5 citations · 21 across the 23 of their papers we have counts for
4 papers · 1 filter
Restricted Strong Convexity of Deep Learning Models with Smooth Activations
Arindam Banerjee, Pedro Cisneros-Velarde, Libin Zhu +1
We consider the problem of optimization of deep learning models with smooth activation functions. While there exist influential results on the problem from the ``near initializatio…
Discrete State-Action Abstraction via the Successor Representation
Amnon Attali, Pedro Cisneros-Velarde, Marco Morales +1
While the difficulty of reinforcement learning problems is typically related to the complexity of their state spaces, Abstraction proposes that solutions often lie in simpler under…
One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning
Pedro Cisneros-Velarde, Boxiang Lyu, Sanmi Koyejo +1
Although parallelism has been extensively used in reinforcement learning (RL), the quantitative effects of parallel exploration are not well understood theoretically. We study the…
Family-wise error rate control in Gaussian graphical model selection via Distributionally Robust Optimization
Chau Tran, Pedro Cisneros-Velarde, Sang-Yun Oh +1
Recently, a special case of precision matrix estimation based on a distributionally robust optimization (DRO) framework has been shown to be equivalent to the graphical lasso. From…