| Subgoal Discovery for Hierarchical Dialogue Policy Learning D Tang, X Li, J Gao, C Wang, L Li, T Jebara Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018 | 55 | 2018 |
| Item Recommendation with Variational Autoencoders and Heterogeneous Priors G Karamanolakis, KR Cherian, AR Narayan, J Yuan, D Tang, T Jebara RecSys Worshop: Deep Learning for Recommender Systems (DLRS), 2018 | 46 | 2018 |
| Monolith: real time recommendation system with collisionless embedding table Z Liu, L Zou, X Zou, C Wang, B Zhang, D Tang, B Zhu, Y Zhu, P Wu, ... arXiv preprint arXiv:2209.07663, 2022 | 36 | 2022 |
| Correlated Variational Auto-Encoders D Tang, D Liang, T Jebara, N Ruozzi International Conference on Machine Learning (ICML), 2019 | 21 | 2019 |
| Rating Distribution Calibration for Selection Bias Mitigation in Recommendations H Liu, D Tang, J Yang, X Zhao, H Liu, J Tang, Y Cheng The ACM Web Conference (WWW), 2048-2057, 2022 | 13 | 2022 |
| Balancing specialized and general skills in llms: The impact of modern tuning and data strategy Z Zhang, C Zheng, D Tang, K Sun, Y Ma, Y Bu, X Zhou, L Zhao arXiv preprint arXiv:2310.04945, 2023 | 9 | 2023 |
| Toward annotator group bias in crowdsourcing H Liu, J Thekinen, S Mollaoglu, D Tang, J Yang, Y Cheng, H Liu, J Tang arXiv preprint arXiv:2110.08038, 2021 | 9 | 2021 |
| Correlated Compressive Sensing for Networked Data T Shi, D Tang, L Xu, T Moscibroda Conference on Uncertainty in Artificial Intelligence (UAI), 2014 | 9 | 2014 |
| The Variational Predictive Natural Gradient D Tang, R Ranganath International Conference on Machine Learning (ICML), 2019 | 5 | 2019 |
| Initialization and Coordinate Optimization for Multi-way Matching D Tang, T Jebara International Conference on Artificial Intelligence and Statistics (AISTATS), 2017 | 5 | 2017 |
| Self-supervised Learning for Alleviating Selection Bias in Recommendation Systems H Liu, D Tang, J Yang, X Zhao, J Tang, Y Cheng International Workshop on Industrial Recommendation Systems, 2021 | 3 | 2021 |
| CowClip: reducing CTR prediction model training time from 12 hours to 10 minutes on 1 GPU Z Zheng, P Xu, X Zou, D Tang, Z Li, C Xi, P Wu, L Zou, Y Zhu, M Chen, ... Proceedings of the AAAI conference on artificial intelligence 37 (9), 11390 …, 2023 | 2 | 2023 |
| ICE-GRT: Instruction Context Enhancement by Generative Reinforcement based Transformers C Zheng, K Sun, D Tang, Y Ma, Y Zhang, C Xi, X Zhou arXiv preprint arXiv:2401.02072, 2024 | 1 | 2024 |
| Learning correlated latent representations with adaptive priors D Tang, D Liang, N Ruozzi, T Jebara arXiv preprint arXiv:1906.06419, 2019 | 1 | 2019 |
| On the duality gap convergence of ADMM methods D Tang, T Zhang arXiv preprint arXiv:1508.03702, 2015 | 1 | 2015 |
| Active Multitask Learning with Committees J Xu, D Tang, T Jebara ICML Workshop: Adaptive & Multitask Learning: Algorithms & Systems (AMTL), 0 | 1* | |
| Unsupervised Representation Learning with Correlations D Tang Columbia University, 2020 | | 2020 |
| Natural Gradients via the Variational Predictive Distribution D Tang, R Ranganath NeurIPS Workshop: Advances in Approximate Bayesian Inference (AABI), 2017 | | 2017 |
Tony JebaraSpotifyVerified email at spotify.com
