Learning Graphical Models
A Proofs from Section 2 448 Algorithm 4: Output ˆ α null G1 (1 η
Return ˆ α We show the following generalization of Proposition 2.1. Moreover, Alg. 4 has sample complexity The sample complexity is clear so we focus on the first statement. Theorem 4.5 in [MU17]) on these events as i varies and noting that Hence recalling (A.2) above, we conclude that The other direction is similar. Using (A.2) in the same way as above, we find First we analyze the expected sample complexity. Finally Alg. 4 has sample complexity We do this using Bayes' rule.
Asymptotically Optimal Quantile Pure Exploration for Infinite-Armed Bandits
We study pure exploration with infinitely many bandit arms generated i.i.d.
3acbe9dc3a1e8d48a57b16e9aef91879-Supplemental-Conference.pdf
Machine learning models often fail on out-of-distribution data.
OpenGSL: A Comprehensive Benchmark for Graph Structure Learning
Graph Structure Learning (GSL), a family of data-centric learning approaches, has garnered substantial attention in recent years. The core concept behind GSL is to jointly optimize the graph structure and the corresponding GNN models.
Causal discovery from observational and interventional data across multiple environments
A fundamental problem in many sciences is the learning of causal structure underlying a system, typically through observation and experimentation. Commonly, one even collects data across multiple domains, such as gene sequencing from different labs, or neural recordings from different species.
