
A Central Limit Theorem, Loss Aversion and MultiArmed Bandits
This paper establishes a central limit theorem under the assumption that...
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Don't Fix What ain't Broke: Nearoptimal Local Convergence of Alternating Gradient DescentAscent for Minimax Optimization
Minimax optimization has recently gained a lot of attention as adversari...
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Computational frameworks for homogenization and multiscale stability analyses of nonlinear periodic metamaterials
This paper presents a consistent computational framework for multiscale ...
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A Unified Analysis of FirstOrder Methods for Smooth Games via Integral Quadratic Constraints
The theory of integral quadratic constraints (IQCs) allows the certifica...
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On the Suboptimality of Negative Momentum for Minimax Optimization
Smooth game optimization has recently attracted great interest in machin...
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Picking Winning Tickets Before Training by Preserving Gradient Flow
Overparameterization has been shown to benefit both the optimization and...
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On Solving Minimax Optimization Locally: A FollowtheRidge Approach
Many tasks in modern machine learning can be formulated as finding equil...
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Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
Increasing the batch size is a popular way to speed up neural network tr...
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Benchmarking ModelBased Reinforcement Learning
Modelbased reinforcement learning (MBRL) is widely seen as having the p...
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Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks
Natural gradient descent has proven effective at mitigating the effects ...
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EigenDamage: Structured Pruning in the KroneckerFactored Eigenbasis
Reducing the test time resource requirements of a neural network while p...
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Computational Design of Finite Strain Auxetic Metamaterials via Topology Optimization and Nonlinear Homogenization
A novel computational framework for designing metamaterials with negativ...
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Functional Variational Bayesian Neural Networks
Variational Bayesian neural networks (BNNs) perform variational inferenc...
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Interplay Between Optimization and Generalization of Stochastic Gradient Descent with Covariance Noise
The choice of batchsize in a stochastic optimization algorithm plays a ...
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Eigenvalue Corrected Noisy Natural Gradient
Variational Bayesian neural networks combine the flexibility of deep lea...
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Three Mechanisms of Weight Decay Regularization
Weight decay is one of the standard tricks in the neural network toolbox...
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Differentiable Compositional Kernel Learning for Gaussian Processes
The generalization properties of Gaussian processes depend heavily on th...
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Noisy Natural Gradient as Variational Inference
Combining the flexibility of deep learning with Bayesian uncertainty est...
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Deformable Convolutional Networks
Convolutional neural networks (CNNs) are inherently limited to model geo...
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Guodong Zhang
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