Search Results for author:
Found 18 papers, 4 papers with code
Holistic Survey of Privacy and Fairness in Machine Learning
Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and trustworthy Machine Learning (ML).
Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting
Forecasting the number of visits to Points-of-Interest (POI) in an urban area is critical for planning and decision-making for various application domains, from urban planning and transportation management to public health and social studies.
On Distribution Dependent Sub-Logarithmic Query Time of Learned Indexing
In this paper, we significantly strengthen this result, showing that under mild assumptions on data distribution, and the same space complexity as non-learned methods, learned indexes can answer queries in $O(\log\log n)$ expected query time.
Fair Spatial Indexing: A paradigm for Group Spatial Fairness
We propose techniques to mitigate location bias in machine learning.
Models and Mechanisms for Spatial Data Fairness
We introduce the concept of spatial data fairness to address the specific challenges of location data and spatial queries.
Gaussian Process for Trajectories
The Gaussian process is a powerful and flexible technique for interpolating spatiotemporal data, especially with its ability to capture complex trends and uncertainty from the input signal.
HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting
Based on the homophily assumption of GNN, we propose a homophily-aware constraint to regularize the optimization of the region graph so that neighboring region nodes on the learned graph share similar crime patterns, thus fitting the mechanism of diffusion convolution.
Integer-arithmetic-only Certified Robustness for Quantized Neural Networks
Adversarial data examples have drawn significant attention from the machine learning and security communities.
SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling
Federated learning enables multiple clients, such as mobile phones and organizations, to collaboratively learn a shared model for prediction while protecting local data privacy.
Towards Accurate Spatiotemporal COVID-19 Risk Scores using High Resolution Real-World Mobility Data
As countries look towards re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been challenging.
Kartta Labs: Collaborative Time Travel
We introduce the modular and scalable design of Kartta Labs, an open source, open data, and scalable system for virtually reconstructing cities from historical maps and photos.
Spatial Privacy Pricing: The Interplay between Privacy, Utility and Price in Geo-Marketplaces
Users concerned about privacy may want to charge more for data that pinpoints their location accurately, but may charge less for data that is more vague.
DETECT: Deep Trajectory Clustering for Mobility-Behavior Analysis
Identifying mobility behaviors in rich trajectory data is of great economic and social interest to various applications including urban planning, marketing and intelligence.
Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs
Existing techniques either cannot be scaled to large-scale bipartite graphs that have limited labels or cannot exploit the unique structure of bipartite graphs, which have distinct node features in two domains.
m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time Series
Multivariate time series (MTS) have become increasingly common in healthcare domains where human vital signs and laboratory results are collected for predictive diagnosis.
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.
Ranked #12 on
Traffic Prediction
on PEMS-BAY
Multivariate Time Series Forecasting
Spatio-Temporal Forecasting
+2
On Identifying Disaster-Related Tweets: Matching-based or Learning-based?
Social media such as tweets are emerging as platforms contributing to situational awareness during disasters.
Label Propagation on K-partite Graphs with Heterophily
In this paper, for the first time, we study label propagation in heterogeneous graphs under heterophily assumption.
