More Like this

1. Learning tensor representations for meta-learning

   Deng, Samuel; Guo, Yilin; Hsu, Daniel; Mandal, Debmalya (January 2022, Twenty-Fifth International Conference on Artificial Intelligence and Statistics)
2. Authentic Learning on Machine Learning for Cybersecurity

   https://doi.org/10.1145/3545947.3576245  

   Lo, Dan; Shahriar, Hossain; Qian, Kai; Whitman, Michael; Wu, Fan; Thomas, Cassandra (March 2022, Proceedings of the 54th ACM Technical Symposium on Computer Science Education (SIGCSE))

   The primary goal of the authentic learning approach is to engage and motivate students in learning real world problem solving. We report our experience in developing k-nearest neighbor (KNN) classification for anomaly user behavior detection, one of the authentic machine learning for cybersecurity (ML4Cybr) learning modules based on 10 cybersecurity (CybrS) cases with machine learning (ML) solutions. All portable labs are made available on Google CoLab. So students can access and practice these hands-on labs anywhere and anytime without software installation and configuration which will engage students in learning concepts immediately and getting more experience for hands-on problem solving skills. 

   more » « less
3. Machine Learning for Yield Learning and Optimization

   https://doi.org/10.1109/TEST.2018.8624733  

   Lin, Yibo; Alawieh, Mohamed Baker; Ye, Wei; Pan, David Z. (October 2018, ITC)
4. Learning a Policy for Opportunistic Active Learning

   Padmakumar, A.; Stone, P.; Mooney, R.J. (October 2018, Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP))

   Active learning identifies data points to label that are expected to be the most useful in improving a supervised model. Opportunistic active learning incorporates active learning into interactive tasks that constrain possible queries during interactions. Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in an interactive object retrieval task. In this work, we use reinforcement learning for such an object retrieval task, to learn a policy that effectively trades off task completion with model improvement that would benefit future tasks. 

   more » « less
5. Offline Multitask Representation Learning for Reinforcement Learning

   Ishfaq, Haque; Nguyen-Tang, Thanh; Feng, Songtao; Arora, Raman; Wang, Mengdi; Yin, Ming; Precup, Doina (December 2024, 38th Conference on Neural Information Processing Systems (NeurIPS 2024))

   We study offline multitask representation learning in reinforcement learning (RL), where a learner is provided with an offline dataset from different tasks that share a common representation and is tasked to learn the shared representation. We theoretically investigate offline multitask low-rank RL, and propose a new algorithm called MORL for offline multitask representation learning. Furthermore, we examine downstream RL in reward-free, offline and online scenarios, where a new task is introduced to the agent that shares the same representation as the upstream offline tasks. Our theoretical results demonstrate the benefits of using the learned representation from the upstream offline task instead of directly learning the representation of the low-rank model. 

   more » « less