conftrace_

concept drift

73 papers

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Co-occurring keywords

online learning (1770) data stream (65) continual learning (1164) drift detection (13) distribution shift (711) ensemble learning (1271) incremental learning (278) time series (434) time series forecasting (360) streaming datum (77)

Papers

DRMD: Deep Reinforcement Learning for Malware Detection Under Concept Drift AAAI 2026

TRACE: A Generalizable Drift Detector for Streaming Data-Driven Optimization AAAI 2026

DFDT: Dynamic Fast Decision Tree for IoT Data Stream Mining on Edge Devices AAAI 2026

Drift-aware Collaborative Assistance Mixture of Experts for Heterogeneous Multistream Learning AAAI 2026

Autonomous Concept Drift Threshold Determination AAAI 2026

DeepBooTS: Dual-Stream Residual Boosting for Drift-Resilient Time-Series Forecasting AAAI 2026

Early Concept Drift Detection via Prediction Uncertainty AAAI 2025

Learning to Extrapolate and Adjust: Two-Stage Meta-Learning for Concept Drift in Online Time Series Forecasting IJCAI 2025

Identifying Predictions That Influence the Future: Detecting Performative Concept Drift in Data Streams AAAI 2025

Online Fraud Detection via Test-Time Retrieval-Based Representation Enrichment AAAI 2025

Kernel Representation Learning for Time Sequence: Algorithm, Theory, and Applications AAAI 2025

Toward Interpretable Time Series Modeling: A Kernel Representation Perspective IJCAI 2025

Prospective Learning: Learning for a Dynamic Future NIPS 2024

Quilt: Robust Data Segment Selection against Concept Drifts AAAI 2024

Recurrent Concept Drifts on Data Streams IJCAI 2024

Intentional Evolutionary Learning for Untrimmed Videos with Long Tail Distribution AAAI 2024

Online Boosting Adaptive Learning under Concept Drift for Multistream Classification AAAI 2024

When Model Meets New Normals: Test-Time Adaptation for Unsupervised Time-Series Anomaly Detection AAAI 2024

Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments NIPS 2024

Instance-Conditional Timescales of Decay for Non-Stationary Learning AAAI 2024

Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift NIPS 2024

Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets COLING 2024

Federated Learning under Distributed Concept Drift AISTATS 2023

Latent Space Evolution under Incremental Learning with Concept Drift (Student Abstract) AAAI 2023

It’s about Time: Rethinking Evaluation on Rumor Detection Benchmarks using Chronological Splits EACL 2023