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1. DeepMapping: Learned Data Mapping for Lossless Compression and Efficient Lookup (DOI: 10.1109/ICDE60146.2024.00008)

   Zhou, L; Candan, K; Zou, J (May 2024, Proceedings of 2024 IEEE 39th International Conference on Data Engineering (ICDE 2024))

   Storing tabular data to balance storage and query efficiency is a long-standing research question in the database community. In this work, we argue and show that a novel {\em DeepMapping} abstraction, which relies on the impressive {\em memorization} capabilities of deep neural networks, can provide better storage cost, better latency, and better run-time memory footprint, all at the same time. Such unique properties may benefit a broad class of use cases in capacity-limited devices. Our proposed DeepMapping abstraction transforms a dataset into multiple key-value mappings and constructs a multi-tasking neural network model that outputs the corresponding \textit{values} for a given input \textit{key}. To deal with memorization errors, DeepMapping couples the learned neural network with a lightweight auxiliary data structure capable of correcting mistakes. The auxiliary structure design further enables DeepMapping to efficiently deal with insertions, deletions, and updates even without retraining the mapping. We propose a multi-task search strategy for selecting the hybrid DeepMapping structures (including model architecture and auxiliary structure) with a desirable trade-off among memorization capacity, size, and efficiency. Extensive experiments with a real-world dataset, synthetic and benchmark datasets, including TPC-H and TPC-DS, demonstrated that the DeepMapping approach can better balance the retrieving speed and compression ratio against several cutting-edge competitors. 

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2. Relational Multi-Task Learning: Modeling Relations between Data and Tasks

   Cao, K; You, J; Leskovec, J (January 2022, International Conference on Representation Learning (ICLR))

   A key assumption in multi-task learning is that at the inference time the multi-task model only has access to a given data point but not to the data point’s labels from other tasks. This presents an opportunity to extend multi-task learning to utilize data point’s labels from other auxiliary tasks, and this way improves performance on the new task. Here we introduce a novel relational multi-task learning setting where we leverage data point labels from auxiliary tasks to make more accurate predictions on the new task. We develop MetaLink, where our key innovation is to build a knowledge graph that connects data points and tasks and thus allows us to leverage labels from auxiliary tasks. The knowledge graph consists of two types of nodes: (1) data nodes, where node features are data embeddings computed by the neural network, and (2) task nodes, with the last layer’s weights for each task as node features. The edges in this knowledge graph capture data-task relationships, and the edge label captures the label of a data point on a particular task. Under MetaLink, we reformulate the new task as a link label prediction problem between a data node and a task node. The MetaLink framework provides flexibility to model knowledge transfer from auxiliary task labels to the task of interest. We evaluate MetaLink on 6 benchmark datasets in both biochemical and vision domains. Experiments demonstrate that MetaLink can successfully utilize the relations among different tasks, outperforming the state-of-the-art methods under the proposed relational multi-task learning setting, with up to 27% improvement in ROC AUC. 

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3. Extending Graph Condensation to Multi-Label Datasets: A Benchmark Study

   Zhang, Liangliang; Bao, Haoran; Ma, Yao (May 2025, Transactions on Machine Learning Research)

   As graph data grows increasingly complicated, training graph neural networks (GNNs) on large-scale datasets presents significant challenges, including computational resource constraints, data redundancy, and transmission inefficiencies. While existing graph condensation techniques have shown promise in addressing these issues, they are predominantly designed for single-label datasets, where each node is associated with a single class label. However, many real-world applications, such as social network analysis and bioinformatics, involve multi-label graph datasets, where one node can have various related labels. To deal with this problem, we extend traditional graph condensation approaches to accommodate multi-label datasets by introducing modifications to synthetic dataset initialization and condensing optimization. Through experiments on eight real-world multi-label graph datasets, we prove the effectiveness of our method. In the experiment, the GCond framework, combined with K-Center initialization and binary cross-entropy loss (BCELoss), generally achieves the best performance. This benchmark for multi-label graph condensation not only enhances the scalability and efficiency of GNNs for multi-label graph data but also offers substantial benefits for diverse real-world applications. Code is available at https://github.com/liangliang6v6/Multi-GC. 

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4. AQUAMAN: An Analytic-Query Offloading Machine

   Xu, Shuotao; Bourgeat, Thomas; Huang, Tianhao; Kim, Hojun; Lee, Sungjin.; Arvind, A. (October 2020, Proceedings of the 53rd Annual IEEE/ACM International Symposium on Microarchitecture)

   null (Ed.)

   Analytic workloads on terabyte data-sets are often run in the cloud, where application and storage servers are separate and connected via network. In order to saturate the storage bandwidth and to hide the long storage latency, such a solution requires an expensive server cluster with sufficient aggregate DRAM capacity and hardware threads. An alternative solution is to push the query computation into storage servers. In this paper we present an in-storage Analytics QUery Offloading MAchiNe (AQUOMAN) to “offload” most SQL operators, including multi-way joins, to SSDs. AQUOMAN executes Table Tasks, which apply a static dataflow graph of SQL operators to relational tables to produce an output table. Table Tasks use a streaming computation model, which allows AQUOMAN to process queries with a reasonable amount of DRAM for intermediate results. AQUOMAN is a general analytic query processor, which can be integrated in the database software stack transparently. We have built a prototype of AQUOMAN in FPGAs, and using TPC-H benchmarks on 1TB data sets, shown that a single instance of 1TB AQUOMAN disk, on average, can free up 70% CPU cycles and reduce DRAM usage by 60%. One way to visualize this saving is to think that if we run queries sequentially and ignore inter-query page cache reuse, MonetDB running on a 4-core, 16GB-DRAM machine with AQUOMAN augmented SSDs performs, on average, as well as a MonetDB running on a 32-core, 128GB-DRAM machine with standard SSDs. 

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5. Mitigating Memorization of Noisy Labels via Regularization between Representations

   Cheng, Hao; Zhu, Zhaowei; Sun, Xing; Liu, Yang (January 2023, International Conference on Learning Representations (ICLR))

   Designing robust loss functions is popular in learning with noisy labels while existing designs did not explicitly consider the overfitting property of deep neural networks (DNNs). As a result, applying these losses may still suffer from overfitting/memorizing noisy labels as training proceeds. In this paper, we first theoretically analyze the memorization effect and show that a lower-capacity model may perform better on noisy datasets. However, it is non-trivial to design a neural network with the best capacity given an arbitrary task. To circumvent this dilemma, instead of changing the model architecture, we decouple DNNs into an encoder followed by a linear classifier and propose to restrict the function space of a DNN by a representation regularizer. Particularly, we require the distance between two self-supervised features to be positively related to the distance between the corresponding two supervised model outputs. Our proposed framework is easily extendable and can incorporate many other robust loss functions to further improve performance. Extensive experiments and theoretical analyses support our claims. Code is available at https://github.com/UCSC-REAL/SelfSup_NoisyLabel. 

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