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Found 7 papers, 3 papers with code
AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods
The choice of batch sizes in stochastic gradient optimizers is critical for model training.
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms
We study the problem of approximate sampling from non-log-concave distributions, e. g., Gaussian mixtures, which is often challenging even in low dimensions due to their multimodality.
Bregman Proximal Langevin Monte Carlo via Bregman--Moreau Envelopes
The proposed algorithms extend existing Langevin Monte Carlo algorithms in two aspects -- the ability to sample nonsmooth distributions with mirror descent-like algorithms, and the use of the more general Bregman--Moreau envelope in place of the Moreau envelope as a smooth approximation of the nonsmooth part of the potential.
Wasserstein Distributionally Robust Optimization with Wasserstein Barycenters
On the other hand, in distributionally robust optimization, we seek data-driven decisions which perform well under the most adverse distribution from a nominal distribution constructed from data samples within a certain discrepancy of probability distributions.
The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applications
We study two state-of-the-art solutions to the multi-agent pickup and delivery (MAPD) problem based on different principles -- multi-agent path-finding (MAPF) and multi-agent reinforcement learning (MARL).
Multi-Agent Path Finding
Multi-agent Reinforcement Learning
+2
A Proximal Block Coordinate Descent Algorithm for Deep Neural Network Training
Training deep neural networks (DNNs) efficiently is a challenge due to the associated highly nonconvex optimization.
Global Convergence of Block Coordinate Descent in Deep Learning
Deep learning has aroused extensive attention due to its great empirical success.
