Separating Covariate Shift from Mechanism Change with Two Discriminators: CJSD, a Conditional Discrepancy with an Exact Covariate-Concept Decomposition

• URL: http://arxiv.org/abs/2608.19885v2
• Date: Fri, 21 Aug 2026 05:32:41 GMT
• Title: Separating Covariate Shift from Mechanism Change with Two Discriminators: CJSD, a Conditional Discrepancy with an Exact Covariate-Concept Decomposition
• Abstract summary: Conditional Jensen-Shannon Discrepancy (CJSD)<n>On a ten-measure battery over 202 dataset pairs, only the two conditional-information estimators -- CJSD and a kNN plug-in for the same estimand -- fail.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: After the inputs X are known, how much additional information does the label Y carry about which dataset a sample came from? That single quantity -- estimable as the difference of two discriminators' held-out cross-entropies, D_CJS = CE(Z|X) - CE(Z|X,Y) -- is exactly the part of a dataset difference that covariate shift cannot explain. We propose the Conditional Jensen-Shannon Discrepancy (CJSD): with a task indicator Z, the chain rule I(Z;X,Y) = I(Z;X) + I(Z;Y|X) splits total task discrepancy exactly into a covariate axis and a functional axis, both estimable from two ordinary classifiers, with no task-specific predictors, generative models, or bootstrap surrogates. We prove a covariate-null property (the functional axis is exactly zero under pure covariate shift, however severe), a drift-mass law (D_CJS/ln2 equals the mass of the disagreement region for deterministic labels), a one-sided misspecification-control inequality (each direction of estimation error is bounded, unconditionally, by the excess risk of a single discriminator), and a fixed-measure metrization via an identifiability lemma. Empirically, on a ten-measure battery over 202 dataset pairs (synthetic, Electricity, Covertype), only the two conditional-information estimators -- CJSD and a kNN plug-in for the same estimand -- separate concept from covariate shift with AUC 1.0; the case for CJSD is the estimator: under controlled dimensionality scaling the kNN plug-in fails from d=64 while the discriminator route holds to d=256 with a swappable classifier, and it alone yields paired confidence intervals and sequential extensions from the same learned object. The same estimator audits the conditional fidelity of synthetic-data generators that marginal and joint QA metrics pass, detects annotation-guideline changes invisible to input-space monitors, and supports null-calibrated fairness audits.

Related papers

• What FID Hides: Detecting, Ranking, and Diagnosing Deviations in Generative Evaluation [6.078726431806193]
  Generative models are commonly ranked by Fréchet Inception Distance (FID) and Kernel Inception Distance (KID)<n>ZID detects a broad range of departures, and its score tracks increasing severity along the corresponding sweeps.
  arXiv  Detail & Related papers  (2026-08-25T17:58:46Z)
• EDGE: a closed-form directed test for the calibration of probabilistic binary classifiers [0.0]
  We propose EDGE, a calibration test for the canonical probabilistic classifier, logistic regression.<n> EDGE reads the same binned predicted-observed table a reliability diagram plots, and projects its standardized bin residuals onto a small pre-specified basis of smooth calibration-distortion.<n>Across link and feature misspecification the pre-specified default led or tied every rival binned test on the fitted index in 19 of 22 detectable scenarios, and stayed computable where the refit-based Stukel score test separates in 20% to 28% of samples.
  arXiv  Detail & Related papers  (2026-08-20T19:06:05Z)
• Evidence Before Expansion: Reuse, Spawn, or Defer in Lifelong Expert Pools [0.0]
  Reuse and spawn are posed as one-sided sequential hypotheses on a conditional (mechanism-level) discrepancy.<n>We prove finite-time anytime validity for the observable surrogate discrepancy of a predictable discriminator sequence.<n>On synthetic multi-concept streams, Electricity, Covertype, and the recurrence-heavy INSECTS benchmark, the instance-accounted restarted bank achieves zero false spawns.
  arXiv  Detail & Related papers  (2026-08-20T10:54:17Z)
• Simplex Relaxation for Discrete Diffusion [58.33696160279345]
  We introduce Simplax, an exact Dirichlet--categorical augmentation that couples each corrupted categorical state with an auxiliary simplex-valued variable.<n>Simplax improves the generative perplexity--entropy tradeoff on unconditional OpenWebText generation.
  arXiv  Detail & Related papers  (2026-08-11T07:58:17Z)
• Two-Step MV-DeepONet: Probabilistic Operator Learning for Uncertainty Propagation Driven by Random Input Fields [2.110303171517621]
  We develop a two-step mean-variance DeepONet (two-step MV-DeepONet) through two principal modifications.<n>We show improved generalization, more structured uncertainty bands, and accurate recovery of off-diagonal correlation patterns compared with Prob-DeepONet.
  arXiv  Detail & Related papers  (2026-08-10T03:21:10Z)
• Dual-Branch Cross-Projection Debiasing through Diffusion-based Disentanglement [66.67463557274358]
  Foundation models trained on biased datasets often rely on spurious correlations between target labels and non-causal attributes.<n>We introduce Confidence-guided Concept Mining (CBCM) to identify reliable spurious attributes without attribute annotations.<n>We propose Dual-branch Cross-projection Debiasing (DCD), a prompt-tuning framework that separates target and spurious representations into two branches.
  arXiv  Detail & Related papers  (2026-06-23T05:28:47Z)
• AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE [51.994752158953084]
  AnchorMoE is an interpretable-by-construction classification framework.<n>It encodes multi-view representations of local patches and routes them to specialized experts.<n>Experiments on real-world and synthetic benchmarks demonstrate that AnchorMoE achieves highly competitive classification performance.
  arXiv  Detail & Related papers  (2026-06-02T13:30:54Z)
• Measuring the Symmetry--Data Exchange Rate [0.2538209532048867]
  We show that a wrong-group control with identical orbit size and matched compute is worse than no constraint.<n>Relative exchange rate beta_diff = 1.28 is consistent in sign and order of magnitude with the theoretical bootstrap 1.0.<n>The methodological contributions -- the relative-rate estimator that cancels the shared-difficulty confound, the wrong-group control, and a pre-specified failure taxonomy -- transfer to any inductive bias whose strength can be parameterised.
  arXiv  Detail & Related papers  (2026-05-31T08:17:40Z)
• The conditional-mean barrier: From deterministic regression to conditional distribution learning [6.160113667162026]
  This tutorial develops a self-contained module centered on the conditional-mean barrier.<n>Adding latent randomness to a squared-loss predictor collapses it back to the conditional mean.<n>We briefly organize common distributional objectives, including negative log-likelihood, moment and observable matching, variational objectives, adversarial divergences, and score matching.
  arXiv  Detail & Related papers  (2026-05-27T07:31:42Z)
• The Need for an External Observer Formalizing the Sufficiency Gap: A Mathematical Extension of Mixture Identifiability and Contextual Grounding in Sequence Models [0.0]
  We construct a binary mixed-regime process with one deterministic textual regime and one random regime governed by an unobserved latent state.<n>The resulting entropy difference is not an ordinary optimization error; it is a sufficiency gap caused by marginalization over an unobserved state.<n>A corrective signal reverses the posterior odds induced by the textual history exactly when its fidelity exceeds the text-only posterior weight assigned to the misleading regime.
  arXiv  Detail & Related papers  (2026-05-26T08:53:11Z)
• Learning Context-conditioned Gaussian Overbounds for Convolution-Based Uncertainty Propagation [4.891998873486944]
  We propose a unified learning framework that trains neural networks to produce context-aware Gaussian overbounds.<n>We provide a scoped analysis of discrete-to-continuous conservatism and compact-domain objective regularity.<n>The framework is modality-agnostic and applicable to learning systems that require conservative, feature-conditioned uncertainty estimates.
  arXiv  Detail & Related papers  (2026-05-15T09:47:05Z)
• Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventions [0.0]
  In genomics experiments, exchangeability often holds only within subsets of interventions that leave a target variable "unaffected"<n>Our contributions are: (i) a contamination-robust conformal coverage theorem that quantifies how misclassification of "unaffected" calibration examples degrades coverage via an explicit function $g(,n)$ of the contamination fraction and calibration set size; and (ii) a task-driven partial causal learning formulation that estimates only the binary descendant indicators $Z_a,i=mathbf1iinmathrmdesc(a)$ needed
  arXiv  Detail & Related papers  (2026-03-02T18:58:22Z)
• LEC: Linear Expectation Constraints for False-Discovery Control in Selective Prediction and Routing Systems [95.35293543918762]
  Large language models (LLMs) often generate unreliable answers, while uncertainty methods fail to fully distinguish correct from incorrect predictions.<n>We address this issue through the lens of false discovery rate (FDR) control, ensuring that among all accepted predictions, the proportion of errors does not exceed a target risk level.<n>We propose LEC, which reinterprets selective prediction as a constrained decision problem by enforcing a Linear Expectation Constraint.
  arXiv  Detail & Related papers  (2025-12-01T11:27:09Z)
• Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty [49.19257648205146]
  We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
  arXiv  Detail & Related papers  (2025-09-26T23:40:47Z)
• Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression [102.24287051757469]
  We study self-supervised covariance estimation in deep heteroscedastic regression.<n>We derive an upper bound on the 2-Wasserstein distance between normal distributions.<n>Experiments over a wide range of synthetic and real datasets demonstrate that the proposed 2-Wasserstein bound coupled with pseudo label annotations results in a computationally cheaper yet accurate deep heteroscedastic regression.
  arXiv  Detail & Related papers  (2025-02-14T22:37:11Z)
• TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression [109.69084997173196]
  Deepscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. Recent works show that this may result in sub-optimal convergence due to the challenges associated with covariance estimation. We study two questions: (1) Does the predicted covariance truly capture the randomness of the predicted mean? Our results show that not only does TIC accurately learn the covariance, it additionally facilitates an improved convergence of the negative log-likelihood.
  arXiv  Detail & Related papers  (2023-10-29T09:54:03Z)
• Sequential Predictive Two-Sample and Independence Testing [114.4130718687858]
  We study the problems of sequential nonparametric two-sample and independence testing. We build upon the principle of (nonparametric) testing by betting.
  arXiv  Detail & Related papers  (2023-04-29T01:30:33Z)
• How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm? [73.80001705134147]
  We provide an exact characterization of the expected generalization error (gen-error) for semi-supervised learning (SSL) with pseudo-labeling via the Gibbs algorithm. The gen-error is expressed in terms of the symmetrized KL information between the output hypothesis, the pseudo-labeled dataset, and the labeled dataset.
  arXiv  Detail & Related papers  (2022-10-15T04:11:56Z)
• Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic Uncertainty [58.144520501201995]
  Bi-Lipschitz regularization of neural network layers preserve relative distances between data instances in the feature spaces of each layer. With the use of an attentive set encoder, we propose to meta learn either diagonal or diagonal plus low-rank factors to efficiently construct task specific covariance matrices. We also propose an inference procedure which utilizes scaled energy to achieve a final predictive distribution.
  arXiv  Detail & Related papers  (2021-10-12T22:04:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.