David T. Arbour

According to our database, David T. Arbour authored at least 36 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Continuous Treatment Effects with Surrogate Outcomes.

Zhenghao Zeng, David Arbour, Avi Feller, Raghavendra Addanki, Ryan A. Rossi, Ritwik Sinha, Edward H. Kennedy

CoRR, 2024

Distributional Off-Policy Evaluation for Slate Recommendations.

Shreyas Chaudhari, David Arbour, Georgios Theocharous, Nikos Vlassis

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Leveraging Graph Diffusion Models for Network Refinement Tasks.

Puja Trivedi, Ryan A. Rossi, David Arbour, Tong Yu, Franck Dernoncourt, Sungchul Kim, Nedim Lipka, Namyong Park, Nesreen K. Ahmed, Danai Koutra

CoRR, 2023

Dynamic Vector Bin Packing for Online Resource Allocation in the Cloud.

Aniket Murhekar, David Arbour, Tung Mai, Anup Rao

CoRR, 2023

Anytime-Valid Confidence Sequences in an Enterprise A/B Testing Platform.

Akash Maharaj, Ritwik Sinha, David Arbour, Ian Waudby-Smith, Simon Z. Liu, Moumita Sinha, Raghavendra Addanki, Aaditya Ramdas, Manas Garg, Viswanathan Swaminathan

Proceedings of the Companion Proceedings of the ACM Web Conference 2023, 2023

Brief Announcement: Dynamic Vector Bin Packing for Online Resource Allocation in the Cloud.

Aniket Murhekar, David Arbour, Tung Mai, Anup B. Rao

Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures, 2023

Finite Population Regression Adjustment and Non-asymptotic Guarantees for Treatment Effect Estimation.

Mehrdad Ghadiri, David Arbour, Tung Mai, Cameron Musco, Anup B. Rao

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Online Forecasting Based Anomaly Detection For Monitoring Large Scale Streaming Data.

Wei Zhang, David Arbour

Proceedings of the IEEE International Conference on Big Data, 2023

Learning Relational Causal Models with Cycles through Relational Acyclification.

Ragib Ahsan, David Arbour, Elena Zheleva

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Off-Policy Evaluation in Embedded Spaces.

Jaron J. R. Lee, David Arbour, Georgios Theocharous

CoRR, 2022

Towards Preserving Server-Side Privacy of On-Device Models.

Akanksha Atrey, Ritwik Sinha, Somdeb Sarkhel, Saayan Mitra, David Arbour, Akash Maharaj, Prashant J. Shenoy

Proceedings of the Companion of The Web Conference 2022, Virtual Event / Lyon, France, April 25, 2022

Generating and Controlling Diversity in Image Search.

Md. Mehrab Tanjim, Ritwik Sinha, Krishna Kumar Singh, Sridhar Mahadevan, David Arbour, Moumita Sinha, Garrison W. Cottrell

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

Non-parametric inference of relational dependence.

Ragib Ahsan, Zahra Fatemi, David Arbour, Elena Zheleva

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Offline Evaluation of Ranked Lists using Parametric Estimation of Propensities.

Vishwa Vinay, Manoj Kilaru, David Arbour

Proceedings of the SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11, 2022

Sample Constrained Treatment Effect Estimation.

Raghavendra Addanki, David Arbour, Tung Mai, Cameron Musco, Anup Rao

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference.

Galen Weld, Peter West, Maria Glenski, David Arbour, Ryan A. Rossi, Tim Althoff

Proceedings of the Sixteenth International AAAI Conference on Web and Social Media, 2022

Online Balanced Experimental Design.

David Arbour, Drew Dimmery, Tung Mai, Anup B. Rao

Proceedings of the International Conference on Machine Learning, 2022

Relational Causal Models with Cycles: Representation and Reasoning.

Ragib Ahsan, David Arbour, Elena Zheleva

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

Constraint Sampling Reinforcement Learning: Incorporating Expertise for Faster Learning.

Tong Mu, Georgios Theocharous, David Arbour, Emma Brunskill

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Heterogeneous Graphlets.

Ryan A. Rossi, Nesreen K. Ahmed, Aldo G. Carranza, David Arbour, Anup B. Rao, Sungchul Kim, Eunyee Koh

ACM Trans. Knowl. Discov. Data, 2021

Causal Inference from Network Data.

Elena Zheleva, David Arbour

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

BOhance: Bayesian Optimization for Content Enhancement.

Trisha Mittal, Vishy Swaminathan, Somdeb Sarkhel, Ritwik Sinha, David Arbour, Saayan Mitra, Dinesh Manocha

Proceedings of the IEEE International Symposium on Multimedia, 2021

Permutation Weighting.

David Arbour, Drew Dimmery, Arjun Sondhi

Proceedings of the 38th International Conference on Machine Learning, 2021

Designing Transportable Experiments Under S-admissability.

My Phan, David Arbour, Drew Dimmery, Anup B. Rao

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Efficient Balanced Treatment Assignments for Experimentation.

David Arbour, Drew Dimmery, Anup B. Rao

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Bayesian Estimation of the Effect of Television Advertising on Web Metrics.

Ritwik Sinha, Shiv Kumar Saini, Moumita Sinha, David Arbour

Proceedings of the 7th IEEE International Conference on Data Science and Advanced Analytics, 2020

Balanced Off-Policy Evaluation in General Action Spaces.

Arjun Sondhi, David Arbour, Drew Dimmery

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

General Identification of Dynamic Treatment Regimes Under Interference.

Eli Sherman, David Arbour, Ilya Shpitser

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Heterogeneous Network Motifs.

Ryan A. Rossi, Nesreen K. Ahmed, Aldo G. Carranza, David Arbour, Anup Rao, Sungchul Kim, Eunyee Koh

CoRR, 2019

2016

Inferring Causal Direction from Relational Data.

David T. Arbour, Katerina Marazopoulou, David D. Jensen

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Inferring Network Effects from Observational Data.

David T. Arbour, Dan Garant, David D. Jensen

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

2014

Refining the Semantics of Social Influence.

Katerina Marazopoulou, David T. Arbour, David D. Jensen

CoRR, 2014

Propensity Score Matching for Causal Inference with Relational Data.

David T. Arbour, Katerina Marazopoulou, Dan Garant, David D. Jensen

Proceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction co-located with 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014), 2014

2013

A Sound and Complete Algorithm for Learning Causal Models from Relational Data.

Marc E. Maier, Katerina Marazopoulou, David T. Arbour, David D. Jensen

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

2010

Evaluation of automatic classroom capture for computer science education.

Paul E. Dickson, David T. Arbour, W. Richards Adrion, Amanda Gentzel

Proceedings of the 15th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education, 2010

2009

First experiences with a classroom recording system.

Paul E. Dickson, W. Richards Adrion, Allen R. Hanson, David T. Arbour

Proceedings of the 14th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education, 2009