| Configuration: GEARS, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 15.5 (12.8) RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average) unitless · lower Uncertainty: SE 12.8. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceGEARS, trained once on a downsampled set on Norman (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 6, Gene Frequency 10 block, row "GEARS", column "Norman" |
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| Configuration: GEARS, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 10.7 (12.1) RMSE (test RMSE of pair viability, training set with each gene seen about 20 times on average) unitless · lower Uncertainty: SE 12.1. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceGEARS, trained once on a downsampled set on Norman (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 20 block, row "GEARS", column "Norman" |
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| Configuration: GEARS, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 16.6 (19.8) RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average) unitless · lower Uncertainty: SE 19.8. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceGEARS, trained once on a downsampled set on Norman (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 4 block, row "GEARS", column "Norman" |
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| Configuration: Linear regression on single-gene effects, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 6.1 (1.3) RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average) unitless · lower Uncertainty: SE 1.3. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceLinear regression on single-gene effects, trained once on a downsampled set on Norman (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 6, Gene Frequency 10 block, row "Linear", column "Norman" |
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| Configuration: Linear regression on single-gene effects, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 6.1 (1.1) RMSE (test RMSE of pair viability, training set with each gene seen about 20 times on average) unitless · lower Uncertainty: SE 1.1. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceLinear regression on single-gene effects, trained once on a downsampled set on Norman (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 20 block, row "Linear", column "Norman" |
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| Configuration: Linear regression on single-gene effects, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 6.2 (1.7) RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average) unitless · lower Uncertainty: SE 1.7. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceLinear regression on single-gene effects, trained once on a downsampled set on Norman (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 4 block, row "Linear", column "Norman" |
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| Configuration: MLP on summed gene embeddings, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 5.7 (1.3) RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average) unitless · lower Uncertainty: SE 1.3. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMLP on summed gene embeddings, trained once on a downsampled set on Norman (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 6, Gene Frequency 10 block, row "MLP", column "Norman" |
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| Configuration: MLP on summed gene embeddings, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 5.0 (1.4) RMSE (test RMSE of pair viability, training set with each gene seen about 20 times on average) unitless · lower Uncertainty: SE 1.4. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMLP on summed gene embeddings, trained once on a downsampled set on Norman (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 20 block, row "MLP", column "Norman" |
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| Configuration: MLP on summed gene embeddings, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 7.7 (0.9) RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average) unitless · lower Uncertainty: SE 0.9. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMLP on summed gene embeddings, trained once on a downsampled set on Norman (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 4 block, row "MLP", column "Norman" |
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| Configuration: NAIAD, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 4.7 (1.2) RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average) unitless · lower Uncertainty: SE 1.2. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD, trained once on a downsampled set on Norman (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 6, Gene Frequency 10 block, row "NAIAD", column "Norman" |
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| Configuration: NAIAD, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 4.7 (0.1) RMSE (test RMSE of pair viability, training set with each gene seen about 20 times on average) unitless · lower Uncertainty: SE 0.1. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD, trained once on a downsampled set on Norman (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 20 block, row "NAIAD", column "Norman" |
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| Configuration: NAIAD, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 5.1 (1.8) RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average) unitless · lower Uncertainty: SE 1.8. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD, trained once on a downsampled set on Norman (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 4 block, row "NAIAD", column "Norman" |
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| Configuration: RECOVER (gene adaptation), trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 5.9 (1.0) RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average) unitless · lower Uncertainty: SE 1.0. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceRECOVER (gene adaptation), trained once on a downsampled set on Norman (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 6, Gene Frequency 10 block, row "RECOVER", column "Norman" |
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| Configuration: RECOVER (gene adaptation), trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 4.7 (0.5) RMSE (test RMSE of pair viability, training set with each gene seen about 20 times on average) unitless · lower Uncertainty: SE 0.5. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceRECOVER (gene adaptation), trained once on a downsampled set on Norman (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 20 block, row "RECOVER", column "Norman" |
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| Configuration: RECOVER (gene adaptation), trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 7.1 (2.8) RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average) unitless · lower Uncertainty: SE 2.8. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceRECOVER (gene adaptation), trained once on a downsampled set on Norman (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 4 block, row "RECOVER", column "Norman" |
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| Configuration: NAIAD with MPE acquisition | Protocol: Retrospective active learning on a fully measured two-gene CRISPR viability screen: top-200 gene pairs found by round 4
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 14 Count (marginal gain (P_4 - P_0)/4 in the screen's 200 strongest measured gene pairs found, round 0 to round 4) count · higher Uncertainty: Not reported by the source: The table prints no error and does not state the number of runs. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with MPE acquisition on Norman (Tables 2 and 3, acquisition functions with NAIAD) pheno-20261022-qin2025-protocol-al-top200 Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 3, row "MPE", column "Norman" |
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| Configuration: NAIAD with MPE acquisition | Protocol: Retrospective active learning on a fully measured two-gene CRISPR viability screen: top-200 gene pairs found by round 4
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 144 True positives (the screen's 200 strongest measured gene pairs found among the 200 strongest measured-plus-predicted values, round 4 of active learning) count · higher Uncertainty: Not reported by the source: The table prints no error and does not state the number of runs. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with MPE acquisition on Norman (Tables 2 and 3, acquisition functions with NAIAD) pheno-20261022-qin2025-protocol-al-top200 Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 2, row "MPE", column "Norman" |
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| Configuration: NAIAD with UCB acquisition | Protocol: Retrospective active learning on a fully measured two-gene CRISPR viability screen: top-200 gene pairs found by round 4
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 2.25 Count (marginal gain (P_4 - P_0)/4 in the screen's 200 strongest measured gene pairs found, round 0 to round 4) count · higher Uncertainty: Not reported by the source: The table prints no error and does not state the number of runs. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with UCB acquisition on Norman (Tables 2 and 3, acquisition functions with NAIAD) pheno-20261022-qin2025-protocol-al-top200 Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 3, row "UCB", column "Norman" |
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| Configuration: NAIAD with UCB acquisition | Protocol: Retrospective active learning on a fully measured two-gene CRISPR viability screen: top-200 gene pairs found by round 4
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 97 True positives (the screen's 200 strongest measured gene pairs found among the 200 strongest measured-plus-predicted values, round 4 of active learning) count · higher Uncertainty: Not reported by the source: The table prints no error and does not state the number of runs. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with UCB acquisition on Norman (Tables 2 and 3, acquisition functions with NAIAD) pheno-20261022-qin2025-protocol-al-top200 Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 2, row "UCB", column "Norman" |
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| Configuration: NAIAD with Uncertainty acquisition | Protocol: Retrospective active learning on a fully measured two-gene CRISPR viability screen: top-200 gene pairs found by round 4
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 1 Count (marginal gain (P_4 - P_0)/4 in the screen's 200 strongest measured gene pairs found, round 0 to round 4) count · higher Uncertainty: Not reported by the source: The table prints no error and does not state the number of runs. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with Uncertainty acquisition on Norman (Tables 2 and 3, acquisition functions with NAIAD) pheno-20261022-qin2025-protocol-al-top200 Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 3, row "Uncertainty", column "Norman" |
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| Configuration: NAIAD with Uncertainty acquisition | Protocol: Retrospective active learning on a fully measured two-gene CRISPR viability screen: top-200 gene pairs found by round 4
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 92 True positives (the screen's 200 strongest measured gene pairs found among the 200 strongest measured-plus-predicted values, round 4 of active learning) count · higher Uncertainty: Not reported by the source: The table prints no error and does not state the number of runs. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with Uncertainty acquisition on Norman (Tables 2 and 3, acquisition functions with NAIAD) pheno-20261022-qin2025-protocol-al-top200 Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 2, row "Uncertainty", column "Norman" |
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| Configuration: NAIAD with Uniform acquisition | Protocol: Retrospective active learning on a fully measured two-gene CRISPR viability screen: top-200 gene pairs found by round 4
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 1 Count (marginal gain (P_4 - P_0)/4 in the screen's 200 strongest measured gene pairs found, round 0 to round 4) count · higher Uncertainty: Not reported by the source: The table prints no error and does not state the number of runs. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with Uniform acquisition on Norman (Tables 2 and 3, acquisition functions with NAIAD) pheno-20261022-qin2025-protocol-al-top200 Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 3, row "Uniform", column "Norman" |
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| Configuration: NAIAD with Uniform acquisition | Protocol: Retrospective active learning on a fully measured two-gene CRISPR viability screen: top-200 gene pairs found by round 4
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 92 True positives (the screen's 200 strongest measured gene pairs found among the 200 strongest measured-plus-predicted values, round 4 of active learning) count · higher Uncertainty: Not reported by the source: The table prints no error and does not state the number of runs. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with Uniform acquisition on Norman (Tables 2 and 3, acquisition functions with NAIAD) pheno-20261022-qin2025-protocol-al-top200 Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 2, row "Uniform", column "Norman" |
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| Configuration: NAIAD with MPE acquisition | Protocol: Retrospective active learning on a fully measured two-gene CRISPR viability screen: top-200 gene pairs found by round 4
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 143.0 (1.5) True positives (the screen's 200 strongest measured gene pairs found among the 200 strongest measured-plus-predicted values, round 4 of active learning) count · higher Uncertainty: SE 1.5. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with MPE acquisition on Norman (Table 4, NAIAD against RECOVER by acquisition function) pheno-20261022-qin2025-protocol-al-top200 Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 4, row "NAIAD MPE", column "Norman" |
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| Configuration: NAIAD with UCB acquisition | Protocol: Retrospective active learning on a fully measured two-gene CRISPR viability screen: top-200 gene pairs found by round 4
Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025) | 110.3 (2.5) True positives (the screen's 200 strongest measured gene pairs found among the 200 strongest measured-plus-predicted values, round 4 of active learning) count · higher Uncertainty: SE 2.5. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with UCB acquisition on Norman (Table 4, NAIAD against RECOVER by acquisition function) pheno-20261022-qin2025-protocol-al-top200 Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 4, row "NAIAD UCB", column "Norman" |
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