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1. Nonverbal task learning

   Kunda, Maithilee ( January 2019 , Proceedings of the Seventh Annual Conference on Advances in Cognitive Systems)

   null (Ed.)

   Nonverbal task learning is defined here as a variant of interactive task learning in which an agent learns the definition of a new task without any verbal information such as task instructions. Instead, the agent must 1) learn the task definition using only a single solved example problem as its training input, and then 2) generalize this definition in order to successfully parse new problems. In this paper, we present a conceptual framework for nonverbal task learning, and we compare and contrast this type of learning with existing learning paradigms in AI. We also discuss nonverbal task learning in the context of nonverbal human intelligence tests, which are standardized tests designed to be given without any verbal instructions so that they can be used by people with language difficulties. 

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2. Learning Task Informed Abstractions

   Fu, Xiang ; Yang, Ge ; Agrawal, Pulkit ; Jaakkola, Tommi ( January 2021 , Proceedings of the 38th International Conference on Machine Learning)

   null (Ed.)

   Current model-based reinforcement learning methods struggle when operating from complex visual scenes due to their inability to prioritize task-relevant features. To mitigate this prob- lem, we propose learning Task Informed Ab- stractions (TIA) that explicitly separates reward- correlated visual features from distractors. For learning TIA, we introduce the formalism of Task Informed MDP (TiMDP) that is realized by train- ing two models that learn visual features via coop- erative reconstruction, but one model is adversari- ally dissociated from the reward signal. Empirical evaluation shows that TIA leads to significant per- formance gains over state-of-the-art methods on many visual control tasks where natural and un- constrained visual distractions pose a formidable challenge. Project page: https://xiangfu.co/tia 

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3. Learning Task Informed Abstractions

   Fu, Xiang ; Yang, Ge ; Agrawal, Pulkit ; Jaakkola, Tommi ( July 2021 , Proceedings of the 38 th International Conference on Machine Learning)
4. Distributed Multi-Task Learning

   Wang, Jialei ; Kolar, Mladen ; Srebro, Nathan ( January 2016 , Journal of machine learning research)

   We consider the problem of distributed multitask learning, where each machine learns a separate, but related, task. Specifically, each machine learns a linear predictor in high-dimensional space, where all tasks share the same small support. We present a communication-efficient estimator based on the debiased lasso and show that it is comparable with the optimal centralized method. 

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5. What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task Learning

   https://doi.org/10.18653/v1/2023.findings-acl.527  

   Pan, Jane ; Gao, Tianyu ; Chen, Howard ; Chen, Danqi ( July 2023 , Association for Computational Linguistics)