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 • 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 • 8 Jul 2019 • Harshavardhan Kamarthi, Priyesh Vijayan, Bryan Wilder, Balaraman Ravindran, Milind Tambe
A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network.
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 • 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.
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.
no code implementations • 1 Dec 2018 • Ameet Deshpande, Harshavardhan Kamarthi, Balaraman Ravindran
Learning options that allow agents to exhibit temporally higher order behavior has proven to be useful in increasing exploration, reducing sample complexity and for various transfer scenarios.
no code implementations • 1 Dec 2018 • Harshavardhan Kamarthi, Kousik Krishnan
We propose a framework of genetic algorithms which use multi-level hierarchies to solve an optimization problem by searching over the space of simpler objective functions.
no code implementations • ICON 2019 • Aakash Srinivasan, Harshavardhan Kamarthi, Devi Ganesan, Sutanu Chakraborti
Past works have attempted to improve these embeddings by incorporating semantic knowledge from lexical resources like WordNet.
no code implementations • 13 Jun 2020 • Siddharth Nishtala, Harshavardhan Kamarthi, Divy Thakkar, Dhyanesh Narayanan, Anirudh Grama, Aparna Hegde, Ramesh Padmanabhan, Neha Madhiwalla, Suresh Chaudhary, Balaraman Ravindran, Milind Tambe
India accounts for 11% of maternal deaths globally where a woman dies in childbirth every fifteen minutes.
no code implementations • 18 Dec 2020 • Aravind Venugopal, Elizabeth Bondi, Harshavardhan Kamarthi, Keval Dholakia, Balaraman Ravindran, Milind Tambe
We therefore first propose a novel GSG model that combines defender allocation, patrolling, real-time drone notification to human patrollers, and drones sending warning signals to attackers.
Decision Making Multiagent Systems
no code implementations • 7 Mar 2021 • Siddharth Nishtala, Lovish Madaan, Aditya Mate, Harshavardhan Kamarthi, Anirudh Grama, Divy Thakkar, Dhyanesh Narayanan, Suresh Chaudhary, Neha Madhiwalla, Ramesh Padmanabhan, Aparna Hegde, Pradeep Varakantham, Balaraman Ravindran, Milind Tambe
India has a maternal mortality ratio of 113 and child mortality ratio of 2830 per 100, 000 live births.
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.
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.
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 • 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.