David D. Jensen

发表

The case for anomalous link detection

Matthew J. Rattigan, David D. Jensen, 2005, MRDM '05.

Identifying When Effect Restoration Will Improve Estimates of Causal Effect

David D. Jensen, Hüseyin Oktay, Akanksha Atrey, 2019, SDM.

Generalization in Deep Reinforcement Learning

M. Littman, David D. Jensen, Emma Tosch, 2018 .

Comment: Strengthening Empirical Evaluation of Causal Inference Methods

David D. Jensen, 2019, Statistical Science.

Non-intuitive conditional independence facts hold in models of network data

Marc E. Maier, David D. Jensen, Katerina Marazopoulou, 2013 .

Stick It to The Man: Correcting for Non-Cooperative Behavior of Subjects in Experiments on Social Networks

David D. Jensen, J. Onaolapo, Emma Tosch, 2022, USENIX Security Symposium.

A Simulation-Based Test of Identifiability for Bayesian Causal Inference

Vikash K. Mansinghka, David D. Jensen, S. Witty, 2021, ArXiv.

Identifying Independence in Relational Models pdf

David Jensen, Marc E. Maier, Marc Maier, 2012, ArXiv.

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

David D. Jensen, Akanksha Atrey, Kaleigh Clary, 2019, ICLR.

Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep RL

David D. Jensen, Akanksha Atrey, Kaleigh Clary, 2019, ICLR 2020.

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

Amanda Gentzel, Dan Garant, David Jensen, 2019, NeurIPS.

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

David Jensen, John Foley, Emma Tosch, 2019, ArXiv.

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

Katherine A. Keith, Brendan T. O'Connor, Brendan O'Connor, 2020, ACL.

Measuring and Characterizing Generalization in Deep Reinforcement Learning pdf

Michael L. Littman, Jun Ki Lee, Emma Tosch, 2018, Applied AI Letters.

Causal Inference using Gaussian Processes with Structured Latent Confounders pdf

Vikash K. Mansinghka, Sam Witty, Kenta Takatsu, 2020, ICML.

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

David D. Jensen, A. Gentzel, Purva Pruthi, 2020, ICML.

Using Experimental Data to Evaluate Methods for Observational Causal Inference pdf

Justin Clarke, Amanda Gentzel, David Jensen, 2020, ArXiv.

Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program pdf

Michael L. Littman, Jeff Druce, James Niehaus, 2021, ArXiv.

Anonymizing Social Networks

David D. Jensen, Carol, Siddharth Srivastava, 2007 .

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

John Foley, Emma Tosch, Kaleigh Clary, 2019, ArXiv.

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

Amanda Gentzel, Purva Pruthi, David Jensen, 2020, ICML.