1. Deep Learning with Differential Privacy
2. WEFE: The Word Embeddings Fairness Evaluation Framework
3. Yu Bai , Yu-Xiang Wang , and Edo Liberty . 2019 . ProxQuant: Quantized Neural Networks via Proximal Operators . In 7th International Conference on Learning Representations, ICLR 2019 , New Orleans, LA, USA, May 6--9 , 2019. OpenReview.net. https://openreview.net/forum?id=HyzMyhCcK7 Yu Bai, Yu-Xiang Wang, and Edo Liberty. 2019. ProxQuant: Quantized Neural Networks via Proximal Operators. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6--9, 2019. OpenReview.net. https://openreview.net/forum?id=HyzMyhCcK7
4. Borja Balle and Yu-Xiang Wang . 2018 . Improving the Gaussian mechanism for differential privacy: Analytical calibration and optimal denoising . In International Conference on Machine Learning. PMLR, 394--403 . Borja Balle and Yu-Xiang Wang. 2018. Improving the Gaussian mechanism for differential privacy: Analytical calibration and optimal denoising. In International Conference on Machine Learning. PMLR, 394--403.
5. Yoshua Bengio , Réjean Ducharme , Pascal Vincent , and Christian Janvin . 2003. A neural probabilistic language model. The journal of machine learning research , Vol. 3 ( 2003 ), 1137--1155. Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin. 2003. A neural probabilistic language model. The journal of machine learning research, Vol. 3 (2003), 1137--1155.