12 publications by Arnav Wadhwa.
Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, Rajiv Ratn Shah
EMNLP · 2020
A graph-aware attentive approach combining market, social-media, and inter-company signals for stock-movement prediction.
Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, Rajiv Ratn Shah
ICDM · 2020
A spatiotemporal hypergraph network that captures higher-order dependencies across stocks via multi-scale temporal convolutions.
Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, Rajiv Ratn Shah
COLING · 2020
A contextual graph-based language model for analyzing parliamentary debates and political cohesion.
Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, Tyler Derr, Rajiv Ratn Shah
AAAI · 2021
A learning-to-rank approach using spatiotemporal hypergraph attention for stock selection.
Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, Rajiv Ratn Shah
EACL · 2021
A time-aware network for stock trading from financial news and tweets.
Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, Rajiv Ratn Shah
NAACL · 2021
A reinforcement-learning approach to quantitative day trading from natural language.
Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, Rajiv Ratn Shah
The Web Conference · 2021
Hyperbolic graph learning for algorithmic trading in scale-free stock markets.
Ramit Sawhney, Shivam Agarwal, Megh Thakkar, Arnav Wadhwa, Rajiv Ratn Shah
SIGIR · 2021
Hyperbolic online time-stream modeling for irregular information flows.
Ramit Sawhney, Arnav Wadhwa, Ayush Mangal, Vivek Mittal, Shivam Agarwal, Rajiv Ratn Shah
UAI · 2021
A curriculum-learning approach to modeling financial uncertainty with multivariate temporal entropy.
Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, Rajiv Ratn Shah
IJCAI · 2021
A time-evolving contextual graph model for speaker-state analysis in political debates.
Rachna Pathak, Anil Saini, Arnav Wadhwa, Himanshu Sharma, Dhiraj Sangwan
Journal of Cultural Heritage · 2021
An object-detection approach for heritage-site damage detection using 3-D point clouds and 2-D visual data.
Rachna Pathak, Arnav Wadhwa, Poras Khetarpal, Neeraj Kumar
IETE Journal of Research · 2023
A comparative assessment of regression techniques for wind-power forecasting.