306 citations · 2.2k across the 100 of their papers we have counts for
25 papers · 1 filter
AlphaMatch: Improving Consistency for Semi-supervised Learning with Alpha-divergence
Chengyue Gong, Dilin Wang, Qiang Liu
Semi-supervised learning (SSL) is a key approach toward more data-efficient machine learning by jointly leverage both labeled and unlabeled data. We propose AlphaMatch, an efficien…
KeepAugment: A Simple Information-Preserving Data Augmentation Approach
Chengyue Gong, Dilin Wang, Meng Li +2
Data augmentation (DA) is an essential technique for training state-of-the-art deep learning systems. In this paper, we empirically show data augmentation might introduce noisy aug…
Debiasing Convolutional Neural Networks via Meta Orthogonalization
Kurtis Evan David, Qiang Liu, Ruth Fong
While deep learning models often achieve strong task performance, their successes are hampered by their inability to disentangle spurious correlations from causative factors, such…
Certified Monotonic Neural Networks
Xingchao Liu, Xing Han, Na Zhang +1
Learning monotonic models with respect to a subset of the inputs is a desirable feature to effectively address the fairness, interpretability, and generalization issues in practice…
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…
Off-Policy Interval Estimation with Lipschitz Value Iteration
Ziyang Tang, Yihao Feng, Na Zhang +2
Off-policy evaluation provides an essential tool for evaluating the effects of different policies or treatments using only observed data. When applied to high-stakes scenarios such…