Hidden Community Detection on Two-Layer Stochastic Models: A Theoretical Perspective. - Jialu Bao; Kun He 001; Xiaodong Xin; Bart Selman; John E. Hopcroft (TAMC, 2020)

Hidden Community Detection on Two-layer Stochastic Models: a Theoretical Prospective. - Jialu Bao; Kun He 001; Xiaodong Xin; Bart Selman; John E. Hopcroft (CoRR, 2020)

A Logic to Reason about Dependence and Independence. - Jialu Bao; Simon Docherty; Justin Hsu; Alexandra Silva 001 (CoRR, 2020)

A Bunched Logic for Conditional Independence. - Jialu Bao; Simon Docherty; Justin Hsu; Alexandra Silva 001 (LICS, 2021)

Data-Driven Invariant Learning for Probabilistic Programs. - Jialu Bao; Drashti Pathak; Justin Hsu; Subhajit Roy 001 (CoRR, 2021)

Structure Amplification on Multi-layer Stochastic Block Models. - Xiaodong Xin; Kun He 001; Jialu Bao; Bart Selman; John E. Hopcroft (CoRR, 2021)

A separation logic for negative dependence. - Jialu Bao; Marco Gaboardi; Justin Hsu; Joseph Tassarotti (Proc. ACM Program. Lang., 2022)

Data-Driven Invariant Learning for Probabilistic Programs (Extended Abstract). - Jialu Bao; Nitesh Trivedi; Drashti Pathak; Justin Hsu; Subhajit Roy 001 (IJCAI, 2023)

A Categorical Approach to DIBI Models. - Tao Gu 002; Jialu Bao; Justin Hsu; Alexandra Silva 001; Fabio Zanasi (FSCD, 2024)

Bluebell: An Alliance of Relational Lifting and Independence for Probabilistic Reasoning. - Jialu Bao; Emanuele D'Osualdo; Azadeh Farzan (Proc. ACM Program. Lang., 2025)

Structure Amplification on Multi-layer Stochastic Block Models. - Kun He 001; Xiaodong Xin; Jialu Bao; Meng Wang 039; Bart Selman; John E. Hopcroft (ACM Trans. Knowl. Discov. Data, 2025)

Data-driven invariant learning for probabilistic programs. - Jialu Bao; Nitesh Trivedi; Drashti Pathak; Justin Hsu; Subhajit Roy 001 (Formal Methods Syst. Des., 2025)