Publications Search

127 entries « 1 of 7 »

Sam Witty, David Jensen, Vikash Mansinghka

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

2021.

Abstract | Links | BibTeX | Tags: Causal Modeling

Terrance E. Boult, Przemyslaw A. Grabowicz, D. S. Prijatelj, R. Stern, L. Holder, J. Alspector, M. Jafarzadeh, T. Ahmad, A. R. Dhamija, C. Li, S. Cruz, A. Shrivastava, C. Vondrick, W. J. Scheirer

A Unifying Framework for Formal Theories of Novelty:Framework, Examples and Discussion Inproceedings

In: AAAI'21 SMPT, 2021, ISSN: 23318422.

Abstract | Links | BibTeX | Tags: Novelty

Amanda M Gentzel, Purva Pruthi, David Jensen

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

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

Abstract | Links | BibTeX | Tags: Causal Modeling

David Jensen

Improving Causal Inference by Increasing Model Expressiveness Inproceedings

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

Abstract | Links | BibTeX | Tags:

Akanksha Atrey, Prashant J. Shenoy, David Jensen

Preserving Privacy in Personalized Models for Distributed Mobile Services Miscellaneous

2021.

Abstract | Links | BibTeX | Tags:

Aarshee Mishra, Przemyslaw A. Grabowicz, Nicholas Perello

Towards Fair and Explainable Supervised Learning Inproceedings

In: ICML Workshop on Socially Responsible Machine Learning, 2021.

Abstract | Links | BibTeX | Tags: Fairness

Sam Witty, Kenta Takatsu, David Jensen, Vikash Mansinghka

Causal Inference using Gaussian Processes with Structured Latent Confounders Inproceedings

In: 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

David Ifeoluwa Adelani, Ryota Kobayashi, Ingmar Weber, Przemyslaw A. Grabowicz

Estimating community feedback effect on topic choice in social media with predictive modeling Journal Article

In: EPJ Data Science, vol. 9, no. 1, pp. 25, 2020, ISSN: 2193-1127.

Abstract | Links | BibTeX | Tags: Computational Social Science, Social feedback, Social influence, User behavior modeling

Akanksha Atrey, Kaleigh Clary, David Jensen

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

In: International Conference on Learning Representations, 2020.

Abstract | Links | BibTeX | Tags: Explainable AI

David Jensen, Javier Burroni, Matthew Rattigan

Object conditioning for causal inference Inproceedings

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

Abstract | Links | BibTeX | Tags:

Katherine A. Keith, David Jensen, Brendan O'Connor

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

In: 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:

Amanda Gentzel, Justin Clarke, David Jensen

Using Experimental Data to Evaluate Methods for Observational Causal Inference Miscellaneous

2020.

Abstract | Links | BibTeX | Tags:

Sam Witty, Alexander Lew, David Jensen, Vikash Mansinghka

Bayesian causal inference via probabilistic program synthesis Miscellaneous

2019.

Abstract | Links | BibTeX | Tags: Probabilistic Programming

David Jensen, others

Comment: Strengthening empirical evaluation of causal inference methods Journal Article

In: Statistical Science, vol. 34, no. 1, pp. 77–81, 2019.

Abstract | Links | BibTeX | Tags:

Huseyin Oktay, Akanksha Atrey, David Jensen

Identifying when effect restoration will improve estimates of causal effect Inproceedings

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

Abstract | Links | BibTeX | Tags:

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

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

In: Proceedings of the ACM on Programming Languages, vol. 3, no. OOPSLA, pp. 1–30, 2019.

Abstract | Links | BibTeX | Tags:

Przemyslaw A. Grabowicz, Nicholas Perello, Kenta Takatsu

Resilience of Supervised Learning Algorithms to Discriminatory Data Perturbations Journal Article

In: 2019.

Abstract | Links | BibTeX | Tags: Fairness

Amanda Gentzel, Dan Garant, David Jensen

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

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

Abstract | Links | BibTeX | Tags: Causal Modeling

Emma Tosch, Kaleigh Clary, John Foley, David Jensen

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

2019.

Abstract | Links | BibTeX | Tags: Explainable AI

Sam Witty, David Jensen

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

In: First Conference on Probabilistic Programming (ProbProg), 2018.

Abstract | Links | BibTeX | Tags: Probabilistic Programming

127 entries « 1 of 7 »