-
Learning with User-Level Privacy
We propose and analyze algorithms to solve a range of learning tasks und...
02/23/2021 ∙ by Daniel Lévy, et al. ∙
5
∙
share
read it
-
Large-Scale Methods for Distributionally Robust Optimization
We propose and analyze algorithms for distributionally robust optimizati...
10/12/2020 ∙ by Daniel Lévy, et al. ∙
0
∙
share
read it
-
Necessary and Sufficient Conditions for Adaptive, Mirror, and Standard Gradient Methods
We study the impact of the constraint set and gradient geometry on the c...
09/23/2019 ∙ by Daniel Lévy, et al. ∙
0
∙
share
read it
-
Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference
Homomorphic encryption enables arbitrary computation over data while it ...
11/25/2018 ∙ by Edward Chou, et al. ∙
0
∙
share
read it
-
Generalizing Hamiltonian Monte Carlo with Neural Networks
We present a general-purpose method to train Markov chain Monte Carlo ke...
11/25/2017 ∙ by Daniel Lévy, et al. ∙
0
∙
share
read it
-
Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces
Policy optimization methods have shown great promise in solving complex ...
11/21/2017 ∙ by Daniel Lévy, et al. ∙
0
∙
share
read it
-
Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search
Inference in log-linear models scales linearly in the size of output spa...
07/11/2017 ∙ by Stephen Mussmann, et al. ∙
0
∙
share
read it
-
Data Noising as Smoothing in Neural Network Language Models
Data noising is an effective technique for regularizing neural network m...
03/07/2017 ∙ by Ziang Xie, et al. ∙
0
∙
share
read it
-
Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks
Mammography is the most widely used method to screen breast cancer. Beca...
12/02/2016 ∙ by Daniel Lévy, et al. ∙
0
∙
share
read it
