Paper proposes a new method for online process discovery.
problem Online process discovery requires limited memory.
method Mapped online process discovery to cache memory management and applied cache replacement policies.
result Implemented and evaluated a new approach for online process discovery.
Private online FDR control for adaptive testing under differential privacy.
problem Controlling false discoveries in adaptive multiple hypothesis testing with privacy constraints.
method Private online algorithms based on non-private results, ensuring privacy and statistical performance.
result Strong guarantees for privacy and statistical performance in FDR and power.
New method controls false discoveries in online testing with deadlines.
problem Controlling false discoveries in online hypothesis testing with decision deadlines.
method Benjamini-Hochberg-type procedure over a moving window of hypotheses with adaptive threshold parameters.
result Controls false discovery rate at every stage and adaptively chosen stopping times.
New rules control false discoveries in online anomaly detection for time series data.
problem Controlling false discoveries in anomaly detection for time series data.
method Novel online false discovery rate control (FDRC) rules for time series anomaly detection.
result Ensures high power in detecting anomalies even when the alternative is rare and test statistics are serially dependent.
e-LOND algorithm controls FDR in online testing with arbitrary dependencies.
problem Online testing of hypotheses with unknown dependencies.
method e-LOND algorithm for FDR control under arbitrary dependence.
result e-LOND provides more power than existing methods through simulations.
The paper develops online methods to control familywise error rate in growing hypothesis testing sequences.
problem Controlling familywise error rate in a growing sequence of hypotheses over time.
method Unified algorithmic concepts for offline and online FWER control, including new adaptive online algorithms.
result Substantial gains in power demonstrated and formally proved in a Gaussian sequence model.
New findings control FDR for online testing methods under positive dependence.
problem Maintaining FDR control for online testing methods under positive dependence.
method Developed new methods to control FDR for online testing procedures under positive dependence.
result SAFFRON and LORD control FDR under positive dependence, not just conditional superuniformity.
Improved online FDR control with decaying memory.
problem Online multiple testing with temporal data.
method Generalized alpha-investing algorithms (GAI) with decaying memory FDR (mem-FDR).
result New algorithms reduce false discovery rate and improve power.
The paper tackles online FDR control in hypothesis testing with contextual features.
problem Controlling false discoveries in online hypothesis testing with contextual features.
method Proposes a new class of online testing procedures that learn significance levels sequentially, incorporating contextual information and previous results.
result Proves that the proposed procedures control online FDR under standard assumptions and outperform existing methods in terms of statistical power.
New method controls false discoveries in real-time data streams.
problem Online testing of hypotheses with strict error constraints and no future data.
method Structure-adaptive sequential testing (SAST) with alpha-investment algorithm.
result Substantial power gain over existing online testing rules.
Multiple hypothesis testing is a core problem in statistical inference and arises in almost every scientific field. Given a set of null hypotheses H(n)=(H1,…,Hn), Benjamini and Hochberg introduced the false discovery rate (FDR), which is the expected proportion of false positives among rejected nu…
AIHT improves online high-dimensional quantile regression by separating support discovery and refinement.
problem Online high-dimensional quantile regression with structural sparsity.
method Adaptive Iterative Hard Thresholding (AIHT) alternates stochastic updates with adaptive hard-thresholding steps.
result AIHT achieves logarithmic regret for the sliding-window objective in high-dimensional settings.
Online method selects candidates from data streams, ensuring irreversible decisions.
problem Conformal selection's incompatibility with irreversible decisions in online scenarios.
method Online Conformal Selection with Accept-to-Reject Changes (OCS-ARC) incorporating online Benjamini-Hochberg procedure.
result OCS-ARC controls FDR at or below nominal level, improving selection power.
We present a framework for online inference in the presence of a nonexhaustively defined set of classes that incorporates supervised classification with class discovery and modeling. A Dirichlet process prior (DPP) model defined over class distributions ensures that both known and unknown class distributions originate …
New algorithms control FDX while achieving more power in online multiple testing.
problem Problems with previous online multiple testing methods, including high FDX and low power.
method Developed new dynamic algorithms that adjust testing levels based on accumulated wealth.
result SupLORD algorithm achieves higher power and FDR control in synthetic experiments.
Robust Bayesian changepoint detection with β-divergences reduces false discovery rates.
problem Detecting changepoints in non-stationary streaming data with high accuracy.
method Doubly robust Bayesian Online Changepoint Detection (BOCD) using β-divergences. result False discovery rates of changepoints reduced from over 90% to 0%.
C-PP-COAD detects anomalies with limited real data, reducing dependency on real calibration data.
problem Limited real calibration data for online anomaly detection.
method Context-aware prediction-powered conformal online anomaly detection (C-PP-COAD).
result Significantly reduces dependency on real calibration data without compromising FDR control.
Novel online algorithm for hierarchical imitation learning.
problem Scalability issue in reinforcement learning and options discovery.
method Online Baum-Welch algorithm for hierarchical imitation learning.
result The online algorithm outperforms the batch version in both discrete and continuous environments.
Unified framework for online FDR control in asynchronous testing.
problem Control of false discovery rate in decentralized, asynchronous testing.
method Unified computational abstraction called 'conflict sets' for handling dependencies.
result Formal FDR guarantees under local dependence, improving on classical methods.
The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery techniques relies on an event log as an input. An event log is a static source of historical data capturing the execution of a business proc…
MO2 learns useful behaviours from past experience for new tasks.
problem Discovering useful behaviours from past experience and transferring them to new tasks.
method Model-Based Offline Options (MO2) framework supporting sample-efficient bottleneck option discovery over continuous state-action spaces.
result MO2 outperforms recent option learning methods on complex long-horizon continuous control tasks.
Forest Fire Clustering discovers cell types from single-cell data.
problem Discovering cell types from large-scale single-cell sequencing data.
method Iterative label propagation and parallelized Monte Carlo simulation.
result Forest Fire Clustering outperforms state-of-the-art methods on diverse benchmarks.
A new method for multiple testing reduces false discoveries while maximizing power.
problem Maximizing statistical power while controlling false discoveries in multiple testing scenarios.
method Adaptive sampling approach inspired by multi-armed bandits to minimize sample size.
result The method achieves sample complexity close to information theoretic lower bounds and outperforms uniform sampling.
GAIF enhances online multiple testing with feedback, improving statistical power.
problem Sequential online multiple testing with delayed feedback.
method GAIF framework using dynamic threshold adjustment and feedback-driven model selection.
result Improves statistical power through feedback-driven model selection.
A new metric, Weighted Regret, unifies FDR and power evaluation in online multiple testing.
problem The asymmetric costs of false positives and false negatives in automated pipelines.
method Introducing Weighted Regret and Decoupled-OMT (DOMT) to unify FDR and power evaluation.
result DOMT achieves an order-optimal sublinear mitigation of threshold depletion in bursty environments.
Model learns to discover and disambiguate entities and relations in text streams.
problem Learning to follow and resolve mentions in a continuous text stream.
method End-to-end trainable memory network for online, one-shot learning.
result Improves disambiguation and discovery skills with minimal supervision.
New method learns decisions from collective preferences without individual covariates.
problem Making decisions online without individual covariates.
method Collaborative filtering, matrix completion bandit, ε-greedy policy, online gradient descent, inverse propensity weighting.
result Method outperforms benchmarks and reveals new discoveries.
Paper tackles dense subgraph discovery with noisy feedback.
problem Discover dense subgraphs in edge-weighted graphs with noisy feedback.
method Proposes polynomial-time and scalable algorithms for dense subgraph discovery.
result Polynomial-time algorithm obtains nearly-optimal solution with high probability.
DiffATD efficiently discovers targets in partially observable environments using diffusion dynamics.
problem Efficiently discovering targets in partially observable environments with limited sampling.
method DiffATD uses diffusion dynamics to maintain a belief distribution over unobserved states, balancing exploration and exploitation.
result DiffATD outperforms baselines and supervised methods in diverse domains.
The false discovery rate (FDR)---the expected fraction of spurious discoveries among all the discoveries---provides a popular statistical assessment of the reproducibility of scientific studies in various disciplines. In this work, we introduce a new method for controlling the FDR in meta-analysis of many decentralized…
New methods identify concepts in trained embeddings reliably without human labels.
problem Identifying interpretable concepts in trained embedding spaces without human labels.
method Explicitly connecting concept discovery to PCA and ICA, proposing novel approaches for dependent concepts.
result Proven methods outperform competitors on a variety of experiments, achieving up to 29% better alignment with ground truth.
Paper proposes NAC for efficient network discovery in incomplete networks.
problem Efficiently discover vertices with specific attributes in incomplete networks.
method Formulates network discovery as a reinforcement learning problem, uses deep reinforcement learning with task-specific network embeddings.
result Offline planning leads to significantly improved performance compared to online discovery algorithms.
New algorithms improve FDR control in sequential hypothesis testing.
problem Balancing offline and online approaches for FDR control.
method Introducing Batch_{BH} and Batch_{St-BH} algorithms.
result Interpolates between offline and online methods, improving FDR control.
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.
Paper proposes using word embeddings to detect trolls in social media debates.
problem Preventing online harassment through rapid detection of offensive posts.
method Word embedding models for identifying fast-changing topics and negative content.
result GloVe model helps in discovering new keywords for trolling detection.
OCTen compresses and speeds up online tensor decomposition.
problem Handling dynamic, growing datasets efficiently.
method Compression-based online parallel CP decomposition.
result OCTen achieves similar or better accuracy with up to 40-200% less memory.
Paper tackles action selection in deep RL, proposing a data-driven approach.
problem High-dimensional action selection in deep RL environments.
method Data-driven approach with knockoff sampling for minimal sufficient actions.
result Method surpasses alternative techniques in performance and rewards.
Automates organizing diverse web data into a hierarchical topic model.
problem Manual classification of all scientific and popular scientific knowledge is impractical.
method Proposes an algorithm to aggregate multiple collections into a single hierarchical topic model.
result Demonstrates a web service for topical exploratory search.
Causal relationships in time series with latent variables are discovered using LPCMCI.
problem Discovering causal relationships in complex, time-series data with hidden variables.
method Evaluated LPCMCI algorithm for finding generators compatible with multi-dimensional, autocorrelated time series with latent variables.
result LPCMCI performs better than random guessing but is not optimal.
This paper discovers classification models from sequential data without prior knowledge.
problem Lack of prior knowledge in defining kernels for online classification.
method Adapts GP-based time-series structure discovery with SMC to learn new features from sequential data.
result Improves classification accuracy by 10% on real-world data.
OLCS-Ranker improves peptide identification accuracy and speed on hard datasets.
problem Efficiently identifying peptides from MS/MS data, especially on hard datasets with many false positives.
method Cost-sensitive online learning model and iterative online learning algorithm.
result OLCS-Ranker outperforms existing methods in accuracy and speed on large datasets.
Paper proposes a recursive GPSSM for efficient online learning.
problem Efficient online learning for dynamical models with limited prior information.
method Recursive Gaussian Process State-Space Model with adaptive capabilities for domains and hyperparameters.
result Superior accuracy, computational efficiency, and adaptability compared to state-of-the-art methods.
We present a scheme for online, unsupervised state discovery and detection from streaming, multi-featured, asynchronous data in high-frequency financial markets. Online feature correlations are computed using an unbiased, lossless Fourier estimator. A high-speed maximum likelihood clustering algorithm is then used to f…
ADDIS improves power in online FDR control for conservative nulls.
problem Lack of power in adaptive FDR control algorithms for conservative nulls.
method ADDIS: adaptive discarding algorithm for online FDR control.
result ADDIS achieves best of both worlds: high power for conservative nulls and no loss for uniformly distributed nulls.
New algorithm detects changes in high-dimensional data with mean and variance.
problem Challenges in detecting changes in high-dimensional data with mean and variance.
method Complete graph-based approach to detect changes of mean and variance from low to high-dimensional online data.
result The proposed method outperforms existing methods in terms of detection power.
Bayesian reflex models AI learning like the autonomic nervous system.
problem Online learning in dynamic AI environments.
method Bayesian online algorithms with belief maintenance, sequential updating, and uncertainty-driven action balancing.
result Unified framework for adaptive AI learning.
Despite our extensive knowledge of biophysical properties of neurons, there is no commonly accepted algorithmic theory of neuronal function. Here we explore the hypothesis that single-layer neuronal networks perform online symmetric nonnegative matrix factorization (SNMF) of the similarity matrix of the streamed data. …
Novel semi-supervised method for online structure learning in noisy data streams.
problem Discovering complex relations in noisy data streams with limited labelled data.
method Combines graph-cut minimization and first-order logic for online, single-pass label completion.
result Improves accuracy of structure learning system by completing missing labels.