| 2023 | AAAI | DiFA: Differentiable Feature Acquisition. | Aritra Ghosh, Andrew S. Lan |
| 2023 | AIED | Balancing Test Accuracy and Security in Computerized Adaptive Testing. | Wanyong Feng, Aritra Ghosh, Stephen Sireci, Andrew S. Lan |
| 2023 | EDM | A Conceptual Model for End-to-End Causal Discovery in Knowledge Tracing. | Nischal Ashok Kumar, Wanyong Feng, Jaewook Lee, Hunter McNichols, Aritra Ghosh, Andrew S. Lan |
| 2022 | AAAI | DiPS: Differentiable Policy for Sketching in Recommender Systems. | Aritra Ghosh, Saayan Mitra, Andrew S. Lan |
| 2022 | AIED | Automated Scoring for Reading Comprehension via In-context BERT Tuning. | Nigel Fernandez, Aritra Ghosh, Naiming Liu, Zichao Wang, Benot Choffin, Richard G. Baraniuk, Andrew S. Lan |
| 2021 | AIED | Option Tracing: Beyond Correctness Analysis in Knowledge Tracing. | Aritra Ghosh, Jay Raspat, Andrew S. Lan |
| 2021 | CVPR | Contrastive Learning Improves Model Robustness Under Label Noise. | Aritra Ghosh, Andrew S. Lan |
| 2021 | IJCAI | BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing. | Aritra Ghosh, Andrew S. Lan |
| 2021 | WACV | Do We Really Need Gold Samples for Sample Weighting under Label Noise? | Aritra Ghosh, Andrew S. Lan |
| 2020 | KDD | Context-Aware Attentive Knowledge Tracing. | Aritra Ghosh, Neil T. Heffernan, Andrew S. Lan |
| 2020 | SDM | Optimal Bidding Strategy without Exploration in Real-time Bidding. | Aritra Ghosh, Saayan Mitra, Somdeb Sarkhel, Viswanathan Swaminathan |
| 2017 | AAAI | Robust Loss Functions under Label Noise for Deep Neural Networks. | Aritra Ghosh, Himanshu Kumar, P. S. Sastry |
| 2017 | PAKDD | On the Robustness of Decision Tree Learning Under Label Noise. | Aritra Ghosh, Naresh Manwani, P. S. Sastry |
| 2016 | CIKM | A Preference Approach to Reputation in Sponsored Search. | Aritra Ghosh, Dinesh Gaurav, Rahul Agrawal |