| Cutset networks: A simple, tractable, and scalable approach for improving the accuracy of chow-liu trees T Rahman, P Kothalkar, V Gogate Machine Learning and Knowledge Discovery in Databases: European Conference …, 2014 | 131 | 2014 |
| Anchoring bias affects mental model formation and user reliance in explainable AI systems M Nourani, C Roy, JE Block, DR Honeycutt, T Rahman, E Ragan, ... Proceedings of the 26th International Conference on Intelligent User …, 2021 | 70 | 2021 |
| Merging Strategies for Sum-Product Networks: From Trees to Graphs. T Rahman, V Gogate UAI, 2016 | 53 | 2016 |
| Learning ensembles of cutset networks T Rahman, V Gogate Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 38 | 2016 |
| Explainable activity recognition in videos: Lessons learned C Roy, M Nourani, DR Honeycutt, JE Block, T Rahman, ED Ragan, ... Applied AI Letters 2 (4), e59, 2021 | 35 | 2021 |
| Investigating the Importance of First Impressions and Explainable AI with Interactive Video Analysis V Nourani, M., Honeycutt, D., Block, J., Roy, C., Rahman, T., Ragan, E., and ... ACM CHI 2020, 2020 | 24* | 2020 |
| Don't explain without verifying veracity: an evaluation of explainable ai with video activity recognition M Nourani, C Roy, T Rahman, ED Ragan, N Ruozzi, V Gogate arXiv preprint arXiv:2005.02335, 2020 | 18 | 2020 |
| Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models T Rahman, S Jin, V Gogate Proceedings of the Twenty-Eighth International Joint Conference on …, 2019 | 18 | 2019 |
| On the importance of user backgrounds and impressions: Lessons learned from interactive AI applications M Nourani, C Roy, JE Block, DR Honeycutt, T Rahman, ED Ragan, ... ACM Transactions on Interactive Intelligent Systems 12 (4), 1-29, 2022 | 14 | 2022 |
| Look ma, no latent variables: Accurate cutset networks via compilation T Rahman, S Jin, V Gogate International Conference on Machine Learning, 5311-5320, 2019 | 12 | 2019 |
| Algorithms for the Nearest Assignment Problem. S Rouhani, T Rahman, V Gogate IJCAI, 5096-5102, 2018 | 12 | 2018 |
| UAI 2014 inference competition V Gogate, T Rahman, S Sarkhel, D Smith, D Venugopal See www. hlt. utdallas. edu/vgogate/uai14-competition, 2014 | 12 | 2014 |
| A novel approach for constrained optimization in graphical models S Rouhani, T Rahman, V Gogate Advances in Neural Information Processing Systems 33, 11949-11960, 2020 | 9 | 2020 |
| Robust learning of tractable probabilistic models R Peddi, T Rahman, V Gogate Uncertainty in Artificial Intelligence, 1572-1581, 2022 | 7 | 2022 |
| Novel upper bounds for the constrained most probable explanation task T Rahman, S Rouhani, V Gogate Advances in Neural Information Processing Systems 34, 9613-9624, 2021 | 7 | 2021 |
| Dynamic cutset networks C Roy, T Rahman, H Dong, N Ruozzi, V Gogate International Conference on Artificial Intelligence and Statistics, 3106-3114, 2021 | 6 | 2021 |
| Explainable activity recognition in videos using Deep Learning and tractable probabilistic models C Roy, M Nourani, S Arya, M Shanbhag, T Rahman, ED Ragan, N Ruozzi, ... ACM Transactions on Interactive Intelligent Systems 13 (4), 1-32, 2023 | 4 | 2023 |
| Conditionally tractable density estimation using neural networks H Dong, C Roy, T Rahman, V Gogate, N Ruozzi International Conference on Artificial Intelligence and Statistics, 6933-6946, 2022 | 2 | 2022 |
| Scalable Learning Approaches for Sum-Product-Cutset Networks T Rahman | 2 | 2016 |
| Learning to solve the constrained most probable explanation task in probabilistic graphical models S Arya, T Rahman, V Gogate International conference on artificial intelligence and statistics, 2791-2799, 2024 | 1 | 2024 |
Vibhav GogateThe University of Texas at DallasE-mail megerősítve itt: utdallas.edu
