Paper introduces threshold invariant fairness to ensure equitable predictions across different groups.
problem Machine learning models can be unfair to certain groups based on sensitive attributes.
method Proposes threshold invariant fairness and uses two approximation methods to equalize risk distributions.
result Demonstrates effectiveness in alleviating threshold sensitivity in fairness models.
Study proposes machine learning to estimate lactate threshold for runners.
problem Inconvenient and expensive blood lactate measurement for recreational runners.
method Recurrent neural networks and standardized temporal axis.
result 89.52% accuracy in estimating lactate threshold.
A new method for choosing thresholds in data sequences without assuming distribution.
problem Choosing thresholds for random sequences without distributional assumptions.
method Data-driven threshold machine (DTM) that estimates three parameters of extreme value distributions and extremal index.
result DTM provides a reliable estimate of thresholds with robustness and computational efficiency.
System classifies lung CT scans into normal or COVID-19 using machine learning.
problem Detecting COVID-19 infection in lung CT scans.
method MLS with CBA+KE thresholding, feature extraction, selection, and classification.
result SVM with FFV achieved 89.80% detection accuracy.
MLShrink integrates machine learning with wavelet shrinkage for denoising.
problem Denoising signals with uncertain magnitudes
method Combines wavelet shrinkage with machine learning
result Preserves simplicity for signal coefficients while allowing data-adaptive decisions for ambiguous coefficients
TBAL reduces manual annotation but requires validated data.
problem Creating large, high-quality labeled datasets.
method Threshold-based auto-labeling using human validation data.
result Sample complexity bounds on validation data needed.
SpaRCe optimizes reservoir computing by learning neuron thresholds to improve performance and prevent forgetting.
problem Improving performance and preventing forgetting in reservoir computing networks.
method Integrates neuron-specific learnable thresholds to optimize sparsity without altering dynamics, learning read-out weights and thresholds via gradient rule.
result Threshold learning improves performance and alleviates catastrophic forgetting.
Optimizes waiting thresholds for node health states in Azure.
problem Minimizing downtime when nodes transition to unhealthy states.
method Customized organic recovery model using node features and gradient descent.
result Optimal thresholds can be found for multiple states in a state machine.
New technique reduces gender discrimination in credit lending models.
problem Bias and unfairness in credit lending predictions.
method Subgroup Threshold Optimizer (STO) technique.
result Reduces gender discrimination by over 90%.
A new SSL method uses instance-dependent thresholds to improve accuracy.
problem Improving semi-supervised learning by better selecting confident unlabeled instances.
method Proposes instance-dependent thresholds that vary based on the ambiguity and error rates of pseudo-labels for each unlabeled instance.
result Demonstrates that instance-dependent thresholds provide a probabilistic guarantee for correct pseudo-labels.
DSSP improves deep learning training speed by dynamically adjusting staleness thresholds.
problem Time-consuming deep learning training on large datasets.
method Dynamic Stale Synchronous Parallel (DSSP) framework that adapts staleness threshold at runtime.
result DSSP converges faster and achieves higher accuracy than other paradigms.
Proposes a novel approach for RUL estimation of aero-engines.
problem Lack of prior knowledge for defining exact failure thresholds in dynamic environments.
method Simultaneous and dynamic prediction of continuous and discrete states within a single learning framework.
result Improves RUL estimation for aero-engines by reducing complexity.
Reveals the first layer of deep networks with high activation thresholds.
problem Learning guarantees for deep neural networks with multiple layers.
method Strengthening parameter recovery guarantees for deep networks with a high threshold assumption.
result Reveals the first layer of a deep neural network under specific activation conditions.
Improves interpretability of anomaly scores in GBRBM-based detection.
problem Difficulty in setting a proper threshold for anomaly scores.
method Proposes a measure based on cumulative distribution and uses simulated annealing for evaluation.
result Established a guideline for setting the threshold using the interpretable measure.
Paper proves fair classification can be done via simple thresholding.
problem Achieving fair binary classification subject to group fairness constraints.
method Proves Bayes optimal fair learning rule is a group-wise thresholding rule over the Bayes regressor with randomization.
result Proposes an efficient unconstrained optimization algorithm for post-processing fair classification.
Study uses supercomputers to improve financial predictions.
problem Improving financial predictions through better exploration of data.
method Refactored and ran algorithm on Fugaku supercomputer, exploring more rules.
result Increasing the number of explored rules improves predictive performance.
This paper is concerned with the hard thresholding operator which sets all but the k largest absolute elements of a vector to zero. We establish a {\em tight} bound to quantitatively characterize the deviation of the thresholded solution from a given signal. Our theoretical result is universal in the sense that it ho…
Framework uses human feedback to safely set OOD detection thresholds, reducing false positives.
problem Challenges in setting OOD detection thresholds for safety-critical applications.
method Mathematically grounded framework leveraging expert feedback to dynamically update thresholds.
result Guaranteed to meet FPR constraint while minimizing human feedback, maintaining FPR at most 5%.
Paper bounds tensor decomposition's RLCT, aiding Bayesian inference.
problem Unclear mathematical property of tensor decomposition.
method Algebraic geometrical method for upper bound derivation.
result Upper bound of real log canonical threshold (RLCT) derived.
Machine learning predicts movie genres from summaries with high accuracy.
problem Predicting movie genres from plot summaries.
method Used Naive Bayes, Word2Vec+XGBoost, Recurrent Neural Networks, and Gated Recurrent Units (GRU) for text classification and multi-label problem.
result GRU neural networks achieve the best result with a Jaccard Index of 50.0%, F-score of 0.56, and hit rate of 80.5%.
A novel algorithm optimizes sparsity in reservoir computing inspired by insect brain.
problem Optimizing sparsity in reservoir computing networks.
method Inspired by insect brain, the algorithm optimizes sparsity levels by adjusting node firing thresholds.
result The algorithm outperforms standard gradient descent on tasks involving better classification, memorization, and convergence.
DaRE forests enable efficient data deletion from random forests.
problem Efficiently removing data from machine learning models.
method Random Forests with data deletion enabled (DaRE).
result Data deletion from DaRE models is orders of magnitude faster than retraining.
A new machine learning method calculates failure probability efficiently and accurately.
problem Computing the probability of failure for complex systems.
method Penalized Profile Support Vector Machine with adaptive sampling and clustering.
result The method minimizes model evaluations while preserving decision boundary geometry.
A new method for optimizing non-decomposable metrics with constraints.
problem Optimizing complex machine learning objectives with thresholded constraints.
method Formulate rate-constrained optimization using the Implicit Function theorem and solve with gradient-based methods.
result Demonstrated effectiveness over existing methods on benchmark datasets.
ATC predicts target domain accuracy using only labeled and unlabeled data.
problem Predicting out-of-distribution performance with limited labeled data.
method Average Thresholded Confidence (ATC) method that learns a threshold on model confidence.
result ATC outperforms previous methods across various types of distribution shifts and datasets.
Unified framework for Bayes-optimal classifiers under group fairness.
problem Mitigating disparate impacts from algorithmic predictions in high-stakes decision-making.
method Unified framework based on Neyman-Pearson argument for deriving Bayes-optimal classifiers under group fairness constraints.
result Proposes FairBayes method that directly controls disparity and achieves optimal fairness-accuracy tradeoff.
Study uses active learning to automate EEG event annotation.
problem Lack of annotated clinical EEG data for machine learning models.
method Active learning algorithm for automated annotation of six types of EEG events.
result Recognition performance improved 2% absolute, capable of auto-annotating.
New metrics CWSA and CWSA+ improve model evaluation under confidence thresholds.
problem Lack of metrics capturing model reliability under confidence thresholds.
method Introducing CWSA and CWSA+ metrics that reward confident accuracy and penalize overconfident mistakes.
result CWSA and CWSA+ outperform classical metrics in trust-sensitive tests.
Improves graph recovery in Gaussian graphical modeling.
problem Calibrating regularization parameters for graph recovery.
method Thresholded adaptive validation applied to graphical lasso.
result Thresholding pipeline improves graph recovery.
Study improves early warning models for currency and stock market crises.
problem Predicting currency and stock market crises.
method Synthetic review and comparison of early warning models, focusing on crisis identifications and predictive models.
result SWARCH model with elastic thresholding methodology most accurately classifies crisis observations.
Paper presents six ELM-based OCC methods for online and offline learning.
problem Time-consuming traditional one-class classifiers.
method Reconstruction and boundary-based ELM methods for online and offline learning.
result Proposed methods outperform traditional classifiers and expand DD toolbox functionality.
COMRADE is a communication-efficient, Byzantine-resilient second-order optimization algorithm.
problem Byzantine failures in distributed optimization.
method COMRADE is a communication-efficient, second-order optimization algorithm that uses a simple norm-based thresholding rule to filter out Byzantine workers.
result COMRADE achieves linear-quadratic convergence and is robust against Byzantine workers.
This paper tackles fair Bayes-optimal classifiers under predictive parity, proving their limitations and proposing a new algorithm.
problem Ensuring fair Bayes-optimal classifiers under predictive parity, especially when group performance levels vary widely.
method Proving the limitations of fair Bayes-optimal classifiers under predictive parity and proposing a new adaptive thresholding algorithm, FairBayes-DPP.
result Fair Bayes-optimal classifiers under predictive parity may not hold if group performance levels vary widely, leading to within-group unfairness.
We present a hybrid continuum-atomistic scheme which combines molecular dynamics (MD) simulations with on-the-fly machine learning techniques for the accurate and efficient prediction of multiscale fluidic systems. By using a Gaussian process as a surrogate model for the computationally expensive MD simulations, we use…
Optimal ski rental strategies with machine learning predictions.
problem Minimizing ski rental costs with uncertain future days.
method Derive optimal randomized algorithms using machine learning predictions.
result Class of optimal algorithms with minimized competitive ratio.
Paper studies non-tight reconstruction threshold in a 4-state model with different in/out block mutations.
problem Non-tight reconstruction threshold in a 4-state symmetric model with different in-block and out-block mutations.
method Inspired by the q1+q2 stochastic block model, rigorously analyzes conditions for non-tightness of the reconstruction threshold. result Rigorously gives conditions for the non-tightness of the reconstruction threshold in a 4-state symmetric model.
Paper proposes a new ML approach using only additions and thresholding.
problem Energy efficiency and reduced complexity for IoT ML devices.
method Margin-Propagation (MP) network for inference and learning without MVMs.
result MP-based classifiers achieve comparable results to traditional ML methods with energy savings.
Proposes tau-FPL for efficient false-positive rate control in linear time.
problem Learning classifiers with strict false-positive rate constraints.
method Scoring-thresholding approach with linear time efficiency.
result Superior performance over existing approaches in false-positive rate control.
Paper improves robust PCA with feature information using iterative hard-thresholding.
problem Separating low-rank and sparse components in data with feature information.
method Iterative hard-thresholding algorithm for robust PCA under weaker assumptions.
result Global convergence and faster convergence rate of the proposed algorithm.
Detects model changes in machine learning with empirical difference test.
problem Detect significant model changes between pre-change and post-change parameters.
method Constructs an empirical difference test (EDT) to approximate GLRT, with low computational complexity and false alarm constraint.
result EDT approximates GLRT and provides a method to set threshold for false alarm constraint.
New method corrects bias in CVaR estimation for extreme risks.
problem Limited data above VaR leads to poor CVaR estimation.
method Bias-corrected peaks-over-threshold (POT) estimation using GPD.
result Asymptotically unbiased CVaR estimator with lower threshold.
This work compares OmniAnomaly with PCA for MTSAD, finding PCA can match or outperform OmniAnomaly.
problem Comparing deep learning models with classical methods in MTSAD under fair evaluation protocols.
method Systematic comparison of OmniAnomaly and PCA on SMD, using identical thresholding and evaluation procedures.
result PCA can achieve performance comparable to OmniAnomaly and even outperform it under certain conditions.
Efficient distributed learning with Byzantine-resilient thresholding and error feedback.
problem Byzantine-resilient distributed learning with communication efficiency.
method Simple thresholding for Byzantine mitigation, compressed gradients and norms for aggregation, error feedback.
result Statistical error rate matches Yin et al.~\cite{dong} but with simpler schemes, and improved convergence with error feedback.
The paper investigates how Gaussian Processes handle adversarial examples and their uncertainty.
problem Adversarial examples' impact on uncertainty in Gaussian Process models.
method Investigates Gaussian Processes in the context of Bayesian inference to study adversarial examples.
result Gaussian Processes show varying levels of uncertainty that reflect adversarial perturbations.
Paper provides linear convergence guarantees for KZIHT and KZPT methods.
problem Solving linear equation systems with sparse constraints.
method Combines Kaczmarz and iterative thresholding methods, using reshuffling data sampling.
result KZIHT and KZPT converge linearly to sparse solutions.
Graph neural networks improve network localization accuracy and efficiency.
problem Network localization in large-scale networks.
method Adopted graph neural networks for nonlinear regression.
result GNN outperforms state-of-the-art benchmarks in network localization.
Study evaluates three class imbalance techniques across diverse datasets.
problem Class imbalance in binary classification tasks.
method Synthetic Minority Over-sampling Technique (SMOTE), Class Weights tuning, Decision Threshold Calibration.
result Decision Threshold Calibration is the most consistently effective technique.
Study finds AUC is most consistent across different prevalence in binary classification.
problem Consistency of model evaluation metrics across varying prevalence in binary classification.
method Analysis of 156 data scenarios with 18 metrics, 5 models, and a random guess model.
result AUC has the smallest variance in evaluating individual models and ranking of models.