115 citations · 816 across the 62 of their papers we have counts for
6 papers · 2 filters
Generalization Bounds for Uniformly Stable Algorithms
Vitaly Feldman, Jan Vondrak
Uniform stability of a learning algorithm is a classical notion of algorithmic stability introduced to derive high-probability bounds on the generalization error (Bousquet and Elis…
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov +3
Sensitive statistics are often collected across sets of users, with repeated collection of reports done over time. For example, trends in users' private preferences or software usa…
Locally Private Learning without Interaction Requires Separation
Amit Daniely, Vitaly Feldman
We consider learning under the constraint of local differential privacy (LDP). For many learning problems known efficient algorithms in this model require many rounds of communicat…
Privacy Amplification by Iteration
Vitaly Feldman, Ilya Mironov, Kunal Talwar +1
Many commonly used learning algorithms work by iteratively updating an intermediate solution using one or a few data points in each iteration. Analysis of differential privacy for…
Privacy-preserving Prediction
Cynthia Dwork, Vitaly Feldman
Ensuring differential privacy of models learned from sensitive user data is an important goal that has been studied extensively in recent years. It is now known that for some basic…
The Everlasting Database: Statistical Validity at a Fair Price
Blake Woodworth, Vitaly Feldman, Saharon Rosset +1
The problem of handling adaptivity in data analysis, intentional or not, permeates a variety of fields, including test-set overfitting in ML challenges and the accumulation of inva…