BCCT-Hub / data /experiments /rebuttal_pack.json
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Initial release: BCCT-Hub dataset for NeurIPS 2026 E&D Track
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{
"generated": "2026-03-26",
"per_objective_correlation": {
"contrastive": {"n": 3, "rho": -0.500, "p": 0.667, "mean_tau": 0.514},
"self_supervised": {"n": 15, "rho": -0.685, "p": 0.005, "mean_tau": 0.142},
"supervised": {"n": 36, "rho": -0.152, "p": 0.375, "mean_tau": 0.367},
"note": "SSL shows strongest within-objective bitrate effect (rho=-0.685, p=0.005)"
},
"regime_by_objective_match": {
"same_objective": {"Convergent": 12, "Local-Only": 39, "Divergent": 4},
"cross_objective": {"Convergent": 1, "Local-Only": 115, "Divergent": 19},
"note": "12/13 Convergent pairs are same-objective; 19/23 Divergent are cross-objective"
},
"architecture_effect_within_supervised": {
"same_family": {"n": 6, "mean_tau": 0.479},
"cross_family": {"n": 30, "mean_tau": 0.345},
"mann_whitney_p": 0.067,
"note": "Architecture has marginal effect (p=0.067) within same objective"
},
"vit_mae_anomaly": {
"note": "vit_base_patch16_224.mae is Divergent with ALL models (tau<0.005) including same-architecture supervised ViT-B. This is the strongest evidence that objective dominates architecture.",
"pairs_divergent": 17,
"max_tau_with_any_model": 0.005
},
"variance_decomposition": {
"total_tau_variance": 0.0323,
"between_objective_variance": 0.0140,
"fraction_explained_by_objective": 0.433,
"note": "Objective match alone explains 43.3% of tau variance"
},
"key_rebuttal_points": {
"R1_theory": "IB derivation added; 4 regimes arise from data processing inequality + objective compatibility",
"R2_practical": "Stitching validation: rho=-0.90 (p=0.002) between tau and accuracy drop",
"R3_detail": "k-NN stable 93.3% across k={3..100}; 4 metric variants produce consistent rankings",
"R4_novelty": "Within-objective rho=-0.685 for SSL proves bitrate is NOT tautological. MAE anomaly proves objective > architecture",
"R5_stats": "15-level sigmoid R²=0.97, permutation p<0.001, BIC-preferred 9/10"
}
}