More Like this

1. COMPARING COLLABORATIVE LEARNING IN VARIOUS LEARNING MODALITIES

   https://doi.org/10.35490/EC3.2023.306  

   Suwal, Ashma; Wu, Wei; Luo, Yupeng (July 2023, Computing in Construction)
2. Transfer Learning in Deep Reinforcement Learning: A Survey

   https://doi.org/10.1109/TPAMI.2023.3292075  

   Zhu, Zhuangdi; Lin, Kaixiang; Jain, Anil K; Zhou, Jiayu (November 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence)
3. Direction Concentration Learning: Enhancing Congruency in Machine Learning

   https://doi.org/10.1109/TPAMI.2019.2963387  

   Luo, Yan; Wong, Yongkang; Kankanhalli, Mohan; Zhao, Qi (December 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence)
4. Selfish Learning: Leveraging the Greed in Social Learning

   Raman, R. K.; Pattabiraman, S. (April 2018, Proceedings of the 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP))

   We introduce a sequential Bayesian binary hypothesis testing problem under social learning, termed selfish learning, where agents work to maximize their individual rewards. In particular, each agent receives a private signal and is aware of decisions made by earlier-acting agents. Beside inferring the underlying hypothesis, agents also decide whether to stop and declare, or pass the inference to the next agent. The employer rewards only correct responses and the reward per worker decreases with the number of employees used for decision making. We characterize decision regions of agents in the infinite and finite horizon. In particular, we show that the decision boundaries in the infinite horizon are the solutions to a Markov Decision Process with discounted costs, and can be solved using value iteration. In the finite horizon, we show that team performance is enhanced upon appropriate incentivization when compared to sequential social learning. 

   more » « less
5. Pausing Policy Learning in Non-stationary Reinforcement Learning

   Lee, Hyunin; Jin, Ming; Lavaei, Javad; Sojoudi, Somayeh (July 2024, International Conference on Machine Learning (ICML))