New method improves precision matrix estimation with prior hub information.
problem Estimating precision matrices in Gaussian graphical models.
method Discriminated Hub Graphical Lasso (DHGL) using prior hub information.
result DHGL outperforms HGL in estimating precision matrices with prior information.
Model improves volatility estimation for LNG markets.
problem LNG markets have spatial structure with different price dynamics at hubs.
method Joint modeling strategy using high-frequency data from thickly-traded hubs.
result Model has superior predictive performance for risk management metrics.
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.
Python package reduces hubness in high-dimensional data.
problem Hubness in high-dimensional data affects machine learning tasks.
method Hubness analysis, reduction, and approximate neighbor search.
result Improves neighbor retrieval and learning tasks in high dimensions.
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.
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…
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.
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.
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.
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.
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.
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…
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 (ψ-LiNGAM). result Identified three types of hub structures and 16 causal flows.
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 O(M1−α1logT) for homogeneous settings, O(MlogT) for heterogeneous. We consider the following general hidden hubs model: an n×n random matrix A with a subset S of k special rows (hubs): entries in rows outside S are generated from the probability distribution p0∼N(0,σ02); for each row in S, some k of its entries are generated from p1∼N(0,σ12), $…
Proposes using dimensionality reduction with personalized page rank to improve GCNs' performance in node classification.
problem GCNs' limitation in considering only a few propagation steps away nodes, leading to hub node bias.
method Utilizes dimensionality reduction techniques conjugate with personalized page rank to fully account for graph topology.
result Significantly outperforms existing methods in node classification tasks on real-world networks.
Study reveals hubs play key role in Bitcoin bubbles.
problem Understanding systemic risk in Bitcoin network during price surges.
method Analysis of Bitcoin transaction network history from 2011-2013.
result Hubs significantly increase systemic risk during Bitcoin bubbles.
This paper proposes using flying platforms for efficient small-cell base station association in 5G networks.
problem Efficient association and placement of backhaul hubs for small-cell base stations in 5G networks.
method Formulates the association problem considering backhaul link and NFP limitations, then presents a distributed greedy search solution.
result Observes favorable performance via numerical comparison with optimal exhaustive search algorithm.
Proposes a block-based model for attributed network embedding.
problem Handles both assortative and disassortative networks.
method Assigns nodes to blocks based on similar linkage patterns, using neural networks to preserve attribute information.
result Consistently outperforms state-of-the-art methods on disassortative networks.
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.
A new ZSL algorithm uses shared sparse representations for unseen classes.
problem Classifying images from unseen classes using only semantic information.
method Coupled dictionary learning to represent visual and semantic features in an intermediate space.
result The proposed method outperforms state-of-the-art ZSL algorithms on benchmark datasets.
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.
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 …
Study uses ANFIS to assess wind power under climate change.
problem Tackles climate change impact on wind power potential.
method Employed ANFIS to match climate model data with reference data.
result Real wind power potential lower than projected.
Study examines stock market connections before, during, and after the 2008 financial crisis.
problem Effects of the 2008 global financial crisis on stock market connectivity.
method Generated complex networks from cross-correlation matrices, using threshold networks and minimal spanning trees.
result During the crisis, countries in different zones had varying levels of connectivity.
Study compares sentiment spillover networks from news and social media in tech companies.
problem Understanding how sentiment information flows between companies through news and social media.
method Network-based transfer entropy method to measure and compare sentiment spillover.
result News shows stronger information flow among tech companies after COVID-19.
Improved algorithm for causal structure learning in large networks.
problem Estimating high-dimensional directed acyclic graphs from noisy data.
method A modified PC-Algorithm that uses small sets of variables for conditioning.
result Significant gains in computational complexity and estimation accuracy, especially in large networks with hub nodes.
Adaptive adjustment of semantic feature space improves zero-shot recognition.
problem Domain shift and hubness problems in zero-shot recognition.
method Adaptive adjustment of semantic feature space.
result Remarkable performance improvement compared to existing methods.
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.
A fast k-NN classifier without negative pairs.
problem Efficient k-NN classification without negative pairs. method Ridge regression for learning dissimilarity function.
result Better k-NN classification accuracy than state-of-the-art methods. Estimates social network structure from random walk subgraphs.
problem Recovering population structure from random walk subgraphs in stochastic block models.
method Maximum likelihood estimation, SAEM algorithm, de-biasing techniques.
result New de-biased estimator provides more accurate recovery of network structure.
Generative framework tackles zero-shot learning with adversarial domain adaptation.
problem Domain shift between seen and unseen class distributions in zero-shot learning.
method End-to-end learning of seen and unseen class distributions, adversarial domain adaptation.
result Superior accuracies compared to state-of-the-art models on various benchmark datasets.
Challenge evaluates voice conversion systems using parallel and non-parallel data.
problem Evaluate and compare different voice conversion systems.
method Hub and Spoke tasks with crowdsourced evaluation.
result Naturalness and similarity ratings provided for submitted systems.
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.
Study analyzes carbon footprint of 1,417 ML models on Hugging Face.
problem Scarce knowledge on measuring and reporting carbon footprint of ML models.
method Repository mining study on Hugging Face Hub API.
result Stalled carbon emissions-reporting models, slight decrease in carbon footprint over 2 years.
We consider a collaborative online learning paradigm, wherein a group of agents connected through a social network are engaged in playing a stochastic multi-armed bandit game. Each time an agent takes an action, the corresponding reward is instantaneously observed by the agent, as well as its neighbours in the social n…
The agent-based model of stock price dynamics on a directed evolving complex network is suggested and studied by direct simulation. The stationary regime is maintained as a result of the balance between the extremal dynamics, adaptivity of strategic variables and reconnection rules. The inherent structure of node agent…
In this paper, we generalize Huber's criterion to multichannel sparse recovery problem of complex-valued measurements where the objective is to find good recovery of jointly sparse unknown signal vectors from the given multiple measurement vectors which are different linear combinations of the same known elementary vec…
SIMCom detects HTs by analyzing communication patterns in SoCs.
problem Timely detection of Hardware Trojans in secure integrated circuits.
method Multi-parameter statistical modeling of communication channels in SoCs.
result SIMCom detects HTs with less than 1% area and power overhead.
Functional connectivity refers to the temporal statistical relationship between spatially distinct brain regions and is usually inferred from the time series coherence/correlation in brain activity between regions of interest. In human functional brain networks, the network structure is often inferred from functional m…
We study the crash dynamics of the Warsaw Stock Exchange (WSE) by using the Minimal Spanning Tree (MST) networks. We find the transition of the complex network during its evolution from a (hierarchical) power law MST network, representing the stable state of WSE before the recent worldwide financial crash, to a superst…
This study examines common pitfalls and reproducibility issues in GANs.
problem Challenges in training GANs and lack of a measure for failure modes.
method Evaluation of common pitfalls, reproducibility issues, and open-source code.
result Discussion and evaluation of common pitfalls and reproducibility issues in GANs.
Deep learning models estimate black hole merger parameters and post-merger properties.
problem Estimating masses and properties of black hole mergers.
method Modified WaveNet architecture with contrastive learning and normalizing flow.
result Deep learning models produce statistically consistent results and similar posterior distributions to traditional Bayesian methods.
BigBiGAN improves unsupervised representation learning using image generation quality.
problem Improving unsupervised representation learning methods.
method Extending BigGAN to include an encoder and modifying the discriminator for representation learning.
result BigBiGAN models achieve state-of-the-art performance in unsupervised representation learning and unconditional image generation.