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Found 9 papers, 1 papers with code
Private Robust Estimation by Stabilizing Convex Relaxations
Prior works obtained private robust algorithms for mean estimation of subgaussian distributions with bounded covariance.
Robust Learning for Congestion-Aware Routing
We consider the problem of routing users through a network with unknown congestion functions over an infinite time horizon.
Scaling up Kernel Ridge Regression via Locality Sensitive Hashing
Random binning features, introduced in the seminal paper of Rahimi and Recht (2007), are an efficient method for approximating a kernel matrix using locality sensitive hashing.
Private Aggregation from Fewer Anonymous Messages
Using a reduction of Balle et al. (2019), our improved analysis of the protocol of Ishai et al. yields, in the same model, an $\left(\varepsilon, \delta\right)$-differentially private protocol for aggregation that, for any constant $\varepsilon > 0$ and any $\delta = \frac{1}{\mathrm{poly}(n)}$, incurs only a constant error and requires only a constant number of messages per party.
Cryptography and Security Data Structures and Algorithms
Oblivious Sketching of High-Degree Polynomial Kernels
Oblivious sketching has emerged as a powerful approach to speeding up numerical linear algebra over the past decade, but our understanding of oblivious sketching solutions for kernel matrices has remained quite limited, suffering from the aforementioned exponential dependence on input parameters.
Data Structures and Algorithms
On the Power of Multiple Anonymous Messages
- Protocols in the multi-message shuffled model with $poly(\log{B}, \log{n})$ bits of communication per user and $poly\log{B}$ error, which provide an exponential improvement on the error compared to what is possible with single-message algorithms.
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model
Federated learning promises to make machine learning feasible on distributed, private datasets by implementing gradient descent using secure aggregation methods.
A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms
We formalize this intuition by showing that, roughly, a continuous signal from a given class can be approximately reconstructed using a number of samples proportional to the *statistical dimension* of the allowed power spectrum of that class.
Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees
Qualitatively, our results are twofold: on the one hand, we show that random Fourier feature approximation can provably speed up kernel ridge regression under reasonable assumptions.
