217 citations · 331 across the 28 of their papers we have counts for
5 papers · 1 filter
Comparing Few to Rank Many: Active Human Preference Learning using Randomized Frank-Wolfe
Kiran Koshy Thekumparampil, Gaurush Hiranandani, Kousha Kalantari +2
We study learning of human preferences from a limited comparison feedback. This task is ubiquitous in machine learning. Its applications such as reinforcement learning from human f…
Dynamic FISTA for Convex Composite Bi-Level Optimization
Roey Merchav, Shoham Sabach, Marc Teboulle
In this paper, we study convex bi-level optimization problems where both the inner and outer levels are given as a composite convex minimization. We propose the Fast Bi-level Proxi…
Learning the Target Network in Function Space
Kavosh Asadi, Yao Liu, Shoham Sabach +2
We focus on the task of learning the value function in the reinforcement learning (RL) setting. This task is often solved by updating a pair of online and target networks while ens…
Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate
Ruichen Jiang, Parameswaran Raman, Shoham Sabach +3
Second-order optimization methods, such as cubic regularized Newton methods, are known for their rapid convergence rates; nevertheless, they become impractical in high-dimensional…
MADA: Meta-Adaptive Optimizers through hyper-gradient Descent
Kaan Ozkara, Can Karakus, Parameswaran Raman +4
Following the introduction of Adam, several novel adaptive optimizers for deep learning have been proposed. These optimizers typically excel in some tasks but may not outperform Ad…