2.1k citations · 5.5k across the 24 of their papers we have counts for
25 papers
Privately generating tabular data using language models
Alexandre Sablayrolles, Yue Wang, Brian Karrer
Privately generating synthetic data from a table is an important brick of a privacy-first world. We propose and investigate a simple approach of treating each row in a table as a s…
Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale
Matthew Le, Apoorv Vyas, Bowen Shi +8
Large-scale generative models such as GPT and DALL-E have revolutionized the research community. These models not only generate high fidelity outputs, but are also generalists whic…
Exact Privacy Analysis of the Gaussian Sparse Histogram Mechanism
Brian Karrer, Daniel Kifer, Arjun Wilkins +1
Sparse histogram methods can be useful for returning differentially private counts of items in large or infinite histograms, large group-by queries, and more generally, releasing a…
Bounding Training Data Reconstruction in Private (Deep) Learning
Chuan Guo, Brian Karrer, Kamalika Chaudhuri +1
Differential privacy is widely accepted as the de facto method for preventing data leakage in ML, and conventional wisdom suggests that it offers strong protection against privacy…
Network experimentation at scale
Brian Karrer, Liang Shi, Monica Bhole +5
We describe our framework, deployed at Facebook, that accounts for interference between experimental units through cluster-randomized experiments. We document this system, includin…
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
Shali Jiang, Daniel R. Jiang, Maximilian Balandat +3
Bayesian optimization is a sequential decision making framework for optimizing expensive-to-evaluate black-box functions. Computing a full lookahead policy amounts to solving a hig…