David D. Jensen

123 papers A* 41A 19B 4C 1Misc 2Journal 40Unranked 16

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	Compositional Models for Estimating Causal Effects. CLeaR Purva Pruthi, David D. Jensen
2025	J	`jnl`	Improving Generative Methods for Causal Evaluation via Simulation-Based Inference. CoRR Pracheta Amaranath, Vinitra Muralikrishnan, Amit Sharma, David D. Jensen
2025	A	`conf`	Open-World Classification with Bayesian Gaussian Mixture Models. AAMAS Justin Clarke, Przemyslaw Grabowicz, David D. Jensen
2025	J	`jnl`	Uncertainty Sets for Distributionally Robust Bandits Using Structural Equation Models. CoRR Katherine Avery, Chinmay Pendse, David D. Jensen
2024	J	`jnl`	Compositional Models for Estimating Causal Effects. CoRR Purva Pruthi, David D. Jensen
2024	—	`conf`	The Effect of Alter Ego Accounts on A/B Tests in Social Networks. WWW (Companion Volume) Katherine Avery, Amir Houmansadr, David D. Jensen
2023	Misc	`conf`	Causal Dynamic Bayesian Networks for Simulation Metamodeling. WSC Pracheta Amaranath, Peter J. Haas, David D. Jensen, Sam Witty
2022	J	`jnl`	Improving the Efficiency of the PC Algorithm by Using Model-Based Conditional Independence Tests. CoRR Erica Cai, Andrew McGregor, David D. Jensen
2022	J	`jnl`	Measuring Interventional Robustness in Reinforcement Learning. CoRR Katherine Avery, Jack Kenney, Pracheta Amaranath, Erica Cai, David D. Jensen
2022	A*	`conf`	Stick It to The Man: Correcting for Non-Cooperative Behavior of Subjects in Experiments on Social Networks. USENIX Security Symposium Kaleigh Clary, Emma Tosch, Jeremiah Onaolapo, David D. Jensen
2021	J	`jnl`	A Simulation-Based Test of Identifiability for Bayesian Causal Inference. CoRR Sam Witty, David D. Jensen, Vikash Mansinghka
2021	J	`jnl`	Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program. CoRR Jeff Druce, James Niehaus, Vanessa Moody, David D. Jensen, Michael L. Littman
2021	A*	`conf`	How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference. ICML Amanda Gentzel, Purva Pruthi, David D. Jensen
2021	A*	`conf`	Improving Causal Inference by Increasing Model Expressiveness. AAAI David D. Jensen
2021	J	`jnl`	PlanAlyzer: assessing threats to the validity of online experiments. Commun. ACM Emma Tosch, Eytan Bakshy, Emery D. Berger, David D. Jensen, J. Eliot B. Moss
2021	A	`conf`	Preserving Privacy in Personalized Models for Distributed Mobile Services. ICDCS Akanksha Atrey, Prashant J. Shenoy, David D. Jensen
2021	J	`jnl`	Preserving Privacy in Personalized Models for Distributed Mobile Services. CoRR Akanksha Atrey, Prashant J. Shenoy, David D. Jensen
2020	A*	`conf`	Causal Inference using Gaussian Processes with Structured Latent Confounders. ICML Sam Witty, Kenta Takatsu, David D. Jensen, Vikash Mansinghka
2020	J	`jnl`	Causal Inference using Gaussian Processes with Structured Latent Confounders. CoRR Sam Witty, Kenta Takatsu, David D. Jensen, Vikash Mansinghka
2020	A*	`conf`	Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning. ICLR Akanksha Atrey, Kaleigh Clary, David D. Jensen
2020	A*	`conf`	Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates. ACL Katherine A. Keith, David D. Jensen, Brendan O'Connor
2020	J	`jnl`	Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates. CoRR Katherine A. Keith, David D. Jensen, Brendan O'Connor
2020	J	`jnl`	Using Experimental Data to Evaluate Methods for Observational Causal Inference. CoRR Amanda Gentzel, Justin Clarke, David D. Jensen
2019	J	`jnl`	Bayesian causal inference via probabilistic program synthesis. CoRR Sam Witty, Alexander K. Lew, David D. Jensen, Vikash Mansinghka
2019	J	`jnl`	Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep RL. CoRR Akanksha Atrey, Kaleigh Clary, David D. Jensen
2019	A	`conf`	Identifying When Effect Restoration Will Improve Estimates of Causal Effect. SDM Hüseyin Oktay, Akanksha Atrey, David D. Jensen
2019	J	`jnl`	Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments. CoRR Kaleigh Clary, Emma Tosch, John Foley, David D. Jensen
2019	A	`conf`	Object Conditioning for Causal Inference. UAI David D. Jensen, Javier Burroni, Matthew J. Rattigan
2019	J	`jnl`	PlanAlyzer: Assessing Threats to the Validity of Online Experiments. CoRR Emma Tosch, Eytan Bakshy, Emery D. Berger, David D. Jensen, J. Eliot B. Moss
2019	J	`jnl`	PlanAlyzer: assessing threats to the validity of online experiments. Proc. ACM Program. Lang. Emma Tosch, Eytan Bakshy, Emery D. Berger, David D. Jensen, J. Eliot B. Moss
2019	A*	`conf`	The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data. NeurIPS Amanda Gentzel, Dan Garant, David D. Jensen
2019	J	`jnl`	The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data. CoRR Amanda Gentzel, Dan Garant, David D. Jensen
2019	J	`jnl`	Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning. CoRR Emma Tosch, Kaleigh Clary, John Foley, David D. Jensen
2018	J	`jnl`	Measuring and Characterizing Generalization in Deep Reinforcement Learning. CoRR Sam Witty, Jun Ki Lee, Emma Tosch, Akanksha Atrey, Michael L. Littman, David D. Jensen
2018	J	`jnl`	ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents. CoRR John Foley, Emma Tosch, Kaleigh Clary, David D. Jensen
2016	J	`jnl`	Causal Discovery for Manufacturing Domains. CoRR Katerina Marazopoulou, Rumi Ghosh, Prasanth Lade, David D. Jensen
2016	A*	`conf`	Controversy Detection in Wikipedia Using Collective Classification. SIGIR Shiri Dori-Hacohen, David D. Jensen, James Allan
2016	J	`jnl`	Evaluating Causal Models by Comparing Interventional Distributions. CoRR Dan Garant, David D. Jensen
2016	A	`conf`	Inferring Causal Direction from Relational Data. UAI David T. Arbour, Katerina Marazopoulou, David D. Jensen
2016	A*	`conf`	Inferring Network Effects from Observational Data. KDD David T. Arbour, Dan Garant, David D. Jensen
2015	—	`conf`	Learning the Structure of Causal Models with Relational and Temporal Dependence. ACI@UAI Katerina Marazopoulou, Marc E. Maier, David D. Jensen
2015	A	`conf`	Learning the Structure of Causal Models with Relational and Temporal Dependence. UAI Katerina Marazopoulou, Marc E. Maier, David D. Jensen
2015	A*	`conf`	Learning to Uncover Deep Musical Structure. AAAI Phillip B. Kirlin, David D. Jensen
2015	A	`conf`	Teaching Computing as Science in a Research Experience. SIGCSE Jerod J. Weinman, David D. Jensen, David Lopatto
2014	A	`conf`	Classifier-Adjusted Density Estimation for Anomaly Detection and One-Class Classification. SDM Lisa Friedland, Amanda Gentzel, David D. Jensen
2014	J	`jnl`	Online Dating Recommendations: Matching Markets and Learning Preferences. CoRR Kun Tu, Bruno F. Ribeiro, Hua Jiang, Xiaodong Wang, David D. Jensen, Benyuan Liu, Don Towsley
2014	—	`conf`	Online dating recommendations: matching markets and learning preferences. WWW (Companion Volume) Kun Tu, Bruno F. Ribeiro, David D. Jensen, Don Towsley, Benyuan Liu, Hua Jiang, Xiaodong Wang
2014	—	`conf`	Propensity Score Matching for Causal Inference with Relational Data. CI@UAI David T. Arbour, Katerina Marazopoulou, Dan Garant, David D. Jensen
2014	J	`jnl`	Refining the Semantics of Social Influence. CoRR Katerina Marazopoulou, David T. Arbour, David D. Jensen
2014	Misc	`conf`	Strategy Mining. FLAIRS Xiaoxi Xu, David D. Jensen, Edwina L. Rissland
2013	A	`conf`	A Sound and Complete Algorithm for Learning Causal Models from Relational Data. UAI Marc E. Maier, Katerina Marazopoulou, David T. Arbour, David D. Jensen
2013	J	`jnl`	A Sound and Complete Algorithm for Learning Causal Models from Relational Data. CoRR Marc E. Maier, Katerina Marazopoulou, David T. Arbour, David D. Jensen
2013	—	`conf`	Copy or Coincidence? A Model for Detecting Social Influence and Duplication Events. ICML (3) Lisa Friedland, David D. Jensen, Michael Lavine
2013	A*	`conf`	Detecting insider threats in a real corporate database of computer usage activity. KDD Ted E. Senator, Henry G. Goldberg, Alex Memory, William T. Young, Brad Rees, Robert Pierce, Daniel Huang, Matthew Reardon, David A. Bader, Edmond Chow, Irfan A. Essa, Joshua Jones, Vinay Bettadapura, Duen Horng Chau, Oded Green, Oguz Kaya, Anita Zakrzewska, Erica Briscoe, Rudolph L. Mappus IV, Robert McColl, Lora Weiss, Thomas G. Dietterich, Alan Fern, Weng-Keen Wong, Shubhomoy Das, Andrew Emmott, Jed Irvine, Jay Yoon Lee, Danai Koutra, Christos Faloutsos, Daniel D. Corkill, Lisa Friedland, Amanda Gentzel, David D. Jensen
2013	J	`jnl`	Reasoning about Independence in Probabilistic Models of Relational Data CoRR Marc E. Maier, Katerina Marazopoulou, David D. Jensen
2011	J	`jnl`	Indexing Network Structure with Shortest-Path Trees. ACM Trans. Knowl. Discov. Data Marc E. Maier, Matthew J. Rattigan, David D. Jensen
2011	—	`conf`	Probabilistic Modeling of Hierarchical Music Analysis. ISMIR Phillip B. Kirlin, David D. Jensen
2011	A*	`conf`	Relational Blocking for Causal Discovery. AAAI Matthew J. Rattigan, Marc E. Maier, David D. Jensen
2010	—	`conf`	Causal discovery in social media using quasi-experimental designs. SOMA@KDD Hüseyin Oktay, Brian J. Taylor, David D. Jensen
2010	A*	`conf`	Learning Causal Models of Relational Domains. AAAI Marc E. Maier, Brian J. Taylor, Hüseyin Oktay, David D. Jensen
2010	A*	`conf`	Leveraging D-Separation for Relational Data Sets. ICDM Matthew J. Rattigan, David D. Jensen
2010	J	`jnl`	Resisting structural re-identification in anonymized social networks. VLDB J. Michael Hay, Gerome Miklau, David D. Jensen, Donald F. Towsley, Chao Li
2010	A	`conf`	The Application of Statistical Relational Learning to a Database of Criminal and Terrorist Activity. SDM Brian Delaney, Andrew S. Fast, William M. Campbell, Clifford J. Weinstein, David D. Jensen
2009	A*	`conf`	Accurate Estimation of the Degree Distribution of Private Networks. ICDM Michael Hay, Chao Li, Gerome Miklau, David D. Jensen
2009	—	`conf`	Knowledge Discovery by Design. KDIR David D. Jensen
2008	J	`jnl`	A bias/variance decomposition for models using collective inference. Mach. Learn. Jennifer Neville, David D. Jensen
2008	—	`conf`	Automatic Identification of Quasi-Experimental Designs for Scientific Discovery. AAAI Fall Symposium: Automated Scientific Discovery David D. Jensen, Andrew S. Fast, Brian J. Taylor, Marc E. Maier, Matthew J. Rattigan
2008	A*	`conf`	Automatic identification of quasi-experimental designs for discovering causal knowledge. KDD David D. Jensen, Andrew S. Fast, Brian J. Taylor, Marc E. Maier
2008	J	`jnl`	Navigating networks by using homophily and degree. Proc. Natl. Acad. Sci. USA Özgür Simsek, David D. Jensen
2008	J	`jnl`	Optimistic pruning for multiple instance learning. Pattern Recognit. Lett. Amy McGovern, David D. Jensen
2008	J	`jnl`	Resisting structural re-identification in anonymized social networks. Proc. VLDB Endow. Michael Hay, Gerome Miklau, David D. Jensen, Donald F. Towsley, Philipp Weis
2008	A*	`conf`	Social networks: looking ahead. KDD Ravi Kumar, Alexander Tuzhilin, Christos Faloutsos, David D. Jensen, Gueorgi Kossinets, Jure Leskovec, Andrew Tomkins
2008	A*	`conf`	Why Stacked Models Perform Effective Collective Classification. ICDM Andrew S. Fast, David D. Jensen
2007	B	`conf`	Beyond Prediction: Directions for Probabilistic and Relational Learning. ILP David D. Jensen
2007	B	`conf`	Bias/Variance Analysis for Relational Domains. ILP Jennifer Neville, David D. Jensen
2007	—	`conf`	Exploiting Network Structure for Active Inference in Collective Classification. ICDM Workshops Matthew J. Rattigan, Marc E. Maier, David D. Jensen, Bin Wu, Xin Pei, Jianbin Tan, Yi Wang
2007	A*	`conf`	Finding tribes: identifying close-knit individuals from employment patterns. KDD Lisa Friedland, David D. Jensen
2007	A*	`conf`	Graph clustering with network structure indices. ICML Matthew J. Rattigan, Marc E. Maier, David D. Jensen
2007	A*	`conf`	Recommending citations for academic papers. SIGIR Trevor Strohman, W. Bruce Croft, David D. Jensen
2007	J	`jnl`	Relational Dependency Networks. J. Mach. Learn. Res. Jennifer Neville, David D. Jensen
2007	A*	`conf`	Relational data pre-processing techniques for improved securities fraud detection. KDD Andrew S. Fast, Lisa Friedland, Marc E. Maier, Brian J. Taylor, David D. Jensen, Henry G. Goldberg, John Komoroske
2006	J	`jnl`	Introduction to the special issue on multi-relational data mining and statistical relational learning. Mach. Learn. Hendrik Blockeel, David D. Jensen, Stefan Kramer
2006	A*	`conf`	MaxProp: Routing for Vehicle-Based Disruption-Tolerant Networks. INFOCOM John Burgess, Brian Gallagher, David D. Jensen, Brian Neil Levine
2006	A	`conf`	Representing documents with named entities for story link detection (SLD). CIKM Chirag Shah, W. Bruce Croft, David D. Jensen
2006	—	`conf`	The NFL Coaching Network: Analysis of the Social Network among Professional Football Coaches. AAAI Fall Symposium: Capturing and Using Patterns for Evidence Detection Andrew S. Fast, David D. Jensen
2006	A*	`conf`	Using structure indices for efficient approximation of network properties. KDD Matthew J. Rattigan, Marc E. Maier, David D. Jensen
2005	A*	`conf`	A Relational Representation for Procedural Task Knowledge. AAAI Stephen Hart, Roderic A. Grupen, David D. Jensen
2005	A*	`conf`	Creating social networks to improve peer-to-peer networking. KDD Andrew S. Fast, David D. Jensen, Brian Neil Levine
2005	A*	`conf`	Decentralized Search in Networks Using Homophily and Degree Disparity. IJCAI Özgür Simsek, David D. Jensen
2005	A*	`conf`	Leveraging Relational Autocorrelation with Latent Group Models. ICDM Jennifer Neville, David D. Jensen
2005	—	`conf`	Leveraging relational autocorrelation with latent group models. Probabilistic, Logical and Relational Learning Jennifer Neville, David D. Jensen
2005	A	`conf`	Privacy Vulnerabilities in Encrypted HTTP Streams. Privacy Enhancing Technologies George Dean Bissias, Marc Liberatore, David D. Jensen, Brian Neil Levine
2005	J	`jnl`	The case for anomalous link discovery. SIGKDD Explor. Matthew J. Rattigan, David D. Jensen
2005	A*	`conf`	Using relational knowledge discovery to prevent securities fraud. KDD Jennifer Neville, Özgür Simsek, David D. Jensen, John Komoroske, Kelly Palmer, Henry G. Goldberg
2004	A*	`conf`	Dependency Networks for Relational Data. ICDM Jennifer Neville, David D. Jensen
2004	A*	`conf`	Why collective inference improves relational classification. KDD David D. Jensen, Jennifer Neville, Brian Gallagher
2003	A*	`conf`	Avoiding Bias when Aggregating Relational Data with Degree Disparity. ICML David D. Jensen, Jennifer Neville, Michael Hay
2003	J	`jnl`	Exploiting relational structure to understand publication patterns in high-energy physics. SIGKDD Explor. Amy McGovern, Lisa Friedland, Michael Hay, Brian Gallagher, Andrew S. Fast, Jennifer Neville, David D. Jensen
2003	A*	`conf`	Identifying Predictive Structures in Relational Data Using Multiple Instance Learning. ICML Amy McGovern, David D. Jensen
2003	A*	`conf`	Information awareness: a prospective technical assessment. KDD David D. Jensen, Matthew J. Rattigan, Hannah Blau
2003	A*	`conf`	Learning relational probability trees. KDD Jennifer Neville, David D. Jensen, Lisa Friedland, Michael Hay
2003	A*	`conf`	Simple Estimators for Relational Bayesian Classifiers. ICDM Jennifer Neville, David D. Jensen, Brian Gallagher
2002	B	`conf`	Autocorrelation and Linkage Cause Bias in Evaluation of Relational Learners. ILP David D. Jensen, Jennifer Neville
2002	A*	`conf`	Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning. ICML David D. Jensen, Jennifer Neville
2000	J	`jnl`	Data Snooping, Dredging and Fishing: The Dark Side of Data Mining, A SIGKDD99 Panel Report. SIGKDD Explor. David D. Jensen
2000	A	`conf`	Knowledge Discovery from Graphs (Invited Talk). GD David D. Jensen
2000	A	`conf`	Language Models for Financial News Recommendation. CIKM Victor Lavrenko, Matthew D. Schmill, Dawn J. Lawrie, Paul Ogilvie, David D. Jensen, James Allan
2000	J	`jnl`	Multiple Comparisons in Induction Algorithms. Mach. Learn. David D. Jensen, Paul R. Cohen
2000	J	`jnl`	Reports on the AAAI Fall Symposia (November 1999 and November 1998). AI Mag. Fawzi Daud, Michael Mateas, Phoebe Sengers, Susan Brennan, Alain Giboin, David R. Traum, Vinay K. Chaudri, Richard Fikes, Donia Scott, Richard Power, David D. Jensen
1999	—	`conf`	Coordinating agent activities in knowledge discovery processes. WACC David D. Jensen, Yulin Dong, Barbara Staudt Lerner, Eric K. McCall, Leon J. Osterweil, Stanley M. Sutton Jr., Alexander E. Wise
1999	A*	`conf`	Efficient Progressive Sampling. KDD Foster J. Provost, David D. Jensen, Tim Oates
1999	—	`conf`	Learning Quantitative Knowledge for Multiagent Coordination. AAAI/IAAI David D. Jensen, Michael Atighetchi, Régis Vincent, Victor R. Lesser
1999	A	`conf`	Statistical challenges to inductive inference in linked data. AISTATS David D. Jensen
1999	—	`conf`	Toward a Theoretical Understanding of Why and When Decision Tree Pruning Algorithms Fail. AAAI/IAAI Tim Oates, David D. Jensen
1998	A*	`conf`	Large Datasets Lead to Overly Complex Models: An Explanation and a Solution. KDD Tim Oates, David D. Jensen
1997	A	`conf`	A Family of Algorithms for Finding Temporal Structure in Data. AISTATS Tim Oates, Matthew D. Schmill, David D. Jensen, Paul R. Cohen
1997	A*	`conf`	Adjusting for Multiple Comparisons in Decision Tree Pruning. KDD David D. Jensen, Matthew D. Schmill
1997	A	`conf`	Adjusting for Multiple Testing in Decision Tree Pruning. AISTATS David D. Jensen
1997	B	`conf`	Building Simple Models: A Case Study with Decision Trees. IDA David D. Jensen, Tim Oates, Paul R. Cohen
1997	A	`conf`	Overfitting Explained. AISTATS Paul R. Cohen, David D. Jensen
1997	A	`conf`	The Effects of Training Set Size on Decision Tree Complexity. AISTATS Tim Oates, David D. Jensen
1997	A*	`conf`	The Effects of Training Set Size on Decision Tree Complexity. ICML Tim Oates, David D. Jensen
1996	C	`conf`	Technology, language, and public decisions: finding common ground for experts and citizens. ISTAS David D. Jensen, Todd M. La Porte