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Coping with Label Shift via Distributionally Robust Optimisation
The label shift problem refers to the supervised learning setting where ...
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Stochastic Optimization with Non-stationary Noise
We investigate stochastic optimization problems under relaxed assumption...
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On Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions
We provide the first non-asymptotic analysis for finding stationary poin...
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Why ADAM Beats SGD for Attention Models
While stochastic gradient descent (SGD) is still the de facto algorithm ...
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Analysis of Gradient Clipping and Adaptive Scaling with a Relaxed Smoothness Condition
We provide a theoretical explanation for the fast convergence of gradien...
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Quantifying Exposure Bias for Neural Language Generation
The exposure bias problem refers to the training-inference discrepancy c...
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A Probe into Understanding GAN and VAE models
Both generative adversarial network models and variational autoencoders ...
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A Probe Towards Understanding GAN and VAE Models
This project report compares some known GAN and VAE models proposed prio...
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R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate
We study smooth stochastic optimization problems on Riemannian manifolds...
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Direct Runge-Kutta Discretization Achieves Acceleration
We study gradient-based optimization methods obtained by directly discre...
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