Mobile Internet Quality Estimation using Self-Tuning Kernel Regression

• URL: http://arxiv.org/abs/2311.05641v1
• Date: Sat, 4 Nov 2023 21:09:46 GMT
• Title: Mobile Internet Quality Estimation using Self-Tuning Kernel Regression
• Authors: Hanyang Jiang, Henry Shaowu Yuchi, Elizabeth Belding, Ellen Zegura, Yao Xie
• Abstract summary: We look into estimating mobile (cellular) internet quality at the scale of a state in the United States. Most of the samples are concentrated in limited areas, while very few are available in the rest. We propose a new adaptive kernel regression approach that employs self-tuning kernels to alleviate the adverse effects of data imbalance.
• Score: 7.6449549886709764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling and estimation for spatial data are ubiquitous in real life, frequently appearing in weather forecasting, pollution detection, and agriculture. Spatial data analysis often involves processing datasets of enormous scale. In this work, we focus on large-scale internet-quality open datasets from Ookla. We look into estimating mobile (cellular) internet quality at the scale of a state in the United States. In particular, we aim to conduct estimation based on highly {\it imbalanced} data: Most of the samples are concentrated in limited areas, while very few are available in the rest, posing significant challenges to modeling efforts. We propose a new adaptive kernel regression approach that employs self-tuning kernels to alleviate the adverse effects of data imbalance in this problem. Through comparative experimentation on two distinct mobile network measurement datasets, we demonstrate that the proposed self-tuning kernel regression method produces more accurate predictions, with the potential to be applied in other applications.

Related papers

• Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenario [11.208740750755025]
  We propose a Spatiotemporal Out-of-Distribution (ST-OOD) benchmark, which comprises six urban scenario: bike-sharing, 311 services, pedestrian traffic speed, traffic flow, ride-hailing demand, and bike-sharing. We extensively evaluate state-of-the-arttemporal models and find that their performance degrades significantly in out-of-distribution settings, with most models performing even worse than a simple Multi-Layer Perceptron (MLP) Our results showed that a slight dropout rate could significantly improve generalization performance on most datasets, with minimal impact on in-distribution performance.
  arXiv  Detail & Related papers  (2024-10-07T04:15:48Z)
• Scalable Spatiotemporal Prediction with Bayesian Neural Fields [3.3299088915999295]
  BayesNF is a novel deep neural network architecture for high-capacity function estimation. We evaluate BayesNF against statistical machine-learning prediction problems from climate and public health datasets.
  arXiv  Detail & Related papers  (2024-03-12T13:47:50Z)
• A Temporally Disentangled Contrastive Diffusion Model for Spatiotemporal Imputation [35.46631415365955]
  We introduce a conditional diffusion framework called C$2$TSD, which incorporates disentangled temporal (trend and seasonality) representations as conditional information. Our experiments on three real-world datasets demonstrate the superior performance of our approach compared to a number of state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-02-18T11:59:04Z)
• Spatial-temporal Forecasting for Regions without Observations [13.805203053973772]
  We study spatial-temporal forecasting for a region of interest without any historical observations. We propose a model named STSM for the task. Our key insight is to learn from the locations that resemble those in the region of interest.
  arXiv  Detail & Related papers  (2024-01-19T06:26:05Z)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [73.03458424369657]
  In real-time forecasting in public health, data collection is a non-trivial and demanding task. 'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
  arXiv  Detail & Related papers  (2021-06-08T14:48:20Z)
• Churn Reduction via Distillation [54.5952282395487]
  We show an equivalence between training with distillation using the base model as the teacher and training with an explicit constraint on the predictive churn. We then show that distillation performs strongly for low churn training against a number of recent baselines.
  arXiv  Detail & Related papers  (2021-06-04T18:03:31Z)
• Transfer learning suppresses simulation bias in predictive models built from sparse, multi-modal data [15.587831925516957]
  Many problems in science, engineering, and business require making predictions based on very few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multidimensional. We combine recent developments in deep learning to build more robust predictive models from multimodal data.
  arXiv  Detail & Related papers  (2021-04-19T23:28:32Z)
• Improving Uncertainty Calibration via Prior Augmented Data [56.88185136509654]
  Neural networks have proven successful at learning from complex data distributions by acting as universal function approximators. They are often overconfident in their predictions, which leads to inaccurate and miscalibrated probabilistic predictions. We propose a solution by seeking out regions of feature space where the model is unjustifiably overconfident, and conditionally raising the entropy of those predictions towards that of the prior distribution of the labels.
  arXiv  Detail & Related papers  (2021-02-22T07:02:37Z)
• Provably Efficient Causal Reinforcement Learning with Confounded Observational Data [135.64775986546505]
  We study how to incorporate the dataset (observational data) collected offline, which is often abundantly available in practice, to improve the sample efficiency in the online setting. We propose the deconfounded optimistic value iteration (DOVI) algorithm, which incorporates the confounded observational data in a provably efficient manner.
  arXiv  Detail & Related papers  (2020-06-22T14:49:33Z)
• TraDE: Transformers for Density Estimation [101.20137732920718]
  TraDE is a self-attention-based architecture for auto-regressive density estimation. We present a suite of tasks such as regression using generated samples, out-of-distribution detection, and robustness to noise in the training data.
  arXiv  Detail & Related papers  (2020-04-06T07:32:51Z)

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.