How Much Can Reliability Drift Under a Fixed Confidence Distribution?

Wenhao Liang; Lin Yue; Wei Emma Zhang; Mingyu Guo; Olaf Maennel; Weitong Chen

cs.AISep 30, 2026

How Much Can Reliability Drift Under a Fixed Confidence Distribution?

Authors: Wenhao Liang, Lin Yue, Wei Emma Zhang, Mingyu Guo, Olaf Maennel, Weitong Chen

Organizations: Adelaide University

Abstract

A classifier's conditional accuracy can change while its confidence distribution stays exactly the same. We study the worst-case movement of the reliability relation under covariate shifts that preserve the distribution of the confidence score, constraining the reweighting within each confidence level by a $χ^2$ budget; the resulting worst case, as a function of the budget, is a fragility profile. On an interval of budgets that can be computed from the source distribution, the profile equals exactly the square root of the budget times the within-level variance of the correctness propensity -- the grouping-loss term of calibration-refinement decompositions. Beyond this interval the profile is governed by the tails of the propensity law, and the entire upward profile determines the centred within-level law; consequently, calibration residual and grouping variance do not determine fragility in general, though they do when labels and predictions are deterministic. Since the propensity is not observed, we restrict reweightings to a learned finite readout within confidence bins, bound the part the restriction misses by the grouping variance remaining inside readout cells, estimate the restricted profile with role-separated labels, and provide a separate split-sample lower confidence bound. On ImageNet this bound is positive in both splits for four of six primary classifiers and nine of twelve additional ones as released, and for three of eighteen after temperature scaling. Held-out drift under optimised reweightings fitted without evaluation labels tracks the estimated profile; an exploratory label-permutation diagnostic yields near-zero agreement for this statistic while largely reproducing the correlation observed for unsigned random reweightings.

Figures & tables

Figure 1: Reliability can move while the confidence distribution does not. (a) Within a confidence level set, mass moves between inputs that share a score but differ in their propensity to be correct;

\mathbb{E}[r\mid S=s]=1

keeps the level’s mass fixed. (b) The score distribution, and so any confidence histogram, is unchanged. (c) The reliability curve moves by at most the fragility profile; a threshold policy keeps its coverage while its error rate rises. Movement is possible exactly where

G_{P}>0

(Proposition 2 ). The schematic is the population construction; the finite-sample one of Section 4 preserves the binned marginal.

Panel of Figure 2 — Figure 2: The profile beyond its variance-controlled regime. Left: the three regimes of Theorem 1 for $\eta\in\{0.2,0.6,1.0\}$ with probabilities $(\tfrac{1}{4},\tfrac{1}{2},\tfrac{1}{4})$ ( $\rho_{+}=0.5$ , $\rho_{\mathrm{sat}}=3$ ). Right: that law and a four-point law on $\{0,0.4,0.7,0.9\}$ with the same mean and variance ( $0.6$ , $0.08$ ) agree for $\rho\leq 0.22$ and separate by $0.119$ at $\rho=3$ . The two-point witnesses of Proposition 4 (iv) have no tail-controlled regime and separate by $0.100$ once both saturate (Appendix M ).

		$T=1$			$T=\hat{T}$
Model	Acc.	ECE	$G_{R}$	FAUC	ECE	$G_{R}$	FAUC
ResNet-50 V1	0.761	0.038	0.0004	0.042	0.024	0.0009	0.049
ResNet-50 V2	0.809	0.412	0.0133	0.135	0.032	0.0009	0.046
ConvNeXt-T	0.825	0.169	0.0081	0.112	0.031	0.0011	0.048
ViT-B/16	0.811	0.057	0.0011	0.048	0.042	0.0005	0.042
Swin-T	0.815	0.068	0.0015	0.056	0.032	0.0004	0.038

Table 1: Primary cohort. ECE:

20

-bin equal-mass out-of-fold estimate;

G_{R}

with the binomial-noise correction of Proposition 9 ; FAUC of the upward plug-in profile. Neither

G_{R}

nor FAUC is a confidence bound. Additional checkpoints: Table 12 .

Appendix figures & tables30 assets

Supplementary material from the paper’s appendix.

Appendix

bins used	target mass kept	mean $L_{\mathrm{comp}}/L_{\mathrm{total}}$	$L_{\mathrm{cond}}>L_{\mathrm{comp}}$	severity 1	severity 5
all	$100\%$	$41.8\%$	400/450	$50.6\%$	$33.0\%$
unflagged	$77.9\%$ ( $55.8\%$ – $98.6\%$ )	$43.9\%$	401/450	$52.7\%$	$37.0\%$

Appendix

Table 2: Low-target-count sensitivity of the ImageNet-C summaries (post-hoc; original partition, fold-pooled records). “Unflagged bins” drops, within each (model, domain), the bins carrying the stored low-count flag of the pooled table (set by the preregistered per-fold rule of fewer than

200

target examples and taken from the first available fold; not a threshold on the fold-averaged count) and renormalises the target bin mass over the rest; every one of the

450

(model, domain) units keeps at least one bin. This changes the estimand to higher-count bins; it is not a revised-pipeline rerun and carries no interval or coverage statement.

Figure 3: Source calibration error against restricted fragility, exploratory. One point per checkpoint and calibration state, all post-audit and regenerated from the same source as Table 1 ; the six checkpoints appear four times each and are not independent observations. Marginally the two are associated (Pearson 0.91, Spearman 0.88); conditioning on $G_{R}$ removes it (partial 0.00, while $G_{R}$ retains 0.86 given ECE). Appendix J gives both parent sets, the rank versions and the leave-one-checkpoint-out ranges, and why none of this establishes non-determination.

Figure 4: The composition-to-total norm ratio decreases with corruption severity. Original protocol, fixed partition; not recomputed. ImageNet-C, macro-averaged over fifteen corruptions and six architectures. The conditional term tracks the total; the governed composition term grows far more slowly, and $L_{\mathrm{comp}}/L_{\mathrm{total}}$ falls from $50.6\%$ at severity 1 to $33.0\%$ at 5. Norms are not additive, so this is a ratio and not a share.

Target	$L_{\mathrm{total}}$	$L_{\mathrm{comp}}$	$L_{\mathrm{cond}}$	median $U$
ImageNet-C (75 domains)	0.103	0.034	0.102	0.63
ImageNetV2	0.058	0.006	0.055	0.37
ImageNet-Sketch	0.240	0.024	0.223	0.89

Appendix

Table 3: Original protocol, fixed partition; not recomputed under the revised data roles. Reliability drift under natural shift, averaged over six checkpoints and domains, with

L

and

U

as defined in Section 6 . The independent-rate check of Appendix P tests the qualitative dominance only; it does not regenerate these coefficients. The

L

are quadratic means of binwise drift, so

L_{\mathrm{total}}\neq L_{\mathrm{comp}}+L_{\mathrm{cond}}

despite the binwise identity being exact.

	changed assignments / violations				max absolute difference
	bins	frozen $R$	rank	ties	cells	$G_{R}$	$\mathcal{F}^{\pm}$	FAUC +	FAUC -	sel. risk
ResNet-50 V1	0	0	0	0	$0$	$0$	$0$	$0$	$0$	$0$
ResNet-50 V2	0	0	0	0	$0$	$0$	$0$	$0$	$0$	$0$
ConvNeXt-T	0	0	0	0	$0$	$0$	$0$	$0$	$0$	$0$
ViT-B/16	0	0	0	0	$0$	$0$	$0$	$0$	$0$	$0$
Swin-T	0	0	0	0	$0$	$0$	$0$	$0$	$0$	$0$

Appendix

Table 4: Structural invariance under the fold-wise monotone map, all six architectures. Counts are over all

250{,}000

train and evaluation bin assignments per model. Every entry is exactly zero; the preregistered tolerance was

10^{-12}

	ECE			binary NLL		ECE 15
	$T{=}1$	monotone	reduction	$T{=}1$	monotone	$T{=}1$	monotone
ResNet-50 V1	0.03614	0.01288	$64.4\%$	0.39579	0.37929	0.03688	0.01349
ResNet-50 V2	0.41178	0.02162	$94.8\%$	0.80867	0.38869	0.41178	0.02064
ConvNeXt-T	0.16933	0.01477	$91.3\%$	0.44668	0.35481	0.16933	0.01312
ViT-B/16	0.05589	0.02577	$53.9\%$	0.36461	0.34621	0.05605	0.02328
Swin-T	0.06794	0.01989	$70.7\%$	0.36261	0.34134	0.06794	0.01740

Appendix

Table 5: Cross-fitted calibration under the fold-wise monotone map. ECE is the

20

-bin equal-mass estimator of Appendix N with the bin partition held fixed; ECE 15 is the equal-width secondary metric. All six architectures exceed the preregistered

20\%

relative-reduction bar.

	$a_{f}$ range	$c_{f}$ range	Spearman	Kendall	pooled ties		edge gap
					before	after
ResNet-50 V1	$0.710$ – $0.719$	$-0.128$ – $-0.091$	$1.000$	$1.000$	29	4	$2.2{\times}10^{-9}$
ResNet-50 V2	$1.430$ – $1.457$	$+2.554$ – $+2.570$	$1.000$	$1.000$	29	4	$2.2{\times}10^{-9}$
ConvNeXt-T	$1.342$ – $1.352$	$+1.026$ – $+1.043$	$1.000$	$1.000$	29	4	$2.3{\times}10^{-9}$
ViT-B/16	$1.343$ – $1.360$	$+0.124$ – $+0.155$	$1.000$	$1.000$	29	4	$0.7{\times}10^{-9}$
Swin-T	$1.251$ – $1.264$	$+0.351$ – $+0.377$	$1.000$	$1.000$	29	4	$1.9{\times}10^{-9}$

Appendix

Table 6: Fitted parameters and rank diagnostics. Spearman and Kendall are within-fold , where a single

g_{f}

acts; no optimisation reached a numerical boundary in any of the

30

fold fits. The edge gap is

\max_{j}|g(\mathrm{edge}_{j})-\mathrm{edge}^{\mathrm{refit}}_{j}|

, the distance between the pushed-forward boundaries the primary analysis uses and independently refitted transformed-score quantiles.

	$g_{\mathrm{flat}}$				$g_{\mathrm{sharp}}$
	ECE	bins chg.	merged	$\max\|\Delta\mathcal{F}\|$	ECE	bins chg.	merged	$\max\|\Delta\mathcal{F}\|$
ResNet-50 V1	0.04713	0	0	$0$	0.09074	0	146	$0$
ResNet-50 V2	0.36709	0	0	$0$	0.46333	0	0	$0$
ConvNeXt-T	0.23640	0	0	$0$	0.10666	0	0	$0$
ViT-B/16	0.16995	0	0	$0$	0.05472	0	0	$0$
Swin-T	0.16788	0	0	$0$	0.05107	0	0	$0$

Appendix

Table 7: Deterministic stress maps. Large deliberate changes in numerical confidence leave every structural quantity exactly unchanged.

	ARI $(R)$	exact $R$ agreement	relative $\|\Delta G_{R}\|$	relative $\|\Delta$ FAUC ${}^{+}\|$
ResNet-50 V1	0.136	0.379	$108.7\%$	$27.2\%$
ResNet-50 V2	0.615	0.795	$1.9\%$	$0.5\%$
ConvNeXt-T	0.584	0.775	$9.9\%$	$4.5\%$
ViT-B/16	0.611	0.796	$25.4\%$	$11.3\%$
Swin-T	0.575	0.770	$27.5\%$	$13.4\%$
EffNet-B0	0.672	0.832	$13.6\%$	$7.2\%$

Appendix

Table 8: Secondary end-to-end analysis, in which the bin quantiles and the low-capacity logistic readout are refitted on the transformed confidence coordinate. Diagnostic only.

Figure 5: Random score-preserving shifts, post-audit. (a) One point per bin–radius cell: two-sided plug-in profile against the median held-out movement over

100

random directions (Spearman

0.901

, most of which a within-bin label-permutation null reproduces; Appendix T ); dashed line

y=x

, equal axes. (b) Per-shift ratio

|\delta_{\mathrm{random}}|/\widehat{\mathcal{F}}

by quartile of the bin’s

G_{R}

: median and 90th percentile (bars, left axis) and the percentage of shifts with ratio above one (line, right axis). Descriptive; the shifts are not independent.

stratum	shifts	median ratio	90th pct.	ratio $>1$ (%)
all	720,000	0.264	0.741	3.86
$G_{R}$ Q1 $[-0.00148,\,0.00012]$	180,000	0.352	0.994	9.84
$G_{R}$ Q2 $[0.00012,\,0.00066]$	180,000	0.270	0.742	3.43
$G_{R}$ Q3 $[0.00066,\,0.00235]$	180,000	0.232	0.638	1.54
$G_{R}$ Q4 $[0.00235,\,0.03797]$	180,000	0.226	0.595	0.63
$T=1$	360,000	0.257	0.708	3.08

Appendix

Table 9: Random score-preserving shifts: the per-shift ratio

|\delta_{\mathrm{random}}|/\widehat{\mathcal{F}}^{\pm}_{B,R}

, with the branch chosen by the sign of

\delta_{\mathrm{random}}

. Shifts within a bin–radius cell share data and are not independent; the percentages are descriptive and are not coverage rates. Post-audit, not preregistered.

	human proxy		cross-target (pre-specified)		same proxy (post-hoc)
model	$G_{H}^{\times}$	vote noise	$G_{R}$	ratio	$G_{R}^{H}$	ratio	$\leq G_{H}^{\times}$
ResNet-56	9.29	9.3%	0.021	0.33%	0.521	6.3% [3.8, 10.1]	50/50
VGG16-BN	8.33	9.9%	0.012	0.17%	0.093	1.7% [0.2, 3.5]	50/50
MobileNetV2	9.23	8.3%	0.035	0.29%	0.251	2.8% [1.3, 4.3]	50/50
pooled				0.25% [0.0, 2.1]		3.3% [1.4, 6.3]	150/150

Appendix

Table 10: CIFAR-10H case study,

J=10

bins and

K=4

groups in each of

5

folds,

50

fold–bin cells per model.

G_{H}^{\times}

is the cross-half covariance of the two annotator-half proxies;

G_{H}^{\times}

G_{R}

and

G_{R}^{H}

are medians over cells, in units of

10^{-3}

. Columns 4–5: the pre-specified cross-target diagnostic, whose restricted term uses the fixed CIFAR-10 label and so a different propensity from

G_{H}^{\times}

; Proposition 5 does not order the two. Columns 6–8: the post-hoc same-proxy diagnostic, in which both terms use

\eta_{H}

. Ratios are medians over cells with interquartile ranges in brackets. Top-1 accuracy on the CIFAR-10 test set: ResNet-56 94.37%, VGG16-BN 94.15%, MobileNetV2 94.05%.

Figure 6: CIFAR-10H case study, post-audit. One point per (model, fold, bin) cell, $150$ in total; dashed line $y=x$ . (a) Human-proxy heterogeneity $G_{H}$ from annotator half A against half B. (b) Post-hoc same-proxy diagnostic: the vote-noise-corrected cross-half $G_{H}^{\times}$ against the debiased restricted $G_{R}^{H}$ computed from the same proxy.

benchmark	partition, state	units	$L_{\mathrm{comp}}/L_{\mathrm{total}}$	range	$L_{\mathrm{cond}}>L_{\mathrm{comp}}$	median $U$
ImageNetV2	original, $T=1$	30	12.2%	[2.1, 29.7]	100%	0.399
	revised, $T=1$	30	12.0%	[2.4, 31.5]	100%	0.357
	revised, $T=\hat{T}$	30	6.3%	[3.0, 12.4]	100%	0.331
ImageNet-Sketch	original, $T=1$	30	9.8%	[3.7, 24.7]	100%	0.891
	revised, $T=1$	30	10.0%	[2.8, 30.0]	100%	0.768
	revised, $T=\hat{T}$	30	7.8%	[1.6, 22.1]	100%	0.661

Appendix

Table 11: Natural shift under the original and the revised partition, with matched aggregation: per (checkpoint, source fold) unit, then the mean over the

30

units. The primary comparison is between the first two rows of each block, both in the as-released state; the temperature-scaled row has no original-partition counterpart and is an additional result, not a partition sensitivity. Units share target images and source data and are not independent. Post-audit, not preregistered.

			$T=1$			$T=\hat{T}$
Model	family	Acc.	ECE	$G_{R}$	FAUC	ECE	$G_{R}$	FAUC
DenseNet-201	DenseNet	0.773	0.032	0.0006	0.043	0.021	0.0009	0.050
ResNeXt-50 32x4d	ResNeXt	0.776	0.065	0.0009	0.049	0.028	0.0015	0.057
RegNetX-4.0GF	RegNet	0.785	0.052	0.0008	0.047	0.025	0.0017	0.061
MobileNetV3-L	MobileNetV3	0.758	0.067	0.0029	0.076	0.021	0.0007	0.047
DeiT-S/16	DeiT	0.799	0.081	0.0010	0.049	0.041	0.0002	0.037

Appendix

Table 12: Post-audit architecture-breadth cohort: twelve additional frozen ImageNet-1K checkpoints, one per family, under the revised data roles of Appendix N (same estimators as Table 1 ). Acc. is the measured top-1 on the 50,000 validation images; every checkpoint reproduces its public reference within

0.04

pp. Not preregistered; the six checkpoints of Table 1 remain the frozen primary cohort.

cohort	(ECE, $G_{R}$ )	(ECE, FAUC)	( $G_{R}$ , FAUC)
primary six	+1.00 / -0.37	+1.00 / -0.83	+1.00 / +0.60
additional twelve	+0.61 / -0.28	+0.81 / -0.36	+0.92 / +0.99
all eighteen	+0.75 / -0.26	+0.85 / -0.36	+0.95 / +0.95

Appendix

Table 13: Cohort summaries. Top: Spearman rank correlations among the three source metrics across checkpoints (one point per checkpoint), as released / temperature-scaled. Bottom: held-out controlled-shift rank agreement over (checkpoint, state) combinations with the aggregation of Appendix N , and random score-preserving shifts (Appendix R.1 ; “corr.” is the Spearman correlation over bin–radius cells between the two-sided plug-in profile and the median, or secondarily the maximum, of

100

held-out movements; most of it is reproduced by the permutation null of Appendix T ). Primary-six values in this table use the revised estimator; Appendix P reports the original-protocol values. All rank correlations are computed from the unrounded source values. Descriptive; checkpoints and cells are not independent samples and no interval is attached.

Model	A fits/B cert.	B fits/A cert.	A fits/B cert.	B fits/A cert.	ECE reduction	lower NLL
	as released $T{=}1$		split-fitted $T{=}\hat{T}$		monotone map
DenseNet-201	$0$	$0$	$0$	$0$	$57.4\%$	yes
ResNeXt-50 32x4d	$0.00068$	$0$	$0.00018$	$0.00058$	not fitted	–
RegNetX-4.0GF	$0$	$0$	$0.00053$	$0.00037$	not fitted	–
MobileNetV3-L	$0.0011$	$0.0011$	$0$	$0.00034$	$85.2\%$	yes
DeiT-S/16	$0.00011$	$0.00064$	$0$	$0$	$70.0\%$	yes

Appendix

Table 14: Additional twelve checkpoints: split-sample lower confidence bound

\mathrm{LCB}^{+}_{\mathrm{model}}(0.3)

of Proposition 7 , one column per split arm (construction and split seed of Appendix N.1 unchanged; each entry carries its own per-analysis

95\%

statement, never joint), and the fold-wise monotone reparameterisation of Appendix Q replicated post-audit (relative reduction of cross-fitted ECE; structural quantities unchanged exactly). The original 4-of-6 outcome and Outcome A are historical and are not redefined.

Figure 7: Source metrics across eighteen checkpoints, as released (top) and temperature-scaled (bottom). One point per checkpoint (blue: primary six; red: additional twelve; marker = family), log axes. Panel titles give the Spearman rank correlation on the six / the twelve / all eighteen, computed from unrounded values. Descriptive; the checkpoints are not independent samples.

Figure 8: Held-out rank agreement against restricted heterogeneity. All eighteen checkpoints in four calibration states (blue: primary six; red: breadth twelve; marker = family). The weakest agreement occurs where $G_{R}$ is smallest.

Figure 9: Random score-preserving shifts on the breadth and combined cohorts. One point per bin–radius cell: two-sided plug-in profile against the median held-out movement over

100

random directions; dashed line

y=x

, equal axes. Descriptive; most of the association is reproduced by the permutation null of Appendix T .

	(ECE, FAUC)	( $G_{R}$ , FAUC)	(ECE, $G_{R}$ )
primary six	+1.00 / $-$ 0.83	+1.00 / +0.60	+1.00 / $-$ 0.37
additional twelve	+0.81 / $-$ 0.36	+0.92 / +0.99	+0.61 / $-$ 0.28
all eighteen	+0.85 / $-$ 0.36	+0.95 / +0.95	+0.75 / $-$ 0.26
leave one family out	[+0.79, +0.90] / [ $-$ 0.50, $-$ 0.28]	[+0.93, +0.98] / [+0.94, +0.96]	[+0.65, +0.90] / [ $-$ 0.42, $-$ 0.16]
one per family	+0.82 / $-$ 0.35	+0.94 / +0.96	+0.71 / $-$ 0.28

Appendix

Table 15: Rank correlations among source metrics across checkpoints (Spearman, unrounded inputs), as released / temperature-scaled. Leave-one-family-out gives the range over the

17

deletions; one-per-family keeps the first checkpoint of each family in the frozen cohort order. Post-hoc and descriptive; checkpoints are not independent samples.

state	pair	$\|\Delta\mathrm{ECE}\|$	FAUC	FAUC ratio	$G_{R}$
$T=\hat{T}$	ResNet-50 V2 / PoolFormer-S24	$1.41\times 10^{-4}$	0.0461 / 0.0770	1.67	0.00086 / 0.00459
$T=\hat{T}$	ResNet-50 V2 / MViTv2-T	$4.54\times 10^{-4}$	0.0461 / 0.0407	1.13	0.00086 / 0.00061
$T=\hat{T}$	Swin-T / MViTv2-T	$0.77\times 10^{-4}$	0.0384 / 0.0407	1.06	0.00038 / 0.00061
$T=\hat{T}$	DenseNet-201 / MobileNetV3-L	$3.72\times 10^{-4}$	0.0495 / 0.0471	1.05	0.00094 / 0.00072
$T=\hat{T}$	RegNetX-4.0GF / ResMLP-12	$1.09\times 10^{-4}$	0.0610 / 0.0384	1.59	0.00166 / 0.00022
$T=1$	ResNet-50 V1 / PoolFormer-S24	$4.98\times 10^{-4}$	0.0423 / 0.0723	1.71	0.00041 / 0.00417

Appendix

Table 16: All checkpoint pairs among the eighteen whose estimated ECE agrees within the original tolerance

5\times 10^{-4}

(all

153

pairs per state enumerated; none within the primary six in either state). Post-hoc enumeration on an enlarged cohort; similar estimated ECE is not equal population calibration error, and no significance is claimed.

cohort	null variant	pooled	fixed radius	within unit
primary six	both roles	0.901 / 0.876 [0.886]	0.828 / 0.747 [0.771]	0.776 / 0.748 [0.777]
	$D_{\mathrm{rate}}$ only	0.901 / 0.835 [0.843]	0.828 / 0.692 [0.714]	0.776 / 0.755 [0.776]
	$D_{\mathrm{eval}}$ only	0.901 / 0.846 [0.854]	0.828 / 0.659 [0.681]	0.776 / 0.743 [0.765]
additional twelve	both roles	0.901 / 0.877 [0.881]	0.838 / 0.755 [0.768]	0.803 / 0.755 [0.774]
	$D_{\mathrm{rate}}$ only	0.901 / 0.851 [0.856]	0.838 / 0.726 [0.741]	0.803 / 0.759 [0.775]
	$D_{\mathrm{eval}}$ only	0.901 / 0.866 [0.872]	0.838 / 0.711 [0.722]	0.803 / 0.764 [0.776]

Appendix

Table 17: Random-shift ordering statistic against the within-bin label-permutation null (

M=100

replicates per variant; correctness permuted within each bin in

D_{\mathrm{rate}}

and/or

D_{\mathrm{eval}}

, with partitions, cell counts, proportions and directions fixed). Entries: observed / null mean [null maximum]. “Fixed radius”: for the observed data and for each replicate, the mean over the six radii of the Spearman correlation over cells at that radius; the null mean and maximum are taken over replicates of that mean. (The r7 version of this table printed, as the bracket, the maximum over radii and replicates of the radius-specific correlations, a looser quantity; corrected here.) “Within unit”: median over (checkpoint, state, fold, radius) units of the Spearman correlation across the

20

bins. Permutations are fold-conditional, so the null does not reproduce cross-fold dependence; no

p

-value is reported. Post-hoc.

$\rho$	$0.01$	$0.03$	$0.1$	$0.3$	$1$	$3$
outside variance regime	0%	0%	0%	45%	99%	100%
excess of $\sqrt{\rho\hat{G}}$	0%	0%	0%	0%	10%	44%
excess of capped variance	0%	0%	0%	0%	9%	10%
matched-variance difference	0%	0%	0%	1%	10%	19%
pairs differing by $>10\%$	0%	0%	0%	6%	48%	72%

Appendix

Table 18: The plug-in profile against its variance summaries on identical cells (all eighteen checkpoints, as released and

T=\hat{T}

3{,}600

bin units). Regime: share of units outside the variance-controlled regime (upward profile). Excess: median relative overstatement of

\widehat{\mathcal{F}}^{+}

\sqrt{\rho\hat{G}}

and by

\min\{\sqrt{\rho\hat{G}},\hat{z}_{+}\}

. Matched variance: median relative profile difference within disjoint consecutive pairs of units whose

\hat{G}

agree within

1\%

(

1{,}742

pairs), and the share of pairs differing by more than

10\%

. Post-hoc; plug-in quantities, no coverage.

			$n=250$		$n=5000$
law of $\eta$	$\mathcal{F}^{+}(3)$	cap slack	full	capped	full	capped
four-point (B)	0.299	0.101	0.022	0.148	0.006	0.102
mixture $\lambda=\tfrac{1}{2}$	0.362	0.038	0.029	0.052	0.006	0.038
three-point (A)	0.400	0.000	0.030	0.030	0.006	0.006
B, variance $/16$	0.075	0.025	0.042	0.052	0.008	0.027

Appendix

Table 19: Known-propensity check, primary arm: one bin, upward,

\rho=3

. Exact profile, population slack of the capped variance, and mean absolute error over

2000

replicates of the full plug-in and the capped variance from the same rates.

law, direction	$\rho$	$\mathcal{F}$	MAE f/c	$\Delta$	MAE f/c	$\Delta$	MAE f/c	$\Delta$
			$n=250$		$n=1000$		$n=5000$
B	0.3	0.155	0.011/0.011	$+0.000$	0.006/0.006	$+0.000$	0.003/0.003	$+0.000$
	1	0.283	0.020/0.020	$+0.000$	0.010/0.010	$+0.000$	0.005/0.005	$+0.000$
	3	0.299	0.022/0.148	$-0.126$	0.014/0.110	$-0.096$	0.006/0.102	$-0.095$
$\lambda=\tfrac{1}{4}$	0.3	0.155	0.011/0.011	$+0.000$	0.005/0.005	$+0.000$	0.002/0.002	$+0.000$
	1	0.279	0.020/0.021	$+0.000$	0.010/0.011	$-0.001$	0.004/0.005	$-0.001$

Appendix

Table 20: Known-propensity magnitude, primary arm (proportional stratified labels,

2000

replicates).

\mathcal{F}

: exact profile. MAE f/c: mean absolute error of the full plug-in and of the capped variance;

\Delta

: mean paired difference

|\text{full}-\mathcal{F}|-|\text{capped}-\mathcal{F}|

(negative favours the full plug-in; Monte-Carlo standard errors are at most

0.002

). Upward unless stated.

condition	sampling	bias f/c, $n=250$	MAE f/c, $n=250$	$n=1000$	$n=5000$
B, up, $\rho=3$	proportional	$+0.010$ / $+0.141$	0.022/0.148	0.014/0.110	0.006/0.102
	equal	$+0.000$ / $+0.096$	0.025/0.097	0.013/0.101	0.006/0.101
	iid, drop empty	$+0.006$ / $+0.120$	0.023/0.130	0.014/0.106	0.006/0.102
	iid, drop $<30$	$-0.017$ / $-0.017$	0.025/0.025	0.020/0.020	0.006/0.102
asym., down, $\rho=1$	proportional	$+0.003$ / $+0.004$	0.020/0.020	0.010/0.010	0.004/0.004
	equal	$+0.004$ / $+0.004$	0.012/0.013	0.006/0.006	0.003/0.003

Appendix

Table 21: Sampling arms at two conditions: bias and MAE of the full plug-in (f) and the capped variance (c).

				$\rho=1$			$\rho=3$
law, ordering	$K$	$G_{R}$	$H_{R}$	gap	B $-\mathcal{F}_{R}$	A $-\mathcal{F}_{R}$	gap	B $-\mathcal{F}_{R}$	A $-\mathcal{F}_{R}$
smooth, feature	1	0.0000	0.0469	0.212	0.217	0.217	0.296	0.375	0.375
	2	0.0385	0.0085	0.016	0.020	0.092	0.088	0.126	0.159
	4	0.0417	0.0053	0.010	0.014	0.072	0.030	0.057	0.126
	8	0.0440	0.0029	0.006	0.008	0.054	0.016	0.036	0.094
	16	0.0458	0.0012	0.003	0.003	0.034	0.003	0.013	0.059

Appendix

Table 22: Nested readouts, exact population values (upward). Gap:

\mathcal{F}_{B}-\mathcal{F}_{B,R}

; B and A: the middle and right-hand bounds of Proposition 6 , reported as excess over

\mathcal{F}_{B,R}

law	$n$	$K=1$	$K=2$	$K=4$	$K=8$	$K=16$
smooth	250	0.000/0.296	0.030/0.094	0.039/0.048	0.040/0.037	0.045/0.043
	1000	0.000/0.296	0.015/0.089	0.020/0.036	0.020/0.023	0.020/0.019
	5000	0.000/0.296	0.007/0.088	0.009/0.031	0.009/0.018	0.008/0.008
rare	250	0.000/0.264	0.038/0.063	0.040/0.046	0.044/0.041	0.044/0.044
	1000	0.000/0.264	0.019/0.053	0.021/0.037	0.021/0.020	0.021/0.021
	5000	0.000/0.264	0.008/0.051	0.010/0.033	0.010/0.012	0.010/0.010

Appendix

Table 23: Nested readouts at a fixed label budget, feature ordering, upward,

\rho=3

: RMSE of the plug-in restricted profile against its own target / against the binned oracle (

1000

replicates).

state	FAUC 4 /FAUC 8	FAUC 2 /FAUC 8	$G_{R}^{\mathrm{deb}}$ : $K{=}4$ / $K{=}8$	$\Delta G^{\mathrm{deb}}/\Delta G^{\mathrm{plug}}$ , $4\to 8$
as released	0.89 [0.76, 0.94]	0.65 [0.56, 0.75]	0.89 [0.85, 1.05]	0.35 [-0.05, 0.79]
temperature-scaled	0.80 [0.73, 0.89]	0.62 [0.48, 0.72]	0.89 [0.83, 1.08]	0.14 [-0.04, 0.61]

Appendix

Table 24: Nested merges on ImageNet, eighteen checkpoints, median [range]. The last column is the share of the plug-in increase in

G_{R}

from four to eight groups that remains after the binomial-noise correction.

Explore similar work

Expectation Consistency Loss: Rethink Confidence Calibration under Covariate Shift
May 20, 2026Jinzong Dong, Zhaohui Jiang, Bo YangCovariate Shift Confidence Estimation
School of Automation, Central South University, Changsha, China.
When to Trust Confidence Thresholding: Calibration Diagnostics for Pseudo-Labelled Regression
May 12, 2026Marcell T. KurbuczDecision Thresholds Logistic Regression
Institute for Global Prosperity, The Bartlett, University College London, 9–11 Endsleigh Gardens, London, WC1H 0EH, United Kingdom
The Label Complexity of Useful Class-Conditional Prediction Sets under Distribution Shift
Jul 20, 2026Weijia Han, Lisha Qu, Tianxin Zhou +2Covariate Shift Online Conformal Prediction
University of Washington

May 20, 2026, cs.LGJ/K move · Enter open · S save

Expectation Consistency Loss: Rethink Confidence Calibration under Covariate Shift

Jinzong Dong, Zhaohui Jiang, Bo Yang

School of Automation, Central South University, Changsha, China.

Covariate Shift Confidence Estimation Confidence Calibration

Open paper AskPDF arXiv

Confidence calibration for classification models is vital in safety-critical decision-making scenarios and has received extensive attention. General confidence calibration methods assume training and test data are independent and identically distributed, limiting their effectiveness under covariate shifts. Previous calibration methods under covariate shift struggle with class-wise or canonical calibrations and often rely on unstable importance weighting when density ratios are large or unbounded. Given the above limitations, this paper rethinks confidence calibration under covariate shifts. First, we derive a necessary and sufficient condition for confidence calibration under covariate shifts, named Expectation consistency condition, which reveals covariate shifts do not necessarily lead to uncalibrated confidence and provides a weaker condition for confidence calibration than global covariate distribution alignment. Then, utilizing Expectation consistency condition, this paper proposes an unsupervised domain adaptation loss to calibrate confidence of the target domain, named Expectation consistency loss (ECL), which is compatible with canonical calibration, class-wise calibration, and top-label calibration. Third, we prove that computing ECL loss has the same sample complexity as Expected Calibration Error (ECE) and provide a theoretically grounded mini-batch trainable scheme for ECL loss. Finally, we validate the effectiveness of our method on both simulated and real-world covariate shift datasets.

Abstract

Explore similar work

Expectation Consistency Loss: Rethink Confidence Calibration under Covariate Shift

When to Trust Confidence Thresholding: Calibration Diagnostics for Pseudo-Labelled Regression

The Label Complexity of Useful Class-Conditional Prediction Sets under Distribution Shift