Two diagnostics improve variational inference quality.
problem Difficulties in evaluating variational approximations.
method PSIS diagnostic for joint distributions and VSBC for point estimates.
result Improves accuracy and reliability of variational approximations.
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.
AI triage and diagnostic system performs similarly to human doctors, with safer triage advice.
problem Improving patient care through more reliable symptom checkers.
method Prospective validation study comparing AI system to human doctors.
result AI system's accuracy in identifying conditions comparable to human doctors, with safer triage advice.
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.
Proposes a new framework for evaluating diagnostic models with multiple co-primary endpoints.
problem Overoptimistic assessments of predictive performance in automated medical testing devices.
method Multiple testing framework for diagnostic accuracy studies with co-primary endpoints, using a parametric simultaneous test procedure and Bayesian approach to determine optimal number of models.
result Our approach leads to a better final diagnostic model and increased statistical power.
Paper presents a probabilistic diagnostic model for identifying and treating supervised learning degradation issues.
problem Degradation problems in supervised learning, including class imbalance, overlapping, small-disjuncts, noisy labels, and sparseness.
method Develops a novel probabilistic diagnostic model to identify and treat degradation issues in supervised learning.
result Early and correct diagnosis of degradation issues allows for selecting appropriate remediation treatments and unbiased performance metrics.
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.
A new method improves fault diagnostics and prognostics for class-imbalanced data.
problem Class imbalance in industrial fault diagnostics and prognostics.
method EWMOTE: EM-based Weighted Minority Oversampling TEchnique.
result EWMOTE achieves better performance on binary and multi-class imbalance learning tasks.
Paper proposes an integrated M&D approach for large multistream data.
problem Inability to progress in monitoring and diagnostics due to high-dimensionality and volume of multistream data.
method Adaptive Principal Component monitoring (APC) and Principal Component Signal Recovery (PCSR).
result The integrated M&D approach enables early detection and streamlined SPC.
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.
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.
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…
Enhances projection pursuit tree classifier with visual diagnostics for better multi-class classification.
problem Rigidity of original algorithm limits performance in complex high-dimensional classification problems.
method Allowing more splits and flexible class groupings in projection pursuit computation, and developing visual diagnostics.
result Demonstrates enhanced classifier performs as intended through interactive visual diagnostics.
Refines diagnostic prediction using causal relationships.
problem Improving accuracy of pain diagnostics prediction.
method Two approaches: 1) Inference of causal relationships, 2) Post-processing refinement.
result Potential for improving pain diagnostics prediction accuracy.
Survey of ML and DL for bearing fault diagnostics.
problem Fault detection and categorization in bearings.
method Review of conventional ML methods and analysis of DL algorithms.
result DL methods outperform conventional ML in fault feature extraction and classification.
GANCS uses GANs to speed up MRI reconstruction while maintaining diagnostic quality.
problem Time and resource intensive MRI reconstruction and loss of diagnostic quality in compressed sensing.
method Generative adversarial networks (GAN) trained on historical patient data to learn diagnostic-quality MR images.
result GANCS reconstructs MRI images in a few milliseconds with high contrast and texture details.
Novel nonparametric method for GLMs improves prediction and inference performance.
problem Improving prediction and inference in GLMs with minimal assumptions.
method Combines binary regression and latent variable formulations, extends parametric versions, introduces new classification statistic.
result Uniformly better prediction and inference performance over parametric formulation, especially with asymmetric data.
Auditor package assesses model performance and detects anomalies.
problem Model failure in real-world data.
method Model-agnostic audit techniques for goodness of fit and performance.
result Flexible and consistent grammar for validating models of any class.
Proposes FACT, a diagnostic for understanding group fairness trade-offs.
problem Group fairness notions often conflict with each other, requiring a cost in model performance.
method Characterizes trade-offs via the fairness-confusion tensor and optimizes accuracy and fairness objectives.
result Demonstrates the use of FACT on synthetic and real datasets to understand accuracy-fairness trade-offs.
Two new feature selection methods improve breast cancer diagnosis accuracy.
problem Improving breast cancer diagnosis accuracy and reducing dataset dimensions.
method Imperialist Competitive Algorithm (ICA) and Bat Algorithm (BA) integrated with ML algorithms.
result BA-based feature selection method outperforms other methods with 99.12% accuracy.
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.
Paper presents a light-weight CNN model for Parkinson's disease diagnosis.
problem Diagnosing Parkinson's disease efficiently and accurately.
method A light-weight CNN model cascading to LSTM for time-series signals.
result The model achieves high-quality results with fewer parameters and operations.
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.
It has been argued that in supervised classification tasks, in practice it may be more sensible to perform model selection with respect to some more focused model selection score, like the supervised (conditional) marginal likelihood, than with respect to the standard marginal likelihood criterion. However, for most Ba…
Paper proposes a diagnostic tool for evaluating model performance out-of-sample.
problem Evaluating model performance on unseen data.
method Uses a finite calibration dataset to assess future losses.
result Provides guarantees under weak assumptions and quantifies distribution shifts.
Deep learning system detects hip fractures as well as radiologists.
problem Automatically identifying hip fractures from x-rays.
method Trained on 53,000 clinical x-rays, system achieves 0.994 ROC curve.
result Equivalent diagnostic performance to human radiologists.
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%.
Paper proposes a new method to detect convergence in SGD.
problem Detecting the transition from fast progress to oscillation in SGD.
method Analyzes Pflug's test and proposes a novel statistical procedure.
result The novel procedure accurately detects stationarity in SGD.
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.
Proposes and evaluates three diagnostic graphics for probabilistic classifiers.
problem Evaluating and comparing probabilistic classifiers.
method Triptych of diagnostic graphics: reliability diagram, ROC curve, Murphy diagram.
result Visual diagnostics reveal distinct aspects of forecast performance.
Computer systems for melanoma detection ranked by sensitivity, not specificity.
problem Improving computer systems for melanoma detection in dermoscopic images.
method Analyzed an open challenge in dermoscopic image classification, comparing five measures of diagnostic accuracy.
result Choice of performance measure significantly impacts ranking of computer systems.
Quickly adapts fault diagnosis models for industrial machines.
problem Fault diagnostic models trained for lab machines fail on industrial ones.
method Net2Net transformation followed by fine-tuning.
result Models can be quickly adapted for new operating conditions.
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.
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.
A theory for interpreting black-box models in medical diagnostics.
problem Lack of computational formulation for interpreting black-box models in medical diagnostics.
method Defining interpretation as a finite communication between a known model and a black-box model, deriving an algorithm for diagnostic interpretability.
result Demonstrated the feasibility of interpreting black-box models in synthetic supervised classification scenarios.
Paper uses transfer learning and Bayesian optimization to reduce DNA sequence design experiments.
problem Designing many similar DNA sequences for specific applications is expensive and time-consuming.
method Combines transfer learning with Bayesian optimization to reduce experiment count.
result Total number of experiments can be significantly reduced by sharing information between tasks.
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.
New method clusters health codes for better risk adjustment.
problem Improving insurer incentives to attract lower-cost enrollees.
method Markov Chain Monte Carlo methods for clustering diagnostic codes.
result Methodology outperforms common alternatives in health expenditure prediction.
Compressed sensing improves MRI scans with data-driven learning.
problem Challenges in applying compressed sensing from research to clinical practice.
method Data-driven learning to address challenges of hand-crafted priors, tuning parameters, and long reconstruction times.
result Compressed sensing can have greater clinical impact with data-driven learning.
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.
Paper develops a diagnostic test for detecting convergence in SGD with constant step size.
problem Detecting convergence in stochastic gradient descent with constant step size.
method Statistical diagnostic test to detect phase transition in convergence.
result The diagnostic region coincides with the convergence region for a class of loss functions.
TRACE analyzes risk changes in models trained on shifted data.
problem Understanding performance changes when a model trained on shifted data is used.
method TRACE framework decomposes risk change into four factors: generalization gaps, model change penalty, and covariate shift penalty.
result TRACE provides a diagnostic tool to understand and quantify risk changes due to covariate shift.
Study highlights robustness issues in healthcare diagnostic models due to distribution shifts.
problem Robustness of diagnostic models in healthcare is compromised by distribution shifts.
method Theoretical analysis and simulation studies to understand and mitigate shortcuts learned by models.
result Ignoring covariates or using invariant learning approaches leads to non-robust predictors.
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.
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.