Neural network accuracy improves with denser training samples.
problem Improving neural network accuracy on unseen test samples.
method Bounding empirical training error smoothed across activation regions and using it to discard high-risk test samples.
result Discarding high-risk test samples based on error bounds improves prediction accuracy by up to 20%.
Learning ReLU networks to high uniform accuracy requires exponentially many samples.
problem Achieving high uniform accuracy on ReLU networks for security-critical applications.
method Quantified the number of training samples needed for any algorithm to guarantee uniform accuracy.
result The minimal number of training samples scales exponentially with network depth and input dimension.
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.
New algorithm improves plant breeding by clustering soybean genotypes more accurately and efficiently.
problem Low accuracy and high computational complexity in clustering plant genotypes.
method Spectral Clustering with Pivotal Sampling for phenotypic data.
result Our algorithm achieves substantially more accuracy than existing methods.
Improved sampling for Bayesian neural networks reduces vanishing acceptance rates and increases predictive accuracy.
problem Sampling inefficiency in Bayesian neural networks, especially with deep architectures and large datasets.
method Approximate blocked Gibbs sampling to partition and sample subgroups of parameters.
result Increased predictive accuracy and quantification of predictive uncertainty in classification tasks.
New study shows Gaussian samplers struggle with heavy-tailed targets, while stable samplers excel.
problem The difficulty of sampling from heavy-tailed distributions using Gaussian versus stable oracles.
method Comparison of Gaussian and stable oracles for proximal samplers.
result Gaussian samplers have a fundamental barrier for high-accuracy guarantees in heavy-tailed sampling, while stable samplers excel.
New sketches for weighted ℓp sampling without replacement improve accuracy and efficiency.
problem Efficiently sampling weighted data with high accuracy and minimal redundancy.
method Design of novel composable sketches for WOR ℓp sampling, based on CountSketch. result First to provide WOR sampling for p>1 and signed updates. Sampling strategies significantly affect feature approximations in ELA, impacting classifier accuracy.
problem The impact of sampling strategies on feature approximations in ELA.
method Analysis of feature approximations from different sampling strategies and sample sizes.
result Feature approximations from different sampling strategies do not converge, affecting classifier accuracy.
Paper improves neural network accuracy by focusing on uncertain samples.
problem Improving neural network accuracy through better instance weighting.
method Estimates sample uncertainty in SGD to re-weight training instances.
result Reliable improvements in accuracy across various network architectures.
Flexics samples patterns with guarantees, addressing flexibility and accuracy issues.
problem Pattern explosion and limited sampling accuracy with existing methods.
method Leverages SAT sampling and pattern mining algorithms to support flexible quality measures and constraints.
result Flexics provides strong guarantees on sampling accuracy while being flexible and efficient.
A new data-driven sampling method improves GraphSAGE's accuracy in node classification.
problem High variance in neighborhood sampling leads to sub-optimum accuracy in GraphSAGE.
method A data-driven node sampling approach using a non-linear regressor trained with reinforcement learning.
result Enhanced GraphSAGE accuracy in inductive node classification benchmarks.
MIRA scores assess conditional distribution accuracy using joint samples.
problem Assessing the accuracy of candidate conditional distributions.
method Analytic expression for Mira score based on equal probability mass regions.
result Mira enables Bayesian model comparison by quantifying alignment with true process.
Two algorithms reduce label complexity in machine learning using stratified sampling.
problem Reducing the number of true labels needed for machine learning evaluation.
method Proposes two algorithms that estimate strata properties and optimize evaluation accuracy.
result Demonstrates algorithms are rate optimal and reduce label complexity.
New method improves deep RL efficiency by adaptively setting accuracy requirements.
problem Improving efficiency in deep reinforcement learning.
method Accuracy-based curriculum learning using adaptive selection of accuracy requirements.
result Adaptive accuracy requirements lead to better learning efficiency than random selection.
New machine learning model faster, more accurate, and can identify hard-to-classify samples.
problem Improving classification models in machine learning.
method Quadratic Multiform Separation approach.
result Produces comparable predictive accuracy, runs faster, and identifies hard-to-classify samples.
Develops a fast, accurate method for comparing networks.
problem Comparing networks with repeated observations and varying sizes/sparsity.
method A novel two-sample hypothesis testing method with theoretical guarantees.
result Outperforms existing tools in speed and accuracy, power-optimal.
Improves neural network accuracy with minimal clean data loss.
problem Adversarial samples degrade neural network performance.
method Moderate adversarial training to improve robustness without sacrificing clean data accuracy.
result Enhanced robustness against adversarial samples with minimal accuracy loss.
Adaptive sampling method optimizes DNN compression for resource-constrained platforms.
problem Efficiently compressing DNNs for resource-constrained platforms with high accuracy.
method Adaptive sampling using genetic algorithm-inspired operations to optimize hyperparameters.
result Adaptive sampling outperforms rule-based and reinforcement learning methods in compression rate and accuracy.
Improved dataset distillation for images and texts boosts model accuracy.
problem Reducing dataset size for faster and more energy-efficient model training.
method Simultaneous distillation of images and soft labels, extending to text datasets.
result 2-4% increase in accuracy for image classification tasks, 20% reduction in distilled samples.
Adversarial training can hurt robust accuracy in small sample size scenarios.
problem Adversarial training improves test accuracy but may degrade robustness in limited data settings.
method Analyzes high-dimensional linear classification with noiseless observations, and observes perceptible attacks on image datasets.
result Adversarial training can negatively impact robust generalization in small sample size regimes.
New method improves deep learning by sampling worst-performing data.
problem Overfitting and poor generalization in deep learning.
method Distributional robust optimization to modify sample contributions.
result Faster convergence and higher accuracy in different scenarios.
The paper optimizes LLM accuracy by stopping early based on consistent answers.
problem Improving LLM accuracy in math and reasoning problems.
method Bayesian stopping policy to save on sampling costs, tracking only the L-1 most frequent answer counts.
result The L=3 stopping policy is sufficient for asymptotic optimality and significantly reduces inference costs.
New metric detects adversarial samples with high accuracy.
problem Vulnerability of deep neural networks to adversarial samples.
method Analyzed adversarial samples through their contributions to principal components of images.
result Proposed new metric (k,p) point for measuring robustness to adversarial samples.
GraphSAINT improves GCN training efficiency and accuracy with graph sampling.
problem Neighbor explosion problem in minibatch training of GCNs.
method GraphSAINT constructs minibatches by sampling the training graph, ensuring fixed well-connected nodes in all layers.
result GraphSAINT achieves new state-of-the-art F1 scores for PPI and Reddit.
Riemannian Proximal Sampler improves sampling on manifold data.
problem Sampling from densities on Riemannian manifolds.
method Uses MBI and RHK oracles for high-accuracy sampling.
result Sampling with ε-accuracy requires O(log(1/ε)) iterations in KL divergence.
Improved sampling accuracy in SG-MCMC methods via non-uniform gradient subsampling.
problem Computational inefficiency and sampling error in stochastic gradient MCMC methods.
method Proposes a non-uniform subsampling scheme to reduce sampling error in EWSG, a variant of SG-MCMC.
result EWSG reduces sampling error compared to uniform subsampling, improving accuracy without sacrificing convergence speed.
Predicts accuracy of classifiers on unseen classes.
problem Unknown accuracy of classifiers on unseen classes.
method Defined rROC to estimate classifier accuracy on unseen classes.
result Robust algorithm CleaneX achieves better predictions.
Bayesian design improves accuracy without extra cost.
problem Nested inference in complex systems limits BED accuracy and efficiency.
method Grouped geometric pooled posterior with EKI formulation.
result Improved accuracy and stable estimators at comparable cost.
Study shows how many samples are needed for eigenvector/eigenvalue accuracy.
problem Guaranteeing eigenvector and eigenvalue accuracy of sample vs actual covariance matrices.
method Proves inner product decrease proportional to eigenvalue distance for various distributions.
result Non-asymptotic concentration bounds and conditions for distinguishing principal components.
Enhances classification accuracy on low data sets using synthetic data.
problem Low sample size in data augmentation.
method Variational Autoencoder and manifold sampling.
result Significant improvement in classification accuracy (e.g., 88.6% vs 80.7%).
Clever sampling methods can be used to improve the handling of big data and increase its usefulness. The subject of this study is remote sensing, specifically airborne laser scanning point clouds representing different classes of ground cover. The aim is to derive a supervised learning model for the classification usin…
Co-learning BO improves global optimization with limited samples.
problem Suboptimal solutions in Bayesian optimization due to poor surrogate accuracy.
method Build multiple Gaussian process models to complement each other and reduce prediction errors.
result CLBO achieves more efficient global optimization with fewer samples.
The study analyzes the accuracy of quantile estimators in risk assessment using tail models.
problem Accurately assessing high quantiles in risk management with unknown distributions and sparse data.
method Used generalized Pareto distribution to model tail risks and calculated quantiles with finite sample bias and variance analysis.
result Determined the finite sample distribution function and bias/variance of quantile estimators.
OTSL improves structure learning accuracy with out-of-sample and resampling strategies.
problem Determining optimal hyperparameters for structure learning algorithms.
method Out-of-sample Tuning for Structure Learning (OTSL) using resampling strategies.
result Improves graphical accuracy of structure learning algorithms.
Deep learning identifies wireless interference sources efficiently.
problem Interference source identification in 2.4 GHz ISM Band.
method Deep learning algorithms trained on 10 MHz band samples.
result CNN architecture reduces training time by 60% with minimal accuracy loss.
Improved particle filters enhance vehicle tracking accuracy.
problem Particle filters struggle with frequent, informative observations.
method Proposes particle filters that sample around recent observations.
result Significant improvement in accuracy and efficiency.
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.
LGES speeds up causal discovery while maintaining accuracy.
problem Causal discovery from observational data with computational and accuracy limitations.
method LGES modifies GES by avoiding certain edge insertions, using prior knowledge, and leveraging interventional data.
result LGES outperforms GES in speed, accuracy, and robustness to misspecified knowledge.
This work improves surrogate models for balancing accuracy and cost in multi-fidelity methods.
problem Balancing accuracy and computational cost in multi-fidelity methods.
method Develops context-aware surrogate models for multi-fidelity importance sampling and Bayesian inverse problems.
result Context-aware surrogate models can lead to runtime speedups of up to one order of magnitude.
Stochastic gradient descent optimizes Nyström samples for kernel matrix approximation.
problem Optimizing Nyström samples for kernel matrix approximation.
method Stochastic gradient descent applied to multisets of landmark points (Nyström samples) using a surrogate criterion (radial SKD).
result Local minimization of the radial SKD yields improved Nyström approximation accuracy.
Tricks adversarial attacks to target specific classes, improving classifier accuracy.
problem Recent adversarial defense approaches have failed to protect classifiers from untargeted attacks.
method Target Training defense tricks untargeted attacks into targeted attacks on designated classes, then derives the real class.
result 86.2% accuracy for CW-L2 (confidence=0) in CIFAR10, outperforming unsecured classifiers.
New method for long-term sampling of complex dynamics on curved spaces.
problem Sampling ergodic dynamics on Riemannian manifolds efficiently over long periods.
method Intrinsic geometric operations for sampling invariant measure without embeddings.
result Outperforms previous methods in long-term sampling efficiency.
Improved classifier accuracy by using more of the class-specific structure in trained models.
problem Softmax ignores valuable information encoded in the full array of class response distributions.
method Developed a hybrid classifier (Softmax-Pooling Hybrid, SPH) that uses Softmax on high-scoring samples and a log-likelihood method on low-scoring samples. result Reduces test set error by 6% to 23% using the exact same trained model.
A neural network-based two-sample test improves classification accuracy.
problem Differentiating between two sub-exponential densities.
method Difference of logit function from trained classification neural network.
result Network complexity scales with intrinsic dimensionality for low-dimensional manifolds.
Prunes neural networks while preserving accuracy, using sensitivity sampling.
problem Sparsifying neural networks while maintaining predictive accuracy.
method Uses sensitivity sampling to construct an importance distribution, then adaptively prunes weights.
result Pruned networks incur minimal loss in performance compared to original networks.
Graphs from features improve classification accuracy in tasks.
problem Traditional classification tasks can be improved by incorporating relational information.
method Construct geometric graphs from features and use them in Graph Convolutional Networks.
result Graphs derived from features increase classification accuracy and improve class separation.
DMIDAS improves long-term forecasting accuracy in healthcare and electricity data.
problem Challenging long-term forecasting accuracy and computational complexity.
method Smoothness regularization and mixed data sampling techniques integrated into NBEATS architecture.
result Improves prediction accuracy by 5% on long forecasting horizons (1000 timestamps) compared to state-of-the-art models.
Algorithm estimates multivariate normal parameters from hidden truncated samples.
problem Estimating parameters of multivariate normal distribution from hidden truncated samples.
method Efficient algorithm using polynomial-time estimation with oracle access to the subset S.
result Estimation of mean and covariance matrix with arbitrary accuracy.