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2. Learning Linear Complementarity Systems

   Jin, Wanxin; Aydinoglu, Alp; Halm, Mathew; Posa, Michael (June 2022, Learning for Dynamics and Control Conference)

   Firoozi, Roya; Mehr, Negar; Yel, Esen; Antonova, Rika; Bohg, Jeannette; Schwager, Mac; Kochenderfer, Mykel (Ed.)

   This paper investigates the learning, or system identification, of a class of piecewise-affine dynamical systems known as linear complementarity systems (LCSs). We propose a violation-based loss which enables efficient learning of the LCS parameterization, without prior knowledge of the hybrid mode boundaries, using gradient-based methods. The proposed violation-based loss incorporates both dynamics prediction loss and a novel complementarity - violation loss. We show several properties attained by this loss formulation, including its differentiability, the efficient computation of first- and second-order derivatives, and its relationship to the traditional prediction loss, which strictly enforces complementarity. We apply this violation-based loss formulation to learn LCSs with tens of thousands of (potentially stiff) hybrid modes. The results demonstrate a state-of-the-art ability to identify piecewise-affine dynamics, outperforming methods which must differentiate through non-smooth linear complementarity problems. 

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3. Consensus Complementarity Control for Multicontact MPC

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4. Bayesian modeling of human–AI complementarity

   https://doi.org/10.1073/pnas.2111547119  

   Steyvers, Mark; Tejeda, Heliodoro; Kerrigan, Gavin; Smyth, Padhraic (March 2022, Proceedings of the National Academy of Sciences)

   Artificial intelligence (AI) and machine learning models are being increasingly deployed in real-world applications. In many of these applications, there is strong motivation to develop hybrid systems in which humans and AI algorithms can work together, leveraging their complementary strengths and weaknesses. We develop a Bayesian framework for combining the predictions and different types of confidence scores from humans and machines. The framework allows us to investigate the factors that influence complementarity, where a hybrid combination of human and machine predictions leads to better performance than combinations of human or machine predictions alone. We apply this framework to a large-scale dataset where humans and a variety of convolutional neural networks perform the same challenging image classification task. We show empirically and theoretically that complementarity can be achieved even if the human and machine classifiers perform at different accuracy levels as long as these accuracy differences fall within a bound determined by the latent correlation between human and machine classifier confidence scores. In addition, we demonstrate that hybrid human–machine performance can be improved by differentiating between the errors that humans and machine classifiers make across different class labels. Finally, our results show that eliciting and including human confidence ratings improve hybrid performance in the Bayesian combination model. Our approach is applicable to a wide variety of classification problems involving human and machine algorithms. 

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5. Crown complementarity rather than crown selection contributes to stem complementarity in genetic mixtures of Pinus taeda L

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