| Tuning hyperparameters without grad students: Scalable and robust bayesian optimisation with dragonfly K Kandasamy, KR Vysyaraju, W Neiswanger, B Paria, CR Collins, ... Journal of Machine Learning Research 21 (81), 1-27, 2020 | 208 | 2020 |
| A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations B Paria, K Kandasamy, B Póczos Uncertainty in Artificial Intelligence (UAI), 267, 2019 | 153 | 2019 |
| Autonomous discovery of battery electrolytes with robotic experimentation and machine learning A Dave, J Mitchell, K Kandasamy, H Wang, S Burke, B Paria, B Póczos, ... Cell Reports Physical Science 1 (12), 2020 | 137 | 2020 |
| Minimizing FLOPs to Learn Efficient Sparse Representations B Paria, CK Yeh, IEH Yen, N Xu, P Ravikumar, B Póczos International Conference on Learning Representations, 2020 | 46 | 2020 |
| Gradient-based constrained sampling from language models S Kumar, B Paria, Y Tsvetkov arXiv preprint arXiv:2205.12558, 2022 | 28 | 2022 |
| An experimental design perspective on model-based reinforcement learning V Mehta, B Paria, J Schneider, S Ermon, W Neiswanger arXiv preprint arXiv:2112.05244, 2021 | 25 | 2021 |
| A neural architecture mimicking humans end-to-end for natural language inference B Paria, KM Annervaz, A Dukkipati, A Chatterjee, S Podder arXiv preprint arXiv:1611.04741, 2016 | 16 | 2016 |
| Hierarchically regularized deep forecasting B Paria, R Sen, A Ahmed, A Das arXiv preprint arXiv:2106.07630, 2021 | 15 | 2021 |
| Constrained sampling from language models via langevin dynamics in embedding spaces S Kumar, B Paria, Y Tsvetkov Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022 | 9 | 2022 |
| Forward stagewise additive model for collaborative multiview boosting A Lahiri, B Paria, PK Biswas IEEE Transactions on Neural Networks and Learning Systems 29 (2), 470-485, 2016 | 8 | 2016 |
| A deep top-down approach to hierarchically coherent probabilistic forecasting A Das, W Kong, B Paria, R Sen | 5 | 2022 |
| Be greedy–a simple algorithm for blackbox optimization using neural networks B Paria, B Pòczos, P Ravikumar, J Schneider, AS Suggala ICML2022 Workshop on Adaptive Experimental Design and Active Learning in the …, 2022 | 5 | 2022 |
| Analytic connectivity in general hypergraphs A Guha, MS Pydi, B Paria, A Dukkipati arXiv preprint arXiv:1701.04548, 2017 | 3 | 2017 |
| PolicyGAN: Training generative adversarial networks using policy gradient B Paria, A Lahiri, PK Biswas 2017 Ninth International Conference on Advances in Pattern Recognition …, 2017 | 2 | 2017 |
| Dirichlet proportions model for hierarchically coherent probabilistic forecasting A Das, W Kong, B Paria, R Sen Uncertainty in Artificial Intelligence, 518-528, 2023 | 1 | 2023 |
| Cost-Aware Bayesian Optimization via Information Directed Sampling B Paria, W Neiswanger, R Ghods, J Schneider, B Póczos Workshop on Real World Experiment Design and Active Learning at ICML, 2020 | 1 | 2020 |
| COMA-boost: co-operative multi agent AdaBoost A Lahiri, B Paria, PK Biswas Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and …, 2016 | 1 | 2016 |
| Regression and Time Series Forecasting R Sen, NIE Shuxin, Y Li, A Das, N Loeff, AT Suresh, P Awasthi, B Paria US Patent App. 17/804,082, 2022 | | 2022 |
| Strategies for Black-Box and Multi-Objective Optimization B Paria Carnegie Mellon University, 2022 | | 2022 |
| A top-down approach to hierarchically coherent probabilistic forecasting A Das, B Paria, R Sen, W Kong CoRR, 2022 | | 2022 |
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityVerified email at cs.cmu.edu
