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Harshavardhan Kamarthi
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2020 – today
- 2024
- [j1]Alexander Rodríguez, Harshavardhan Kamarthi, Pulak Agarwal, Javen Ho, Mira Patel, Suchet Sapre, B. Aditya Prakash:
Machine learning for data-centric epidemic forecasting. Nat. Mac. Intell. 6(10): 1122-1131 (2024) - [c11]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: 7832-7840 - [c10]Haoxin Liu, Harshavardhan Kamarthi, Lingkai Kong, Zhiyuan Zhao, Chao Zhang, B. Aditya Prakash:
Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning. ICML 2024 - [c9]Harshavardhan Kamarthi
, Aditya B. Sasanur
Large Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity. KDD 2024: 5230-5239 - [i17]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 abs/2402.16132 (2024) - [i16]Haoxin Liu, Shangqing Xu, Zhiyuan Zhao, Lingkai Kong, Harshavardhan Kamarthi, Aditya B. Sasanur, Megha Sharma, Jiaming Cui, Qingsong Wen
Time-MMD: A New Multi-Domain Multimodal Dataset for Time Series Analysis. CoRR abs/2406.08627 (2024) - [i15]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 abs/2406.09130 (2024) - [i14]Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash:
Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting. CoRR abs/2407.02641 (2024) - [i13]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 abs/2407.02657 (2024) - 2023
- [c8]Harshavardhan Kamarthi
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting. KDD 2023: 1057-1072 - [c7]Lingkai Kong
Uncertainty Quantification in Deep Learning. KDD 2023: 5809-5810 - [i12]Harshavardhan Kamarthi, B. Aditya Prakash:
PEMS: Pre-trained Epidemic Time-series Models. CoRR abs/2311.07841 (2023) - [i11]Harshavardhan Kamarthi, B. Aditya Prakash:
Large Pre-trained time series models for cross-domain Time series analysis tasks. CoRR abs/2311.11413 (2023) - 2022
- [c6]Harshavardhan Kamarthi, Alexander Rodríguez, B. Aditya Prakash:
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future. ICLR 2022 - [c5]Alexander Rodríguez, Harshavardhan Kamarthi, B. Aditya Prakash:
Epidemic Forecasting with a Data-Centric Lens. KDD 2022: 4822-4823 - [c4]Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash:
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting. WWW 2022: 3174-3185 - [i10]Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash:
PROFHIT: Probabilistic Robust Forecasting for Hierarchical Time-series. CoRR abs/2206.07940 (2022) - [i9]Alexander Rodríguez, Harshavardhan Kamarthi, Pulak Agarwal, Javen Ho, Mira Patel, Suchet Sapre, B. Aditya Prakash:
Data-Centric Epidemic Forecasting: A Survey. CoRR abs/2207.09370 (2022) - 2021
- [c3]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: 1353-1361 - [c2]Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash:
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting. NeurIPS 2021: 19796-19807 - [i8]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 abs/2103.09052 (2021) - [i7]Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash:
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting. CoRR abs/2106.03904 (2021) - [i6]Harshavardhan Kamarthi, Alexander Rodríguez, B. Aditya Prakash:
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future. CoRR abs/2106.04420 (2021) - [i5]Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash:
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting. CoRR abs/2109.07438 (2021) - 2020
- [c1]Harshavardhan Kamarthi, Priyesh Vijayan, Bryan Wilder, Balaraman Ravindran, Milind Tambe:
Influence Maximization in Unknown Social Networks: Learning Policies for Effective Graph Sampling. AAMAS 2020: 575-583 - [i4]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 abs/2006.07590 (2020) - [i3]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 abs/2012.10389 (2020)
2010 – 2019
- 2019
- [i2]Harshavardhan Kamarthi, Priyesh Vijayan, Bryan Wilder, Balaraman Ravindran, Milind Tambe:
Learning policies for Social network discovery with Reinforcement learning. CoRR abs/1907.11625 (2019) - [i1]Aakash Srinivasan, Harshavardhan Kamarthi, Devi Ganesan, Sutanu Chakraborti:
Integrating Lexical Knowledge in Word Embeddings using Sprinkling and Retrofitting. CoRR abs/1912.06889 (2019)
Coauthor Index
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last updated on 2024-12-05 21:41 CET by the dblp team
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