| Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations G Zhao, B Zhou, K Wang, R Jiang, M Xu MICCAI 2018, 2018 | 53 | 2018 |
| Automatic localization and identification of mitochondria in cellular electron cryo-tomography using faster-RCNN R Li, X Zeng, SE Sigmund, R Lin, B Zhou, C Liu, K Wang, R Jiang, ... BMC bioinformatics 20, 75-85, 2019 | 32 | 2019 |
| Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning N Kallus, X Mao, K Wang, Z Zhou International Conference of Machine Learning (ICML) 2022, 2022 | 28 | 2022 |
| Provable benefits of representational transfer in reinforcement learning A Agarwal, Y Song, W Sun, K Wang, M Wang, X Zhang The Thirty Sixth Annual Conference on Learning Theory, 2114-2187, 2023 | 23 | 2023 |
| Multi-task learning for macromolecule classification, segmentation and coarse structural recovery in cryo-tomography C Liu, X Zeng, KW Wang, Q Guo, M Xu BMVC: proceedings of the British Machine Vision Conference. British Machine …, 2018 | 13 | 2018 |
| Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR K Wang, N Kallus, W Sun International Conference of Machine Learning (ICML) 2023, 2023 | 9 | 2023 |
| Deep multi-modal structural equations for causal effect estimation with unstructured proxies S Deshpande, K Wang, D Sreenivas, Z Li, V Kuleshov Advances in Neural Information Processing Systems 35, 10931-10944, 2022 | 9 | 2022 |
| Learning bellman complete representations for offline policy evaluation J Chang, K Wang, N Kallus, W Sun International Conference on Machine Learning, 2938-2971, 2022 | 8 | 2022 |
| Feature decomposition based saliency detection in electron cryo-tomograms B Zhou, Q Guo, K Wang, X Zeng, X Gao, M Xu 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2018 | 8 | 2018 |
| The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning K Wang, K Zhou, R Wu, N Kallus, W Sun NeurIPS 2023, 2023 | 6 | 2023 |
| Scalable and provably accurate algorithms for differentially private distributed decision tree learning K Wang, T Dick, MF Balcan Workshop on Privacy Preserving AI @ AAAI, 2020, 2020 | 6 | 2020 |
| Image-derived generative modeling of pseudo-macromolecular structures-towards the statistical assessment of Electron CryoTomography template matching KW Wang, X Zeng, X Liang, Z Huo, EP Xing, M Xu BMVC 2018, 2018 | 6 | 2018 |
| More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning K Wang, O Oertell, A Agarwal, N Kallus, W Sun arXiv preprint arXiv:2402.07198, 2024 | 2 | 2024 |
| Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes A Bennett, N Kallus, M Oprescu, W Sun, K Wang arXiv preprint arXiv:2404.00099, 2024 | | 2024 |
| Risk-Sensitive RL with Optimized Certainty Equivalents via Reduction to Standard RL K Wang, D Liang, N Kallus, W Sun arXiv preprint arXiv:2403.06323, 2024 | | 2024 |
| Switching the Loss Reduces the Cost in Batch Reinforcement Learning A Ayoub, K Wang, V Liu, S Robertson, J McInerney, D Liang, N Kallus, ... arXiv preprint arXiv:2403.05385, 2024 | | 2024 |
| JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning K Wang, J Wang, Y Li, N Kallus, I Trummer, W Sun arXiv preprint arXiv:2307.11704, 2023 | | 2023 |
Nathan KallusCornell UniversityVerified email at cornell.edu
