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1. Bayesian optimization for automated model selection

   Gustavo Malkomes, Chip Schaff (March 2017, Nips)

   Despite the success of kernel-based nonparametric methods, kernel selection still requires considerable expertise, and is often described as a “black art.” We present a sophisticated method for automatically searching for an appropriate kernel from an infinite space of potential choices. Previous efforts in this direction have focused on traversing a kernel grammar, only examining the data via computation of marginal likelihood. Our proposed search method is based on Bayesian optimization in model space, where we reason about model evidence as a function to be maximized. We explicitly reason about the data distribution and how it induces similarity between potential model choices in terms of the explanations they can offer for observed data. In this light, we construct a novel kernel between models to explain a given dataset. Our method is capable of finding a model that explains a given dataset well without any human assistance, often with fewer computations of model evidence than previous approaches, a claim we demonstrate empirically. 

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2. Automated Model Selection with Bayesian Quadrature

   Chai, Henry; Ton, Jean-François; Osborne, Michael A.; Garnett, Roman (January 2019, Proceedings of the 36th International Conference on Machine Learning)

   We present a novel technique for tailoring Bayesian quadrature (BQ) to model selection. The state-of-the-art for comparing the evidence of multiple models relies on Monte Carlo methods, which converge slowly and are unreliable for computationally expensive models. Although previous research has shown that BQ offers sample efficiency superior to Monte Carlo in computing the evidence of an individual model, applying BQ directly to model comparison may waste computation producing an overly-accurate estimate for the evidence of a clearly poor model. We propose an automated and efficient algorithm for computing the most-relevant quantity for model selection: the posterior model probability. Our technique maximizes the mutual information between this quantity and observations of the models’ likelihoods, yielding efficient sample acquisition across disparate model spaces when likelihood observations are limited. Our method produces more-accurate posterior estimates using fewer likelihood evaluations than standard Bayesian quadrature and Monte Carlo estimators, as we demonstrate on synthetic and real-world examples. 

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3. Accurate assembly of circular RNAs with TERRACE

   https://doi.org/10.1101/gr.279106.124  

   Zahin, Tasfia; Shi, Qian; Zang, Xiaofei Carl; Shao, Mingfu (September 2024, Genome Research)

   Circular RNA (circRNA) is a class of RNA molecules that forms a closed loop with their 5′ and 3′ ends covalently bonded. CircRNAs are known to be more stable than linear RNAs, have distinct properties and functions, and are promising biomarkers. Existing methods for assembling circRNAs heavily rely on the annotated transcriptomes, hence exhibiting unsatisfactory accuracy without a high-quality transcriptome. We present TERRACE, a new algorithm for full-length assembly of circRNAs from paired-end total RNA-seq data. TERRACE uses the splice graph as the underlying data structure that organizes the splicing and coverage information. We transform the problem of assembling circRNAs into finding paths that “bridge” the three fragments in the splice graph induced by back-spliced reads. We adopt a definition for optimal bridging paths and a dynamic programming algorithm to calculate such optimal paths. TERRACE features an efficient algorithm to detect back-spliced reads missed by RNA-seq aligners, contributing to its much-improved sensitivity. It also incorporates a new machine-learning approach trained to assign a confidence score to each assembled circRNA, which is shown to be superior to using abundance for scoring. On both simulations and biological data sets, TERRACE consistently outperforms existing methods by a large margin in sensitivity while achieving better or comparable precision. In particular, when the annotations are not provided, TERRACE assembles 123%–413% more correct circRNAs than state-of-the-art methods. TERRACE presents a significant advance in assembling full-length circRNAs from RNA-seq data, and we expect it to be widely used in future research on circRNAs. 

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4. Meta-peering: Towards automated ISP peer selection

   https://doi.org/10.1145/3472305.3472325  

   Dey, Prasun Kanti; Mustafa, Shahzeb; Yuksel, Murat (July 2021, Proceedings of ACM/IRTF Applied Networking Research Workshop 2021 (ANRW))

   null (Ed.)
5. Automated material selection based on detected construction progress

   https://doi.org/10.22260/ISARC2022/0056  

   Stephans, Tyler; McClymonds, Austin; Leicht, Robert; Wagner, Alan (July 2022, international symposium on automation and construction)

   One of the many ways in which automation may help the construction industry is on-site material management. This paper presents an automated process where materials are selected for staging by detecting construction progress from site images. The materials are then delivered to their respective workface locations by a robot. The effectiveness of the material selection process is assessed using a simulated and physical construction site. We demonstrate that our process is successful under a number of different conditions and environments. Our system contributes to the feasibility of autonomously managing materials on a construction site and reveals potential avenues for future research. 

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