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Da Tang
Da Tang
Research Scientist
在 cs.columbia.edu 的电子邮件经过验证
标题
引用次数
引用次数
年份
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
582018
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
482018
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
382022
Correlated Variational Auto-Encoders
D Tang, D Liang, T Jebara, N Ruozzi
International Conference on Machine Learning (ICML), 2019
202019
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
142022
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
142021
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
112023
Correlated Compressive Sensing for Networked Data
T Shi, D Tang, L Xu, T Moscibroda
Conference on Uncertainty in Artificial Intelligence (UAI), 2014
92014
The Variational Predictive Natural Gradient
D Tang, R Ranganath
International Conference on Machine Learning (ICML), 2019
52019
Initialization and Coordinate Optimization for Multi-way Matching
D Tang, T Jebara
International Conference on Artificial Intelligence and Statistics (AISTATS), 2017
52017
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
32021
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
22023
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
12024
Learning correlated latent representations with adaptive priors
D Tang, D Liang, N Ruozzi, T Jebara
arXiv preprint arXiv:1906.06419, 2019
12019
On the duality gap convergence of ADMM methods
D Tang, T Zhang
arXiv preprint arXiv:1508.03702, 2015
12015
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
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