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Learning with User-Level Privacy
We propose and analyze algorithms to solve a range of learning tasks und...
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Large-Scale Methods for Distributionally Robust Optimization
We propose and analyze algorithms for distributionally robust optimizati...
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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...
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Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference
Homomorphic encryption enables arbitrary computation over data while it ...
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Generalizing Hamiltonian Monte Carlo with Neural Networks
We present a general-purpose method to train Markov chain Monte Carlo ke...
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Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces
Policy optimization methods have shown great promise in solving complex ...
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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...
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Data Noising as Smoothing in Neural Network Language Models
Data noising is an effective technique for regularizing neural network m...
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Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks
Mammography is the most widely used method to screen breast cancer. Beca...
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