RepUCB and RepLinUCB deliver replicable regret bounds O(K² log²T / ρ² ⋅ sum) for MAB and Õ((d + d³/ρ)√T) for linear bandits, improving the prior best by O(d/ρ) via optimistic exploration and a new replicable ridge estimator.
National Academies of Sciences, Engineering, and Medicine (2019)
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ARA uses LLMs to build workflow graphs linking sources, methods, and outputs in papers, then scores reproducibility, reaching ~61% accuracy on 213 ReScience C articles and outperforming priors on ReproBench and GoldStandardDB.
Agent-based AI workflows repair injected reproducibility failures in R social-science code at 69-96% success, substantially outperforming prompt-based LLM approaches at 31-79%.
Visualization researchers propose traceability—recording abundant annotated artifacts, reporting curated research threads, and enabling reading via interfaces—as a way to ensure rigor and transparency in inherently unreproducible design processes.
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Replicable Bandits with UCB based Exploration
RepUCB and RepLinUCB deliver replicable regret bounds O(K² log²T / ρ² ⋅ sum) for MAB and Õ((d + d³/ρ)√T) for linear bandits, improving the prior best by O(d/ρ) via optimistic exploration and a new replicable ridge estimator.
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ARA: Agentic Reproducibility Assessment For Scalable Support Of Scientific Peer-Review
ARA uses LLMs to build workflow graphs linking sources, methods, and outputs in papers, then scores reproducibility, reaching ~61% accuracy on 213 ReScience C articles and outperforming priors on ReproBench and GoldStandardDB.
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Automating Computational Reproducibility in Social Science: Comparing Prompt-Based and Agent-Based Approaches
Agent-based AI workflows repair injected reproducibility failures in R social-science code at 69-96% success, substantially outperforming prompt-based LLM approaches at 31-79%.
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Reflections on Traceability for Visualization Research
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Is K-fold cross validation the best model selection method for Machine Learning?
K-fold CUBV combines cross-validation with PAC-Bayesian upper bounds on actual risk to provide a more robust criterion for validating ML accuracy and reducing false positives than standard CV.