329 papers
This paper proposes sFRC, a novel method that performs Fourier Ring Correlation analysis over small patches to robustly detect and quantify hallucinations in deep learning-based medical image restoration across various undersampled imaging problems.
This paper proposes that memory consolidation serves a computational role beyond mere stabilization, utilizing "predictive forgetting" to compress stored representations into a form that optimizes generalization by selectively retaining information that predicts future outcomes, a process necessitated by high-capacity encoding constraints and validated through simulations across diverse neural and transformer models.
This paper presents the first structural-assumption-free causal discovery method for linear non-Gaussian latent-variable cyclic models by establishing a graphical criterion for distributional equivalence, introducing edge rank constraints, and providing an algorithm to recover models up to this equivalence class.
This paper demonstrates that the architectural inductive biases of locality and weight sharing in convolutional neural networks fundamentally alter implicit regularization by coupling learned filters to low-dimensional patch manifolds, thereby enabling generalization on high-dimensional spherical data where fully connected networks provably fail.
This paper demonstrates that for high-dimensional random data, gradient descent on shallow ReLU networks exhibits an implicit bias that approximates the minimum -norm solution with high probability, bridging the gap between worst-case non-existence and exact orthogonality results through a novel primal-dual analysis.
This paper extends the Edge of Stability phenomenon to non-Euclidean gradient descent by introducing a generalized sharpness measure based on directional smoothness, demonstrating that diverse optimizers exhibit similar stability thresholds and oscillatory behaviors across arbitrary geometric norms.
This paper introduces fedCI and fedCI-IOD, a novel framework for privacy-preserving federated causal discovery that enables conditional independence testing and latent confounding analysis across heterogeneous, distributed datasets with non-identical variables and mixed data types.
This paper introduces the Sequential Thresholding of Coefficient of Variation (STCV), a novel sparse regression algorithm that replaces magnitude-based thresholding with a dimensionless statistical metric to ensure robust and accurate identification of governing equations in non-linear dynamics, even when data is normalized and noisy.
This paper proposes a novel, computationally efficient framework for learning optimal individualized decision rules that incorporate demographic parity and conditional demographic parity constraints to mitigate discriminatory effects, supported by theoretical convergence guarantees and empirical validation.
This paper demonstrates that distribution shift is the primary cause of performance degradation in deeper layers of Vision Transformers and reveals that optimal out-of-distribution probing requires selecting between feedforward network activations and normalized self-attention outputs depending on the severity of the shift.
This paper proposes Bayesian Supervised Causal Clustering (BSCC), a novel framework that identifies homogeneous patient subgroups by simultaneously clustering individuals based on their covariate profiles and treatment effects, and validates its practical utility through simulations and real-world data from the International Stroke Trial.
This paper proposes using asymmetric Shapley values as a superior metric for quantifying the importance of high-dimensional genomic features in clinical prediction models, addressing limitations of traditional approaches by accounting for collinearity and known causal directions, and provides efficient algorithms validated through a colorectal cancer progression study.
This paper demonstrates that predictive inference is governed by four distinct, pairwise non-nested admissibility geometries—Blackwell risk dominance, anytime-valid supermartingales, marginal coverage, and Cesàro approachability—each offering a unique certificate of optimality and proving that admissibility is irreducibly relative to the chosen criterion rather than a universal property.
This paper establishes a comprehensive statistical theory for globally optimal empirical risk minimization decision trees by deriving sharp oracle inequalities and minimax optimal rates over a novel piecewise sparse heterogeneous anisotropic Besov space, thereby providing rigorous theoretical guarantees for their performance in high-dimensional regression and classification under both sub-Gaussian and heavy-tailed noise settings.
This paper provides a comprehensive statistical review of synthetic data generated by modern AI models, outlining their benefits and limitations while offering principled frameworks and practical recommendations to ensure their valid and reliable use in scientific inference and prediction.
This paper establishes a unified thermodynamic response framework for singular Bayesian models, demonstrating that posterior tempering induces a hierarchy of observables that naturally interpret complex learning-theoretic quantities like the real log canonical threshold and WAIC as free-energy derivatives, thereby revealing phase-transition-like structural reorganizations in models such as neural networks and Gaussian mixtures.
This paper introduces SurvHTE-Bench, the first comprehensive benchmark for evaluating heterogeneous treatment effect estimation in survival analysis, featuring a diverse suite of synthetic, semi-synthetic, and real-world datasets to enable rigorous, fair, and reproducible comparisons of causal survival methods.
This paper presents a sample-optimal, locally differentially private algorithm for hypothesis selection that achieves the information-theoretic lower bound of using only rounds of interaction, thereby demonstrating the provable power of interactivity to overcome the sample complexity barrier inherent in non-interactive approaches.
This paper investigates the applicability of classical generalization principles to list PAC learning, demonstrating that while uniform convergence remains equivalent to learnability, the sample compression conjecture fails as there exist list-learnable classes that cannot be compressed, even with arbitrarily large output lists.
This paper extends tracking guarantees for time-varying variational inequalities to non-monotone functions and periodic problems without sublinear solution paths, while also demonstrating that the associated discrete dynamical systems can exhibit either convergence or provably chaotic behavior.