Da Tang
Da Tang
Research Scientist at ByteDance
Verified email at bytedance.com - Homepage
Cited by
Cited by
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
Item Recommendation with Variational Autoencoders and Heterogenous Priors
G Karamanolakis, KR Cherian, AR Narayan, J Yuan, D Tang, T Jebara
RecSys Worshop: Deep Learning for Recommender Systems (DLRS), 2018
Correlated Variational Auto-Encoders
D Tang, D Liang, T Jebara, N Ruozzi
International Conference on Machine Learning (ICML), 2019
Correlated Compressive Sensing for Networked Data
T Shi, D Tang, L Xu, T Moscibroda
Conference on Uncertainty in Artificial Intelligence (UAI), 2014
Initialization and Coordinate Optimization for Multi-way Matching
D Tang, T Jebara
International Conference on Artificial Intelligence and Statistics (AISTATS), 2017
Learning correlated latent representations with adaptive priors
D Tang, D Liang, N Ruozzi, T Jebara
arXiv preprint arXiv:1906.06419, 2019
The Variational Predictive Natural Gradient
D Tang, R Ranganath
International Conference on Machine Learning (ICML), 2019
On the duality gap convergence of ADMM methods
D Tang, T Zhang
arXiv preprint arXiv:1508.03702, 2015
Unsupervised Representation Learning with Correlations
D Tang
Columbia University, 2020
Natural Gradients via the Variational Predictive Distribution
D Tang, R Ranganath
NeurIPS Workshop: Advances in Approximate Bayesian Inference (AABI), 2017
Active Multitask Learning with Committees
J Xu, D Tang, T Jebara
ICML Workshop: Adaptive & Multitask Learning: Algorithms & Systems (AMTL), 0
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Articles 1–11