174 citations · 700 across the 32 of their papers we have counts for
6 papers · 1 filter
Acceleration and Averaging in Stochastic Mirror Descent Dynamics
Walid Krichene, Peter L. Bartlett
We formulate and study a general family of (continuous-time) stochastic dynamics for accelerated first-order minimization of smooth convex functions. Building on an averaging formu…
Underdamped Langevin MCMC: A non-asymptotic analysis
Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett +1
We study the underdamped Langevin diffusion when the log of the target distribution is smooth and strongly concave. We present a MCMC algorithm based on its discretization and show…
Recovery Guarantees for One-hidden-layer Neural Networks
Kai Zhong, Zhao Song, Prateek Jain +2
In this paper, we consider regression problems with one-hidden-layer neural networks (1NNs). We distill some properties of activation functions that lead to $\mathit{local~strong~c…
Spectrally-normalized margin bounds for neural networks
Peter Bartlett, Dylan J. Foster, Matus Telgarsky
This paper presents a margin-based multiclass generalization bound for neural networks that scales with their margin-normalized "spectral complexity": their Lipschitz constant, mea…
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning
Dong Yin, Ashwin Pananjady, Max Lam +3
It has been experimentally observed that distributed implementations of mini-batch stochastic gradient descent (SGD) algorithms exhibit speedup saturation and decaying generalizati…
Convergence of Langevin MCMC in KL-divergence
Xiang Cheng, Peter Bartlett
Langevin diffusion is a commonly used tool for sampling from a given distribution. In this work, we establish that when the target density is such that is smoo…