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Found 29 papers, 2 papers with code
Online Multi-Kernel Learning with Graph-Structured Feedback
The selection of the dictionary has crucial impact on both the performance and complexity of MKL.
FairWire: Fair Graph Generation
Faced with the bias amplification in graph generation models that is brought to light in this work, we further propose a fair graph generation framework, FairWire, by leveraging our fair regularizer design in a generative model.
Budgeted Online Model Selection and Fine-Tuning via Federated Learning
Although employing a larger set of candidate models naturally leads to more flexibility in model selection, this may be infeasible in cases where prediction tasks are performed on edge devices with limited memory.
Long-term Fairness For Real-time Decision Making: A Constrained Online Optimization Approach
However, the widespread integration of machine learning also makes it necessary to ensure machine learning-driven decision-making systems do not violate ethical principles and values of society in which they operate.
Personalized Online Federated Learning with Multiple Kernels
Federated learning enables a group of learners (called clients) to train an MKL model on the data distributed among clients to perform online non-linear function approximation.
Fairness-aware Optimal Graph Filter Design
We show that the optimal design of said filters can be cast as a convex problem in the graph spectral domain.
Model Extraction Attacks Against Reinforcement Learning Based Controllers
We introduce the problem of model-extraction attacks in cyber-physical systems in which an attacker attempts to estimate (or extract) the feedback controller of the system.
FairGAT: Fairness-aware Graph Attention Networks
Although it is shown that the use of graph structures in learning results in the amplification of algorithmic bias, the influence of the attention design in GATs on algorithmic bias has not been investigated.
Fairness-Aware Graph Filter Design
Graphs are mathematical tools that can be used to represent complex real-world systems, such as financial markets and social networks.
Change Point Detection Approach for Online Control of Unknown Time Varying Dynamical Systems
We propose a novel change point detection approach for online learning control with full information feedback (state, disturbance, and cost feedback) for unknown time-varying dynamical systems.
Explaining Dynamic Graph Neural Networks via Relevance Back-propagation
Graph Neural Networks (GNNs) have shown remarkable effectiveness in capturing abundant information in graph-structured data.
FairNorm: Fair and Fast Graph Neural Network Training
In addition, it is empirically shown that the proposed framework leads to faster convergence compared to the naive baseline where no normalization is employed.
Graph-Assisted Communication-Efficient Ensemble Federated Learning
At each learning round, the server selects a subset of pre-trained models to construct the ensemble model based on the structure of a graph, which characterizes the server's confidence in the models.
Fair Node Representation Learning via Adaptive Data Augmentation
Our analysis reveals that both nodal features and graph structure lead to bias in the obtained representations.
Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network
This paper considers predicting future statuses of multiple agents in an online fashion by exploiting dynamic interactions in the system.
Online Learning with Uncertain Feedback Graphs
In this context, the relationship among experts can be captured by a feedback graph, which can be used to assist the learner's decision making.
Multiple Kernel Representation Learning on Networks
In particular, we propose a weighted matrix factorization model that encodes random walk-based information about nodes of the network.
Fairness-Aware Node Representation Learning
Node representation learning has demonstrated its effectiveness for various applications on graphs.
Graph-Aided Online Multi-Kernel Learning
To improve the accuracy of function approximation and reduce the computational complexity, the present paper studies data-driven selection of kernels from the dictionary that provide satisfactory function approximations.
Online Graph-Adaptive Learning with Scalability and Privacy
Moreover, new nodes can emerge over time, which can necessitate real-time evaluation of their nodal attributes.
Semi-Blind Inference of Topologies and Dynamical Processes over Graphs
Alleviating the limited flexibility of existing approaches, this work advocates structural models for graph processes and develops novel algorithms for joint inference of the network topology and processes from partial nodal observations.
Canonical Correlation Analysis of Datasets with a Common Source Graph
Canonical correlation analysis (CCA) is a powerful technique for discovering whether or not hidden sources are commonly present in two (or more) datasets.
Nonlinear Dimensionality Reduction on Graphs
The novel framework encompasses most of the existing dimensionality reduction methods, but it is also capable of capturing and preserving possibly nonlinear correlations that are ignored by linear methods.
Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics
To further boost performance in dynamic environments, an adaptive multi-kernel learning scheme (termed AdaRaker) is developed.
Tensor Decompositions for Identifying Directed Graph Topologies and Tracking Dynamic Networks
The present paper advocates a novel SEM-based topology inference approach that entails factorization of a three-way tensor, constructed from the observed nodal data, using the well-known parallel factor (PARAFAC) decomposition.
Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivity
To unify these complementary perspectives, linear structural vector autoregressive models (SVARMs) that leverage both contemporaneous and time-lagged nodal data have recently been put forth.
Online Categorical Subspace Learning for Sketching Big Data with Misses
The deterministic Probit and Tobit models treat data as quantized values of an analog-valued process lying in a low-dimensional subspace, while the probabilistic Logit model relies on low dimensionality of the data log-likelihood ratios.
Kernel-Based Structural Equation Models for Topology Identification of Directed Networks
Interestingly, pursuit of the novel kernel-based approach yields a convex regularized estimator that promotes edge sparsity, and is amenable to proximal-splitting optimization methods.
Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals
In this paper, we develop a new sparse Bayesian learning method for recovery of block-sparse signals with unknown cluster patterns.
