5 citations · 31 across the 19 of their papers we have counts for
7 papers · 1 filter
Provable Hierarchical Lifelong Learning with a Sketch-based Modular Architecture
Zihao Deng, Zee Fryer, Brendan Juba +2
We propose a modular architecture for the lifelong learning of hierarchically structured tasks. Specifically, we prove that our architecture is theoretically able to learn tasks th…
Conditional Linear Regression for Heterogeneous Covariances
Brendan Juba, Leda Liang
Often machine learning and statistical models will attempt to describe the majority of the data. However, there may be situations where only a fraction of the data can be fit well…
Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions
Zihao Deng, Siddartha Devic, Brendan Juba
Many reinforcement learning (RL) environments in practice feature enormous state spaces that may be described compactly by a "factored" structure, that may be modeled by Factored M…
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks
Atish Agarwala, Abhimanyu Das, Brendan Juba +4
Can deep learning solve multiple tasks simultaneously, even when they are unrelated and very different? We investigate how the representations of the underlying tasks affect the ab…
List Learning with Attribute Noise
Mahdi Cheraghchi, Elena Grigorescu, Brendan Juba +2
We introduce and study the model of list learning with attribute noise. Learning with attribute noise was introduced by Shackelford and Volper (COLT 1988) as a variant of PAC learn…
Conditional Linear Regression
Diego Calderon, Brendan Juba, Sirui Li +2
Work in machine learning and statistics commonly focuses on building models that capture the vast majority of data, possibly ignoring a segment of the population as outliers. Howev…