7.7k citations
- California Institute of TechnologyUS551 papers
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25 papers · 2 filters
Randomly Projected Additive Gaussian Processes for Regression
Ian A. Delbridge, David S. Bindel, Andrew Gordon Wilson
Gaussian processes (GPs) provide flexible distributions over functions, with inductive biases controlled by a kernel. However, in many applications Gaussian processes can struggle…
Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning
Jicong Fan, Yuqian Zhang, Madeleine Udell
This paper develops new methods to recover the missing entries of a high-rank or even full-rank matrix when the intrinsic dimension of the data is low compared to the ambient dimen…
Can You Really Backdoor Federated Learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh +1
The decentralized nature of federated learning makes detecting and defending against adversarial attacks a challenging task. This paper focuses on backdoor attacks in the federated…
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees
Ruqi Zhang, Christopher De Sa
Gibbs sampling is a Markov chain Monte Carlo method that is often used for learning and inference on graphical models. Minibatching, in which a small random subset of the graph is…
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling
Fanghui Liu, Xiaolin Huang, Yudong Chen +2
In this paper, we propose a fast surrogate leverage weighted sampling strategy to generate refined random Fourier features for kernel approximation. Compared to the current state-o…
: A Divide-and-conquer Algorithm for Large-scale Kernel Learning with Application to Clustering
Ke Alexander Wang, Xinran Bian, Pan Liu +1
Divide-and-conquer is a general strategy to deal with large scale problems. It is typically applied to generate ensemble instances, which potentially limits the problem size it can…