Search Results for author: Cyrus Shahabi

Found 18 papers, 4 papers with code

Holistic Survey of Privacy and Fairness in Machine Learning

no code implementations28 Jul 2023 Sina Shaham, Arash Hajisafi, Minh K Quan, Dinh C Nguyen, Bhaskar Krishnamachari, Charith Peris, Gabriel Ghinita, Cyrus Shahabi, Pubudu N. Pathirana

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

no code implementations28 Jun 2023 Arash Hajisafi, Haowen Lin, Sina Shaham, Haoji Hu, Maria Despoina Siampou, Yao-Yi Chiang, Cyrus Shahabi

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.

Decision Making Management +2

On Distribution Dependent Sub-Logarithmic Query Time of Learned Indexing

no code implementations19 Jun 2023 Sepanta Zeighami, Cyrus Shahabi

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.


Models and Mechanisms for Spatial Data Fairness

no code implementations4 Apr 2022 Sina Shaham, Gabriel Ghinita, Cyrus Shahabi

We introduce the concept of spatial data fairness to address the specific challenges of location data and spatial queries.

Decision Making Fairness

Gaussian Process for Trajectories

no code implementations7 Oct 2021 Kien Nguyen, John Krumm, Cyrus Shahabi

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.

Gaussian Processes

HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting

no code implementations27 Sep 2021 Chenyu Wang, Zongyu Lin, Xiaochen Yang, Jiao Sun, Mingxuan Yue, Cyrus Shahabi

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.

Crime Prediction Graph Learning

Integer-arithmetic-only Certified Robustness for Quantized Neural Networks

no code implementations ICCV 2021 Haowen Lin, Jian Lou, Li Xiong, Cyrus Shahabi

Adversarial data examples have drawn significant attention from the machine learning and security communities.


SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling

no code implementations21 Aug 2021 Haowen Lin, Jian Lou, Li Xiong, Cyrus Shahabi

Federated learning enables multiple clients, such as mobile phones and organizations, to collaboratively learn a shared model for prediction while protecting local data privacy.

Data Augmentation Federated Learning +1

Towards Accurate Spatiotemporal COVID-19 Risk Scores using High Resolution Real-World Mobility Data

1 code implementation14 Dec 2020 Sirisha Rambhatla, Sepanta Zeighami, Kameron Shahabi, Cyrus Shahabi, Yan Liu

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

no code implementations7 Oct 2020 Sasan Tavakkol, Feng Han, Brandon Mayer, Mark Phillips, Cyrus Shahabi, Yao-Yi Chiang, Raimondas Kiveris

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

no code implementations25 Aug 2020 Kien Nguyen, John Krumm, Cyrus Shahabi

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.

Decision Making

DETECT: Deep Trajectory Clustering for Mobility-Behavior Analysis

no code implementations3 Mar 2020 Mingxuan Yue, Yaguang Li, Haoze Yang, Ritesh Ahuja, Yao-Yi Chiang, Cyrus Shahabi

Identifying mobility behaviors in rich trajectory data is of great economic and social interest to various applications including urban planning, marketing and intelligence.

Clustering Marketing +1

Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs

1 code implementation27 Jun 2019 Chaoyang He, Tian Xie, Yu Rong, Wenbing Huang, Junzhou Huang, Xiang Ren, Cyrus Shahabi

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.

Recommendation Systems Representation Learning

m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time Series

no code implementations26 Aug 2017 Minh Nguyen, Sanjay Purushotham, Hien To, Cyrus Shahabi

Multivariate time series (MTS) have become increasingly common in healthcare domains where human vital signs and laboratory results are collected for predictive diagnosis.

Time Series Time Series Analysis +1

On Identifying Disaster-Related Tweets: Matching-based or Learning-based?

1 code implementation4 May 2017 Hien To, Sumeet Agrawal, Seon Ho Kim, Cyrus Shahabi

Social media such as tweets are emerging as platforms contributing to situational awareness during disasters.

Sentiment Analysis

Label Propagation on K-partite Graphs with Heterophily

no code implementations21 Jan 2017 Dingxiong Deng, Fan Bai, Yiqi Tang, Shuigeng Zhou, Cyrus Shahabi, Linhong Zhu

In this paper, for the first time, we study label propagation in heterogeneous graphs under heterophily assumption.

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