Robust Deep Hawkes Process under Label Noise of Both Event and Occurrence

• URL: http://arxiv.org/abs/2407.17164v2
• Date: Mon, 29 Jul 2024 06:55:36 GMT
• Title: Robust Deep Hawkes Process under Label Noise of Both Event and Occurrence
• Authors: Xiaoyu Tan, Bin Li, Xihe Qiu, Jingjing Huang, Yinghui Xu, Wei Chu,
• Abstract summary: Robust Deep Hawkes Process (RDHP) is a framework to overcome the impact of label noise on the intensity function of Hawkes models. RDHP can effectively perform classification and regression tasks, even in the presence of noise related to events and their timing. This is the first study to successfully address both event and time label noise in deep Hawkes process models.
• Score: 29.058557502374544
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Integrating deep neural networks with the Hawkes process has significantly improved predictive capabilities in finance, health informatics, and information technology. Nevertheless, these models often face challenges in real-world settings, particularly due to substantial label noise. This issue is of significant concern in the medical field, where label noise can arise from delayed updates in electronic medical records or misdiagnoses, leading to increased prediction risks. Our research indicates that deep Hawkes process models exhibit reduced robustness when dealing with label noise, particularly when it affects both event types and timing. To address these challenges, we first investigate the influence of label noise in approximated intensity functions and present a novel framework, the Robust Deep Hawkes Process (RDHP), to overcome the impact of label noise on the intensity function of Hawkes models, considering both the events and their occurrences. We tested RDHP using multiple open-source benchmarks with synthetic noise and conducted a case study on obstructive sleep apnea-hypopnea syndrome (OSAHS) in a real-world setting with inherent label noise. The results demonstrate that RDHP can effectively perform classification and regression tasks, even in the presence of noise related to events and their timing. To the best of our knowledge, this is the first study to successfully address both event and time label noise in deep Hawkes process models, offering a promising solution for medical applications, specifically in diagnosing OSAHS.

Related papers

• Measuring the Robustness of Audio Deepfake Detectors [59.09338266364506]
  This work systematically evaluates the robustness of 10 audio deepfake detection models against 16 common corruptions. Using both traditional deep learning models and state-of-the-art foundation models, we make four unique observations.
  arXiv  Detail & Related papers  (2025-03-21T23:21:17Z)
• Noise effects on the diagnostics of quantum chaos [0.0]
  This paper investigates the effects of noise on the diagnostics of quantum chaos, focusing on three primary tools: the spectral form factor (SFF), Krylov complexity, and out-of-time correlators (OTOCs) In the strong noise limit, the SFF, two-point correlation function, and OTOCs become ineffective in distinguishing chaotic behavior.
  arXiv  Detail & Related papers  (2025-03-03T08:06:19Z)
• Enhanced quantum hypothesis testing via the interplay between coherent evolution and noises [6.716993528282281]
  The role of noise in Quantum Hypothesis Testing (QHT) has not been thoroughly explored. We devise and experimentally implement a noise-assisted QHT protocol in the setting of ultralow-field nuclear magnetic resonance spin systems. Our experimental results demonstrate that the success probability of QHT under the noisy dynamics can indeed surpass the ceiling set by unitary evolution alone.
  arXiv  Detail & Related papers  (2024-08-05T07:32:24Z)
• Extracting Biomedical Entities from Noisy Audio Transcripts [5.180763052209895]
  This paper introduces a novel dataset, BioASR-NER, designed to bridge the ASR-NLP gap in the biomedical domain. We present an innovative transcript-cleaning method using GPT4, investigating both zero-shot and few-shot methodologies. Our study further delves into an error analysis, shedding light the types of errors in transcription software, corrections by GPT4, and the challenges GPT4 faces.
  arXiv  Detail & Related papers  (2024-03-26T03:58:52Z)
• An early warning indicator trained on stochastic disease-spreading models with different noises [8.228025953197855]
  Early warning signals (EWSs) are indispensable for effective public health mitigation strategies. The dynamics of real-world disease spread, influenced by diverse sources of noise, pose a significant challenge in developing reliable EWSs. This study contributes to advancing early warning capabilities by addressing the intricate dynamics inherent in real-world disease spread.
  arXiv  Detail & Related papers  (2024-03-24T16:49:55Z)
• SoftPatch: Unsupervised Anomaly Detection with Noisy Data [67.38948127630644]
  This paper considers label-level noise in image sensory anomaly detection for the first time. We propose a memory-based unsupervised AD method, SoftPatch, which efficiently denoises the data at the patch level. Compared with existing methods, SoftPatch maintains a strong modeling ability of normal data and alleviates the overconfidence problem in coreset.
  arXiv  Detail & Related papers  (2024-03-21T08:49:34Z)
• DiffSED: Sound Event Detection with Denoising Diffusion [70.18051526555512]
  We reformulate the SED problem by taking a generative learning perspective. Specifically, we aim to generate sound temporal boundaries from noisy proposals in a denoising diffusion process. During training, our model learns to reverse the noising process by converting noisy latent queries to the groundtruth versions.
  arXiv  Detail & Related papers  (2023-08-14T17:29:41Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• Improve Noise Tolerance of Robust Loss via Noise-Awareness [60.34670515595074]
  We propose a meta-learning method which is capable of adaptively learning a hyper parameter prediction function, called Noise-Aware-Robust-Loss-Adjuster (NARL-Adjuster for brevity) Four SOTA robust loss functions are attempted to be integrated with our algorithm, and comprehensive experiments substantiate the general availability and effectiveness of the proposed method in both its noise tolerance and performance.
  arXiv  Detail & Related papers  (2023-01-18T04:54:58Z)
• Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation [72.0276067144762]
  We discuss and define the two common types of label noise in medical images. We propose an uncertainty estimation-based framework to handle these two label noise amid the medical image classification task.
  arXiv  Detail & Related papers  (2021-02-28T14:56:45Z)
• Analysing the Noise Model Error for Realistic Noisy Label Data [14.766574408868806]
  We study the quality of estimated noise models from the theoretical side by deriving the expected error of the noise model. We also publish NoisyNER, a new noisy label dataset from the NLP domain.
  arXiv  Detail & Related papers  (2021-01-24T17:45:15Z)
• Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model [80.91927573604438]
  This paper proposes a simple yet universal probabilistic model, which explicitly relates noisy labels to their instances. Experiments on datasets with both synthetic and real-world label noise verify that the proposed method yields significant improvements on robustness.
  arXiv  Detail & Related papers  (2021-01-14T05:43:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.