34 citations · 76 across the 7 of their papers we have counts for
11 papers · 1 filter
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach
Mao Ye, Bo Liu, Stephen Wright +2
Bilevel optimization (BO) is useful for solving a variety of important machine learning problems including but not limited to hyperparameter optimization, meta-learning, continual…
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments
Lizhen Nie, Mao Ye, Qiang Liu +1
Motivated by the rising abundance of observational data with continuous treatments, we investigate the problem of estimating the average dose-response curve (ADRF). Available param…
Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough
Mao Ye, Lemeng Wu, Qiang Liu
Despite the great success of deep learning, recent works show that large deep neural networks are often highly redundant and can be significantly reduced in size. However, the theo…
Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data
Mao Ye, Dhruv Choudhary, Jiecao Yu +6
Large scale deep learning provides a tremendous opportunity to improve the quality of content recommendation systems by employing both wider and deeper models, but this comes at gr…
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks
Denny Zhou, Mao Ye, Chen Chen +6
For deploying a deep learning model into production, it needs to be both accurate and compact to meet the latency and memory constraints. This usually results in a network that is…
SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions
Mao Ye, Chengyue Gong, Qiang Liu
State-of-the-art NLP models can often be fooled by human-unaware transformations such as synonymous word substitution. For security reasons, it is of critical importance to develop…