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 Elias Bareinboim

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Average citations per article1.95
Citation Count39
Publication count20
Publication years2011-2017
Available for download3
Average downloads per article110.33
Downloads (cumulative)331
Downloads (12 Months)107
Downloads (6 Weeks)17
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1
August 2017 IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 0

Reinforcement learning (RL) agents have been deployed in complex environments where interactions are costly, and learning is usually slow. One prominent task in these settings is to reuse interactions performed by other agents to accelerate the learning process. Causal inference provides a family of methods to infer the effects of ...

2
May 2017 AAMAS '17: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems
Publisher: International Foundation for Autonomous Agents and Multiagent Systems
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 6,   Downloads (12 Months): 39,   Downloads (Overall): 39

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We leverage causal inference tools to support a principled and more robust transfer of knowledge in reinforcement learning (RL) settings. In particular, we tackle the problem of transferring knowledge across bandit agents in settings where causal effects cannot be identified by Pearl's {do-calculus} nor standard off-policy learning techniques. Our new ...
Keywords: transfer learning, causal inference, reinforcement learning

3
July 2016 IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 0

In this paper, we extend graph-based identification methods by allowing background knowledge in the form of non-zero parameter values. Such information could be obtained, for example, from a previously conducted randomized experiment, from substantive understanding of the domain, or even an identification technique. To incorporate such information systematically, we propose ...

4 published by ACM
January 2016 ACM Transactions on Intelligent Systems and Technology (TIST) - Special Issue on Causal Discovery and Inference: Volume 7 Issue 2, January 2016
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 10,   Downloads (12 Months): 45,   Downloads (Overall): 140

Full text available: PDFPDF

5
January 2016 IEEE Intelligent Systems: Volume 31 Issue 1, January 2016
Publisher: IEEE Educational Activities Department
Bibliometrics:
Citation Count: 0

IEEE Intelligent Systems once again selected 10 young AI scientists as " AI's 10 to Watch." This acknowledgment and celebration not only recognizes these young scientists and makes a positive impact in their academic career but also promotes the community and cutting-edge AI research among next-generation AI researchers, the industry, ...

6
December 2015 NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1
Publisher: MIT Press
Bibliometrics:
Citation Count: 3

The Multi-Armed Bandit problem constitutes an archetypal setting for sequential decision-making, permeating multiple domains including engineering, business, and medicine. One of the hallmarks of a bandit setting is the agent's capacity to explore its environment through active intervention, which contrasts with the ability to collect passive data by estimating associational ...

7
January 2015 AAAI'15: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 1

Controlling for selection and confounding biases are two of the most challenging problems that appear in data analysis in the empirical sciences as well as in artificial intelligence tasks. The combination of previously studied methods for each of these biases in isolation is not directly applicable to certain non-trivial cases ...

8 published by ACM
December 2014 AI Matters: Volume 1 Issue 2, December 2014
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 1,   Downloads (12 Months): 23,   Downloads (Overall): 152

Full text available: PDFPDF
This article is a short summary of the full dissertation thesis that was defended in 2014 at the University of California, Los Angeles.

9
December 2014 NIPS'14: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 1
Publisher: MIT Press
Bibliometrics:
Citation Count: 0

This paper addresses the problem of mz -transportability, that is, transferring causal knowledge collected in several heterogeneous domains to a target domain in which only passive observations and limited experimental data can be collected. The paper first establishes a necessary and sufficient condition for deciding the feasibility of mz -transportability, ...

10
July 2014 CI'14: Proceedings of the UAI 2014 Conference on Causal Inference: Learning and Prediction - Volume 1274
Publisher: CEUR-WS.org
Bibliometrics:
Citation Count: 0

The problem of generalizability of empirical findings (experimental and observational) to new environments, settings, and populations is one of the central problems in causal inference. Experiments in the sciences are invariably conducted with the intent of being used elsewhere (e.g., outside the laboratory), where conditions are likely to be different. ...

11
July 2014 AAAI'14: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 10

Selection bias is caused by preferential exclusion of units from the samples and represents a major obstacle to valid causal and statistical inferences; it cannot be removed by randomized experiments and can rarely be detected in either experimental or observational studies. In this paper, we provide complete graphical and algorithmic ...

12
December 2013 NIPS'13: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 1
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 0

This paper considers the problem of transferring experimental findings learned from multiple heterogeneous domains to a target domain, in which only limited experiments can be performed. We reduce questions of transportability from multiple domains and with limited scope to symbolic derivations in the causal calculus, thus extending the original setting ...

13
July 2013 AAAI'13: Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 4

We address the problem of transferring causal knowledge learned in one environment to another, potentially different environment, when only limited experiments may be conducted at the source. This generalizes the treatment of transportability introduced in [Pearl and Bareinboim, 2011; Bareinboim and Pearl, 2012b], which deals with transferring causal information when ...

14
August 2012 UAI'12: Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence
Publisher: AUAI Press
Bibliometrics:
Citation Count: 4

We address the problem of estimating the effect of intervening on a set of variables X from experiments on a different set, Z , that is more accessible to manipulation. This problem, which we call z -identifiability, reduces to ordinary identifiability when Z = Ø and, like the latter, can ...

15
July 2012 AAAI'12: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 5

The study of transportability aims to identify conditions under which causal information learned from experiments can be reused in a different environment where only passive observations can be collected. The theory introduced in [Pearl and Bareinboim, 2011] (henceforth [PB, 2011]) defines formal conditions for such transfer but falls short of ...

16
December 2011 ICDMW '11: Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 0

We address the problem of transferring information learned from experiments to a different environment, in which only passive observations can be collected. We introduce a formal representation called ``selection diagrams'' for expressing knowledge about differences and commonalities between environments and, using this representation, we derive procedures for deciding whether effects ...
Keywords: experiments, causal relations, transportability

17
August 2011 AAAI'11: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 3

Selection bias, caused by preferential exclusion of units (or samples) from the data, is a major obstacle to valid causal inferences, for it cannot be removed or even detected by randomized experiments. This paper highlights several graphical and algebraic methods capable of mitigating and sometimes eliminating this bias. These nonparametric ...

18
August 2011 AAAI'11: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 6

We address the problem of transferring information learned from experiments to a different environment, in which only passive observations can be collected. We introduce a formal representation called "selection diagrams" for expressing knowledge about differences and commonalities between environments and, using this representation, we derive procedures for deciding whether effects ...

19
July 2011 GKR'11: Proceedings of the Second international conference on Graph Structures for Knowledge Representation and Reasoning
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 2

The standard definition of causal Bayesian networks (CBNs) invokes a global condition according to which the distribution resulting from any intervention can be decomposed into a truncated product dictated by its respective mutilated subgraph. We analyze alternative formulations which emphasizes local aspects of the causal process and can serve therefore ...

20
January 2011 Bioinformatics: Volume 27 Issue 2, January 2011
Publisher: Oxford University Press
Bibliometrics:
Citation Count: 0

Summary: We present an approach to statistically pinpoint differentially expressed proteins that have quantitation values near the quantitation threshold and are not identified in all replicates (marginal cases). Our method uses a Bayesian strategy to combine parametric statistics with an empirical distribution built from the reproducibility quality of the technical ...



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