Harshavardhan Kamarthi

I’m Harsha. I’m a Machine Learning PhD student in Department of Computational Science and Engineering at Georgia Institute of Technology . I am affiliated with AdityaLab and am advised by Dr. B Aditya Prakash. I gratuated from Indian Institute of Technology Mardas and am fortunate to have worked with Dr. Balaraman Ravindran and Dr. Sutanu Chakraborti.

My research interests broadly revolves around the fields of Generative Modeling, Epidemic Forecasting, AI for public health, Reinforcement Learning and Deep Learning. My current research interests are:

Generative modeling for multi-source and time-series forecasting with focus on uncertainty quantification and calibration.
Spatio-temporal modeling with deep learning methods that are robust to noisy data
Epidemic modeling and forecasting
Reinforcement Learning in real-world including Network problems like graph exploration, influence maximization.

news

Mar 1, 2023	PROFHIT: Probabilistic Robust Forecasting for Hierarchical Time-series on state-of-art hierarchical probabilistic forecasting is accepted at KDD 2023!
Feb 1, 2022	Our paper CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting on State-of-Art multi-modal calibrated forecasting is accepted at WWW 2022!
Feb 1, 2022	Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future on leveraging revision data to imporve real-time forecasting and model evaluation is accepted at ICLR 2022!
Oct 1, 2021	Our paper When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting on State-of-Art calibrated and explainable Epidemic forecasting is accepted at NeurIPS 2021!
Jan 1, 2021	Starting PhD in Machine Learning at Georgia Tech at AdityaLab advised by Dr B Aditya Prakash!

selected publications

PROFHIT: Probabilistic Robust Forecasting for Hierarchical Time-series

Kamarthi, Harshavardhan, Kong, Lingkai, Rodrı́guez, Alexander, Zhang, Chao, and Prakash, B Aditya

KDD 2023

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Many multivariate time-series that have underlying hierarchical relations. Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series. We propose PROFHIT, a state-of-art probabilistic model that provides accurate probabilitic forecasts that adhere to hierarchical relations at distributional level and adapts to datasets with different levels of adherance to the hierarchy.
@article{kamarthi2023profhit, abbr = {profhit23}, title = {PROFHIT: Probabilistic Robust Forecasting for Hierarchical Time-series}, author = {Kamarthi, Harshavardhan and Kong, Lingkai and Rodr{\'\i}guez, Alexander and Zhang, Chao and Prakash, B Aditya}, journal = {KDD}, year = {2023}, selected = {true}, pdf = {https://arxiv.org/abs/2206.07940}, bibtex_show = {true}, code = {https://github.com/AdityaLab/CAMul} }
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting

Kamarthi, Harshavardhan, Kong, Lingkai, Rodrı́guez, Alexander, Zhang, Chao, and Prakash, B Aditya

ACM Web Conference (WWW) 2022

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Probabilistic time-series forecasting enables reliable decision making across many domains. Most forecasting problems have diverse sources of data containing multiple modalities and structures. Leveraging information as well as uncertainty from these data sources for well-calibrated and accurate forecasts is an important challenging problem. Most previous work on multi-modal learning and forecasting simply aggregate intermediate representations from each data view by simple methods of summation or concatenation and do not explicitly model uncertainty for each data-view. We propose a general probabilistic multi-view forecasting framework CAMul, that can learn representations and uncertainty from diverse data sources. It integrates the knowledge and uncertainty from each data view in a dynamic context-specific manner assigning more importance to useful views to model a well-calibrated forecast distribution. 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.
@article{kamarthi2021camul, abbr = {camul21}, title = {CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting}, author = {Kamarthi, Harshavardhan and Kong, Lingkai and Rodr{\'\i}guez, Alexander and Zhang, Chao and Prakash, B Aditya}, journal = {ACM Web Conference (WWW)}, year = {2022}, selected = {true}, pdf = {https://arxiv.org/abs/2109.07438}, bibtex_show = {true}, code = {https://github.com/AdityaLab/CAMul} }
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future

Kamarthi, Harshavardhan, Rodrı́guez, Alexander, and Prakash, B Aditya

Preprint 2021

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In real-time forecasting in public health, data collection is a non-trivial and demanding task. Often after initially released, it undergoes several revisions later (maybe due to human or technical constraints) - as a result, it may take weeks until the data reaches to a stable value. This so-called ’backfill’ phenomenon and its effect on model performance has been barely studied in the prior literature. In this paper, we introduce the multi-variate backfill problem using COVID-19 as the motivating example. We construct a detailed dataset composed of relevant signals over the past year of the pandemic. We then systematically characterize several patterns in backfill dynamics and leverage our observations for formulating a novel problem and neural framework Back2Future that aims to refines a given model’s predictions in real-time. 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. In addition, we show that our model improves model evaluation too; hence policy-makers can better understand the true accuracy of forecasting models in real-time.
@article{kamarthi2021back2future, abbr = {back2future21}, title = {Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future}, author = {Kamarthi, Harshavardhan and Rodr{\'\i}guez, Alexander and Prakash, B Aditya}, journal = {Preprint}, year = {2021}, selected = {true}, pdf = {https://arxiv.org/abs/2106.04420}, bibtex_show = {true}, code = {https://github.com/AdityaLab/Back2Future} }
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting

Kamarthi, Harshavardhan, Kong, Lingkai, Rodrı́guez, Alexander, Zhang, Chao, and Prakash, B Aditya

Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) 2021

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Accurate and trustworthy epidemic forecasting is an important problem that has impact on public health planning and disease mitigation. Most existing epidemic forecasting models disregard uncertainty quantification, resulting in mis-calibrated predictions. Recent works in deep neural models for uncertainty-aware time-series forecasting also have several limitations; e.g. it is difficult to specify meaningful priors in Bayesian NNs, while methods like deep ensembling are computationally expensive in practice. In this paper, we fill this important gap. 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. EPIFNP leverages a dynamic stochastic correlation graph to model the correlations between sequences in a non-parametric way, and designs different stochastic latent variables to capture functional uncertainty from different perspectives. Our extensive experiments in a real-time flu forecasting setting show that EPIFNP significantly outperforms previous state-of-the-art models in both accuracy and calibration metrics, up to 2.5x in accuracy and 2.4x in calibration. Additionally, due to properties of its generative process,EPIFNP learns the relations between the current season and similar patterns of historical seasons,enabling interpretable forecasts. Beyond epidemic forecasting, the EPIFNP can be of independent interest for advancing principled uncertainty quantification in deep sequential models for predictive analytics.
@article{kamarthi2021doubt, abbr = {epifnp21}, title = {When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting}, author = {Kamarthi, Harshavardhan and Kong, Lingkai and Rodr{\'\i}guez, Alexander and Zhang, Chao and Prakash, B Aditya}, journal = {Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS)}, year = {2021}, selected = {true}, pdf = {https://arxiv.org/abs/2106.03904}, bibtex_show = {true}, code = {https://github.com/AdityaLab/EpiFNP} }
Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertainty

Venugopal, Aravind, Bondi, Elizabeth, Kamarthi, Harshavardhan, Dholakia, Keval, Ravindran, Balaraman, and Tambe, Milind

20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2020

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Green Security Games (GSGs) have been successfully used in the protection of valuable resources such as fisheries, forests and wildlife. While real-world deployment involves both resource allocation and subsequent coordinated patrolling with communication and real-time, uncertain information, previous game models do not fully address both of these stages simultaneously. Furthermore, adopting existing solution strategies is difficult since they do not scale well for larger, more complex variants of the game models. 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. The model further incorporates uncertainty for real-time decision-making within a team of drones and human patrollers. Second, we present CombSGPO, a novel and scalable algorithm based on reinforcement learning, to compute a defender strategy for this game model. CombSGPO performs policy search over a multi-dimensional, discrete action space to compute an allocation strategy that is best suited to a best-response patrolling strategy for the defender, learnt by training a multi-agent Deep Q-Network. We show via experiments that CombSGPO converges to better strategies and is more scalable than comparable approaches. Third, we provide a detailed analysis of the coordination and signaling behavior learnt by CombSGPO, showing group formation between defender resources and patrolling formations based on signaling and notifications between resources. Importantly, we find that strategic signaling emerges in the final learnt strategy. Finally, we perform experiments to evaluate these strategies under different levels of uncertainty.
@article{venugopal2020reinforcement, abbr = {patrol20}, title = {Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertainty}, author = {Venugopal, Aravind and Bondi, Elizabeth and Kamarthi, Harshavardhan and Dholakia, Keval and Ravindran, Balaraman and Tambe, Milind}, journal = {20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)}, year = {2020}, pdf = {https://arxiv.org/abs/2012.10389}, bibtex_show = {true}, selected = {true} }
Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling

Kamarthi, Harshavardhan, Vijayan, Priyesh, Wilder, Bryan, Ravindran, Balaraman, and Tambe, Milind

19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Nominated for Best Paper Award<\b> 2019

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A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network. Current state-of-the-art methods rely on hand-crafted sampling algorithms; these methods sample nodes and their neighbours in a carefully constructed order and choose opinion leaders from this discovered network to maximize influence spread in the (unknown) complete network. In this work, we propose a reinforcement learning framework for network discovery that automatically learns useful node and graph representations that encode important structural properties of the network. At training time, the method identifies portions of the network such that the nodes selected from this sampled subgraph can effectively influence nodes in the complete network. The realization of such transferable network structure based adaptable policies is attributed to the meticulous design of the framework that encodes relevant node and graph signatures driven by an appropriate reward scheme. We experiment with real-world social networks from four different domains and show that the policies learned by our RL agent provide a 10-36% improvement over the current state-of-the-art method.
@article{kamarthi2019influence, abbr = {influence19}, title = {Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling}, author = {Kamarthi, Harshavardhan and Vijayan, Priyesh and Wilder, Bryan and Ravindran, Balaraman and Tambe, Milind}, journal = {19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), <b>Nominated for Best Paper Award<\b> }, note = {Nominated for best paper award}, year = {2019}, selected = {true}, pdf = {https://arxiv.org/abs/1907.11625}, bibtex_show = {true}, code = {https://github.com/kage08/graph_sample_rl} }