Cover Image

Learning Bayesian and Markov Networks with an Unreliable Oracle

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Harviainen, Juha, Parviainen, Pekka, Sharma, Vidya Sagar
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Graph Reconstruction with a Connected Components Oracle
by: Harviainen, Juha, et al.
Published: (2025)

A Fixed-Parameter Tractable Algorithm for Counting Markov Equivalence Classes with the same Skeleton
by: Sharma, Vidya Sagar
Published: (2023)

Improving Decision Trees through the Lens of Parameterized Local Search
by: Harviainen, Juha, et al.
Published: (2025)

Exact and Approximate Algorithms for Polytree Learning
by: Harviainen, Juha, et al.
Published: (2026)

Structural perspective on constraint-based learning of Markov networks
by: Korhonen, Tuukka, et al.
Published: (2024)

Scaling Up Bayesian DAG Sampling
by: Nikzad, Daniele, et al.
Published: (2025)

Optimal Decision Tree Pruning Revisited: Algorithms and Complexity
by: Harviainen, Juha, et al.
Published: (2025)

Quantum Speedups for Bayesian Network Structure Learning
by: Harviainen, Juha, et al.
Published: (2023)

Marginal Pseudo-Likelihood Learning of Markov Network structures
by: Pensar, Johan, et al.
Published: (2014)

Evaluating Prediction Uncertainty Estimates from BatchEnsemble
by: Blørstad, Morten, et al.
Published: (2026)

Improved and Oracle-Efficient Online $\ell_1$-Multicalibration
by: Ghuge, Rohan, et al.
Published: (2025)

Understanding Domain-Size Generalization in Markov Logic Networks
by: Chen, Florian, et al.
Published: (2024)

Bayesian Multi-Scale Neural Network for Crowd Counting
by: Sagar, Abhinav
Published: (2020)

Faster Fixed Parameter Tractable Algorithms for Counting Markov Equivalence Classes with Special Skeletons
by: Sharma, Vidya Sagar
Published: (2023)

Bayesian Functional Connectivity and Graph Convolutional Network for Working Memory Load Classification
by: Gangapuram, Harshini, et al.
Published: (2024)

Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios
by: G, Vidya Sagar, et al.
Published: (2025)

Fairness, Accuracy, and Unreliable Data
by: Stangl, Kevin
Published: (2024)

Robust Transfer Learning with Unreliable Source Data
by: Fan, Jianqing, et al.
Published: (2023)

Secure Vertical Federated Learning Under Unreliable Connectivity
by: Qiu, Xinchi, et al.
Published: (2023)

Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approach
by: Wang, Yanmeng, et al.
Published: (2025)

Autoencoders for Anomaly Detection are Unreliable
by: Bouman, Roel, et al.
Published: (2025)

Unreliable Uncertainty Estimates with Monte Carlo Dropout
by: Djupskås, Aslak, et al.
Published: (2025)

Membership Testing in Markov Equivalence Classes via Independence Query Oracles
by: Zhang, Jiaqi, et al.
Published: (2024)

Piecewise Deterministic Markov Processes for Bayesian Neural Networks
by: Goan, Ethan, et al.
Published: (2023)

MAMAF-Net: Motion-Aware and Multi-Attention Fusion Network for Stroke Diagnosis
by: Degerli, Aysen, et al.
Published: (2023)

Federated Learning for Predicting Mild Cognitive Impairment to Dementia Conversion
by: Sharma, Gaurang, et al.
Published: (2025)

Unifying Summary Statistic Selection for Approximate Bayesian Computation
by: Hoffmann, Till, et al.
Published: (2022)

Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networks
by: Wu, Zheshun, et al.
Published: (2023)

Oracle-efficient Hybrid Learning with Constrained Adversaries
by: Okoroafor, Princewill, et al.
Published: (2026)

Learning Interpretable Models Using Uncertainty Oracles
by: Ghose, Abhishek, et al.
Published: (2019)

OFAL: An Oracle-Free Active Learning Framework
by: Khorsand, Hadi, et al.
Published: (2025)

Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles
by: Kim, Jung-hun, et al.
Published: (2017)

Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning
by: Wu, Chia-Yuan, et al.
Published: (2026)

In-Context Black-Box Optimization with Unreliable Feedback
by: Blumer, Nicolas Samuel, et al.
Published: (2026)

Attribution-Guided Model Rectification of Unreliable Neural Network Behaviors
by: Yang, Peiyu, et al.
Published: (2026)

Regret-Oracle Complexity Tradeoffs in Agnostic Online Learning
by: Attias, Idan, et al.
Published: (2026)

Data-dependent and Oracle Bounds on Forgetting in Continual Learning
by: Friedman, Lior, et al.
Published: (2024)

Oracle-Efficient Hybrid Online Learning with Unknown Distribution
by: Wu, Changlong, et al.
Published: (2024)

Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data Contamination
by: Wu, Mingqi, et al.
Published: (2025)

Is Oracle Pruning the True Oracle?
by: Feng, Sicheng, et al.
Published: (2024)