More Like this

1. Deep Descriptive Clustering

   Zhang, H; Davidson, I (August 2021, IJCAI)

   null (Ed.)

   Recent work on explainable clustering allows describing clusters when the features are interpretable. However, much modern machine learning focuses on complex data such as images, text, and graphs where deep learning is used but the raw features of data are not interpretable. This paper explores a novel setting for performing clustering on complex data while simultaneously generating explanations using interpretable tags. We propose deep descriptive clustering that performs sub-symbolic representation learning on complex data while generating explanations based on symbolic data. We form good clusters by maximizing the mutual information between empirical distribution on the inputs and the induced clustering labels for clustering objectives. We generate explanations by solving an integer linear programming that generates concise and orthogonal descriptions for each cluster. Finally, we allow the explanation to inform better clustering by proposing a novel pairwise loss with self-generated constraints to maximize the clustering and explanation module's consistency. Experimental results on public data demonstrate that our model outperforms competitive baselines in clustering performance while offering high-quality cluster-level explanations. 

   more » « less
2. AutoTap: Synthesizing and Repairing Trigger-Action Programs Using LTL Properties

   https://doi.org/10.1109/ICSE.2019.00043  

   Zhang, Lefan; He, Weijia; Martinez, Jesse; Brackenbury, Noah; Lu, Shan; Ur, Blase (January 2019, Proceedings of the 41st International Conference on Software Engineering)

   End-user programming, particularly trigger-action programming (TAP), is a popular method of letting users express their intent for how smart devices and cloud services interact. Unfortunately, sometimes it can be challenging for users to correctly express their desires through TAP. This paper presents AutoTap, a system that lets novice users easily specify desired properties for devices and services. AutoTap translates these properties to linear temporal logic (LTL) and both automatically synthesizes property-satisfying TAP rules from scratch and repairs existing TAP rules. We designed AutoTap based on a user study about properties users wish to express. Through a second user study, we show that novice users made significantly fewer mistakes when expressing desired behaviors using AutoTap than using TAP rules. Our experiments show that AutoTap is a simple and effective option for expressive end-user programming. 

   more » « less
3. Fast and interpretable consensus clustering via minipatch learning

   https://doi.org/10.1371/journal.pcbi.1010577  

   Gan, Luqin; Allen, Genevera I. (October 2022, PLOS Computational Biology)

   Rigoutsos, Isidore (Ed.)

   Consensus clustering has been widely used in bioinformatics and other applications to improve the accuracy, stability and reliability of clustering results. This approach ensembles cluster co-occurrences from multiple clustering runs on subsampled observations. For application to large-scale bioinformatics data, such as to discover cell types from single-cell sequencing data, for example, consensus clustering has two significant drawbacks: (i) computational inefficiency due to repeatedly applying clustering algorithms, and (ii) lack of interpretability into the important features for differentiating clusters. In this paper, we address these two challenges by developing IMPACC: Interpretable MiniPatch Adaptive Consensus Clustering. Our approach adopts three major innovations. We ensemble cluster co-occurrences from tiny subsets of both observations and features, termed minipatches, thus dramatically reducing computation time. Additionally, we develop adaptive sampling schemes for observations, which result in both improved reliability and computational savings, as well as adaptive sampling schemes of features, which lead to interpretable solutions by quickly learning the most relevant features that differentiate clusters. We study our approach on synthetic data and a variety of real large-scale bioinformatics data sets; results show that our approach not only yields more accurate and interpretable cluster solutions, but it also substantially improves computational efficiency compared to standard consensus clustering approaches. 

   more » « less
4. Multi-layer Bundling as a New Approach for Determining Multi-scale Correlations Within a High-Dimensional Dataset

   https://doi.org/10.1007/s11538-024-01335-8  

   Fazli, Mehran; Bertram, Richard; Striegel, Deborah_A (July 2024, Bulletin of Mathematical Biology)

   Abstract The growing complexity of biological data has spurred the development of innovative computational techniques to extract meaningful information and uncover hidden patterns within vast datasets. Biological networks, such as gene regulatory networks and protein-protein interaction networks, hold critical insights into biological features’ connections and functions. Integrating and analyzing high-dimensional data, particularly in gene expression studies, stands prominent among the challenges in deciphering these networks. Clustering methods play a crucial role in addressing these challenges, with spectral clustering emerging as a potent unsupervised technique considering intrinsic geometric structures. However, spectral clustering’s user-defined cluster number can lead to inconsistent and sometimes orthogonal clustering regimes. We propose theMulti-layer Bundling (MLB)method to address this limitation, combining multiple prominent clustering regimes to offer a comprehensive data view. We call the outcome clusters “bundles”. This approach refines clustering outcomes, unravels hierarchical organization, and identifies bridge elements mediating communication between network components. By layering clustering results, MLB provides a global-to-local view of biological feature clusters enabling insights into intricate biological systems. Furthermore, the method enhances bundle network predictions by integrating thebundle co-cluster matrixwith the affinity matrix. The versatility of MLB extends beyond biological networks, making it applicable to various domains where understanding complex relationships and patterns is needed. 

   more » « less
5. Bandit learning- based online user clustering and selection for cellular networks. In pages 1–10, September 2022.

   Tariq, I.; Sen, R.; Novlan, T.; Akoum S.; Majmundar M.; de Veciana, G.; Shakkottai S. (September 2022, IEEE WIOPT)

   Current wireless networks employ sophisticated multi-user transmission techniques to fully utilize the physical layer resources for data transmission. At the MAC layer, these techniques rely on a semi-static map that translates the channel quality of users to the potential transmission rate (more precisely, a map from the Channel Quality Index to the Modulation and Coding Scheme) for user selection and scheduling decisions. However, such a static map does not adapt to the actual deployment scenario and can lead to large performance losses. Furthermore, adaptively learning this map can be inefficient, particularly when there are a large number of users. In this work, we make this learning efficient by clustering users. Specifically, we develop an online learning approach that jointly clusters users and channel-states, and learns the associated rate regions of each cluster. This approach generates a scenario-specific map that replaces the static map that is currently used in practice. Furthermore, we show that our learning algorithm achieves sub- linear regret when compared to an omniscient genie. Next, we develop a user selection algorithm for multi-user scheduling using the learned user-clusters and associated rate regions. Our algorithms are validated on the WiNGS simulator from AT&T Labs, that implements the PHY/MAC stack and simulates the channel. We show that our algorithm can efficiently learn user clusters and the rate regions associated with the user sets for any observed channel state. Moreover, our simulations show that a deployment-scenario-specific map significantly outperforms the current static map approach for resource allocation at the MAC layer. 

   more » « less