We study the problem of locally private mean estimation of high-dimensio...
We consider online learning problems in the realizable setting, where th...
We study the relationship between two desiderata of algorithms in statis...
In non-private stochastic convex optimization, stochastic gradient metho...
Online prediction from experts is a fundamental problem in machine learn...
The construction of most supervised learning datasets revolves around
co...
We study the problem of mean estimation of ℓ_2-bounded vectors under the...
We develop algorithms for private stochastic convex optimization that ad...
We study adaptive methods for differentially private convex optimization...
We develop a new primitive for stochastic optimization: a low-bias, low-...
Stochastic convex optimization over an ℓ_1-bounded domain is ubiquitous
...
We develop two notions of instance optimality in differential privacy,
i...
We study the planted clique problem in which a clique of size k is plant...
Differential Privacy (DP) provides strong guarantees on the risk of
comp...
Standard stochastic optimization methods are brittle, sensitive to steps...