Faculty–Resident “Co-learning”

Cooperative Co-learning:

Co-Clustering for Multitask Learning

This paper presents a new multitask learning framework that learns a shared representation among the tasks, incorporating both task and feature clusters. The jointlyinduced clusters yield a shared latent subspace where task relationships are learned more effectively and more generally than in state-of-the-art multitask learning methods. The proposed general framework enables the derivation of m...

Supervised Learning and Co-training

Co-training under the Conditional Independence Assumption is among the models which demonstrate how radically the need for labeled data can be reduced if a huge amount of unlabeled data is available. In this paper, we explore how much credit for this saving must be assigned solely to the extra-assumptions underlying the Co-training model. To this end, we compute general (almost tight) upper and...

Co-learning Segmentation in Marketplaces

We present the problem of automatic co-niching in which potential suppliers of some product or service need to determine which offers to make to the marketplace at the same time as potential buyers need to determine which offers (if any) to purchase. Because both groups typically face incomplete or uncertain information needed for these decisions, participants in repeated market interactions en...

Accelerating Multi-agent Reinforcement Learning with Dynamic Co-learning

We introduce an approach to adaptively identify opportunities to periodically transfer experiences between agents in large-scale, stochastic, homogeneous, multi-agent systems. This algorithm operates in an on-line, distributed manner, using supervisor-directed transfer, leading to more rapid acquisition of appropriate policies in systems with a large number of cooperating reinforcement learning...