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79 entries « 1 of 4 »

A Simulation-Based Test of Identifiability for Bayesian Causal Inference Miscellaneous

Sam Witty, David Jensen, Vikash Mansinghka

2021.

Abstract | Links | BibTeX | Tags: Causal Modeling

How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference Inproceedings

Amanda M Gentzel, Purva Pruthi, David Jensen

International Conference on Machine Learning, pp. 3660–3671, PMLR 2021.

Abstract | Links | BibTeX | Tags: Causal Modeling

Improving Causal Inference by Increasing Model Expressiveness Inproceedings

David Jensen

Proceedings of the AAAI Conference on Artificial Intelligence, pp. 15053–15057, 2021.

Abstract | Links | BibTeX | Tags:

Preserving Privacy in Personalized Models for Distributed Mobile Services Miscellaneous

Akanksha Atrey, Prashant J Shenoy, David Jensen

2021.

Abstract | Links | BibTeX | Tags:

Causal Inference using Gaussian Processes with Structured Latent Confounders Inproceedings

Sam Witty, Kenta Takatsu, David Jensen, Vikash Mansinghka

Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, pp. 10313–10323, PMLR, 2020.

Abstract | Links | BibTeX | Tags: Causal Modeling

Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning Inproceedings

Akanksha Atrey, Kaleigh Clary, David Jensen

International Conference on Learning Representations, 2020.

Abstract | Links | BibTeX | Tags: Explainable AI

Object conditioning for causal inference Inproceedings

David Jensen, Javier Burroni, Matthew Rattigan

Uncertainty in Artificial Intelligence, pp. 1072–1082, PMLR 2020.

Abstract | Links | BibTeX | Tags:

Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates Inproceedings

Katherine A Keith, David Jensen, Brendan O'Connor

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pp. 5332–5344, Association for Computational Linguistics, 2020.

Abstract | Links | BibTeX | Tags:

Using Experimental Data to Evaluate Methods for Observational Causal Inference Miscellaneous

Amanda Gentzel, Justin Clarke, David Jensen

2020.

Abstract | Links | BibTeX | Tags:

Bayesian causal inference via probabilistic program synthesis Miscellaneous

Sam Witty, Alexander Lew, David Jensen, Vikash Mansinghka

2019.

Abstract | Links | BibTeX | Tags: Probabilistic Programming

Comment: Strengthening empirical evaluation of causal inference methods Journal Article

David Jensen, others

Statistical Science, 34 (1), pp. 77–81, 2019.

Abstract | Links | BibTeX | Tags:

Identifying when effect restoration will improve estimates of causal effect Inproceedings

Huseyin Oktay, Akanksha Atrey, David Jensen

Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 190–198, Society for Industrial and Applied Mathematics 2019.

Abstract | Links | BibTeX | Tags:

PlanAlyzer: assessing threats to the validity of online experiments Journal Article

Emma Tosch, Eytan Bakshy, Emery D Berger, David Jensen, Eliot J B Moss

Proceedings of the ACM on Programming Languages, 3 (OOPSLA), pp. 1–30, 2019.

Abstract | Links | BibTeX | Tags:

The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data Inproceedings

Amanda Gentzel, Dan Garant, David Jensen

Advances in Neural Information Processing Systems, Curran Associates, Inc., 2019.

Abstract | Links | BibTeX | Tags: Causal Modeling

Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning Miscellaneous

Emma Tosch, Kaleigh Clary, John Foley, David Jensen

2019.

Abstract | Links | BibTeX | Tags: Explainable AI

Causal Graphs vs. Causal Programs: The Case of Conditional Branching Inproceedings

Sam Witty, David Jensen

First Conference on Probabilistic Programming (ProbProg), 2018.

Abstract | Links | BibTeX | Tags: Probabilistic Programming

Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments Miscellaneous

Kaleigh Clary, Emma Tosch, John Foley, David Jensen

2018.

Abstract | Links | BibTeX | Tags: Explainable AI

Measuring and characterizing generalization in deep reinforcement learning Miscellaneous

Sam Witty, Jun Ki Lee, Emma Tosch, Akanksha Atrey, Michael Littman, David Jensen

2018.

Abstract | Links | BibTeX | Tags: Explainable AI

Toybox: Better Atari Environments for Testing Reinforcement Learning Agents Inproceedings

John Foley, Emma Tosch, Kaleigh Clary, David Jensen

NeurIPS 2018 Workshop on Systems for ML, 2018.

Abstract | Links | BibTeX | Tags: Explainable AI

A/B Testing in Networks with Adversarial Members Journal Article

Kaleigh Clary, David Jensen

2017.

Abstract | Links | BibTeX | Tags:

79 entries « 1 of 4 »