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1. Hierarchical computing for hierarchical models in ecology

   https://doi.org/10.1111/2041-210X.13513  

   McCaslin, Hanna M.; Feuka, Abigail B.; Hooten, Mevin B.; O'Hara, ed., Robert B. (November 2020, Methods in Ecology and Evolution)

   Abstract Bayesian hierarchical models allow ecologists to account for uncertainty and make inference at multiple scales. However, hierarchical models are often computationally intensive to fit, especially with large datasets, and researchers face trade‐offs between capturing ecological complexity in statistical models and implementing these models.We present a recursive Bayesian computing (RB) method that can be used to fit Bayesian models efficiently in sequential MCMC stages to ease computation and streamline hierarchical inference. We also introduce transformation‐assisted RB (TARB) to create unsupervised MCMC algorithms and improve interpretability of parameters. We demonstrate TARB by fitting a hierarchical animal movement model to obtain inference about individual‐ and population‐level migratory characteristics.Our recursive procedure reduced computation time for fitting our hierarchical movement model by half compared to fitting the model with a single MCMC algorithm. We obtained the same inference fitting our model using TARB as we obtained fitting the model with a single algorithm.For complex ecological statistical models, like those for animal movement, multi‐species systems, or large spatial and temporal scales, the computational demands of fitting models with conventional computing techniques can limit model specification, thus hindering scientific discovery. Transformation‐assisted RB is one of the most accessible methods for reducing these limitations, enabling us to implement new statistical models and advance our understanding of complex ecological phenomena. 

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2. Hierarchical MultiClass AdaBoost

   https://doi.org/10.1109/BigData52589.2021.9671291  

   Chelmis, Charalampos; Qi, Wenting (December 2021, 2021 IEEE International Conference on Big Data (Big Data))
3. Fair Hierarchical Clustering

   Ahmadian, S; Epasto, A; Knittel, M; Kumar, R; Mahdian, M; Moseley, B; Pham, P; Vassilvitskii, S; Wang, Y (December 2020, Advances in neural information processing systems)

   null (Ed.)

   As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair. Recently, there has been an interest in defining a notion of fairness that mitigates over-representation in traditional clustering. In this paper we extend this notion to hierarchical clustering, where the goal is to recursively partition the data to optimize a specific objective. For various natural objectives, we obtain simple, efficient algorithms to find a provably good fair hierarchical clustering. Empirically, we show that our algorithms can find a fair hierarchical clustering, with only a negligible loss in the objective. 

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4. Fair Hierarchical Clustering

   Ahmadian, Sara; Epasto, Alessandro; Knittel, Marina; Kumar, Ravi; Mahdian, Mohammad; Moseley, Benjamin; Pham, Philip; Vassilvitskii, Sergei; Wang, Yuyan (January 2020, 34th Conference on Neural Information Processing Systems)

   null (Ed.)
5. Hierarchical Neural Dynamic Policies

   https://doi.org/10.15607/rss.2021.xvii.023  

   Bahl, Shikhar; Gupta, Abhinav; Pathak, Deepak (July 2021, Robotics: Science and Systems)

   We tackle the problem of generalization to unseen configurations for dynamic tasks in the real world while learning from high-dimensional image input. The family of nonlinear dynamical system-based methods have successfully demonstrated dynamic robot behaviors but have difficulty in generalizing to unseen configurations as well as learning from image inputs. Recent works approach this issue by using deep network policies and reparameterize actions to embed the structure of dynamical systems but still struggle in domains with diverse configurations of image goals, and hence, find it difficult to generalize. In this paper, we address this dichotomy by leveraging embedding the structure of dynamical systems in a hierarchical deep policy learning framework, called Hierarchical Neural Dynamical Policies (H-NDPs). Instead of fitting deep dynamical systems to diverse data directly, H-NDPs form a curriculum by learning local dynamical system-based policies on small regions in state-space and then distill them into a global dynamical system-based policy that operates only from high-dimensional images. H-NDPs additionally provide smooth trajectories, a strong safety benefit in the real world. We perform extensive experiments on dynamic tasks both in the real world (digit writing, scooping, and pouring) and simulation (catching, throwing, picking). We show that H-NDPs are easily integrated with both imitation as well as reinforcement learning setups and achieve state-of-the-art results. 

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