847 research outputs found
Performance of Regularization for Sparse Convex Optimization
Despite widespread adoption in practice, guarantees for the LASSO and Group
LASSO are strikingly lacking in settings beyond statistical problems, and these
algorithms are usually considered to be a heuristic in the context of sparse
convex optimization on deterministic inputs. We give the first recovery
guarantees for the Group LASSO for sparse convex optimization with
vector-valued features. We show that if a sufficiently large Group LASSO
regularization is applied when minimizing a strictly convex function , then
the minimizer is a sparse vector supported on vector-valued features with the
largest norm of the gradient. Thus, repeating this procedure selects
the same set of features as the Orthogonal Matching Pursuit algorithm, which
admits recovery guarantees for any function with restricted strong
convexity and smoothness via weak submodularity arguments. This answers open
questions of Tibshirani et al. and Yasuda et al. Our result is the first to
theoretically explain the empirical success of the Group LASSO for convex
functions under general input instances assuming only restricted strong
convexity and smoothness. Our result also generalizes provable guarantees for
the Sequential Attention algorithm, which is a feature selection algorithm
inspired by the attention mechanism proposed by Yasuda et al.
As an application of our result, we give new results for the column subset
selection problem, which is well-studied when the loss is the Frobenius norm or
other entrywise matrix losses. We give the first result for general loss
functions for this problem that requires only restricted strong convexity and
smoothness
Deep Time-Series Clustering: A Review
We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives
Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
Dynamic networks are used in a wide range of fields, including social network
analysis, recommender systems, and epidemiology. Representing complex networks
as structures changing over time allow network models to leverage not only
structural but also temporal patterns. However, as dynamic network literature
stems from diverse fields and makes use of inconsistent terminology, it is
challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a
lot of attention in recent years for their ability to perform well on a range
of network science tasks, such as link prediction and node classification.
Despite the popularity of graph neural networks and the proven benefits of
dynamic network models, there has been little focus on graph neural networks
for dynamic networks. To address the challenges resulting from the fact that
this research crosses diverse fields as well as to survey dynamic graph neural
networks, this work is split into two main parts. First, to address the
ambiguity of the dynamic network terminology we establish a foundation of
dynamic networks with consistent, detailed terminology and notation. Second, we
present a comprehensive survey of dynamic graph neural network models using the
proposed terminologyComment: 28 pages, 9 figures, 8 table
Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey
In graph machine learning, data collection, sharing, and analysis often
involve multiple parties, each of which may require varying levels of data
security and privacy. To this end, preserving privacy is of great importance in
protecting sensitive information. In the era of big data, the relationships
among data entities have become unprecedentedly complex, and more applications
utilize advanced data structures (i.e., graphs) that can support network
structures and relevant attribute information. To date, many graph-based AI
models have been proposed (e.g., graph neural networks) for various domain
tasks, like computer vision and natural language processing. In this paper, we
focus on reviewing privacy-preserving techniques of graph machine learning. We
systematically review related works from the data to the computational aspects.
We first review methods for generating privacy-preserving graph data. Then we
describe methods for transmitting privacy-preserved information (e.g., graph
model parameters) to realize the optimization-based computation when data
sharing among multiple parties is risky or impossible. In addition to
discussing relevant theoretical methodology and software tools, we also discuss
current challenges and highlight several possible future research opportunities
for privacy-preserving graph machine learning. Finally, we envision a unified
and comprehensive secure graph machine learning system.Comment: Accepted by SIGKDD Explorations 2023, Volume 25, Issue
Maat: Performance Metric Anomaly Anticipation for Cloud Services with Conditional Diffusion
Ensuring the reliability and user satisfaction of cloud services necessitates
prompt anomaly detection followed by diagnosis.
Existing techniques for anomaly detection focus solely on real-time
detection, meaning that anomaly alerts are issued as soon as anomalies occur.
However, anomalies can propagate and escalate into failures, making
faster-than-real-time anomaly detection highly desirable for expediting
downstream analysis and intervention.
This paper proposes Maat, the first work to address anomaly anticipation of
performance metrics in cloud services.
Maat adopts a novel two-stage paradigm for anomaly anticipation, consisting
of metric forecasting and anomaly detection on forecasts.
The metric forecasting stage employs a conditional denoising diffusion model
to enable multi-step forecasting in an auto-regressive manner.
The detection stage extracts anomaly-indicating features based on domain
knowledge and applies isolation forest with incremental learning to detect
upcoming anomalies.
Thus, our method can uncover anomalies that better conform to human
expertise.
Evaluation on three publicly available datasets demonstrates that Maat can
anticipate anomalies faster than real-time comparatively or more effectively
compared with state-of-the-art real-time anomaly detectors.
We also present cases highlighting Maat's success in forecasting abnormal
metrics and discovering anomalies.Comment: This paper has been accepted by the Research track of the 38th
IEEE/ACM International Conference on Automated Software Engineering (ASE
2023
Hypergraph Learning with Line Expansion
Previous hypergraph expansions are solely carried out on either vertex level
or hyperedge level, thereby missing the symmetric nature of data co-occurrence,
and resulting in information loss. To address the problem, this paper treats
vertices and hyperedges equally and proposes a new hypergraph formulation named
the \emph{line expansion (LE)} for hypergraphs learning. The new expansion
bijectively induces a homogeneous structure from the hypergraph by treating
vertex-hyperedge pairs as "line nodes". By reducing the hypergraph to a simple
graph, the proposed \emph{line expansion} makes existing graph learning
algorithms compatible with the higher-order structure and has been proven as a
unifying framework for various hypergraph expansions. We evaluate the proposed
line expansion on five hypergraph datasets, the results show that our method
beats SOTA baselines by a significant margin
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