Daniel Lévy

Adapting to Function Difficulty and Growth Conditions in Private Optimization

2021 · arXiv (Cornell University)

We develop algorithms for private stochastic convex optimization that adapt to the hardness of the specific function we wish to optimize. While previous work provide worst-case bounds for arbitrary convex functions, it is often the …

Data Noising as Smoothing in Neural Network Language Models

2017 · arXiv (Cornell University)

Data noising is an effective technique for regularizing neural network models. While noising is widely adopted in application domains such as vision and speech, commonly used noising primitives have not been developed for discrete sequence-level …