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- research-articleDecember 2024
A General Concave Fairness Framework for Influence Maximization Based on Poverty Reward
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 19, Issue 1Article No.: 14, Pages 1–23https://doi.org/10.1145/3701737Influence maximization (IM) aims to find a group of influential nodes as initial spreaders to maximize the influence spread over a network. Yet, traditional IM algorithms have not been designed with fairness in mind, resulting in discrimination against ...
- research-articleSeptember 2024JUST ACCEPTED
NeuralCODE: Neural Compartmental Ordinary Differential Equations Model with AutoML for Interpretable Epidemic Forecasting
ACM Transactions on Knowledge Discovery from Data (TKDD), Just Accepted https://doi.org/10.1145/3694688In order to prevent the re-emergence of an epidemic, predicting its trend while gaining insight into the intrinsic factors affecting it is a key issue in urban governance. Traditional SIR-like compartment models provide insight into the explanatory ...
- research-articleMarch 2023
Scheduling Hyperparameters to Improve Generalization: From Centralized SGD to Asynchronous SGD
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 17, Issue 2Article No.: 29, Pages 1–37https://doi.org/10.1145/3544782This article1 studies how to schedule hyperparameters to improve generalization of both centralized single-machine stochastic gradient descent (SGD) and distributed asynchronous SGD (ASGD). SGD augmented with momentum variants (e.g., heavy ball momentum (...
- research-articleDecember 2020
Dynamic Graph Mining for Multi-weight Multi-destination Route Planning with Deadlines Constraints
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 15, Issue 1Article No.: 3, Pages 1–32https://doi.org/10.1145/3412363Route planning satisfied multiple requests is an emerging branch in the route planning field and has attracted significant attention from the research community in recent years. The prevailing studies focus only on seeking a route by minimizing a single ...
- research-articleSeptember 2020
Time-Warped Sparse Non-negative Factorization for Functional Data Analysis
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 6Article No.: 72, Pages 1–23https://doi.org/10.1145/3408313This article proposes a novel time-warped sparse non-negative factorization method for functional data analysis. The proposed method on the one hand guarantees the extracted basis functions and their coefficients to be positive and interpretable, and on ...
- research-articleSeptember 2020
Probabilistic Modeling for Frequency Vectors Using a Flexible Shifted-Scaled Dirichlet Distribution Prior
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 6Article No.: 69, Pages 1–35https://doi.org/10.1145/3406242Burstiness and overdispersion phenomena of count vectors pose significant challenges in modeling such data accurately. While the dependency assumption of the multinomial distribution causes its failure to model frequency vectors in several machine ...
- research-articleJune 2017
Query-Driven Learning for Predictive Analytics of Data Subspace Cardinality
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 11, Issue 4Article No.: 47, Pages 1–46https://doi.org/10.1145/3059177Fundamental to many predictive analytics tasks is the ability to estimate the cardinality (number of data items) of multi-dimensional data subspaces, defined by query selections over datasets. This is crucial for data analysts dealing with, e.g., ...
- research-articleApril 2017
A Randomized Rounding Algorithm for Sparse PCA
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 11, Issue 3Article No.: 38, Pages 1–26https://doi.org/10.1145/3046948We present and analyze a simple, two-step algorithm to approximate the optimal solution of the sparse PCA problem. In the proposed approach, we first solve an ℓ1-penalized version of the NP-hard sparse PCA optimization problem and then we use a ...
- research-articleAugust 2014
Random Projections for Linear Support Vector Machines
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 8, Issue 4Article No.: 22, Pages 1–25https://doi.org/10.1145/2641760Let X be a data matrix of rank ρ, whose rows represent n points in d-dimensional space. The linear support vector machine constructs a hyperplane separator that maximizes the 1-norm soft margin. We develop a new oblivious dimension reduction technique ...
- research-articleJune 2014
On the Sample Complexity of Random Fourier Features for Online Learning: How Many Random Fourier Features Do We Need?
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 8, Issue 3Article No.: 13, Pages 1–19https://doi.org/10.1145/2611378We study the sample complexity of random Fourier features for online kernel learning—that is, the number of random Fourier features required to achieve good generalization performance. We show that when the loss function is strongly convex and smooth, ...
- research-articleDecember 2013
Solving inverse frequent itemset mining with infrequency constraints via large-scale linear programs
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 7, Issue 4Article No.: 18, Pages 1–39https://doi.org/10.1145/2541268.2541271Inverse frequent set mining (IFM) is the problem of computing a transaction database D satisfying given support constraints for some itemsets, which are typically the frequent ones. This article proposes a new formulation of IFM, called IFMI (IFM with ...
- research-articleFebruary 2011
Fast Algorithms for Approximating the Singular Value Decomposition
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 5, Issue 2Article No.: 13, Pages 1–36https://doi.org/10.1145/1921632.1921639A low-rank approximation to a matrix A is a matrix with significantly smaller rank than A, and which is close to A according to some norm. Many practical applications involving the use of large matrices focus on low-rank approximations. By reducing the ...
- research-articleFebruary 2011
Temporal Link Prediction Using Matrix and Tensor Factorizations
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 5, Issue 2Article No.: 10, Pages 1–27https://doi.org/10.1145/1921632.1921636The data in many disciplines such as social networks, Web analysis, etc. is link-based, and the link structure can be exploited for many different data mining tasks. In this article, we consider the problem of temporal link prediction: Given link data ...
- research-articleOctober 2010
Efficient algorithms for large-scale local triangle counting
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 4, Issue 3Article No.: 13, Pages 1–28https://doi.org/10.1145/1839490.1839494In this article, we study the problem of approximate local triangle counting in large graphs. Namely, given a large graph G=(V,E) we want to estimate as accurately as possible the number of triangles incident to every node v∈ V in the graph. We consider ...
- research-articleOctober 2008
Incremental tensor analysis: Theory and applications
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 2, Issue 3Article No.: 11, Pages 1–37https://doi.org/10.1145/1409620.1409621How do we find patterns in author-keyword associations, evolving over time? Or in data cubes (tensors), with product-branchcustomer sales information? And more generally, how to summarize high-order data cubes (tensors)? How to incrementally update ...