5288-4e9c-a452-8c8999353d82 · outbound

This paper cites Landscapes and their correlation functions,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Landscapes and their correlation functions,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.829606Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 081e4c07-64e6-49a7-834d-c9008759a13a · outbound

This paper cites An empirical comparison of model validation tech- niques for defect prediction models,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning An empirical comparison of model validation tech- niques for defect prediction models,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.816167Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c892cb98-135d-4bef-b0c3-751310863be7 · outbound

This paper cites The impact of automated parameter optimization on defect prediction models,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning The impact of automated parameter optimization on defect prediction models,

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-10T22:23:53.447942Z

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Observation 1357a84d-f954-46c1-a6c6-d4416ad587dc · outbound

This paper cites Multidimensional knapsack problem: A fitness landscape analysis,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Multidimensional knapsack problem: A fitness landscape analysis,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.802285Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c37d7a10-438c-4b61-935f-c78c66e65ff1 · outbound

This paper cites Auto- matic database management system tuning through large-scale machine learning,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Auto- matic database management system tuning through large-scale machine learning,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.786635Z

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Observation b9f1f1b0-d302-408a-9c48-be9b19f0c1b0 · outbound

This paper cites An inquiry into machine learning-based auto- matic configuration tuning services on real-world database man- agement systems,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning An inquiry into machine learning-based auto- matic configuration tuning services on real-world database man- agement systems,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.771438Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 71420879-6050-4780-b47e-250efb9e4046 · outbound

This paper cites Configcrusher: towards white-box performance analysis for configurable systems,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Configcrusher: towards white-box performance analysis for configurable systems,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.755705Z

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Observation d41f1ac3-d850-473e-bf22-ec7605f74da9 · outbound

This paper cites Morphling: Fast, near-optimal auto- configuration for cloud-native model serving,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Morphling: Fast, near-optimal auto- configuration for cloud-native model serving,

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-10T22:23:53.478928Z

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Observation b208b0ea-6ec0-4844-8ccf-6cae784cb6ff · outbound

This paper cites A practical guide to select quality indicators for assessing pareto-based search algorithms in search-based software engineering,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning A practical guide to select quality indicators for assessing pareto-based search algorithms in search-based software engineering,

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-10T22:23:53.483548Z

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Observation db0c41b4-c0ac-48a4-b2bf-15652f54c405 · outbound

This paper cites Giving back: Contributions congruent to library dependency changes in a software ecosystem,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Giving back: Contributions congruent to library dependency changes in a software ecosystem,

Reference 94

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T22:23:54.127618Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 88932b5c-e3bd-4309-9baf-f9f944a29314 · outbound

This paper cites Twins or false friends? a study on energy consumption and performance of configurable software,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Twins or false friends? a study on energy consumption and performance of configurable software,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.740381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5dd78a11-d728-4dc1-b16f-d720734e8a48 · outbound

This paper cites Twins or false friends? A study on energy consumption and performance of configurable software,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Twins or false friends? A study on energy consumption and performance of configurable software,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.724778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 424b2f4a-60bb-4c2b-8ca2-b6875ca83c2f · outbound

This paper cites Hyperparameter Optimization for Effort Estimation.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Hyperparameter Optimization for Effort Estimation

Reference 97

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:23:53.962730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 19cebbe8-1bea-4bef-8fb9-3771e113e7ec · outbound

This paper cites Locat: Low-overhead online con- figuration auto-tuning of spark sql applications,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Locat: Low-overhead online con- figuration auto-tuning of spark sql applications,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.709173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a7132ee6-d544-4077-ba32-a7474fb40248 · outbound

This paper cites Hey, you have given me too many knobs!: understanding and dealing with over-designed configuration in system software,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Hey, you have given me too many knobs!: understanding and dealing with over-designed configuration in system software,

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-10T22:23:53.517881Z

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Observation 0c68e450-f41b-4cec-b14a-d098bb52d65c · outbound

This paper cites Distilled lifelong self-adaptation for configurable systems,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Distilled lifelong self-adaptation for configurable systems,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:23:56.693771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 089f83d5-5e81-489d-ac30-0e8e961bf0c5 · outbound

This paper cites Datasize-aware high dimensional configurations auto-tuning of in-memory cluster computing,.

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning Datasize-aware high dimensional configurations auto-tuning of in-memory cluster computing,

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-10T22:23:53.527858Z

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