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Venues (Conferences, Journals, ...)
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GrowBag graphs for keyword ? (Num. hits/coverage)
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Results
Found 32 publication records. Showing 32 according to the selection in the facets
| Hits ?▲ |
Authors |
Title |
Venue |
Year |
Link |
Author keywords |
| 1 | Osman Mian, David Kaltenpoth, Michael Kamp |
Regret-based Federated Causal Discovery.  |
CD@KDD  |
2022 |
DBLP BibTeX RDF |
|
| 1 | Thuc Duy Le, Lin Liu 0003, Emre Kiciman, Sofia Triantafyllou, Huan Liu 0001 |
Preface: The 2022 ACM SIGKDD Workshop on Causal Discovery. |
CD@KDD |
2022 |
DBLP BibTeX RDF |
|
| 1 | Thuc Duy Le, Lin Liu 0003, Emre Kiciman, Sofia Triantafyllou, Huan Liu 0001 (eds.) |
The KDD'22 Workshop on Causal Discovery, 15 August 2022, Washington DC, USA |
CD@KDD |
2022 |
DBLP BibTeX RDF |
|
| 1 | Konstantina Valogianni, Balaji Padmanabhan |
Causal ABMs: Learning Plausible Causal Models using Agent-based Modeling. |
CD@KDD |
2022 |
DBLP BibTeX RDF |
|
| 1 | Christopher Hagedorn, Constantin Lange, Johannes Huegle, Rainer Schlosser |
GPU Acceleration for Information-theoretic Constraint-based Causal Discovery. |
CD@KDD |
2022 |
DBLP BibTeX RDF |
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| 1 | Laila Melkas, Rafael Savvides, Suyog Chandramouli, Jarmo Mäkelä, Tuomo Nieminen, Ivan Mammarella, Kai Puolamäki |
Interactive Causal Structure Discovery in Earth System Sciences. |
CD@KDD |
2021 |
DBLP BibTeX RDF |
|
| 1 | Keisuke Kiritoshi, Tomonori Izumitani, Kazuki Koyama, Tomomi Okawachi, Keisuke Asahara, Shohei Shimizu |
Estimating individual-level optimal causal interventions combining causal models and machine learning models. |
CD@KDD |
2021 |
DBLP BibTeX RDF |
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| 1 | Ehsan Mokhtarian, Sina Akbari, AmirEmad Ghassami, Negar Kiyavash |
A Recursive Markov Boundary-Based Approach to Causal Structure Learning. |
CD@KDD |
2021 |
DBLP BibTeX RDF |
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| 1 | Eric V. Strobl, Shyam Visweswaran |
Dirac Delta Regression: Conditional Density Estimation with Clinical Trials. |
CD@KDD |
2021 |
DBLP BibTeX RDF |
|
| 1 | Thuc Duy Le, Jiuyong Li, Gregory Cooper, Sofia Triantafyllou, Elias Bareinboim, Huan Liu 0001, Negar Kiyavash (eds.) |
The KDD 2021 Workshop on Causal Discovery, CD@KDD 2021, Singapore, August 15, 2021. |
CD@KDD |
2021 |
DBLP BibTeX RDF |
|
| 1 | Thuc Duy Le, Jiuyong Li, Gregory Cooper, Sofia Triantafyllou, Elias Bareinboim, Huan Liu 0001, Negar Kiyavash |
Preface: The 2021 ACM SIGKDD Workshop on Causal Discovery. |
CD@KDD |
2021 |
DBLP BibTeX RDF |
|
| 1 | Thuc Duy Le, Lin Liu 0003, Kun Zhang 0001, Emre Kiciman, Peng Cui 0001, Aapo Hyvärinen |
Preface: The 2020 ACM SIGKDD Workshop on Causal Discovery. |
CD@KDD |
2020 |
DBLP BibTeX RDF |
|
| 1 | Jonathan D. Young, Bryan Andrews, Gregory F. Cooper, Xinghua Lu |
Learning Latent Causal Structures with a Redundant Input Neural Network. |
CD@KDD |
2020 |
DBLP BibTeX RDF |
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| 1 | Ankit Sharma, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, Lovekesh Vig, Gautam Shroff |
Hi-CI: Deep Causal Inference in High Dimensions. |
CD@KDD |
2020 |
DBLP BibTeX RDF |
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| 1 | Sisi Ma, Roshan Tourani |
Predictive and Causal Implications of using Shapley Value for Model Interpretation. |
CD@KDD |
2020 |
DBLP BibTeX RDF |
|
| 1 | Thuc Duy Le, Lin Liu 0003, Kun Zhang 0001, Emre Kiciman, Peng Cui 0001, Aapo Hyvärinen (eds.) |
Proceedings of the 2020 KDD Workshop on Causal Discovery (CD@KDD 2020), San Diego, CA, USA, 24 August 2020. |
CD@KDD |
2020 |
DBLP BibTeX RDF |
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| 1 | Yunzhe Li 0003, Kun Kuang, Bo Li 0064, Peng Cui 0001, Jianrong Tao, Hongxia Yang, Fei Wu 0001 |
Continuous Treatment Effect Estimation via Generative Adversarial De-confounding. |
CD@KDD |
2020 |
DBLP BibTeX RDF |
|
| 1 | Charles K. Assaad, Emilie Devijver, Éric Gaussier, Ali Aït-Bachir |
Scaling Causal Inference in Additive Noise Models. |
CD@KDD |
2019 |
DBLP BibTeX RDF |
|
| 1 | Thuc Duy Le, Jiuyong Li, Kun Zhang 0001, Emre Kiciman, Peng Cui 0001, Aapo Hyvärinen |
Preface: The 2019 ACM SIGKDD Workshop on Causal Discovery. |
CD@KDD |
2019 |
DBLP BibTeX RDF |
|
| 1 | Alexander Lin, Amil Merchant, Suproteem K. Sarkar, Alexander D'Amour |
Universal Causal Evaluation Engine: An API for empirically evaluating causal inference models. |
CD@KDD |
2019 |
DBLP BibTeX RDF |
|
| 1 | Shuyang Du, James Lee, Farzin Ghaffarizadeh |
Improve User Retention with Causal Learning. |
CD@KDD |
2019 |
DBLP BibTeX RDF |
|
| 1 | Bryan Andrews, Joseph D. Ramsey, Gregory F. Cooper |
Learning High-dimensional Directed Acyclic Graphs with Mixed Data-types. |
CD@KDD |
2019 |
DBLP BibTeX RDF |
|
| 1 | Christopher Schmidt, Johannes Huegle, Philipp Bode, Matthias Uflacker |
Load-Balanced Parallel Constraint-Based Causal Structure Learning on Multi-Core Systems for High-Dimensional Data. |
CD@KDD |
2019 |
DBLP BibTeX RDF |
|
| 1 | Thuc Duy Le, Jiuyong Li, Kun Zhang 0001, Emre Kiciman, Peng Cui 0001, Aapo Hyvärinen (eds.) |
Proceedings of the 2019 ACM SIGKDD Workshop on Causal Discovery, CD@KDD 2019, Anchorage, Alaska, USA, August 5, 2019 |
CD@KDD |
2019 |
DBLP BibTeX RDF |
|
| 1 | Sandeep Soni, Shawn Ling Ramirez, Jacob Eisenstein |
Detecting Social Influence in Event Cascades by Comparing Discriminative Rankers. |
CD@KDD |
2019 |
DBLP BibTeX RDF |
|
| 1 | Eric V. Strobl |
Improved Causal Discovery from Longitudinal Data Using a Mixture of DAGs. |
CD@KDD |
2019 |
DBLP BibTeX RDF |
|
| 1 | Mandar S. Chaudhary, Nagiza F. Samatova |
Causal Relationship Prediction with Continuous Additive Noise Models. |
CD@KDD |
2018 |
DBLP BibTeX RDF |
|
| 1 | Thuc Duy Le, Kun Zhang 0001, Emre Kiciman, Aapo Hyvärinen, Lin Liu 0003 (eds.) |
Proceedings of 2018 ACM SIGKDD Workshop on Causal Discovery, CD@KDD 2018, London, UK, 20 August 2018. |
CD@KDD |
2018 |
DBLP BibTeX RDF |
|
| 1 | Vineet K. Raghu, Allen Poon, Panayiotis V. Benos |
Evaluation of Causal Structure Learning Methods on Mixed Data Types. |
CD@KDD |
2018 |
DBLP BibTeX RDF |
|
| 1 | Thuc Duy Le, Kun Zhang 0001, Emre Kiciman, Aapo Hyvärinen, Lin Liu 0003 |
Preface: The 2018 ACM SIGKDD Workshop on Causal Discovery. |
CD@KDD |
2018 |
DBLP BibTeX RDF |
|
| 1 | Min Zheng 0001, Jan Claassen, Samantha Kleinberg |
Automated Identification of Causal Moderators in Time-Series Data. |
CD@KDD |
2018 |
DBLP BibTeX RDF |
|
| 1 | Daniel Malinsky, Peter Spirtes |
Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding. |
CD@KDD |
2018 |
DBLP BibTeX RDF |
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Displaying result #1 - #32 of 32 (100 per page; Change: )
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