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09/06/2023
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks
We present a natural extension to E(n)-equivariant graph neural networks...
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09/09/2021
Distributionally Robust Multilingual Machine Translation
Multilingual neural machine translation (MNMT) learns to translate multi...
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08/05/2021
Adapting to Function Difficulty and Growth Conditions in Private Optimization
We develop algorithms for private stochastic convex optimization that ad...
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02/23/2021
Learning with User-Level Privacy
We propose and analyze algorithms to solve a range of learning tasks und...
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10/12/2020
Large-Scale Methods for Distributionally Robust Optimization
We propose and analyze algorithms for distributionally robust optimizati...
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09/23/2019
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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11/25/2018
Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference
Homomorphic encryption enables arbitrary computation over data while it ...
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11/25/2017
Generalizing Hamiltonian Monte Carlo with Neural Networks
We present a general-purpose method to train Markov chain Monte Carlo ke...
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11/21/2017
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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07/11/2017
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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03/07/2017
Data Noising as Smoothing in Neural Network Language Models
Data noising is an effective technique for regularizing neural network m...
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12/02/2016