How to incorporate monotonicity in deep networks while preserving flexibility? A Gupta, N Shukla, L Marla, A Kolbeinsson, K Yellepeddi arXiv preprint arXiv:1909.10662, 2019 | 57 | 2019 |
Dynamic pricing for airline ancillaries with customer context N Shukla, A Kolbeinsson, K Otwell, L Marla, K Yellepeddi Proceedings of the 25th ACM SIGKDD International Conference on knowledge …, 2019 | 50 | 2019 |
Galactic air improves ancillary revenues with dynamic personalized pricing A Kolbeinsson, N Shukla, A Gupta, L Marla, K Yellepeddi INFORMS Journal on Applied Analytics 52 (3), 233-249, 2022 | 12* | 2022 |
Distribution shift in airline customer behavior during COVID-19 A Garg, N Shukla, L Marla, S Somanchi arXiv preprint arXiv:2111.14938, 2021 | 10 | 2021 |
From average customer to individual traveler: A field experiment in airline ancillary pricing N Shukla, A Kolbeinsson, L Marla, K Yellepeddi Available at SSRN 3518854, 2020 | 8 | 2020 |
Adaptive model selection framework: An application to airline pricing N Shukla, A Kolbeinsson, L Marla, K Yellepeddi arXiv preprint arXiv:1905.08874, 2019 | 8 | 2019 |
Pender: Incorporating shape constraints via penalized derivatives A Gupta, L Marla, R Sun, N Shukla, A Kolbeinsson Proceedings of the AAAI Conference on Artificial Intelligence 35 (13), 11536 …, 2021 | 6 | 2021 |
Deep Contrastive Anomaly Detection for Airline Ancillaries Prediction P Yang, A Kolbeinsson, N Shukla, JA Barria 2022 21st IEEE International Conference on Machine Learning and Applications …, 2022 | | 2022 |
Negotiating Networks in Oligopoly Markets for Price-Sensitive Products N Shukla, K Yellepeddi arXiv preprint arXiv:2110.13303, 2021 | | 2021 |
Leveraging Time Dependency in Graphs A Kolbeinsson, N Shukla, D Solutions, A Gupta, L Marla | | |
Flappy Bird Hack using Deep Reinforcement Learning with Double Q-learning J Kong, U Champaign, N Shukla, S Bansal, Z Zhou, Z Na | | |