| Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set |
Davin Choo, Chandler Squires, Arnab Bhattacharyya, David Sontag |
Conference on Causal Learning and Reasoning (CLeaR) |
2025 |
arXiv
|
| Learnability of Parameter-Bounded Bayes Nets |
Arnab Bhattacharyya, Davin Choo, Sutanu Gayen, Dimitrios Myrisiotis |
AAAI Conference on Artificial Intelligence (AAAI) |
2025 |
arXiv
|
| Online bipartite matching with imperfect advice |
Davin Choo, Themistoklis Gouleakis, Chun Kai Ling, Arnab Bhattacharyya |
International Conference on Machine Learning (ICML) |
2024 |
Conference link,
ICML,
Code,
arXiv
|
| Envy-free house allocation with minimum subsidy |
Davin Choo, Yan Hao Ling, Warut Suksompong, Nicholas Teh, Jian Zhang |
Operations Research Letters (ORL) |
2024 |
ORL,
arXiv
|
| Causal discovery under off-target interventions |
Davin Choo, Kirankumar Shiragur, Caroline Uhler |
International Conference on Artificial Intelligence and Statistics (AISTATS) |
2024 |
AISTATS,
Code,
arXiv
|
| Learning bounded degree polytrees with samples |
Davin Choo, Joy Qiping Yang, Arnab Bhattacharyya, Clément L. Canonne |
International Conference on Algorithmic Learning Theory (ALT) |
2024 |
ALT,
arXiv
|
| The Sharp Power Law of Local Search on Expanders |
Simina Brânzei, Davin Choo, Nicholas Recker |
Symposium on Discrete Algorithms (SODA) |
2024 |
SODA,
arXiv
|
| Learning and Testing Latent-Tree Ising Models Efficiently |
Yuval Dagan, Constantinos Daskalakis, Anthimos-Vardis Kandiros, Davin Choo |
Conference on Learning Theory (COLT) |
2023 |
Conference link,
COLT,
arXiv
|
| Adaptivity Complexity for Causal Graph Discovery |
Davin Choo, Kirankumar Shiragur |
Conference on Uncertainty in Artificial Intelligence (UAI) |
2023 |
UAI,
Code,
arXiv,
Video
|
| New metrics and search algorithms for weighted causal DAGs |
Davin Choo, Kirankumar Shiragur |
International Conference on Machine Learning (ICML) |
2023 |
Conference link,
ICML,
Code,
arXiv
|
| Active causal structure learning with advice |
Davin Choo, Themistoklis Gouleakis, Arnab Bhattacharyya |
International Conference on Machine Learning (ICML) |
2023 |
Conference link,
ICML,
Code,
arXiv
|
| Subset verification and search algorithms for causal DAGs |
Davin Choo, Kirankumar Shiragur |
International Conference on Artificial Intelligence and Statistics (AISTATS) |
2023 |
AISTATS,
Code,
arXiv,
Video
|
| Verification and search algorithms for causal DAGs |
Davin Choo, Kirankumar Shiragur, Arnab Bhattacharyya |
Conference on Neural Information Processing Systems (NeurIPS) |
2022 |
Conference link,
NeurIPS,
Code,
arXiv,
Slides,
Longer slides
|
| Learning Sparse Fixed-Structure Gaussian Bayesian Networks |
Arnab Bhattacharyya, Davin Choo, Rishikesh Gajjala, Sutanu Gayen, Yuhao Wang |
International Conference on Artificial Intelligence and Statistics (AISTATS) |
2022 |
AISTATS,
Code,
arXiv
|
| The Complexity of Sparse Tensor PCA |
Davin Choo, Tommaso d'Orsi |
Conference on Neural Information Processing Systems (NeurIPS) |
2021 |
Conference link,
NeurIPS,
arXiv,
Video,
Slides
|
| Massively Parallel Correlation Clustering in Bounded Arboricity Graphs |
Mélanie Cambus, Davin Choo, Havu Miikonen, Jara Uitto |
International Symposium on Distributed Computing (DISC) |
2021 |
DISC,
arXiv,
Video
|
| k-means++: few more steps yield constant approximation |
Davin Choo, Christoph Grunau, Julian Portmann, Václav Rozhoň |
International Conference on Machine Learning (ICML) |
2020 |
Conference link,
ICML,
arXiv,
Video,
Slides
|
| BOSPHORUS: Bridging ANF and CNF Solvers |
Davin Choo, Mate Soos, Kian Ming A. Chai, Kuldeep S. Meel |
Proceedings of Design, Automation, and Test in Europe (DATE) |
2019 |
DATE,
Paper,
arXiv,
Code,
Kuldeep's website link
|
| 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 |
Machine Learning for Molecules and Materials (NeurIPS Workshop) |
2018 |
arXiv
|
