Geometric QHD tests improve hub detection in correlated data.
problem Detecting hubs in correlated data with evolving correlations.
method Geometric QHD tests combining QCD and QHD, clustering.
result Improved hub detection in correlated data.
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48 results for “Quickest Hub Discovery”
Financial markets for Liquified Natural Gas (LNG) are an important and rapidly-growing segment of commodities markets. Like other commodities markets, there is an inherent spatial structure to LNG markets, with different price dynamics for different points of delivery hubs. Certain hubs support highly liquid markets, a…
Robust quickest change detection method for unknown score functions.
problem Detecting changes in data streams with unknown pre- and post-change distributions.
method Selects least-favorable distributions and robustifies score-based detection algorithm.
result Demonstrates improved performance in simulations.
We develop a new method called Discriminated Hub Graphical Lasso (DHGL) based on Hub Graphical Lasso (HGL) by providing prior information of hubs. We apply this new method in two situations: with known hubs and without known hubs. Then we compare DHGL with HGL using several measures of performance. When some hubs are k…
This paper introduces scikit-hubness, a Python package for efficient nearest neighbor search in high-dimensional spaces. Hubness is an aspect of the curse of dimensionality, and is known to impair various learning tasks, including classification, clustering, and visualization. scikit-hubness provides algorithms for hub…
New CUSUM algorithm detects changes in unnormalized models.
problem Change detection in models with unknown normalization constants.
method Score-based CUSUM (SCUSUM) algorithm based on Fisher divergence and Hyvärinen score.
result Asymptotic optimality of the SCUSUM algorithm demonstrated.
Non-parametric estimators improve quickest changepoint detection under irregular sequence lengths.
problem Limited and irregular sequence lengths hinder application of ARL and ADD in QCD.
method Analogies with survival analysis to model detection probabilities under truncation.
result KM-ARL and KM-ADD non-parametric estimators are asymptotically unbiased.
Paper introduces a new method to identify brain hubs using both structural and functional connectivity.
problem Hub node identification in brain networks using only functional connectivity.
method Graph signal processing framework that models functional activity as graph signals on structural connectivity.
result The proposed GraFHub framework identifies hub nodes more accurately than conventional methods.
Paper proves identifiability and consistency of hub model for network inference.
problem Identifying network structure from group behavior.
method Hub model and variants, proving identifiability and consistency under mild conditions.
result Identifiability and estimation consistency of hub model and its variants proved.
A distributed SGD method for heterogeneous networks with hubs and workers.
problem Learning in heterogeneous multi-level networks with worker heterogeneity and varying communication.
method Multi-Level Local SGD: distributed SGD with hub-and-spoke paradigm and hub averaging.
result The method converges with error dependent on worker heterogeneity, hub network topology, and iterations.
Optimizes quickest detection of drift in Brownian motion with false negatives.
problem Quickest detection of drift in Brownian motion with false negatives.
method Formulated as an optimal multiple stopping problem, then equivalent to a recursive optimal stopping problem, solved using free boundary methods.
result Explicit formulae for expected cost and optimal strategy found.
Graphon Mixturesstat.ML
Generative model captures hubs and dense communities in social networks.
problem Capturing both hubs and dense communities in social networks.
method Graphon mixture model with a new condition on sparse graphs.
result Estimation of hub normalized degree and graphon for sparse components.
This paper discusses the effect of hubness in zero-shot learning, when ridge regression is used to find a mapping between the example space to the label space. Contrary to the existing approach, which attempts to find a mapping from the example space to the label space, we show that mapping labels into the example spac…
GICDM corrects hubness in embedding spaces for better generative model evaluation.
problem Hubness phenomenon distorts distances in high-dimensional embedding spaces.
method Generative ICDM (GICDM) using multi-scale extension to correct neighborhood estimation.
result GICDM resolves hubness-induced failures and improves metric behavior.
Proposes a co-hub node model for multiview graph learning.
problem Identifying shared graphical structures in heterogeneous datasets.
method Enforces structured sparsity on co-hub nodes across multiple views.
result Demonstrates improved precision and interpretive insight in learning multiview graphs.
Learning the hub graphical Lasso model with the structured sparsity via an efficient algorithmmath.OC
Efficiently estimates hub graphical models with structured sparsity.
problem Computational difficulty in fitting graphical models with hub nodes, especially in high-dimensional data.
method Two-phase algorithm: ADMM for initial point generation and SSN-ALM for accurate solution.
result Significantly improves estimation accuracy and efficiency compared to existing methods.
Optimizes latency and false alarm probability in change detection problems.
problem Balancing latency and false alarms in non-stationary environments.
method Develops order-optimal change detectors under specified latency and false alarm levels.
result Derives a universal lower bound on latency and develops order-optimal detectors.
Master algorithm fails to detect non-stationarity in practical settings.
problem Non-Stationary Reinforcement Learning without prior knowledge.
method Master algorithm tested under various conditions, including piecewise stationary multi-armed bandits.
result Master's non-stationarity detection is ineffective for practical horizons, leading to performance similar to random restarting.
New protocol identifies impossible edge orientations in causal graphs.
problem Causal-discovery algorithms cannot distinguish edge directions without assumptions.
method Discrete impossibility certificates and oracle queries.
result Upper bound of expert interactions for DAG recovery.
Change detection (CD) in time series data is a critical problem as it reveal changes in the underlying generative processes driving the time series. Despite having received significant attention, one important unexplored aspect is how to efficiently utilize additional correlated information to improve the detection and…
We consider the problem of learning a high-dimensional graphical model in which certain hub nodes are highly-connected to many other nodes. Many authors have studied the use of an l1 penalty in order to learn a sparse graph in high-dimensional setting. However, the l1 penalty implicitly assumes that each edge is equall…
We consider the following general hidden hubs model: an random matrix with a subset of special rows (hubs): entries in rows outside are generated from the probability distribution ; for each row in , some of its entries are generated from , $…
New algorithm detects changes quickly without knowing parameters, near optimally.
problem Quickest change detection with unknown parameters.
method Leverages theoretical asymptotic properties to derive a scalable approximate algorithm with near optimal performance.
result Detects changes in constant complexity with near optimal performance.
BiLiNGAM model reveals brain emotion circuit development in adolescents.
problem Understanding brain emotion circuit development during adolescence.
method Bayesian incorporated linear non-Gaussian acyclic model (BiLiNGAM) for multiple DAGs estimation.
result BiLiNGAM reveals unique developmental hub structures and group-specific patterns in emotion-related intra- and inter-modular connectivity.
A Distributed Approach for Networked Flying Platform Association with Small Cells in 5G+ Networkscs.NI
The densification of small-cell base stations in a 5G architecture is a promising approach to enhance the coverage area and facilitate the ever increasing capacity demand of end users. However, the bottleneck is an intelligent management of a backhaul/fronthaul network for these small-cell base stations. This involves …
Optimizes sensor usage for detecting abrupt changes in sensor data.
problem Efficiently detect abrupt changes in sensor data with limited resources.
method Proposes a computationally efficient online sensing scheme that balances exploration and exploitation.
result Expected delay bounds match information-theoretic lower bounds, establishing optimality of the method.
New method detects changes in high-dimensional Markov processes without explicit likelihood evaluation.
problem Quickest change detection in Markov processes with unknown transition kernels.
method Learn conditional score from sample pairs, develop score-based CUSUM procedure.
result Exponential lower bounds on mean time to false alarm and asymptotic upper bounds on detection delay.
Optimizes quickest change detection with bounded means under ARL constraint.
problem Quickest detection of changepoints with bounded means under ARL constraint.
method Derives universal lower and upper bounds for detection delay.
result Achieves universal lower bound in the bounded mean detection setting.
Study sample complexity of robust binary hypothesis testing under different contamination models.
problem Analyzing the sample complexity of robust binary hypothesis testing under various contamination models.
method Examined three standard contamination models: ε-additive (Huber), ε-subtractive, and ε-total variation (TV). Provided explicit formulas for least favourable distributions and compared sample complexities across models.
result Sample complexities are highly unstable in the contamination parameter ε and comparable up to constant-factor rescaling of ε across models.
DAGgr aggregates multiple DAGs to stabilize causal structure learning.
problem Stability in learning causal structure from data.
method Model averaging of candidate DAGs weighted by predictive likelihood, with acyclicity enforced.
result DAGgr consistently outperforms individual DAGs and bootstrap-aggregation baselines.
New method identifies causal brain connections from fMRI data.
problem Identifying causal brain interactions from statistical associations.
method ψ-learning incorporated linear non-Gaussian acyclic model (-LiNGAM).
result Identified three types of hub structures and 16 causal flows.
New method detects market liquidity changes using order book data.
problem Detecting changes in market liquidity.
method Marked Hawkes processes and minimax quickest detection problem for doubly-stochastic Poisson process.
result Optimal stopping rule for detecting intensity changes in market liquidity.
Network-based indicators of Bitcoin bubblesphysics.soc-ph
The functioning of the cryptocurrency Bitcoin relies on the open availability of the entire history of its transactions. This makes it a particularly interesting socio-economic system to analyse from the point of view of network science. Here we analyse the evolution of the network of Bitcoin transactions between users…
New algorithm tackles multi-agent bandits with heavy-tailed data.
problem Maximizing system performance in multi-agent settings with heavy-tailed data.
method Algorithm exploits hub-like structures and synchronization among clients.
result Regret bound of for homogeneous settings, for heterogeneous.
Polynomial chaos surrogates quantify epistemic uncertainty in AI-driven scientific models.
problem Uncertainty in reward estimates hinders interpretability in sequential generative models.
method Fit polynomial chaos expansions to trained models to propagate epistemic uncertainty and quantify sensitivity.
result Interpretable decomposition of reward components driving generative decisions.
This paper begins to explore the determinants of the topological properties of the international - trade network (ITN). We fit bilateral-trade flows using a standard gravity equation to build a "residual" ITN where trade-link weights are depurated from geographical distance, size, border effects, trade agreements, and …
We consider the effects of the 2008 global financial crisis on the global stock market before, during, and after the crisis. We generate complex networks from a cross-correlation matrix such as the threshold network (TN) and the minimal spanning tree (MST). In the threshold network, we assign a threshold value by using…
Recently, Graph Convolutional Networks (GCNs) and their variants have been receiving many research interests for learning graph-related tasks. While the GCNs have been successfully applied to this problem, some caveats inherited from classical deep learning still remain as open research topics in the context of the nod…
This paper combines data-driven and model-driven methods for real-time misinformation detection. Our algorithm, named QuickStop, is an optimal stopping algorithm based on a probabilistic information spreading model obtained from labeled data. The algorithm consists of an offline machine learning algorithm for learning …
New method tackles parcel routing with AI.
problem Routing parcels efficiently through a network of hubs.
method Combines graph neural networks with model-free RL.
result Extracts small feature graphs from the environment state.
The paper proposes using low rank assumption to improve causal structure learning in DAGs.
problem Challenges in learning causal structures in high-dimensional, non-sparse DAGs.
method Exploits low rank assumption of DAG adjacency matrix to adapt causal structure learning methods.
result Maximum rank is highly related to hubs, suggesting low rank for scale-free networks.
We consider the problem of quickest change-point detection in data streams. Classical change-point detection procedures, such as CUSUM, Shiryaev-Roberts and Posterior Probability statistics, are optimal only if the change-point model is known, which is an unrealistic assumption in typical applied problems. Instead we p…
Algorithm detects causal change points quickly with adaptive interventions.
problem Detecting changes in causal models with interventions.
method Centralization technique, Kullback-Leibler divergence for intervention selection, adaptive intervention policy.
result Theoretical first-order optimality and validation through simulations and real-world studies.
Attributed network embedding has attracted plenty of interest in recent years. It aims to learn task-independent, low-dimensional, and continuous vectors for nodes preserving both topology and attribute information. Most of the existing methods, such as random-walk based methods and GCNs, mainly focus on the local info…
Modeling Climate Change Impact on Wind Power Resources Using Adaptive Neuro-Fuzzy Inference Systemphysics.ao-ph
Climate change impacts and adaptations are the subjects to ongoing issues that attract the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable devel…
Analyzes Indian chemical industry post-Covid.
problem Global uncertainty impacts chemical industry performance.
method Fundamental analysis of key players and trends.
result Various geopolitical and macroeconomic trends shape industry performance.
Optimizes hydrokinetic turbine design using morphing and Bayesian optimization.
problem Designing optimal hydrokinetic turbine shapes due to high cost and geometric constraints.
method Design-by-Morphing (DbM) and Mixed variable, Multi-Objective Bayesian Optimization (MixMOBO).
result Optimized shapes lead to maximum power output with minimal evaluations.
Paper uses queue theory to model financial signals with relativistic delay.
problem Relativistic delay in financial trading signals.
method Modified M/M/G queue theory.
result Describes propagation of trading signals with finite velocity.