75 citations · 75 across the 2 of their papers we have counts for
9 papers
Provably Optimal Learning Algorithms for Assistance Games
Nivasini Ananthakrishnan, Mark Bedaywi, Michael I. Jordan +2
This paper studies an online variant of the assistance games framework, where an informed agent and an uninformed agent repeatedly interact over timesteps to optimize a common…
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
Kaichao You, Ximei Wang, Mingsheng Long +1
Deep unsupervised domain adaptation (Deep UDA) methods successfully leverage rich labeled data in a source domain to boost the performance on related but unlabeled data in a target…
A Control-Theoretic Perspective on Optimal High-Order Optimization
Tianyi Lin, Michael. I. Jordan
We provide a control-theoretic perspective on optimal tensor algorithms for minimizing a convex function in a finite-dimensional Euclidean space. Given a function $Φ: \mathbb{R}^d…
Transfer Q-learning
Elynn Chen, Sai Li, Michael I. Jordan
Time-inhomogeneous finite-horizon Markov decision processes (MDP) are frequently employed to model decision-making in dynamic treatment regimes and other statistical reinforcement…
First-order methods almost always avoid saddle points: the case of vanishing step-sizes
Ioannis Panageas, Georgios Piliouras, Xiao Wang
In a series of papers \cite{LSJR16, PP17, LPP}, it was established that some of the most commonly used first order methods almost surely (under random initializations) and with ste…
QuTE: decentralized multiple testing on sensor networks with false discovery rate control
Aaditya Ramdas, Jianbo Chen, Martin J. Wainwright +1
This paper designs methods for decentralized multiple hypothesis testing on graphs that are equipped with provable guarantees on the false discovery rate (FDR). We consider the set…