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Publication search results
found 19 matches
- 2023
- Inyoung Jun, Scott A. Cohen, Sarah E. Ser, Simone Marini, Robert J. Lucero, Jiang Bian, Mattia Prosperi:
Optimizing Dynamic Antibiotic Treatment Strategies against Invasive Methicillin-Resistant Staphylococcus Aureus Infections using Causal Survival Forests and G-Formula on Statewide Electronic Health Record Data. CDPD 2023: 98-115 - Thuc Duy Le, Jiuyong Li, Robert Ness, Sofia Triantafillou, Shohei Shimizu, Peng Cui, Kun Kuang, Jian Pei, Fei Wang, Mattia Prosperi:
Preface: The 2023 ACM SIGKDD Workshop on Causal Discovery, Prediction and Decision. CDPD 2023: 1-2 - Wang Lu, Jindong Wang, Yidong Wang, Xing Xie:
Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution. CDPD 2023: 75-97 - Lorenzo Masoero, Doug Hains, James McQueen:
Leveraging covariate adjustments at scale in online A/B testing. CDPD 2023: 25-48 - Oliver Schacht, Sven Klaassen, Philipp Schwarz, Martin Spindler, Daniel Grünbaum, Sebastian Imhof:
Causally Learning an Optimal Rework Policy. CDPD 2023: 3-24 - Ruoxuan Xiong, Alex Chin, Sean Taylor:
Bias-Variance Tradeoffs for Designing Simultaneous Temporal Experiments. CDPD 2023: 115-131 - Shengyu Zhang, Yunze Tong, Kun Kuang, Fuli Feng, Jiezhong Qiu, Jin Yu, Zhou Zhao, Hongxia Yang, Zhongfei Zhang, Fei Wu:
Stable Prediction on Graphs with Agnostic Distribution Shifts. CDPD 2023: 49-74 - Thuc Duy Le, Jiuyong Li, Robert Ness, Sofia Triantafillou, Shohei Shimizu, Peng Cui, Kun Kuang, Jian Pei, Fei Wang, Mattia Prosperi:
The KDD'23 Workshop on Causal Discovery, Prediction and Decision, 07 August 2023, Long Beach, CA, USA. Proceedings of Machine Learning Research 218, PMLR 2023 [contents] - 2022
- Christopher Hagedorn, Constantin Lange, Johannes Huegle, Rainer Schlosser:
GPU Acceleration for Information-theoretic Constraint-based Causal Discovery. CD@KDD 2022: 30-60 - Thuc Duy Le, Lin Liu, Emre Kiciman, Sofia Triantafyllou, Huan Liu:
Preface: The 2022 ACM SIGKDD Workshop on Causal Discovery. CD@KDD 2022: 1-2 - Osman Mian, David Kaltenpoth, Michael Kamp:
Regret-based Federated Causal Discovery. CD@KDD 2022: 61-69 - Konstantina Valogianni, Balaji Padmanabhan:
Causal ABMs: Learning Plausible Causal Models using Agent-based Modeling. CD@KDD 2022: 3-29 - Thuc Duy Le, Lin Liu, Emre Kiciman, Sofia Triantafyllou, Huan Liu:
The KDD'22 Workshop on Causal Discovery, 15 August 2022, Washington DC, USA. Proceedings of Machine Learning Research 185, PMLR 2022 [contents] - 2020
- Thuc Duy Le, Lin Liu, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen:
Preface: The 2020 ACM SIGKDD Workshop on Causal Discovery. CD@KDD 2020: 1-3 - Yunzhe Li, Kun Kuang, Bo Li, Peng Cui, Jianrong Tao, Hongxia Yang, Fei Wu:
Continuous Treatment Effect Estimation via Generative Adversarial De-confounding. CD@KDD 2020: 4-22 - Sisi Ma, Roshan Tourani:
Predictive and Causal Implications of using Shapley Value for Model Interpretation. CD@KDD 2020: 23-38 - Ankit Sharma, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, Lovekesh Vig, Gautam Shroff:
Hi-CI: Deep Causal Inference in High Dimensions. CD@KDD 2020: 39-61 - Jonathan D. Young, Bryan Andrews, Gregory F. Cooper, Xinghua Lu:
Learning Latent Causal Structures with a Redundant Input Neural Network. CD@KDD 2020: 62-91 - Thuc Duy Le, Lin Liu, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen:
Proceedings of the 2020 KDD Workshop on Causal Discovery (CD@KDD 2020), San Diego, CA, USA, 24 August 2020. Proceedings of Machine Learning Research 127, PMLR 2020 [contents]
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