Graph-based feedback improves bandit algorithms' performance.
problem Stochastic multi-armed bandit problem with graph feedback.
method Analysis of Thompson Sampling and UCB algorithms in graph-based feedback setting.
result Regret bounds that combine graph structure and arm means gaps.
Improved Boltzmann exploration reduces regret in stochastic MAB problems.
problem Improving regret in stochastic multi-armed bandit problems.
method A modified Boltzmann exploration with a doubling trick.
result Achieves O ( K log 1 + α T ) O(K\log^{1+α} T) O ( K log 1 + α T ) regret, improving on previous results. DFNets uses feedback-looped filters for better graph CNN performance.
problem Improving CNN performance on graph structured data.
method DFNets incorporates feedback-looped spectral graph filters.
result DFNets outperforms state-of-the-art methods in document and entity classification tasks.
We derive upper and lower bounds for the policy regret of T T T -round online learning problems with graph-structured feedback, where the adversary is nonoblivious but assumed to have a bounded memory. We obtain upper bounds of O ~ ( T 2 / 3 ) \widetilde O(T^{2/3}) O ( T 2/3 ) and O ~ ( T 3 / 4 ) \widetilde O(T^{3/4}) O ( T 3/4 ) for strongly-observable and weakly-observab…
Unified framework for expert selection with bandit and lower-bound feedback.
problem Selecting the best expert in scenarios with bandit feedback and lower-bound information.
method Introduces a new feedback model combining bandit and lower-bound information, proving optimal regret bounds for modified Exp3 algorithms.
result Optimal regret bounds for modified Exp3 algorithms, generalizing both bandit and full-information settings.
New algorithm reduces regret in combinatorial causal bandits without graph structure.
problem Minimizing regret in combinatorial causal bandits without graph structure.
method Design of algorithms for binary general causal models and BGLMs without graph skeleton.
result Achieves O ( T ln T ) O(\sqrt{T}\ln T) O ( T ln T ) expected regret for causal models and O ( T 2 3 ln T ) O(T^{\frac{2}{3}}\ln T) O ( T 3 2 ln T ) for BGLMs. We present and study a partial-information model of online learning, where a decision maker repeatedly chooses from a finite set of actions, and observes some subset of the associated losses. This naturally models several situations where the losses of different actions are related, and knowing the loss of one action p…
We consider stochastic multi-armed bandit problems with graph feedback, where the decision maker is allowed to observe the neighboring actions of the chosen action. We allow the graph structure to vary with time and consider both deterministic and Erdős-Rényi random graph models. For such a graph feedback model, we fir…
Optimizes arm selection with side information in Gaussian bandits.
problem Optimizing arm selection with side information in Gaussian bandits.
method Constructs an LP-based asymptotic instance-dependent lower bound on the regret and develops the first known asymptotically optimal algorithm.
result First known asymptotically optimal algorithm for Gaussian bandits with side information.
IDS algorithm optimizes sequential decisions in various monitoring settings.
problem Optimizing sequential decisions in complex monitoring scenarios.
method Information-directed sampling (IDS) algorithm for linear partial monitoring.
result IDS achieves nearly worst-case rate optimality in finite-action games.
Eigen-GNN enhances GNNs by preserving graph structures.
problem Existing shallow GNNs fail to effectively preserve graph structures.
method Integrates eigenspace of graph structures into GNNs as a dimensionality reduction module.
result Eigen-GNN boosts GNNs' ability to preserve graph structures without increasing depth.
Graph attention auto-encoder reconstructs graph structure and attributes.
problem Lack of methods to reconstruct graph structure and node attributes in graph auto-encoders.
method Stacked encoder/decoder layers with self-attention mechanisms, regularized node representations to reconstruct graph structure.
result Competitive performance on node classification benchmarks, including inductive learning.
We consider a sequential learning problem with Gaussian payoffs and side information: after selecting an action i i i , the learner receives information about the payoff of every action j j j in the form of Gaussian observations whose mean is the same as the mean payoff, but the variance depends on the pair ( i , j ) (i,j) ( i , j ) (and may…
We introduce a stochastic heterogeneous interacting-agent model for the short-time non-equilibrium evolution of excess demand and price in a stylized asset market. We consider a combination of social interaction within peer groups and individually heterogeneous fundamentalist trading decisions which take into account t…
GraphTSNE visualizes graph data by integrating graph structure and node features.
problem Lack of suitable visualization techniques for graph-structured data.
method GraphTSNE combines t-SNE with graph convolutional networks to visualize graph data.
result GraphTSNE produces better visualizations of graph data compared to existing methods.
Proposes DIAL-GNN for joint graph structure and embedding learning.
problem Joint learning of graph structure and embeddings.
method Adapted graph regularization, iterative method for graph structure learning.
result Consistently outperforms state-of-the-art baselines in downstream tasks and computational time.
A new online learning algorithm for graph-structured sparsity.
problem Efficiently handling graph-structured sparsity constraints in online learning settings.
method Proposes extsc{GraphDA} algorithm that projects gradients and variables onto subspaces.
result Improves classification performance and captures graph-structured features effectively.
Investigates how neural network graph structure impacts predictive performance.
problem Lack of understanding between neural network graph structure and predictive performance.
method Developed relational graph representation to analyze neural networks, identifying a 'sweet spot' for improved performance.
result Identified a 'sweet spot' in relational graph structure that significantly improves neural network predictive performance.
This paper improves GNN robustness by aligning feature and adjacency matrix learning.
problem Improving robustness of graph neural networks (GNN) in noisy graph data.
method Proposes a novel regularized GSL approach that aligns feature information and graph information, incorporating sparse dimensional reduction.
result Demonstrates superior performance in noisy graph structures compared to competitive baselines.
Two new methods improve graph embedding without needing a complete graph structure.
problem Graph autoencoders' performance depends on the adjacency matrix quality.
method BAGE and VBAGE: unsupervised graph embedding via adaptive graph learning.
result The methods expand GAEs' applicability to datasets without graph structure.
Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and con…
New method solves graph-structured sparsity problems efficiently.
problem Graph-structured sparsity optimization in complex models.
method Stochastic gradient-based approach for non-convex graph-structured sparsity.
result Linear convergence up to a constant error.
IDGL learns better graph structure and embeddings iteratively.
problem Improving graph neural network node embeddings and graph structure.
method Iterative Deep Graph Learning framework that dynamically stops when graph structure optimizes for downstream tasks.
result IDGL consistently outperforms state-of-the-art baselines on nine benchmarks.
Processes such as disease propagation and information diffusion often spread over some latent network structure which must be learned from observation. Given a set of unlabeled training examples representing occurrences of an event type of interest (e.g., a disease outbreak), our goal is to learn a graph structure that…
Estimates manifold dimension using local graph structure.
problem Estimating the intrinsic dimension of manifolds from data.
method Regression on local PCA coordinates, focusing on local graph structure.
result Proposed QE and TLS estimators outperform existing methods.
Study improves epidemic forecasting with a sparsified GSRNN.
problem Epidemic forecasting on real-world health data.
method Graph-structured recurrent neural network (GSRNN) with sparsification via transformed- ℓ 1 \ell_1 ℓ 1 penalty. result Maintained prediction accuracy with 70% of network weights being zero.
GraphSTONE uses topic models to capture graph structures, improving GCN performance.
problem GCNs focus too much on node features and not enough on graph structures.
method GraphSTONE employs topic models of graphs to capture structural topics, which guide the aggregation of node features.
result GraphSTONE outperforms GCNs in performance, efficiency, and interpretability.
GraphITE estimates individual effects of graph-structured treatments.
problem Estimating individual effects of complex treatment structures.
method Graph neural networks and Hilbert-Schmidt Independence Criterion regularization.
result GraphITE outperforms baselines in estimating treatment effects for large numbers of treatments.
Graph Structured Prediction Energy Networks model correlations for joint inference.
problem Joint inference over multiple variables with high-order correlations.
method Energy Networks for modeling explicit local and implicit higher-order correlations.
result Tractable inference with explicit modeling of correlations.
Method learns graph structure for multi-task learning, revealing interpretable relationships.
problem Learning relationships among tasks in multi-task learning.
method Simultaneously learns graph structure and model parameters, optimizing the graph structure with the model parameters.
result Reduces generalization error and reveals interpretable sparse graph among tasks.
Method learns graph-structured data segments using deep learning.
problem Segmenting data structured by an adjacency graph.
method Graph-structured contrastive loss for deep learning.
result Achieved state-of-the-art performance on 3D point cloud segmentation.
New algorithms for causal bandits without knowing the graph structure.
problem Causal bandit problems with unknown graph structure.
method Developed novel causal bandit algorithms for causal trees, forests, and general graphs without prior knowledge of the causal graph.
result Regret guarantees significantly improved over standard MAB algorithms under mild conditions.
This work proposes a method to learn graph structure for multivariate time series forecasting.
problem Improving multivariate time series forecasting by leveraging pairwise information.
method Learning a probabilistic graph model through optimizing mean performance over graph distribution parameterized by a neural network.
result Our method outperforms existing approaches in simplicity, efficiency, and performance.
Graph Information Bottleneck (GIB) optimizes graph representations for robustness against adversarial attacks.
problem Challenges in learning graph representations due to structure and feature information.
method GIB is an information-theoretic principle that balances expressiveness and robustness by maximizing mutual information between representation and target, while constraining mutual information with input data.
result GIB-based models are more robust to adversarial attacks, achieving up to 31% improvement.
New model preserves graph structure in large datasets.
problem Lack of permutation invariance in graph generation models for large graphs.
method Uses graph embeddings to create a scalable generative model.
result Model maintains structure in large graphs without losing invariance.
Proposes HypCSE for enhanced hierarchical clustering.
problem Challenges in existing hierarchical clustering methods.
method Hyperbolic Continuous Structural Entropy (HypCSE) neural networks.
result Superior performance on seven datasets.
End-to-end neural network extracts graph structure from EEG signals for improved emotional video classification.
problem Challenges in achieving accurate EEG classification for emotional video analysis.
method Proposes an end-to-end neural network model that learns an appropriate multi-layer graph structure from raw EEG signals.
result Improves performance in emotional video classification compared to manually defined connectivity structures.
node2coords learns interpretable graph node representations robust to graph perturbations.
problem Need representations that capture graph structure and are robust to perturbations.
method Proposes a graph representation learning algorithm using Wasserstein barycenters.
result Learned representations are interpretable and stable to graph perturbations.
New insights into cascade feedback linearization of control systems.
problem Obtaining a cascade feedback linearization for invariant control systems.
method Introducing truncated versions of operators from the calculus of variations to prove new theorems.
result Established new geometry and foundational theorems for future work.
GraphTEE estimates treatment effects on graph-structured targets, mitigating bias.
problem Understanding treatment effects on graph-structured targets with observational bias.
method GraphTEE framework focusing on confounding variable sets and new regularization.
result GraphTEE mitigates bias better than previous methods.
GISST interprets GNNs by combining attention and sparsity for graph structure and node feature importance.
problem Lack of joint consideration of graph structure and node features in GNN interpretation.
method Model-agnostic framework using attention mechanism and sparsity regularization.
result GISST achieves superior node feature and edge explanation precision in synthetic and real-world datasets.
New method learns from either positive or negative feedback alone.
problem Limited applicability of existing preference optimization methods in scenarios with only unpaired feedback.
method Decouples learning from positive and negative feedback, using expectation-maximization (EM) to optimize probability of positive outcomes and explicitly incorporate negative examples.
result Stable learning from negative feedback alone demonstrated.
Proposes learning graph structure and GCN parameters together.
problem Inability of GNNs to use graphs that are incomplete or corrupted.
method Solves a bilevel program to learn a discrete graph structure.
result Outperforms related methods in experiments.
GraphVRNN generates graphs with latent variables and node attributes.
problem Generating diverse and complex graph structures.
method Probabilistic autoregressive model for graph generation.
result GraphVRNN can model complicated distributions and generate plausible structures and node attributes.
SANS uses graph structure to find meaningful negatives for entity and relation embeddings.
problem Finding hard negatives for entity and relation embeddings in knowledge graphs.
method Structure Aware Negative Sampling (SANS) that selects negatives from a node's k-hop neighborhood.
result SANS finds semantically meaningful negatives and is competitive with state-of-the-art approaches.
Estimates change-points and graph structures in a time-varying Ising model.
problem Detecting and understanding changes in a time-varying Ising model.
method Maximizing a penalized conditional log-likelihood to estimate neighborhood of each node, enforcing sparsity and piece-wise constant graph structures.
result First change-points consistency theorems for unknown number of change-points in time-varying Ising model.
New method learns graph structure and uncertainty from data.
problem Learning latent graph structures and their uncertainty from data.
method Proposes a sampling-based method to learn latent graph structure and uncertainty simultaneously.
result Proves that suitable loss functions on stochastic model outputs solve both learning latent graph structure and achieving optimal predictions.
User preferences for items can be inferred from either explicit feedback, such as item ratings, or implicit feedback, such as rental histories. Research in collaborative filtering has concentrated on explicit feedback, resulting in the development of accurate and scalable models. However, since explicit feedback is oft…