Papers Active Learning
“Active Learning” 태그가 달린 논문 3,569편 · 필터 해제
FunnelAL: Retrieve-then-Rank Active Learning for Single-Class Discovery
We present FunnelAL, a retrieve-then-rank active learning system for single-class discovery, which adapts the multi-stage funnel architecture of industrial recommender systems to data annotation. Large-scale supervised l…
Image ClassificationActive LearningEmbodied Active Learning under Limited Annotation and Navigation Budget for Object Detection
This paper studies how to adapt a computer vision object detector to an unknown environment under both a robot navigation time and annotation budget constraint. Our approach selects informative robot trajectories and ima…
Robot NavigationObject DetectionActive LearningEvaluating Epistemic Uncertainty: Beyond OOD Detection and Active Learning
Current evaluation of epistemic uncertainty relies on tasks such as out-ofdistribution detection and active learning. However, the Bayes-optimal decision strategies for these tasks do not coincide with the scores commonl…
Active LearningFull-data accuracy with fewer labels for training and fine-tuning machine-learning force fields
Machine-learning force fields (MLFFs) are reliable only near their training distribution, making efficient construction of diverse training sets a major bottleneck for both train-from-scratch and foundation fine-tuning w…
Active LearningInfluence Diagnostics in High-dimensional M-estimation: Precise Asymptotics
The impact of a given training point on a statistical model is classically measured through its leave-one-out influence, which quantifies the effect of its removal from the training set on the model accuracy. While the s…
Active LearningRobust Bayesian Decision Making under Adversarial Uncertainty
Scientific experiments are often designed to maximize information gain, yet in many applications the primary objective is to support reliable downstream decision-making. Existing decision-aware experimental design and ac…
Active LearningDecision MakingOptimal Top-$k$ Identification from Pairwise Comparisons
We study the active learning problem of fixed-confidence top-$k$ identification from noisy pairwise comparisons. In this problem, an algorithm sequentially chooses pairs of items to compare, observes the outcomes, and st…
Active LearningSafe Reinforcement Learning using Ideas from Model Predictive Control
Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics. A persistent challenge, however, is ensuring st…
Reinforcement LearningActive LearningDeterminantal point process sampling for bioacoustic active learning
Eco-acoustic monitoring generates vast volumes of audio data, making active learning a promising approach for reducing annotation effort while efficiently training reliable biodiversity classifiers. This report presents …
Active LearningThe Approximation Ratio for the Risk of Myopic Bayesian Active Learning for Linear Regression
Active learning studies the fundamental question: what data should we choose to observe? The greedy algorithm in optimal experiment design is a common heuristic and also equivalent to myopic Bayesian active learning for …
Active LearningActive Learning on Adversarially Corrupted Graphs
Motivated by real-world scenarios where malicious entities tamper with existing networks, we define a model where an adversary seeks to hide a set of \emph{corrupted vertices} inside a graph $G^*$. To this end, the adver…
Active LearningA Methodology for Investigating AI Patterns Prevalence in Software Repositories
As Artificial Intelligence(AI)-based applications take off, a clear understanding of AI patterns can uplift the quality of AI applications. Many AI patterns have been proposed in the literature; however, their prevalence…
Active LearningDetecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning
Despite the increasing sophistication of industrial AI systems, the ability to reliably detect subtle and noisy anomalies in complex time series data remains a critical yet unresolved challenge. In large-scale industrial…
Time Series Anomaly DetectionActive LearningActive Learning for Cascaded Object Detection: Balancing Coverage and Uncertainty in Table Extraction Pipelines
Table extraction from business documents relies on a cascaded pipeline where Table Detection (TD) first localizes tables and Table Structure Recognition (TSR) then recovers their internal layout. Building task-specific t…
Image ClassificationObject DetectionTable DetectionActive LearningGroup-invariant Coresets for Data-efficient Active Learning
Active learning reduces labeling cost by querying the most informative unlabeled samples, but standard coreset methods ignore known data symmetries and can waste budget on transformed versions of the same instance. We pr…
Active LearningReinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs
Metacognition is a critical component of intelligence that describes the ability to monitor and regulate one's own cognitive processes. Yet LLMs exhibit systemic deficiencies in key metacognitive faculties: they hallucin…
Reinforcement LearningActive LearningA Mechanism-Driven Theory of Phase Transitions in Active Learning
Active learning (AL) performance is known to be budget-dependent, yet regimes are typically defined by heuristic label counts that fail to generalize across datasets or architectures. We characterize AL dynamics by refra…
Active LearningThe CRISTAL Method: Neurosymbolic analysis from AI-synthesized world models
This project introduces the CRISTAL Method (Coherent Reliable Intentional Synthesis of Truthful Analysis Logic), a neurosymbolic framework for automating complex analysis workflows, with fundamental investment analysis a…
Bayesian InferenceActive LearningSituation Perception: A Necessary Primitive to Artificial Superintelligence
Current large language models are extraordinary statistical engines. They compress vast amounts of text into useful patterns and can explain science, write code, imitate reasoning, and participate in philosophical conver…
Active LearningTransformer-Based Active Learning for Data-Efficient Vaccine Epitope Selection in PRRS
High-fidelity molecular docking simulations can produce biologically relevant estimates of epitope-receptor binding affinity but are computationally expensive and therefore limit the number of candidates that can be scre…
Hyperparameter OptimizationIncremental LearningActive Learning