Efficiently fine-tunes patient-independent seizure detection models with tensor kernel machine.
problem Improving seizure detection accuracy for wearable devices.
method Transfer learning with tensor kernel machine using canonical polyadic decomposition.
result Patient fine-tuned model achieves high performance with smaller model size.
New algorithm detects concept drifts from unlabeled data streams.
problem Detecting concept drifts from unlabeled data streams.
method Margin Density Drift Detection (MD3) algorithm.
result MD3 reliably detects drifts with fewer false alarms.
GOCPD detects change points by maximizing the probability of two independent models.
problem Large false discovery rates in online change point detection methods.
method GOCPD uses ternary search to find change points by maximizing the probability of two independent models.
result GOCPD accelerates CPD with logarithmic complexity for single change point detection.
New method aggregates Gaussian experts by detecting conditional independence violations.
problem Aggregation of dependent Gaussian experts leads to sub-optimal solutions.
method Uses Gaussian graphical model to detect and correct conditional independence violations.
result Improves aggregation of Gaussian experts, outperforming SOTA DGP approaches.
This work identifies redundant tests in conditional-independence-based discovery that can improve graphical model accuracy.
problem Reliability and sensitivity of conditional-independence-based discovery algorithms.
method Analysis of redundant tests and their impact on error detection and correction.
result Redundant tests can improve graphical model accuracy but not all are beneficial.
Unified framework for online LLM watermark detection using e-processes.
problem Detecting AI-generated text from human-written content in online settings.
method Unified framework based on e-processes for anytime-valid hypothesis testing on independence.
result Proposed methods achieve competitive performance in watermark detection.
Detect hidden confounding in observational data using multiple environments.
problem Detect hidden confounding in observational data.
method Theoretical framework and simulation studies to test for hidden confounding.
result The proposed procedure correctly predicts hidden confounding, especially when bias is large.
This work improves independence tests for high-dimensional data.
problem Detecting subtle dependencies between high-dimensional random variables with complex distributions.
method Develops two approaches to learn powerful independence tests using variational mutual information and HSIC.
result Optimized HSIC tests generally outperform other approaches on detecting structured dependence.
The paper develops robust tests for detecting independence in synchronous stochastic systems with finite sample guarantees.
problem Detecting independence in synchronous stochastic systems with finite sample guarantees.
method Combines confidence region estimates with permutation tests and dependence measures to detect nonlinear dependence.
result Consistent hypothesis tests for detecting independence under mild assumptions.
Detects anomalous patterns in non-independent data streams.
problem Low detection power for subtle, emerging irregularities in non-iid data.
method Combines Gaussian processes with subset scanning techniques.
result Powerful, interpretable methods for anomalous pattern detection.
Detecting edge correlation between two graphs sharpens a threshold based on densest subgraph.
problem Detecting edge correlation between two Erdős-Rényi graphs.
method Formulated as a hypothesis testing problem, connecting to densest subgraph detection.
result Sharp information-theoretic threshold established for edge correlation detection.
Parallelizes MCMC for spatially dependent models.
problem Inference in models with spatially determined conditional independence.
method Coloring a factor graph over regions of latent space.
result Significant speedups achieved with no loss in inference quality.
New framework detects anomalies in videos without training data.
problem Detect anomalies in videos without training data or temporal context.
method Discriminative learning approach, independent of anomaly order.
result Achieves state-of-the-art results on anomaly detection.
Efficient test detects independence with optimized features.
problem Detecting independence between variables efficiently.
method Analytic kernel embeddings and adaptive feature selection.
result Optimized features improve test performance and consistency.
This research designs a data-driven partition to test independence between continuous variables.
problem Testing independence between continuous random variables.
method Empirical log-likelihood statistic and data-driven tree-structured partition.
result Strongly consistent test of independence over probability families.
Maximizes anomaly detection intervals for multivariate time series.
problem Detect anomalies in multivariate time series data.
method Maximizes Kullback-Leibler divergence between data within and outside intervals.
result Improves anomaly detection compared to independent time step methods.
New method detects biomarker-treatment interactions in clinical trials.
problem Detecting interactions between high-dimensional biomarkers and treatments in randomized trials.
method Two-stage penalized regression screening using ridge regression for multivariate screening.
result Ridge regression screening provides greater power than traditional methods in correlated data.
Deep learning detects novel changes in time series data.
problem Detecting novel changes in time series with unknown probability structures.
method Causally extracts an innovations sequence for novelty detection.
result Minimax optimality established for the novelty detection method.
A new test detects non-linear independence in censored survival data.
problem Detecting non-linear independence between survival times and covariates.
method A kernel log-rank test using reproducing kernel Hilbert spaces.
result The test correctly rejects the null hypothesis under any alternative.
Geometric pruning rules improve change point detection in multiple time series.
problem Detecting multiple changes in multiple independent time series.
method Dynamic programming algorithms with inequality-based and geometric pruning rules.
result Geometric pruning rules offer close-to-linear time complexity for multiple independent time series.
IAE extracts innovations sequences for non-Gaussian processes.
problem Extracting innovations sequences for non-Gaussian processes.
method Causal convolutional neural network.
result IAE effectively detects anomalies in non-Gaussian data.
New method detects changes in data streams efficiently.
problem Quickest change-point detection in data streams.
method Inductive Conformal Martingales for quickest change-point detection.
result Inductive Conformal Martingales are efficient under general conditions.
Deep RNN detects FoG episodes in Parkinson's disease with high accuracy.
problem Detecting freezing episodes in Parkinson's disease patients.
method Deep Recurrent Neural Network (RNN) with Long Short-Term Memory cells on 3D-accelerometer measurements.
result Frequency domain features from trunk sensor achieve an AUC score of 93% in subject-independent method.
Study on detecting change points in features for agnostic learning.
problem Detecting change points in features that become relevant over time.
method Proposed an approach to provably determine change points in an agnostic supervised learning setting.
result Efficient method with same asymptotic performance as original approach.
BET improves nonparametric dependence detection by avoiding power loss.
problem Power loss in nonparametric dependence detection methods.
method Binary expansion statistics (BEStat) and binary expansion testing (BET) framework.
result BET avoids non-uniform consistency and achieves the minimax rate in sample size requirement.
Decentralized detection avoids sharing data, controls false discoveries.
problem Global false discovery rate control in decentralized novelty detection.
method Quantized surrogate models for low-precision sharing, preserving exchangeability.
result Quantized composite scores maintain competitive statistical power with reduced communication.
Model-X test detects conditional independence in streaming data.
problem Detecting conditional independence in data streams with arbitrary dependency.
method Sequential testing inspired by model-X and testing by betting.
result Significantly reduces type-I error rate and enhances data efficiency.
We study the spherical cap packing problem with a probabilistic approach. Such probabilistic considerations result in an asymptotic sharp universal uniform bound on the maximal inner product between any set of unit vectors and a stochastically independent uniformly distributed unit vector. When the set of unit vectors …
MMD test detects adversarial attacks by addressing kernel limitations and non-independence issues.
problem MMD test's failure to detect adversarial attacks.
method Replaced Gaussian kernel with deep kernel, maximized test power, and used wild bootstrap for non-independence.
result MMD test is aware of adversarial attacks.
Proposes LatLapMED for detecting high-utility anomalies.
problem Detecting statistically rare instances with real-world significance.
method Uses EM algorithm to combine entropy minimization and maximum entropy discrimination.
result Superior performance over existing anomaly detection methods.
New metrics for high-dimensional data improve on energy distance.
problem Testing equality of distributions and independence in high dimensions.
method Proposed new metrics inheriting properties of energy distance and others.
result Improved metrics detect homogeneity and independence in high dimensions.
Study detects a specific type of link using annular Khovanov homology.
problem Detecting a specific type of three-strand weaving link.
method Combines braid detection with rigidity theorem to determine (σ1σ2−1)N up to conjugacy. result Annular Khovanov homology detects the underlying unoriented annular link KN. Paper develops new method for detecting latent structure in large symmetric data matrices.
problem Testing for latent structure in large symmetric data matrices.
method Introduces Wilcoxon--Wigner random matrices based on normalized rank statistics.
result Establishes asymptotic Gaussian fluctuations for leading eigenvalue and eigenvector of Wilcoxon--Wigner matrices.
A new process-level model explains how humans detect dependencies quickly.
problem Understanding how humans handle probabilistic independence relations efficiently.
method Developed a rational, distributed, message-passing model called D*.
result D* shows a tendency to quickly detect dependencies, outperforming other algorithms.
A new method detects changes in data sequences by comparing backward and forward confidence sequences.
problem Detecting changes in data sequences over time.
method Constructing and comparing backward and forward confidence sequences.
result The method successfully detects changes with strong nonasymptotic guarantees.
The paper studies structure detection in high-temperature ferromagnetic models.
problem Distinguishing between empty models and models with a specific subgraph structure.
method Matching upper and lower bounds for minimax testing, and computational hardness results.
result Arboricity drives the testability of the problem, and there are no polynomial time tests under certain conditions.
New method combines simulations and data for anomaly detection.
problem Detecting new particle signals without direct evidence.
method Hybrid approach using reweighting and interpolation.
result Improved background estimation and classification.
New kernel tests detect differences between distributions exponentially quickly.
problem Characterize the asymptotic performance of kernel two-sample tests.
method Established exponentially consistent kernel two-sample tests for unknown distributions.
result Exponential decay rate of type-II error probability is optimal and independent of kernels.
Challenge uses unsupervised learning to detect new physics signals at LHC.
problem Detecting new physics signals at the LHC using unsupervised machine learning.
method Developed and evaluated anomaly detection algorithms on a large dataset.
result Benchmark dataset of >1 Billion simulated LHC events for future studies.
Deep learning autoencoder detects bee colony anomalies.
problem Early detection of bee swarms and other unusual behaviors.
method Deep Recurrent Autoencoder model trained on sensor data.
result Autoencoder detects anomalies independent of their origin.
Enhanced neural networks detect thin boundaries between different types of anomalies.
problem Detecting thin boundaries between different types of anomalies in outlier detection.
method Defined new types of anomalies, enhanced Multi-Layer Perceptron Neural Network with Genetic Algorithm.
result Reduced error in anomaly detection compared to baseline methods.
EHBOS enhances HBOS by capturing feature interactions, improving anomaly detection.
problem Limited ability of HBOS to detect anomalies in datasets with feature interactions.
method Incorporates two-dimensional histograms to capture feature pair dependencies.
result EHBOS outperforms HBOS on datasets with critical feature interactions, achieving notable improvements in ROC AUC.
The paper develops p-values for outlier detection using conformal inference.
problem Detecting outliers in nonparametric data sets.
method Conformal inference framework for constructing marginally valid but mutually dependent p-values.
result Valid p-values for outlier detection with conditional independence and marginal false discovery rate control.
Proposes a model to detect changes in multivariate time series data.
problem Detect abrupt changes in multivariate time series data considering dependencies and correlations.
method Integrates graph neural networks into an encoder-decoder framework to model correlation structures and dynamics.
result Advantageous performance on CPD tasks over strong baselines, classifying changes as correlation or independent.
Improved CNNs detect Alzheimer's with 14% accuracy boost.
problem Early detection of Alzheimer's Disease using MRI scans.
method Optimized 3D CNNs with instance normalization, spatial downsampling, model widening, and age information.
result 14% increase in test accuracy distinguishing AD, MCI, and controls.
We say that a given knot J⊂S3 is detected by its knot Floer homology and A-polynomial if whenever a knot K⊂S3 has the same knot Floer homology and the same A-polynomial as J, then K=J. In this paper we show that every torus knot T(p,q) is detected by its knot Floer homology and A-polynom…
Detects knots in thickened surfaces using instanton homology.
problem Detecting knots in thickened surfaces using homology.
method Uses Asaeda-Przytycki-Sikora (APS) homology and sutured instanton homology.
result Detects the unknot in (−1,1)imesΣ and characterizes minimal sutured instanton homology. LCIT tests conditional independence using latent representations.
problem Detecting conditional independencies in statistical and machine learning tasks.
method Generative framework for learning latent representations of target variables X and Y, then testing for remaining dependencies.
result LCIT outperforms state-of-the-art baselines consistently under different metrics and settings.