Harshavardhan Kamarthi

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 27

Top Topics

Reinforcement Learning

Influence Maximization

Realistic Settings

Top Venues

Publications

Haoxin Liu, Shangqing Xu, Zhiyuan Zhao, Lingkai Kong, Harshavardhan Kamarthi, Aditya B. Sasanur, Megha Sharma, Jiaming Cui, Qingsong Wen, Chao Zhang, B. Aditya Prakash
Time-MMD: A New Multi-Domain Multimodal Dataset for Time Series Analysis. CoRR (2024)
Harshavardhan Kamarthi, Aditya B. Sasanur, Xinjie Tong, Xingyu Zhou, James Peters, Joe Czyzyk, B. Aditya Prakash
Large Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity. CoRR (2024)
Haoxin Liu, Harshavardhan Kamarthi, Lingkai Kong, Zhiyuan Zhao, Chao Zhang, B. Aditya Prakash
Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning. CoRR (2024)
Harshavardhan Kamarthi, Aditya B. Sasanur, Xinjie Tong, Xingyu Zhou, James Peters, Joe Czyzyk, B. Aditya Prakash
Large Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity. KDD (2024)
Haoxin Liu, Zhiyuan Zhao, Jindong Wang, Harshavardhan Kamarthi, B. Aditya Prakash
LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting. ACL (Findings) (2024)
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash
Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting. CoRR (2024)
Haoxin Liu, Zhiyuan Zhao, Jindong Wang, Harshavardhan Kamarthi, B. Aditya Prakash
LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting. CoRR (2024)
Harshavardhan Kamarthi, B. Aditya Prakash
PEMS: Pre-trained Epidemic Time-series Models. CoRR (2023)
Harshavardhan Kamarthi, B. Aditya Prakash
Large Pre-trained time series models for cross-domain Time series analysis tasks. CoRR (2023)
Lingkai Kong, Harshavardhan Kamarthi, Peng Chen, B. Aditya Prakash, Chao Zhang
Uncertainty Quantification in Deep Learning. KDD (2023)
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting. KDD (2023)
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting. WWW (2022)
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash
PROFHIT: Probabilistic Robust Forecasting for Hierarchical Time-series. CoRR (2022)
Harshavardhan Kamarthi, Alexander Rodríguez, B. Aditya Prakash
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future. ICLR (2022)
Alexander Rodríguez, Harshavardhan Kamarthi, Pulak Agarwal, Javen Ho, Mira Patel, Suchet Sapre, B. Aditya Prakash
Data-Centric Epidemic Forecasting: A Survey. CoRR (2022)
Alexander Rodríguez, Harshavardhan Kamarthi, B. Aditya Prakash
Epidemic Forecasting with a Data-Centric Lens. KDD (2022)
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting. CoRR (2021)
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting. CoRR (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
Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes. CoRR (2021)
Harshavardhan Kamarthi, Alexander Rodríguez, B. Aditya Prakash
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future. CoRR (2021)
Aravind Venugopal, Elizabeth Bondi, Harshavardhan Kamarthi, Keval Dholakia, Balaraman Ravindran, Milind Tambe
Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertainty. AAMAS (2021)
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting. NeurIPS (2021)
Aravind Venugopal, Elizabeth Bondi, Harshavardhan Kamarthi, Keval Dholakia, Balaraman Ravindran, Milind Tambe
Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertainty. CoRR (2020)
Siddharth Nishtala, Harshavardhan Kamarthi, Divy Thakkar, Dhyanesh Narayanan, Anirudh Grama, Ramesh Padmanabhan, Neha Madhiwalla, Suresh Chaudhary, Balaraman Ravindran, Milind Tambe
Missed calls, Automated Calls and Health Support: Using AI to improve maternal health outcomes by increasing program engagement. CoRR (2020)
Harshavardhan Kamarthi, Priyesh Vijayan, Bryan Wilder, Balaraman Ravindran, Milind Tambe
Influence Maximization in Unknown Social Networks: Learning Policies for Effective Graph Sampling. AAMAS (2020)
Aakash Srinivasan, Harshavardhan Kamarthi, Devi Ganesan, Sutanu Chakraborti
Integrating Lexical Knowledge in Word Embeddings using Sprinkling and Retrofitting. CoRR (2019)
Harshavardhan Kamarthi, Priyesh Vijayan, Bryan Wilder, Balaraman Ravindran, Milind Tambe
Learning policies for Social network discovery with Reinforcement learning. CoRR (2019)