Paper improves predictive distributions for rare events using a simple framework.
problem Local miscalibration of predictive distributions for rare events.
method Semiparametric diagnostic transport maps to correct tail probabilities.
result Semiparametric maps improve predictions for severe weather hazards.
This paper simplifies deep ReLU networks into local linear models for better interpretability.
problem Limited transparency and interpretability of deep neural networks, especially ReLU networks.
method Local linear representation and equivalent set of local linear models (LLMs).
result Simplified deep ReLU networks for better interpretability and diagnostics.
Develops a diagnostic framework for interest rate model calibration, showing equivalence to Weighted Least Squares and revealing boundary-dominated leverage and local parameter instability.
problem Calibration of stochastic interest rate models
method Diagnostic framework using non-linear regression and analytical tractability of At-The-Money caps
result Reveals boundary-dominated leverage and local parameter instability
L-C2ST improves local diagnostics for SBI approximations.
problem Evaluating trustworthiness of posterior approximations in SBI.
method Local evaluation of posterior estimators at any observation.
result Offers better statistical power and interpretability.
The paper introduces a diagnostic method to detect grokking transitions in models before test accuracy improves.
problem Detecting the transition from training to generalization in machine learning models.
method Summarize task-dependent observables as empirical distributions, map them to Wasserstein/quantile coordinates, and analyze using Hankel dynamic mode decomposition.
result The diagnostic method achieves AUROC \(\approx\) 0.93 for grokking-vs-non-grokking discrimination at the run level.
The paper breaks down AUC into cluster-level components for better model diagnostics.
problem Global AUC masks weaknesses in specific subpopulations, leading to financial or operational risks.
method Formal decomposition of AUC into intra- and inter-cluster components, comparing with other performance metrics.
result Allows practitioners to evaluate and diagnose model performance within and across clusters.
LADaR framework calibrates machine learning models for instance-wise predictions.
problem Challenges in assessing and calibrating predictive distributions for complex inputs.
method Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR) framework and extttCal−PIT algorithm. result Achieves better instance-wise calibration than existing methods in galaxy distance estimation.
The paper introduces diagnostic transport maps to improve the reliability of rare event predictions.
problem Improper calibration of predictive distributions, especially for rare events.
method Diagnostic transport maps to adjust base model's probabilities for better calibration.
result Diagnostic transport maps improve predictive performance for rare events, including 24-hour rapid intensity change.
We consider the problem of locating a point-source heart arrhythmia using data from a standard diagnostic procedure, where a reference catheter is placed in the heart, and arrival times from a second diagnostic catheter are recorded as the diagnostic catheter moves around within the heart. We model this situation as a …
This work proposes validation diagnostics for SBI algorithms using Normalizing Flows.
problem Lack of appropriate validation methods for SBI algorithms with complex, high-dimensional data.
method Develops validation diagnostics based on Normalizing Flows with theoretical guarantees.
result Offers theoretical guarantees on consistency of NF-based estimators.
GeoTop resolves topological ambiguity in diagnostic imaging using geometric-topological analysis.
problem Topological equivalence between benign and malignant structures in diagnostic images.
method Combines Topological Data Analysis and Lipschitz-Killing Curvatures to resolve ambiguity.
result Achieves 3.6% accuracy improvement and reduces false positives/negatives by 15-18%.
We speed up Gaussian process cross-validation calculations and improve model diagnostics.
problem Efficiently calculating cross-validation residuals and their covariances in Gaussian processes.
method Generalized fast Gaussian process leave-one-out formulae to multiple-fold cross-validation, highlighting covariance structures.
result Correcting for residual covariances in cross-validation improves back to Maximum Likelihood Estimation.
Applying machine learning in the health care domain has shown promising results in recent years. Interpretable outputs from learning algorithms are desirable for decision making by health care personnel. In this work, we explore the possibility of utilizing causal relationships to refine diagnostic prediction. We focus…
Establishes statistical and computational bounds for influence diagnostics.
problem Identifying influential datapoints or subsets in machine learning models.
method Finite-sample statistical bounds and computational complexity for influence functions and approximate maximum influence perturbations.
result Established statistical and computational guarantees for influence diagnostics.
The article explains how to estimate confusion matrices for classifiers using unlabeled data.
problem Estimating sensitivity and specificity of binary medical diagnostic tests without gold standard tests.
method Modifying diagnostic test solutions to estimate confusion matrices for classifiers on unlabeled data.
result The approach can be used to estimate accuracy statistics for supervised or unsupervised binary classifiers on unlabeled data.
New framework uses geometry of embeddings to predict robustness.
problem Monitoring robustness in models without OOD labels.
method Constructs graphs from embeddings, measures spectral complexity and curvature.
result Representation geometry predicts robustness reliably.
Proposes a tool to contrast global vs personalized models in clinical prediction.
problem Balancing global vs personalized models in clinical prediction.
method Localized regression approach using autoencoder for dimension reduction.
result Identification of patient subgroups where global models fall short.
AI enhances cancer diagnostics using spectroscopy.
problem Early and accurate cancer diagnosis.
method Combining AI with spectroscopy-based techniques.
result AI improves cancer diagnostics speed and safety.
Paper introduces a diagnostic for approximate inference methods.
problem Estimating errors in probabilistic inference algorithms, especially for approximate methods.
method Repeatedly simulate datasets from the prior and perform inference on each, estimating a symmetric KL-divergence.
result A diagnostic for approximate inference methods can be estimated using symmetric KL-divergence.
Framework calculates positional influence in causal residual Transformers.
problem Understanding positional influence in causal residual Transformers.
method Adjoint-sensitivity framework for positional influence in causal residual Transformers.
result Exact evolution of adjoint-energy influence density and decomposition into residual transmission, nonlocal Volterra, and local channels.
Machine learning improves diagnostic test accuracy for bovine tuberculosis.
problem Improving diagnostic test sensitivity for bovine tuberculosis.
method Machine learning to assess risk landscapes and predict infection.
result Test sensitivity improved, detecting 240 more infected herds per year.
AI predicts medical specialty diagnostic choices from EHR records.
problem Predicting timely medical specialty diagnostic workups for patients.
method Ensemble of feed-forward neural networks trained on EHR data.
result Significantly higher accuracy compared to traditional checklists.
Local Gaussian correlation struggles in tails but a new method improves it.
problem Local Gaussian correlation's limitations in tail dependence.
method A new adaptive bandwidth method for LGC, optimizing for local effective sample size.
result Adaptive bandwidths outperform global ones in moderate dependence, but not in strong or weak dependence.
Graph-based approach repairs programs from diagnostic feedback.
problem Learning to repair programs from limited labeled data and compiler error messages.
method Introduces program-feedback graph and graph neural network for reasoning, and self-supervised learning with unlabeled programs.
result DrRepair significantly outperforms prior work, achieving high repair rates.
The paper analyzes convergence in SGD with momentum and proposes a diagnostic test.
problem Detecting convergence in stochastic gradient descent with momentum.
method Analyzes the transient and stationary phases of SGD with momentum, constructs a statistical diagnostic test.
result The proposed diagnostic test effectively detects convergence in the stationary phase of SGD with momentum.
Portable, Wearable and Wireless electrocardiogram (ECG) Systems have the potential to be used as point-of-care for cardiovascular disease diagnostic systems. Such wearable and wireless ECG systems require automatic detection of cardiovascular disease. Even in the primary care, automation of ECG diagnostic systems will …
Paper proposes a coupling-based diagnostic for SGD stepsize optimization.
problem Optimizing stepsize for SGD convergence.
method Coupling-based convergence diagnostic for monitoring stationarity.
result Proposed stepsize scheme achieves superior performance across convex and non-convex problems.
Proposes a model to interpret complex ML algorithms.
problem Complex ML models are hard to interpret.
method Uses model-based regression trees and interpretable main-effects models.
result Surrogate model provides interpretable results with good predictive performance.
TADDAA improves accuracy diagnostics for variational approximations.
problem Challenges in evaluating the accuracy of variational approximations.
method Uses many short parallel MCMC chains to obtain lower bounds on the error of each posterior functional of interest.
result Validates the practical utility and computational efficiency of TADDAA on various models.
Proposes a diagnostic method to evaluate factor models using cap-axis integrals.
problem Improving factor model evaluation in low-dimensional spaces.
method Lifts pricing errors into a bridge-alpha curve along the market-capitalization rank axis.
result The cap-axis norm is distinct from Sharpe gain and size exposure.
Proposes a diagnostic method to evaluate factor models using cap-axis integrals.
problem Improving factor model evaluation for low-dimensional models.
method Lifts pricing errors into a bridge-alpha curve along the market-capitalization rank axis.
result The cap-axis norm is distinct from Sharpe gain and size exposure.
The receiver operating characteristic (ROC) curve is a very useful tool for analyzing the diagnostic/classification power of instruments/classification schemes as long as a binary-scale gold standard is available. When the gold standard is continuous and there is no confirmative threshold, ROC curve becomes less useful…
Improves visualization of high-dimensional data by correcting misleading artifacts in neighbor embedding methods.
problem Misleading visual artifacts in t-SNE and UMAP due to lack of data-independent manifold learning interpretations.
method LOO-map framework that extends embedding maps to the entire input space, identifying and correcting map discontinuities.
result Developed point-wise diagnostic scores to detect unreliable embedding points and improve hyperparameter selection.
The high-dimensionality and volume of large scale multistream data has inhibited significant research progress in developing an integrated monitoring and diagnostics (M&D) approach. This data, also categorized as big data, is becoming common in manufacturing plants. In this paper, we propose an integrated M\&D approach…
Language models fail to process hallucinated responses, and this study diagnoses the failure.
problem Language models fail to process hallucinated responses, leading to over-concentration or diffuse attention.
method The study uses forced scoring of benchmark-labeled responses to compute attention shapes and analyze the symmetric component of the degree-normalized attention operator.
result The study proves that every transpose-invariant spectral diagnostic of the attention operator is orientation-blind and bounds the sensitivity of any Lipschitz diagnostic by the asymmetry coefficient \(G\).
Framework for analyzing dynamic topological changes in point clouds using persistent homology and dynamic optimal transport.
problem Analyzing transient structural reorganizations during dynamic phase transitions in time-evolutionary point clouds.
method Hierarchical dynamic evaluation framework driven by topological and hypergraph reconstruction strategy.
result Combining transport-based alignment with multi-scale entropy diagnostics for dynamic topological analysis.
A new pricing controller handles resource constraints to infer target prices effectively.
problem Resource constraints prevent fixed-price inference, leading to support exclusion.
method Formalizes support-exclusion failure, designs a target-aware controller, and uses a realized information clock.
result The controller can certify feasible target bands and log continuous local densities, leading to polynomial rates of inference.
Recent studies have shown promising results in using Deep Learning to detect malignancy in whole slide imaging. However, they were limited to just predicting positive or negative finding for a specific neoplasm. We attempted to use Deep Learning with a convolutional neural network algorithm to build a lymphoma diagnost…
Paper evaluates whether AI is a bubble or a productivity revolution.
problem Determining if AI investments are a bubble or a sustainable technology.
method Hybrid review and diagnostic framework combining asset pricing foundations and modern econometric methods.
result AI investments show both genuine fundamentals and bubble-like fragilities.
Method extracts time-localized clusters to explain deep learning models in ECG analysis.
problem Limited understanding of deep learning models in ECG analysis.
method Extracts time-localized clusters from model's internal representations.
result Enhances trust in AI-driven diagnostics and reveals clinically relevant patterns.
Interpretable deep learning is a fundamental building block towards safer AI, especially when the deployment possibilities of deep learning-based computer-aided medical diagnostic systems are so eminent. However, without a computational formulation of black-box interpretation, general interpretability research rely hea…
Study robustness of global feature effect explanations in machine learning models.
problem Vulnerability of global feature effect explanations to data and model perturbations.
method Theoretical bounds and experimental evaluation of partial dependence plots and accumulated local effects.
result Quantifies the gap between best and worst-case scenarios of misinterpreting machine learning predictions globally.
Online symptom checkers have significant potential to improve patient care, however their reliability and accuracy remain variable. We hypothesised that an artificial intelligence (AI) powered triage and diagnostic system would compare favourably with human doctors with respect to triage and diagnostic accuracy. We per…
Sparse point observations can provide useful local constraints, but their benefit for radar-like fields depends on the training loss, uncertainty representation, and how observation support is encoded in the model.
problem Improving dense radar-field forecasts with sparse point observations
method A multimodal graph neural network nowcasting system over the Nordic radar domain
result Each source improves a different part of the forecast problem
Improved 3D ECG feature attributions for clinical interpretation.
problem Lack of interpretability in deep learning models for 12-lead ECG analysis.
method Cross-modal mapping of feature attributions from 12-lead ECG models onto CineECG 3D space.
result Mapped feature attributions yield higher Dice scores than standard 12-lead attributions.
New approach interprets machine learning models through feature space transformations.
problem Interpreting models with strongly dependent features in high-dimensional spaces.
method Feature space transformations, including PCA and partial orthogonalization.
result Enhances model interpretation tools for domain experts.
Reducing ICD-10 code granularity improves cost model accuracy and stability.
problem High-dimensional regression with ICD-10 codes leads to unstable coefficient estimates.
method Log-linear analytics approach to cost model regularization through diagnostic code merging.
result Reducing ICD-10 code granularity from 7 characters to 6 or fewer improves model interpretability and consistency.
New diagnostics detect variability in individual risk estimates from machine learning models in healthcare.
problem Variability in individual risk estimates from machine learning models in healthcare, leading to unreliable treatment decisions.
method Proposed evaluation framework using empirical prediction interval width and empirical decision flip rate diagnostics.
result Randomness in optimization and initialization can lead to substantial individual-level variability in risk estimates, affecting clinical decisions.