| Promises and Pitfalls of Threshold-based Auto-labeling H Vishwakarma, H Lin, F Sala, R Korlakai Vinayak Advances in Neural Information Processing Systems 36, 2024 | 2 | 2024 |
| Good data from bad models: Foundations of threshold-based auto-labeling H Vishwakarma, H Lin, F Sala, RK Vinayak arXiv preprint arXiv:2211.12620, 2022 | 1 | 2022 |
| Understanding Threshold-based Auto-labeling: The Good, the Bad, and the Terra Incognita H Vishwakarma, H Lin, F Sala, R Vinayak NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in …, 2023 | | 2023 |
| Human-in-the-Loop Out-of-Distribution Detection with False Positive Rate Control H Vishwakarma, H Lin, R Vinayak NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in …, 2023 | | 2023 |
| Geometry of the Minimum Volume Confidence Sets H Lin, M Li, D Pimentel-Alarcón, ML Malloy 2022 IEEE International Symposium on Information Theory (ISIT), 3180-3185, 2022 | | 2022 |
| Adaptive Out-of-Distribution Detection with Human-in-the-Loop H Lin, H Vishwakarma, RK Vinayak Workshop on Human-Machine Collaboration and Teaming, International …, 2022 | | 2022 |
| Machine Learning for Glucose Prediction to Identify Diabetes-related Metabolic Pathways G Agarwal, C Frink, B Hu, Z Hu, E Kim, H Lin ProjectX Undergraduate Machine Learning Research Competition, 2021 | | 2021 |
