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Found 10 papers, 3 papers with code
Improved Differentially Private and Lazy Online Convex Optimization
We study the task of $(\epsilon, \delta)$-differentially private online convex optimization (OCO).
Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components
We then study the framework in the linear setting, where the problem reduces to that of estimating the sum of a rank-$r$ and a $k$-column sparse matrix using a small number of linear measurements.
Private Matrix Approximation and Geometry of Unitary Orbits
Consider the following optimization problem: Given $n \times n$ matrices $A$ and $\Lambda$, maximize $\langle A, U\Lambda U^*\rangle$ where $U$ varies over the unitary group $\mathrm{U}(n)$.
When Does Differentially Private Learning Not Suffer in High Dimensions?
Large pretrained models can be privately fine-tuned to achieve performance approaching that of non-private models.
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams
Motivated by recent applications requiring differential privacy over adaptive streams, we investigate the question of optimal instantiations of the matrix mechanism in this setting.
Differentially Private Model Personalization
We study personalization of supervised learning with user-level differential privacy.
Node-Level Differentially Private Graph Neural Networks
Graph Neural Networks (GNNs) are a popular technique for modelling graph-structured data and computing node-level representations via aggregation of information from the neighborhood of each node.
Model-Agnostic Private Learning
In the PAC model, we analyze our construction and prove upper bounds on the sample complexity for both the realizable and the non-realizable cases.
Nearly Optimal Private LASSO
In addition, we show that this error bound is nearly optimal amongst all differentially private algorithms.
(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings
The technique leads to the first nonprivate algorithms for private online learning in the bandit setting.
