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1. SET: Searching Effective Supervised Learning Augmentations in Large Tabular Data Repositories

   Liu, Jiaxiang; Huang, Zezhou; Wu, Eugene (June 2024, GUIDE-AI Workshop)

   Successful supervised learning models rely on predictive features, which rarely come from a single dataset. As a result, relevant datasets need to be integrated before training the actual model. This raises one natural question: \textit{``how can one efficiently search for predictive features from relevant datasets for integration with responsible AI guarantees?"}. This paper formalizes the question as the \textit{data augmentation search problem} with an objective of minimizing the search latency. We propose \sys, an interactive system that intakes a supervised learning task and searches for a set of join-compatible datasets that optimally improve the performance of the task. Specifically, \sys manages a corpus of relational datasets, uses linear regression as a \textit{proxy model} to evaluate augmentation candidates, and applies \textit{factorized machine learning} to accelerate model training and evaluation algorithmically. Furthermore, \sys leverages system and hardware optimizations to maximize parallelism across augmentation searches. These allow \sys to search for a good augmentation plan over 1 million datasets with a latency of $1.4$ seconds. 

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2. Learning Sample-Specific Policies for Sequential Image Augmentation

   https://doi.org/10.1145/3474085.3475602  

   Li, Pu; Liu, Xiaobai; Xie, Xiaohui (October 2021, MM '21: Proceedings of the 29th ACM International Conference on Multimedia)

   null (Ed.)

   This paper presents a policy-driven sequential image augmentation approach for image-related tasks. Our approach applies a sequence of image transformations (e.g., translation, rotation) over a training image, one transformation at a time, with the augmented image from the previous time step treated as the input for the next transformation. This sequential data augmentation substantially improves sample diversity, leading to improved test performance, especially for data-hungry models (e.g., deep neural networks). However, the search for the optimal transformation of each image at each time step of the sequence has high complexity due to its combination nature. To address this challenge, we formulate the search task as a sequential decision process and introduce a deep policy network that learns to produce transformations based on image content. We also develop an iterative algorithm to jointly train a classifier and the policy network in the reinforcement learning setting. The immediate reward of a potential transformation is defined to encourage transformations producing hard samples for the current classifier. At each iteration, we employ the policy network to augment the training dataset, train a classifier with the augmented data, and train the policy net with the aid of the classifier. We apply the above approach to both public image classification benchmarks and a newly collected image dataset for material recognition. Comparisons to alternative augmentation approaches show that our policy-driven approach achieves comparable or improved classification performance while using significantly fewer augmented images. The code is available at https://github.com/Paul-LiPu/rl_autoaug. 

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3. A learning-based framework for spatial join processing: estimation, optimization and tuning

   https://doi.org/10.1007/s00778-024-00836-1  

   Vu, Tin; Belussi, Alberto; Migliorini, Sara; Eldawy, Ahmed (February 2024, The VLDB Journal)

   Abstract The importance and complexity of spatial join operation resulted in the availability of many join algorithms, some of which are tailored for big-data platforms like Hadoop and Spark. The choice among them is not trivial and depends on different factors. This paper proposes the first machine-learning-based framework for spatial join query optimization which can accommodate both the characteristics of spatial datasets and the complexity of the different algorithms. The main challenge is how to develop portable cost models that once trained can be applied to any pair of input datasets, because they are able to extract the important input characteristics, such as data distribution and spatial partitioning, the logic of spatial join algorithms, and the relationship between the two input datasets. The proposed system defines a set of features that can be computed efficiently for the data to catch the intricate aspects of spatial join. Then, it uses these features to train five machine learning models that are used to identify the best spatial join algorithm. The first two are regression models that estimate two important measures of the spatial join performance and they act as the cost model. The third model chooses the best partitioning strategy to use with spatial join. The fourth and fifth models further tune two important parameters, number of partitions and plane-sweep direction, to get the best performance. Experiments on large-scale synthetic and real data show the efficiency of the proposed models over baseline methods. 

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4. Establishing a Generalizable Framework for Generating Cost-Aware Training Data and Building Unique Context-Aware Walltime Prediction Regression Models

   Swathi Vallabhajosyula; Rajiv Ramnath (December 2022, 20th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA 2022))

   This paper describes a generalizable framework for creating context-aware wall-time prediction models for HPC applications. This framework: (a) cost-effectively generates comprehensive application-specific training data, (b) provides an application-independent machine learning pipeline that trains different regression models over the training datasets, and (c) establishes context-aware selection criteria for model selection. We explain how most of the training data can be generated on commodity or contention-free cyberinfrastructure and how the predictive models can be scaled to the production environment with the help of a limited number of resource-intensive generated runs (we show almost seven-fold cost reductions along with better performance). Our machine learning pipeline does feature transformation, and dimensionality reduction, then reduces sampling bias induced by data imbalance. Our context-aware model selection algorithm chooses the most appropriate regression model for a given target application that reduces the number of underpredictions while minimizing overestimation errors. Index Terms—AI4CI, Data Science Workflow, Custom ML Models, HPC, Data Generation, Scheduling, Resource Estimations 

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5. Good Feature Selection for Least Squares Pose Optimization in VO/VSLAM

   https://doi.org/10.1109/IROS.2018.8593641  

   Zhao, Yipu; Vela, Patricio A. (October 2018, IEEE/RSJ International Conference of Intelligent Robots and Systems)

   This paper aims to select features that contribute most to the pose estimation in VO/VSLAM. Unlike existing feature selection works that are focused on efficiency only, our method significantly improves the accuracy of pose tracking, while introducing little overhead. By studying the impact of feature selection towards least squares pose optimization, we demonstrate the applicability of improving accuracy via good feature selection. To that end, we introduce the Max-logDet metric to guide the feature selection, which is connected to the conditioning of least squares pose optimization problem. We then describe an efficient algorithm for approximately solving the NP-hard Max-logDet problem. Integrating MaxlogDet feature selection into a state-of-the-art visual SLAM system leads to accuracy improvements with low overhead, as demonstrated via evaluation on a public benchmark. 

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