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Found 19 papers, 5 papers with code
Causal Bandits with General Causal Models and Interventions
First, the structural causal models (SCMs) are assumed to be unknown and drawn arbitrarily from a general class $\mathcal{F}$ of Lipschitz-continuous functions.
Score-based Causal Representation Learning: Linear and General Transformations
The paper addresses both the identifiability and achievability aspects.
Blackout Mitigation via Physics-guided RL
This paper considers the sequential design of remedial control actions in response to system anomalies for the ultimate objective of preventing blackouts.
Robust Causal Bandits for Linear Models
Cumulative regret is adopted as the design criteria, based on which the objective is to design a sequence of interventions that incur the smallest cumulative regret with respect to an oracle aware of the entire causal model and its fluctuations.
General Identifiability and Achievability for Causal Representation Learning
For identifiability, the paper establishes that perfect recovery of the latent causal model and variables is guaranteed under uncoupled interventions.
Optimal Best Arm Identification with Fixed Confidence in Restless Bandits
It is shown that under every policy, the state-action visitation proportions satisfy a specific approximate flow conservation constraint and that these proportions match the optimal proportions dictated by the lower bound under any asymptotically optimal policy.
Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation
To accomplish this challenging task, first, a spectral sensitivity map is introduced to characterize the generalization weaknesses of models in the frequency domain.
GRNN-based Real-time Fault Chain Prediction
The search process is formalized as a partially observable Markov decision process (POMDP), which is subsequently solved via a time-varying graph recurrent neural network (GRNN) that judiciously accounts for the inherent temporal and spatial structures of the data generated by the system.
Score-based Causal Representation Learning with Interventions
The objectives are: (i) recovering the unknown linear transformation (up to scaling) and (ii) determining the directed acyclic graph (DAG) underlying the latent variables.
Best Arm Identification in Stochastic Bandits: Beyond $β-$optimality
Two key metrics for assessing bandit algorithms are computational efficiency and performance optimality (e. g., in sample complexity).
When Neural Networks Fail to Generalize? A Model Sensitivity Perspective
Domain generalization (DG) aims to train a model to perform well in unseen domains under different distributions.
Causal Bandits for Linear Structural Equation Models
Two linear mechanisms, one soft intervention and one observational, are assumed for each node, giving rise to $2^N$ possible interventions.
Active Sampling of Multiple Sources for Sequential Estimation
Additionally, each process $i\in\{1, \dots, K\}$ has a private parameter $\alpha_i$.
SPRT-based Efficient Best Arm Identification in Stochastic Bandits
Based on this test statistic, a BAI algorithm is designed that leverages the canonical sequential probability ratio tests for arm selection and is amenable to tractable analysis for the exponential family of bandits.
Best Arm Identification in Contaminated Stochastic Bandits
Owing to the adversarial contamination of the rewards, each arm's mean is only partially identifiable.
Scalable Intervention Target Estimation in Linear Models
This paper considers the problem of estimating the unknown intervention targets in a causal directed acyclic graph from observational and interventional data.
Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination
Owing to the adversarial contamination of the rewards, each arm's mean is only partially identifiable.
The Broadcast Approach in Communication Networks
However, when the variations are infrequent, their temporal average can deviate significantly from the channel's ergodic mode, rendering a lack of instantaneous performance guarantees.
Information Theory Information Theory
Structure Learning with Side Information: Sample Complexity
Leveraging such side information can be abstracted as inferring structures of distinct graphical models that are {\sl partially} similar.
