13 citations · 63 across the 14 of their papers we have counts for
14 papers
Scalable Membership Inference Attacks via Quantile Regression
Martin Bertran, Shuai Tang, Michael Kearns +3
Membership inference attacks are designed to determine, using black box access to trained models, whether a particular example was used in training or not. Membership inference can…
Improved Differentially Private Regression via Gradient Boosting
Shuai Tang, Sergul Aydore, Michael Kearns +5
We revisit the problem of differentially private squared error linear regression. We observe that existing state-of-the-art methods are sensitive to the choice of hyperparameters -…
Private Synthetic Data for Multitask Learning and Marginal Queries
Giuseppe Vietri, Cedric Archambeau, Sergul Aydore +6
We provide a differentially private algorithm for producing synthetic data simultaneously useful for multiple tasks: marginal queries and multitask machine learning (ML). A key inn…
Spectrally Adaptive Common Spatial Patterns
Mahta Mousavi, Eric Lybrand, Shuangquan Feng +3
The method of Common Spatial Patterns (CSP) is widely used for feature extraction of electroencephalography (EEG) data, such as in motor imagery brain-computer interface (BCI) syst…
Deep Transfer Learning with Ridge Regression
Shuai Tang, Virginia R. de Sa
The large amount of online data and vast array of computing resources enable current researchers in both industry and academia to employ the power of deep learning with neural netw…
Similarity of Neural Networks with Gradients
Shuai Tang, Wesley J. Maddox, Charlie Dickens +2
A suitable similarity index for comparing learnt neural networks plays an important role in understanding the behaviour of the highly-nonlinear functions, and can provide insights…
An Empirical Study on Post-processing Methods for Word Embeddings
Shuai Tang, Mahta Mousavi, Virginia R. de Sa
Word embeddings learnt from large corpora have been adopted in various applications in natural language processing and served as the general input representations to learning syste…
Improving Sentence Representations with Consensus Maximisation
Shuai Tang, Virginia R. de Sa
Consensus maximisation learning can provide self-supervision when different views are available of the same data. The distributional hypothesis provides another form of useful self…
Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning
Shuai Tang, Virginia R. de Sa
The encoder-decoder models for unsupervised sentence representation learning tend to discard the decoder after being trained on a large unlabelled corpus, since only the encoder is…
Multi-view Sentence Representation Learning
Shuai Tang, Virginia R. de Sa
Multi-view learning can provide self-supervision when different views are available of the same data. The distributional hypothesis provides another form of useful self-supervision…
Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding
Shuai Tang, Hailin Jin, Chen Fang +2
Context plays an important role in human language understanding, thus it may also be useful for machines learning vector representations of language. In this paper, we explore an a…
Trimming and Improving Skip-thought Vectors
Shuai Tang, Hailin Jin, Chen Fang +2
The skip-thought model has been proven to be effective at learning sentence representations and capturing sentence semantics. In this paper, we propose a suite of techniques to tri…
Rethinking Skip-thought: A Neighborhood based Approach
Shuai Tang, Hailin Jin, Chen Fang +2
We study the skip-thought model with neighborhood information as weak supervision. More specifically, we propose a skip-thought neighbor model to consider the adjacent sentences as…
What Happened to My Dog in That Network: Unraveling Top-down Generators in Convolutional Neural Networks
Patrick W. Gallagher, Shuai Tang, Zhuowen Tu
Top-down information plays a central role in human perception, but plays relatively little role in many current state-of-the-art deep networks, such as Convolutional Neural Network…