Efficient method estimates classifier accuracy with unlabeled data and logical constraints.
problem Estimating classifier accuracy using only unlabeled data with logical constraints.
method Based on the agreement of classifiers and logical constraints violations.
result Accuracy estimates within a few percent of true accuracy, outperforming existing solutions.
Online learning improves big data accuracy quickly.
problem Heterogeneity in big data analysis.
method Online machine learning for big data.
result Online learning converges quickly to batch accuracy.
Adversarial training can degrade standard accuracy even when optimal for robust accuracy.
problem Tradeoff between standard and robust accuracy in adversarial training.
method Analyzes adversarial training's impact on standard accuracy, even when optimal for robust accuracy.
result Even with optimal predictors, adversarial training can still degrade standard accuracy.
Accuracy on in-distribution data correlates with out-of-distribution data when data is noisy or contains nuisance features.
problem Correlation between in-distribution and out-of-distribution accuracy in noisy or feature-rich data.
method Analyzes the impact of noise and nuisance features on model performance.
result Accuracy on in-distribution and out-of-distribution data can become negatively correlated in noisy or feature-rich data.
Proposes a new adversarial model to avoid accuracy vs. adversarial accuracy tradeoff.
problem Inherent tradeoff between accuracy and adversarial accuracy in existing adversarial robustness definitions.
method Introduces Voronoi-epsilon adversary that balances perturbation constraints.
result Voronoi-epsilon adversary avoids accuracy vs. adversarial accuracy tradeoff even with large ε. Paper improves classification accuracy using synthetic data and probabilistic models.
problem Low classification accuracy in supervised learning algorithms.
method Using probabilistic mixture models to identify sub-labels and generate synthetic data.
result Better classification accuracy achieved through synthetic data generation.
MobileNet CNN achieves high accuracy in skin disease classification on Android.
problem Skin disease classification using smartphone technology.
method Transfer learning on MobileNet, imbalanced dataset handling (sampling and preprocessing), and data augmentation.
result Oversampling and data augmentation on preprocessing input data achieved 94.4% accuracy.
SemiNAS reduces NAS cost by predicting accuracy of unlabeled architectures.
problem Costly evaluation of architectures limits NAS efficiency.
method SemiNAS uses unlabeled architectures to train an accuracy predictor.
result SemiNAS achieves comparable accuracy with less data.
The paper analyzes adversarial training effects on classification accuracy.
problem Understanding adversarial training's impact on standard and robust accuracy.
method Derived precise statistical analysis for binary classification problems with Gaussian data.
result Theoretical explanation of standard and robust accuracy trends for adversarial training.
Patch Gaussian augmentation improves model robustness without sacrificing accuracy.
problem Challenges in building robust models without sacrificing accuracy.
method Adds Gaussian noise to randomly selected patches in images.
result Achieves state-of-the-art performance on benchmarks while improving clean data accuracy.
Calibrated ensembles improve both ID and OOD accuracy in distribution shift.
problem Desired balance between in-distribution and out-of-distribution accuracy.
method Ensemble standard and robust models, calibrating on ID data only.
result ID-calibrated ensembles outperform state-of-the-art methods on multiple datasets.
Vecchia approximations provide the best accuracy-runtime trade-off for Gaussian process approximations.
problem High computational cost of Gaussian processes for large data sets.
method Systematic comparison of different Gaussian process approximations.
result Vecchia approximations consistently provide the best accuracy-runtime trade-off.
CNNs adapted for graphs match image CNNs without prior knowledge.
problem Matching CNN performance on graph-structured data without prior knowledge.
method Strided convolutions and data augmentation on graphs.
result Significant accuracy improvement on fMRI data.
The paper analyzes how extra data affects the accuracy of Bayesian clustering models.
problem The impact of additional data on the accuracy of Bayesian clustering models.
method Theoretical analysis of a complex probabilistic model that includes both initial and additional data.
result The complexity of the model increases, but the additional data can improve accuracy under certain conditions.
Paper explores tradeoff between standard and robust accuracy for latent models.
problem Tradeoff between standard accuracy and robust accuracy in adversarial training.
method Revisits adversarial training for latent models, considering Gaussian mixture and generalized linear models.
result Low-dimensional manifold structure mitigates the tradeoff between standard and robust accuracy.
Self-training with noisy student-teacher boosts keyword spotting accuracy.
problem Robust keyword spotting in challenging conditions.
method Aggressive data augmentation and self-training with noisy student-teacher approach.
result Significant accuracy improvement in difficult conditions, up to 60%.
PARSEC compresses text for sentiment analysis with minimal loss in accuracy.
problem Compressing text data for sentiment analysis without losing accuracy.
method Uses Parts-of-Speech tags to compress text intelligently.
result Accurate compression is possible with minimal loss in sentiment classification accuracy.
Optimizes glmnet configuration for better accuracy and efficiency.
problem Inappropriate glmnet configuration leads to inaccurate solutions and increased computation time.
method Data-driven framework using neural networks to predict accuracy and computation time from dataset characteristics and configuration.
result Automatic selection of optimal configuration maximizing accuracy under a time constraint.
Researchers study fairness-accuracy tradeoffs in predictive models for multiple groups.
problem Understanding the tradeoff between fairness and accuracy in models serving multiple demographic groups.
method Characterizing the fairness-accuracy (FA) Pareto frontier, approximating it from limited data, and bounding the worst-case gap.
result Derivation of worst-case-optimal estimators and uniform finite-sample bounds for the entire FA frontier.
Unhinged loss minimization fails to improve classifier accuracy for simple data.
problem Accuracy of classifiers minimizing the unhinged loss.
method Minimizing the unhinged loss function.
result Minimizing the unhinged loss yields classifiers with accuracy no better than random guessing for simple data.
Data augmentation methods improve worst-case model performance.
problem Ensuring fair predictions across subpopulations in large models.
method Linear last layer retraining with data augmentation techniques.
result Optimal worst-group accuracy achieved for Gaussian latent representation distribution.
New work shows limits of certifying neural network robustness.
problem Certified training improves robustness but decreases accuracy.
method Bayes error analysis to investigate robustness limits.
result Upper bound for certified robust accuracy established.
A new metric estimates classifier accuracy using only training data.
problem Assessing classifier accuracy without cross-validation.
method Bayesian Area Under the ROC Curve (CBAUC) metric for linear classifiers.
result The CBAUC is faster and more accurate than conventional AUC estimators.
Astraea improves federated learning accuracy on imbalanced data.
problem Accuracy degradation in federated learning due to imbalanced data distribution.
method Self-balancing federated learning framework with data augmentation and client rescheduling.
result Astraea shows +5.59% and +5.89% improvement in top-1 accuracy on imbalanced datasets.
New test set shows drop in CIFAR-10 classifier accuracy.
problem Questioning the reliability of current machine learning accuracy numbers.
method Created a new test set of unseen images from CIFAR-10.
result Drop in accuracy from 4% to 10% for deep learning models.
Automates fairness and accuracy optimization in deep learning models for tabular data.
problem Improving fairness and accuracy in neural models for tabular data.
method Employed multi-objective Neural Architecture Search (NAS) and Hyperparameter Optimization (HPO) to find new models.
result Jointly optimized architectures that consistently outperform single-objective fairness mitigation methods.
Dream Distillation compresses models without data, achieving high accuracy.
problem Model compression without real data.
method Data-independent model compression framework.
result Achieves 88.5% accuracy on CIFAR-10 test set.
Improved prediction accuracy in linear models with missing data.
problem Improving prediction accuracy in linear models with missing data.
method Introduced Soft Weighted Prediction (SWP) algorithm and optimized it for missing data scenarios.
result Improved Mean Squared Error (MSE) on test set compared to state-of-the-art methods.
Federated learning struggles with non-IID data, but a strategy improves model accuracy.
problem Federated learning accuracy drops significantly with non-IID data.
method Identified weight divergence as the cause, quantified by EMD, and proposed a solution of sharing a subset of globally shared data.
result Accuracy can be increased by 30% for CIFAR-10 with only 5% globally shared data.
Efficiently reduces data augmentation size with similar accuracy.
problem Explosive growth in dataset size due to data augmentation.
method Subsampling policies based on model influence and loss.
result Achieves a 90% reduction in augmentation set size while maintaining accuracy.
New algorithms improve causal direction inference accuracy using parallel ensemble methods.
problem Stability of causal direction inference results from observational data.
method Parallel ensemble frameworks to map and improve inference accuracy.
result Significant improvement in accuracy of causal direction inference.
The paper studies adversarial training for linear regression models.
problem Understanding the tradeoffs between robust and standard accuracy in adversarial training.
method Characterizes the fundamental tradeoff and specific adversarial training approach for linear regression with Gaussian features.
result Precise characterization of the standard and robust accuracy tradeoff in high-dimensional settings.
Improved SSC clustering with reduced computation time and accuracy.
problem Heavy computational burden in Sparse Subspace Clustering.
method RCOMP-SSC algorithm that restricts connections during OMP iterations.
result Improved clustering accuracy with reduced computational time.
Diffusion models' speed-accuracy relations derived from thermodynamics.
problem Understanding the trade-off between model speed and accuracy.
method Connecting diffusion models to thermodynamics and optimal transport.
result Speed-accuracy relations derived, providing insights into optimal learning protocols.
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.
Study shows skewed data labels significantly impact decentralized ML accuracy.
problem Skewed data labels across devices/locations cause significant accuracy loss in decentralized ML.
method Detailed experimental study on skewed data labels, presenting SkewScout system-level approach.
result Skewed data labels are a fundamental challenge for decentralized learning, affecting many applications and models.
Paper shows adversarial training can be fooled by new type of noise.
problem Adversarial training can be fooled by new types of noise.
method Designing ADVIN, a new type of inducing noise.
result ADVIN can degrade adversarial training robustness by 99.9%.
Improved CNN model accuracy and generalizability through data pre-processing.
problem Enhancing accuracy and generalizability of CNN-based LULC classification.
method Trials of different data preparation methods, including patch selection, size, and augmentations.
result Combining multiple grids and rotations of patches improved model accuracy and generalizability.
The paper explores how to balance accuracy and computational resources in learning to rank.
problem Balancing accuracy and computational resources in learning to rank for large datasets.
method Developed a hierarchy of rank-breaking mechanisms to trade off data points for computational resources.
result Theoretical guarantees on the proposed rank-breaking mechanisms provide trade-offs between accuracy and computational resources.
TDA improves accuracy of machine learning models for repeated measurements.
problem Limited accuracy of machine learning models for repeated measurements.
method Samples from data space, builds network graph based on data topology.
result TDA classifier achieves high accuracy (up to 96.8%) in repeated measurement datasets.
New research shows no trade-off between fairness and accuracy in machine learning.
problem The trade-off between fairness and accuracy in machine learning is a widely accepted belief.
method Using mismatched hypothesis testing and Chernoff information, the study demonstrates that optimal fairness and accuracy can be achieved simultaneously.
result There is no inherent trade-off between fairness and accuracy in ideal distributions, but it exists when measured with respect to biased datasets.
Machine learning can predict cancer with 100% accuracy on a dataset.
problem Accuracy of cancer predictions using machine learning.
method Extensive experiments on the Wisconsin Diagnostic Breast Cancer dataset.
result Machine learning algorithms can be easily misled to achieve 100% accuracy.
Fairness constraints can improve accuracy from biased data.
problem Learning from biased training data can produce biased and suboptimal classifiers.
method Examined fairness-constrained ERM and other recovery methods.
result Equal Opportunity fairness constraint combined with ERM provably recovers Bayes Optimal Classifier under various bias models.
Improved accuracy in a commercial assistant by smartly selecting new training data.
problem Expensive and time-consuming to annotate new data for ML systems.
method Automatically identifies new helpful examples suitable for human annotation.
result The proposed method leads to higher accuracy improvements with a fixed annotation budget.
The paper tackles fairness and accuracy in ML models under domain shifts.
problem Designing fair and accurate ML models that perform well in unseen domains.
method Theoretical bounds and sufficient conditions for fairness and accuracy transfer under domain generalization.
result A learning algorithm that ensures fair and accurate models even when deployment environments change.
Paper calculates optimal use of cheap and expensive data for model accuracy.
problem Optimal design of experiments for variable fidelity data.
method Minimax error analysis for Gaussian process regression.
result Variable fidelity data can improve model accuracy within budget constraints.
ESRF reduces ARF ensemble size without sacrificing accuracy.
problem Over-provisioning of ARF ensemble leads to high CPU and memory consumption.
method ESRF uses a swap and elastic component to dynamically adjust the number of classifiers.
result ESRF reduces the number of classifiers by up to one third without sacrificing accuracy.
This paper explores data science applications in economics using a taxonomy of models and hybrid models showing higher accuracy.
problem Investigating data science applications in economics.
method Systematic literature review using Prisma method.
result Hybrid models showed higher prediction accuracy than other algorithms.