1 code implementation • 28 Mar 2024 • Zewen Liu, Guancheng Wan, B. Aditya Prakash, Max S. Y. Lau, Wei Jin
In this paper, we endeavor to furnish a comprehensive review of GNNs in epidemic tasks and highlight potential future directions.
no code implementations • 25 Feb 2024 • Haoxin Liu, Zhiyuan Zhao, Jindong Wang, Harshavardhan Kamarthi, B. Aditya Prakash
Time-series forecasting (TSF) finds broad applications in real-world scenarios.
2 code implementations • 29 Jan 2024 • Mohammad Hashemi, Shengbo Gong, Juntong Ni, Wenqi Fan, B. Aditya Prakash, Wei Jin
In this survey, we aim to provide a comprehensive understanding of graph reduction methods, including graph sparsification, graph coarsening, and graph condensation.
no code implementations • 19 Nov 2023 • Harshavardhan Kamarthi, B. Aditya Prakash
Large pre-trained models have been instrumental in significant advancements in domains like language and vision making model training for individual downstream tasks more efficient as well as provide superior performance.
no code implementations • 14 Nov 2023 • Harshavardhan Kamarthi, B. Aditya Prakash
We tackle various important challenges specific to pre-training for epidemic time-series such as dealing with heterogeneous dynamics and efficiently capturing useful patterns from multiple epidemic datasets by carefully designing the SSL tasks to learn important priors about the epidemic dynamics that can be leveraged for fine-tuning to multiple downstream tasks.
1 code implementation • 17 Oct 2023 • Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash
We close both these gap and propose PROFHiT, which is a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
1 code implementation • 9 Oct 2023 • Zhiyuan Zhao, Alexander Rodriguez, B. Aditya Prakash
Time-series forecasting is a critical challenge in various domains and has witnessed substantial progress in recent years.
no code implementations • 11 Aug 2023 • Lingkai Kong, Wenhao Mu, Jiaming Cui, Yuchen Zhuang, B. Aditya Prakash, Bo Dai, Chao Zhang
However, existing end-to-end DFL methods are hindered by three significant bottlenecks: model mismatch error, sample average approximation error, and gradient approximation error.
1 code implementation • 21 Jul 2023 • Zhiyuan Zhao, Xueying Ding, B. Aditya Prakash
Physics-Informed Neural Networks (PINNs) have emerged as a promising deep learning framework for approximating numerical solutions to partial differential equations (PDEs).
1 code implementation • 17 Jul 2023 • Lingkai Kong, Jiaming Cui, Haotian Sun, Yuchen Zhuang, B. Aditya Prakash, Chao Zhang
However, existing diffusion-based graph generative models are mostly one-shot generative models that apply Gaussian diffusion in the dequantized adjacency matrix space.
1 code implementation • 25 Nov 2022 • Lingkai Kong, Jiaming Cui, Yuchen Zhuang, Rui Feng, B. Aditya Prakash, Chao Zhang
Decision-focused learning (DFL) was recently proposed for stochastic optimization problems that involve unknown parameters.
1 code implementation • 20 Jul 2022 • Ayush Chopra, Alexander Rodríguez, Jayakumar Subramanian, Arnau Quera-Bofarull, Balaji Krishnamurthy, B. Aditya Prakash, Ramesh Raskar
Mechanistic simulators are an indispensable tool for epidemiology to explore the behavior of complex, dynamic infections under varying conditions and navigate uncertain environments.
no code implementations • 19 Jul 2022 • Alexander Rodríguez, Harshavardhan Kamarthi, Pulak Agarwal, Javen Ho, Mira Patel, Suchet Sapre, B. Aditya Prakash
The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole.
1 code implementation • 16 Jun 2022 • Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash
We close both these gap and propose PROFHiT, which is a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
1 code implementation • 21 Feb 2022 • Alexander Rodríguez, Jiaming Cui, Naren Ramakrishnan, Bijaya Adhikari, B. Aditya Prakash
We introduce EINNs, a framework crafted for epidemic forecasting that builds upon the theoretical grounds provided by mechanistic models as well as the data-driven expressibility afforded by AI models, and their capabilities to ingest heterogeneous information.
1 code implementation • 15 Sep 2021 • Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash
We use CAMul for multiple domains with varied sources and modalities and show that CAMul outperforms other state-of-art probabilistic forecasting models by over 25\% in accuracy and calibration.
1 code implementation • ICLR 2022 • Harshavardhan Kamarthi, Alexander Rodríguez, B. Aditya Prakash
Our extensive experiments demonstrate that our method refines the performance of top models for COVID-19 forecasting, in contrast to non-trivial baselines, yielding 18% improvement over baselines, enabling us obtain a new SOTA performance.
1 code implementation • NeurIPS 2021 • Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash
We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP, which directly models the probability density of the forecast value.
no code implementations • 24 Dec 2020 • Alexander Rodríguez, Bijaya Adhikari, Naren Ramakrishnan, B. Aditya Prakash
Forecasting influenza like illnesses (ILI) has rapidly progressed in recent years from an art to a science with a plethora of data-driven methods.
no code implementations • 22 Dec 2020 • Sorour E. Amiri, Bijaya Adhikari, John Wenskovitch, Alexander Rodriguez, Michelle Dowling, Chris North, B. Aditya Prakash
The analyst can express her agreement/disagreement with the visualization of the network summary via iterative feedback, e. g. closing or moving documents ("nodes") together.
1 code implementation • 7 Dec 2020 • Alexander Rodríguez, Bijaya Adhikari, Andrés D. González, Charles Nicholson, Anil Vullikanti, B. Aditya Prakash
In contrast, we study the harder problem of inferring failed components given partial information of some `serviceable' reachable nodes and a small sample of point probes, being the first often more practical to obtain.
1 code implementation • 23 Sep 2020 • Alexander Rodríguez, Nikhil Muralidhar, Bijaya Adhikari, Anika Tabassum, Naren Ramakrishnan, B. Aditya Prakash
Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic.
no code implementations • 22 Feb 2017 • Mohammad Raihanul Islam, B. Aditya Prakash, Naren Ramakrishnan
Recent successes in word embedding and document embedding have motivated researchers to explore similar representations for networks and to use such representations for tasks such as edge prediction, node label prediction, and community detection.
no code implementations • 22 Feb 2017 • Bijaya Adhikari, Yao Zhang, Naren Ramakrishnan, B. Aditya Prakash
Motivated by the recent successes of embeddings in natural language processing, researchers have tried to find network embeddings in order to exploit machine learning algorithms for mining tasks like node classification and edge prediction.
Ranked #4 on Malware Detection on Android Malware Dataset