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09/22/2022
Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation
Despite decades of research, existing navigation systems still face real...
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04/22/2022
You Only Linearize Once: Tangents Transpose to Gradients
Automatic differentiation (AD) is conventionally understood as a family ...
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03/31/2022
Learning from many trajectories
We initiate a study of supervised learning from many independent sequenc...
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05/31/2021
Efficient and Modular Implicit Differentiation
Automatic differentiation (autodiff) has revolutionized machine learning...
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05/20/2021
Decomposing reverse-mode automatic differentiation
We decompose reverse-mode automatic differentiation into (forward-mode) ...
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05/24/2019
The advantages of multiple classes for reducing overfitting from test set reuse
Excessive reuse of holdout data can lead to overfitting. However, there ...
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11/08/2018
Measuring the Effects of Data Parallelism on Neural Network Training
Recent hardware developments have made unprecedented amounts of data par...
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08/10/2016
Estimation from Indirect Supervision with Linear Moments
In structured prediction problems where we have indirect supervision of ...
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02/22/2016
Principal Component Projection Without Principal Component Analysis
We show how to efficiently project a vector onto the top principal compo...
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02/18/2016
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity
We develop a general duality between neural networks and compositional k...
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06/24/2015
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization
We develop a family of accelerated stochastic algorithms that minimize s...
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12/20/2014
Competing with the Empirical Risk Minimizer in a Single Pass
In many estimation problems, e.g. linear and logistic regression, we wis...
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12/21/2013