Chemical Structure Elucidation from Mass Spectrometry by Matching Substructures. - Jing Lim; Joshua Wong; Minn Xuan Wong; Lee Han Eric Tan; Hai Leong Chieu; Davin Choo; Neng Kai Nigel Neo (CoRR, 2018)

Bosphorus: Bridging ANF and CNF Solvers. - Davin Choo; Mate Soos; Kian Ming Adam Chai; Kuldeep S. Meel (DATE, 2019)

k-means++: few more steps yield constant approximation. - Davin Choo; Christoph Grunau; Julian Portmann; Václav Rozhon (ICML, 2020)

The Complexity of Sparse Tensor PCA. - Davin Choo; Tommaso d'Orsi (NeurIPS, 2021)

Massively Parallel Correlation Clustering in Bounded Arboricity Graphs. - Mélanie Cambus; Davin Choo; Havu Miikonen; Jara Uitto (DISC, 2021)

Learning Sparse Fixed-Structure Gaussian Bayesian Networks. - Arnab Bhattacharyya 001; Davin Choo; Rishikesh Gajjala; Sutanu Gayen; Yuhao Wang (AISTATS, 2022)

Verification and search algorithms for causal DAGs. - Davin Choo; Kirankumar Shiragur; Arnab Bhattacharyya 001 (NeurIPS, 2022)

Subset verification and search algorithms for causal DAGs. - Davin Choo; Kirankumar Shiragur (AISTATS, 2023)

Learning and Testing Latent-Tree Ising Models Efficiently. - Anthimos Vardis Kandiros; Constantinos Daskalakis; Yuval Dagan; Davin Choo (COLT, 2023)

Active causal structure learning with advice. - Davin Choo; Themistoklis Gouleakis; Arnab Bhattacharyya 001 (ICML, 2023)

New metrics and search algorithms for weighted causal DAGs. - Davin Choo; Kirankumar Shiragur (ICML, 2023)

Adaptivity Complexity for Causal Graph Discovery. - Davin Choo; Kirankumar Shiragur (UAI, 2023)

Causal Discovery under Off-Target Interventions. - Davin Choo; Kirankumar Shiragur; Caroline Uhler (AISTATS, 2024)

Learning bounded-degree polytrees with known skeleton. - Davin Choo; Joy Qiping Yang; Arnab Bhattacharyya 001; Clément L. Canonne (ALT, 2024)

Online bipartite matching with imperfect advice. - Davin Choo; Themistoklis Gouleakis; Chun Kai Ling; Arnab Bhattacharyya 001 (ICML, 2024)

The Sharp Power Law of Local Search on Expanders. - Simina Brânzei; Davin Choo; Nicholas J. Recker (SODA, 2024)

A short note about the learning-augmented secretary problem. - Davin Choo; Chun Kai Ling (CoRR, 2024)

Learning multivariate Gaussians with imperfect advice. - Arnab Bhattacharyya 001; Davin Choo; Philips George John; Themis Gouleakis (CoRR, 2024)

Envy-free house allocation with minimum subsidy. - Davin Choo; Yan Hao Ling; Warut Suksompong; Nicholas Teh; Jian Zhang (Oper. Res. Lett., 2024)

Learnability of Parameter-Bounded Bayes Nets. - Arnab Bhattacharyya 001; Davin Choo; Sutanu Gayen; Dimitrios Myrisiotis (AAAI, 2025)

Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set. - Davin Choo; Chandler Squires; Arnab Bhattacharyya 001; David Sontag (CLeaR, 2025)

A partition cover approach to tokenization. - Jia Peng Lim; Davin Choo; Hady W. Lauw (CoRR, 2025)

On Sequential Fault-Intolerant Process Planning. - Andrzej Kaczmarczyk 001; Davin Choo; Niclas Boehmer; Milind Tambe; Haifeng Xu (CoRR, 2025)

Learning-Augmented Online Bipartite Fractional Matching. - Davin Choo; Billy Jin; Yongho Shin (CoRR, 2025)

Adaptive Frontier Exploration on Graphs with Applications to Network-Based Disease Testing. - Davin Choo; Yuqi Pan; Tonghan Wang 001; Milind Tambe; Alastair van Heerden; Cheryl Johnson (CoRR, 2025)

Approximate Proportionality in Online Fair Division. - Davin Choo; Winston Fu; Derek Khu; Tzeh Yuan Neoh; Tze-Yang Poon; Nicholas Teh (CoRR, 2025)