Technology, language, and public decisions: finding common ground for experts and citizens. - David D. Jensen; Todd M. La Porte (ISTAS, 1996)

A Family of Algorithms for Finding Temporal Structure in Data. - Tim Oates 001; Matthew D. Schmill; David D. Jensen; Paul R. Cohen (AISTATS, 1997)

Overfitting Explained. - Paul R. Cohen; David D. Jensen (AISTATS, 1997)

Adjusting for Multiple Testing in Decision Tree Pruning. - David D. Jensen (AISTATS, 1997)

The Effects of Training Set Size on Decision Tree Complexity. - Tim Oates 001; David D. Jensen (ICML, 1997)

Building Simple Models: A Case Study with Decision Trees. - David D. Jensen; Tim Oates 001; Paul R. Cohen (IDA, 1997)

Adjusting for Multiple Comparisons in Decision Tree Pruning. - David D. Jensen; Matthew D. Schmill (KDD, 1997)

Large Datasets Lead to Overly Complex Models: An Explanation and a Solution. - Tim Oates 001; David D. Jensen (KDD, 1998)

Learning Quantitative Knowledge for Multiagent Coordination. - David D. Jensen; Michael Atighetchi; Régis Vincent; Victor R. Lesser (AAAI/IAAI, 1999)

Toward a Theoretical Understanding of Why and When Decision Tree Pruning Algorithms Fail. - Tim Oates 001; David D. Jensen (AAAI/IAAI, 1999)

Statistical challenges to inductive inference in linked data. - David D. Jensen (AISTATS, 1999)

Efficient Progressive Sampling. - Foster J. Provost; David D. Jensen; Tim Oates 001 (KDD, 1999)

Coordinating agent activities in knowledge discovery processes. - David D. Jensen; Yulin Dong; Barbara Staudt Lerner; Eric K. McCall; Leon J. Osterweil; Stanley M. Sutton Jr.; Alexander E. Wise (WACC, 1999)

Language Models for Financial News Recommendation. - Victor Lavrenko; Matthew D. Schmill; Dawn J. Lawrie; Paul Ogilvie; David D. Jensen; James Allan (CIKM, 2000)

Knowledge Discovery from Graphs (Invited Talk). - David D. Jensen (GD, 2000)

Reports on the AAAI Fall Symposia (November 1999 and November 1998). - 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 (AI Mag., 2000)

Multiple Comparisons in Induction Algorithms. - David D. Jensen; Paul R. Cohen (Mach. Learn., 2000)

Data Snooping, Dredging and Fishing: The Dark Side of Data Mining, A SIGKDD99 Panel Report. - David D. Jensen (SIGKDD Explor., 2000)

Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning. - David D. Jensen; Jennifer Neville (ICML, 2002)

Autocorrelation and Linkage Cause Bias in Evaluation of Relational Learners. - David D. Jensen; Jennifer Neville (ILP, 2002)

Simple Estimators for Relational Bayesian Classifiers. - Jennifer Neville; David D. Jensen; Brian Gallagher (ICDM, 2003)

Avoiding Bias when Aggregating Relational Data with Degree Disparity. - David D. Jensen; Jennifer Neville; Michael Hay (ICML, 2003)

Identifying Predictive Structures in Relational Data Using Multiple Instance Learning. - Amy McGovern; David D. Jensen (ICML, 2003)

Information awareness: a prospective technical assessment. - David D. Jensen; Matthew J. Rattigan; Hannah Blau (KDD, 2003)

Learning relational probability trees. - Jennifer Neville; David D. Jensen; Lisa Friedland; Michael Hay (KDD, 2003)

Exploiting relational structure to understand publication patterns in high-energy physics. - Amy McGovern; Lisa Friedland; Michael Hay; Brian Gallagher; Andrew S. Fast; Jennifer Neville; David D. Jensen (SIGKDD Explor., 2003)

Dependency Networks for Relational Data. - Jennifer Neville; David D. Jensen (ICDM, 2004)

Why collective inference improves relational classification. - David D. Jensen; Jennifer Neville; Brian Gallagher (KDD, 2004)

A Relational Representation for Procedural Task Knowledge. - Stephen Hart; Roderic A. Grupen; David D. Jensen (AAAI, 2005)

Leveraging Relational Autocorrelation with Latent Group Models. - Jennifer Neville; David D. Jensen (ICDM, 2005)

Decentralized Search in Networks Using Homophily and Degree Disparity. - Özgür Simsek; David D. Jensen (IJCAI, 2005)

Creating social networks to improve peer-to-peer networking. - Andrew S. Fast; David D. Jensen; Brian Neil Levine (KDD, 2005)

Using relational knowledge discovery to prevent securities fraud. - Jennifer Neville; Özgür Simsek; David D. Jensen; John Komoroske; Kelly Palmer; Henry G. Goldberg (KDD, 2005)

Privacy Vulnerabilities in Encrypted HTTP Streams. - George Dean Bissias; Marc Liberatore; David D. Jensen; Brian Neil Levine (Privacy Enhancing Technologies, 2005)

The case for anomalous link discovery. - Matthew J. Rattigan; David D. Jensen (SIGKDD Explor., 2005)

The NFL Coaching Network: Analysis of the Social Network among Professional Football Coaches. - Andrew S. Fast; David D. Jensen (AAAI Fall Symposium: Capturing and Using Patterns for Evidence Detection, 2006)

Representing documents with named entities for story link detection (SLD). - Chirag Shah 001; W. Bruce Croft; David D. Jensen (CIKM, 2006)

MaxProp: Routing for Vehicle-Based Disruption-Tolerant Networks. - John Burgess; Brian Gallagher; David D. Jensen; Brian Neil Levine (INFOCOM, 2006)

Using structure indices for efficient approximation of network properties. - Matthew J. Rattigan; Marc E. Maier; David D. Jensen (KDD, 2006)

Introduction to the special issue on multi-relational data mining and statistical relational learning. - Hendrik Blockeel; David D. Jensen; Stefan Kramer 001 (Mach. Learn., 2006)

Exploiting Network Structure for Active Inference in Collective Classification. - Matthew J. Rattigan; Marc E. Maier; David D. Jensen; Bin Wu 001; Xin Pei; Jianbin Tan; Yi Wang 010 (ICDM Workshops, 2007)

Graph clustering with network structure indices. - Matthew J. Rattigan; Marc E. Maier; David D. Jensen (ICML, 2007)

Beyond Prediction: Directions for Probabilistic and Relational Learning. - David D. Jensen (ILP, 2007)

Bias/Variance Analysis for Relational Domains. - Jennifer Neville; David D. Jensen (ILP, 2007)

Relational data pre-processing techniques for improved securities fraud detection. - Andrew S. Fast; Lisa Friedland; Marc E. Maier; Brian J. Taylor; David D. Jensen; Henry G. Goldberg; John Komoroske (KDD, 2007)

Finding tribes: identifying close-knit individuals from employment patterns. - Lisa Friedland; David D. Jensen (KDD, 2007)

Recommending citations for academic papers. - Trevor Strohman; W. Bruce Croft; David D. Jensen (SIGIR, 2007)

Relational Dependency Networks. - Jennifer Neville; David D. Jensen (J. Mach. Learn. Res., 2007)

Automatic Identification of Quasi-Experimental Designs for Scientific Discovery. - David D. Jensen; Andrew S. Fast; Brian J. Taylor; Marc E. Maier; Matthew J. Rattigan (AAAI Fall Symposium: Automated Scientific Discovery, 2008)

Why Stacked Models Perform Effective Collective Classification. - Andrew S. Fast; David D. Jensen (ICDM, 2008)

Automatic identification of quasi-experimental designs for discovering causal knowledge. - David D. Jensen; Andrew S. Fast; Brian J. Taylor; Marc E. Maier (KDD, 2008)

Social networks: looking ahead. - Ravi Kumar 001; Alexander Tuzhilin; Christos Faloutsos; David D. Jensen; Gueorgi Kossinets; Jure Leskovec; Andrew Tomkins (KDD, 2008)

A bias/variance decomposition for models using collective inference. - Jennifer Neville; David D. Jensen (Mach. Learn., 2008)

Navigating networks by using homophily and degree. - Özgür Simsek; David D. Jensen (Proc. Natl. Acad. Sci. USA, 2008)

Optimistic pruning for multiple instance learning. - Amy McGovern; David D. Jensen (Pattern Recognit. Lett., 2008)

Resisting structural re-identification in anonymized social networks. - Michael Hay; Gerome Miklau; David D. Jensen; Donald F. Towsley; Philipp Weis (Proc. VLDB Endow., 2008)

Knowledge Discovery by Design. - David D. Jensen (KDIR, 2009)

Accurate Estimation of the Degree Distribution of Private Networks. - Michael Hay; Chao Li 003; Gerome Miklau; David D. Jensen (ICDM, 2009)

Learning Causal Models of Relational Domains. - Marc E. Maier; Brian J. Taylor; Hüseyin Oktay; David D. Jensen (AAAI, 2010)

Leveraging D-Separation for Relational Data Sets. - Matthew J. Rattigan; David D. Jensen (ICDM, 2010)

Causal discovery in social media using quasi-experimental designs. - Hüseyin Oktay; Brian J. Taylor; David D. Jensen (SOMA@KDD, 2010)

The Application of Statistical Relational Learning to a Database of Criminal and Terrorist Activity. - Brian Delaney; Andrew S. Fast; William M. Campbell; Clifford J. Weinstein; David D. Jensen (SDM, 2010)

Resisting structural re-identification in anonymized social networks. - Michael Hay; Gerome Miklau; David D. Jensen; Donald F. Towsley; Chao Li 003 (VLDB J., 2010)

Relational Blocking for Causal Discovery. - Matthew J. Rattigan; Marc E. Maier; David D. Jensen (AAAI, 2011)

Probabilistic Modeling of Hierarchical Music Analysis. - Phillip B. Kirlin; David D. Jensen (ISMIR, 2011)

Indexing Network Structure with Shortest-Path Trees. - Marc E. Maier; Matthew J. Rattigan; David D. Jensen (ACM Trans. Knowl. Discov. Data, 2011)

Copy or Coincidence? A Model for Detecting Social Influence and Duplication Events. - Lisa Friedland; David D. Jensen; Michael Lavine (ICML (3), 2013)

Detecting insider threats in a real corporate database of computer usage activity. - Ted E. Senator; Henry G. Goldberg; Alex Memory; William T. Young; Brad Rees; Robert Pierce; Daniel Huang 003; Matthew Reardon; David A. Bader; Edmond Chow; Irfan A. Essa; Joshua Jones; et al. (KDD, 2013)

A Sound and Complete Algorithm for Learning Causal Models from Relational Data. - Marc E. Maier; Katerina Marazopoulou; David T. Arbour; David D. Jensen (UAI, 2013)

Reasoning about Independence in Probabilistic Models of Relational Data - Marc E. Maier; Katerina Marazopoulou; David D. Jensen (CoRR, 2013)

Strategy Mining. - Xiaoxi Xu; David D. Jensen; Edwina L. Rissland (FLAIRS, 2014)

Classifier-Adjusted Density Estimation for Anomaly Detection and One-Class Classification. - Lisa Friedland; Amanda Gentzel; David D. Jensen (SDM, 2014)

Propensity Score Matching for Causal Inference with Relational Data. - David T. Arbour; Katerina Marazopoulou; Dan Garant; David D. Jensen (CI@UAI, 2014)

Online dating recommendations: matching markets and learning preferences. - Kun Tu; Bruno F. Ribeiro; David D. Jensen; Don Towsley; Benyuan Liu; Hua Jiang; Xiaodong Wang (WWW (Companion Volume), 2014)

Refining the Semantics of Social Influence. - Katerina Marazopoulou; David T. Arbour; David D. Jensen (CoRR, 2014)

Learning to Uncover Deep Musical Structure. - Phillip B. Kirlin; David D. Jensen (AAAI, 2015)

Teaching Computing as Science in a Research Experience. - Jerod J. Weinman; David D. Jensen; David Lopatto (SIGCSE, 2015)

Learning the Structure of Causal Models with Relational and Temporal Dependence. - Katerina Marazopoulou; Marc E. Maier; David D. Jensen (ACI@UAI, 2015)

Inferring Network Effects from Observational Data. - David T. Arbour; Dan Garant; David D. Jensen (KDD, 2016)

Controversy Detection in Wikipedia Using Collective Classification. - Shiri Dori-Hacohen; David D. Jensen; James Allan (SIGIR, 2016)

Inferring Causal Direction from Relational Data. - David T. Arbour; Katerina Marazopoulou; David D. Jensen (UAI, 2016)

Evaluating Causal Models by Comparing Interventional Distributions. - Dan Garant; David D. Jensen (CoRR, 2016)

Causal Discovery for Manufacturing Domains. - Katerina Marazopoulou; Rumi Ghosh; Prasanth Lade; David D. Jensen (CoRR, 2016)

ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents. - John Foley; Emma Tosch; Kaleigh Clary; David D. Jensen (CoRR, 2018)

Measuring and Characterizing Generalization in Deep Reinforcement Learning. - Sam Witty; Jun Ki Lee; Emma Tosch; Akanksha Atrey; Michael L. Littman; David D. Jensen (CoRR, 2018)

The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data. - Amanda Gentzel; Dan Garant; David D. Jensen (NeurIPS, 2019)

Identifying When Effect Restoration Will Improve Estimates of Causal Effect. - Hüseyin Oktay; Akanksha Atrey; David D. Jensen (SDM, 2019)

Object Conditioning for Causal Inference. - David D. Jensen; Javier Burroni; Matthew J. Rattigan (UAI, 2019)

Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments. - Kaleigh Clary; Emma Tosch; John Foley; David D. Jensen (CoRR, 2019)

Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning. - Emma Tosch; Kaleigh Clary; John Foley; David D. Jensen (CoRR, 2019)

Bayesian causal inference via probabilistic program synthesis. - Sam Witty; Alexander K. Lew; David D. Jensen; Vikash Mansinghka 001 (CoRR, 2019)

Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep RL. - Akanksha Atrey; Kaleigh Clary; David D. Jensen (CoRR, 2019)

Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates. - Katherine A. Keith; David D. Jensen; Brendan O'Connor 001 (ACL, 2020)

Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning. - Akanksha Atrey; Kaleigh Clary; David D. Jensen (ICLR, 2020)

Causal Inference using Gaussian Processes with Structured Latent Confounders. - Sam Witty; Kenta Takatsu; David D. Jensen; Vikash Mansinghka 001 (ICML, 2020)

Using Experimental Data to Evaluate Methods for Observational Causal Inference. - Amanda Gentzel; Justin Clarke; David D. Jensen (CoRR, 2020)

Improving Causal Inference by Increasing Model Expressiveness. - David D. Jensen (AAAI, 2021)

Preserving Privacy in Personalized Models for Distributed Mobile Services. - Akanksha Atrey; Prashant J. Shenoy; David D. Jensen (ICDCS, 2021)

How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference. - Amanda Gentzel; Purva Pruthi; David D. Jensen (ICML, 2021)

PlanAlyzer: assessing threats to the validity of online experiments. - Emma Tosch; Eytan Bakshy; Emery D. Berger; David D. Jensen; J. Eliot B. Moss (Commun. ACM, 2021)

A Simulation-Based Test of Identifiability for Bayesian Causal Inference. - Sam Witty; David D. Jensen; Vikash Mansinghka 001 (CoRR, 2021)

Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program. - Jeff Druce; James Niehaus; Vanessa Moody; David D. Jensen; Michael L. Littman (CoRR, 2021)

Stick It to The Man: Correcting for Non-Cooperative Behavior of Subjects in Experiments on Social Networks. - Kaleigh Clary; Emma Tosch; Jeremiah Onaolapo; David D. Jensen (USENIX Security Symposium, 2022)

Measuring Interventional Robustness in Reinforcement Learning. - Katherine Avery; Jack Kenney; Pracheta Amaranath; Erica Cai; David D. Jensen (CoRR, 2022)

Improving the Efficiency of the PC Algorithm by Using Model-Based Conditional Independence Tests. - Erica Cai; Andrew McGregor 001; David D. Jensen (CoRR, 2022)

Causal Dynamic Bayesian Networks for Simulation Metamodeling. - Pracheta Amaranath; Peter J. Haas; David D. Jensen; Sam Witty (WSC, 2023)

The Effect of Alter Ego Accounts on A/B Tests in Social Networks. - Katherine Avery; Amir Houmansadr; David D. Jensen (WWW (Companion Volume), 2024)

Compositional Models for Estimating Causal Effects. - Purva Pruthi; David D. Jensen (CLeaR, 2025)

Open-World Classification with Bayesian Gaussian Mixture Models. - Justin Clarke; Przemyslaw Grabowicz; David D. Jensen (AAMAS, 2025)

Uncertainty Sets for Distributionally Robust Bandits Using Structural Equation Models. - Katherine Avery; Chinmay Pendse; David D. Jensen (CoRR, 2025)