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ORCIDDavid D. Jensen
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- affiliation: University of Massachusetts Amherst, College of Information and Computer Sciences
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2020 – today
- 2024
[c80]Katherine Avery
, Amir Houmansadr, David D. Jensen:
The Effect of Alter Ego Accounts on A/B Tests in Social Networks. WWW (Companion Volume) 2024: 565-568- [i24]Purva Pruthi, David D. Jensen:
Compositional Models for Estimating Causal Effects. CoRR abs/2406.17714 (2024) - 2023
- [c79]Pracheta Amaranath, Peter J. Haas, David D. Jensen, Sam Witty:
Causal Dynamic Bayesian Networks for Simulation Metamodeling. WSC 2023: 746-757 - 2022
- [c78]Kaleigh Clary, Emma Tosch, Jeremiah Onaolapo, David D. Jensen:
Stick It to The Man: Correcting for Non-Cooperative Behavior of Subjects in Experiments on Social Networks. USENIX Security Symposium 2022: 3771-3788 - [i23]Katherine Avery, Jack Kenney, Pracheta Amaranath, Erica Cai, David D. Jensen:
Measuring Interventional Robustness in Reinforcement Learning. CoRR abs/2209.09058 (2022) - [i22]Erica Cai, Andrew McGregor, David D. Jensen:
Improving the Efficiency of the PC Algorithm by Using Model-Based Conditional Independence Tests. CoRR abs/2211.06536 (2022) - 2021
- [j15]Emma Tosch, Eytan Bakshy, Emery D. Berger, David D. Jensen, J. Eliot B. Moss:
PlanAlyzer: assessing threats to the validity of online experiments. Commun. ACM 64(9): 108-116 (2021) - [c77]David D. Jensen:
Improving Causal Inference by Increasing Model Expressiveness. AAAI 2021: 15053-15057 - [c76]Akanksha Atrey, Prashant J. Shenoy, David D. Jensen:
Preserving Privacy in Personalized Models for Distributed Mobile Services. ICDCS 2021: 875-886 - [c75]Amanda Gentzel, Purva Pruthi, David D. Jensen:
How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference. ICML 2021: 3660-3671 - [i21]Akanksha Atrey, Prashant J. Shenoy, David D. Jensen:
Preserving Privacy in Personalized Models for Distributed Mobile Services. CoRR abs/2101.05855 (2021) - [i20]Sam Witty, David D. Jensen, Vikash Mansinghka:
A Simulation-Based Test of Identifiability for Bayesian Causal Inference. CoRR abs/2102.11761 (2021) - [i19]Jeff Druce, James Niehaus, Vanessa Moody, David D. Jensen, Michael L. Littman:
Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program. CoRR abs/2106.05506 (2021) - 2020
- [c74]Katherine A. Keith, David D. Jensen, Brendan O'Connor:
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates. ACL 2020: 5332-5344 - [c73]Akanksha Atrey, Kaleigh Clary, David D. Jensen:
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning. ICLR 2020 - [c72]Sam Witty, Kenta Takatsu, David D. Jensen, Vikash Mansinghka:
Causal Inference using Gaussian Processes with Structured Latent Confounders. ICML 2020: 10313-10323 - [i18]Katherine A. Keith, David D. Jensen, Brendan O'Connor:
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates. CoRR abs/2005.00649 (2020) - [i17]Sam Witty, Kenta Takatsu, David D. Jensen, Vikash Mansinghka:
Causal Inference using Gaussian Processes with Structured Latent Confounders. CoRR abs/2007.07127 (2020) - [i16]Amanda Gentzel, Justin Clarke, David D. Jensen:
Using Experimental Data to Evaluate Methods for Observational Causal Inference. CoRR abs/2010.03051 (2020)
2010 – 2019
- 2019
- [j14]Emma Tosch, Eytan Bakshy, Emery D. Berger, David D. Jensen, J. Eliot B. Moss:
PlanAlyzer: assessing threats to the validity of online experiments. Proc. ACM Program. Lang. 3(OOPSLA): 182:1-182:30 (2019) - [c71]Amanda Gentzel, Dan Garant, David D. Jensen:
The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data. NeurIPS 2019: 11717-11727 - [c70]Hüseyin Oktay, Akanksha Atrey, David D. Jensen:
Identifying When Effect Restoration Will Improve Estimates of Causal Effect. SDM 2019: 190-198 - [c69]David D. Jensen, Javier Burroni, Matthew J. Rattigan:
Object Conditioning for Causal Inference. UAI 2019: 1072-1082 - [i15]Kaleigh Clary, Emma Tosch, John Foley, David D. Jensen:
Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments. CoRR abs/1904.06312 (2019) - [i14]Emma Tosch, Kaleigh Clary, John Foley, David D. Jensen:
Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning. CoRR abs/1905.02825 (2019) - [i13]Emma Tosch, Eytan Bakshy, Emery D. Berger, David D. Jensen, J. Eliot B. Moss:
PlanAlyzer: Assessing Threats to the Validity of Online Experiments. CoRR abs/1909.13649 (2019) - [i12]Amanda Gentzel, Dan Garant, David D. Jensen:
The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data. CoRR abs/1910.05387 (2019) - [i11]Sam Witty, Alexander K. Lew, David D. Jensen, Vikash Mansinghka:
Bayesian causal inference via probabilistic program synthesis. CoRR abs/1910.14124 (2019) - [i10]Akanksha Atrey, Kaleigh Clary, David D. Jensen:
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep RL. CoRR abs/1912.05743 (2019) - 2018
- [i9]John Foley, Emma Tosch, Kaleigh Clary, David D. Jensen:
ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents. CoRR abs/1812.02850 (2018) - [i8]Sam Witty, Jun Ki Lee, Emma Tosch, Akanksha Atrey, Michael L. Littman, David D. Jensen:
Measuring and Characterizing Generalization in Deep Reinforcement Learning. CoRR abs/1812.02868 (2018) - 2016
- [c68]David T. Arbour, Dan Garant, David D. Jensen:
Inferring Network Effects from Observational Data. KDD 2016: 715-724 - [c67]Shiri Dori-Hacohen, David D. Jensen, James Allan:
Controversy Detection in Wikipedia Using Collective Classification. SIGIR 2016: 797-800 - [c66]David T. Arbour, Katerina Marazopoulou, David D. Jensen:
Inferring Causal Direction from Relational Data. UAI 2016 - [i7]Katerina Marazopoulou, Rumi Ghosh, Prasanth Lade, David D. Jensen:
Causal Discovery for Manufacturing Domains. CoRR abs/1605.04056 (2016) - [i6]Dan Garant, David D. Jensen:
Evaluating Causal Models by Comparing Interventional Distributions. CoRR abs/1608.04698 (2016) - 2015
- [c65]Phillip B. Kirlin, David D. Jensen:
Learning to Uncover Deep Musical Structure. AAAI 2015: 1770-1776 - [c64]Jerod J. Weinman, David D. Jensen, David Lopatto:
Teaching Computing as Science in a Research Experience. SIGCSE 2015: 24-29 - [c63]Katerina Marazopoulou, Marc E. Maier, David D. Jensen:
Learning the Structure of Causal Models with Relational and Temporal Dependence. ACI@UAI 2015: 66-75 - [c62]Katerina Marazopoulou, Marc E. Maier, David D. Jensen:
Learning the Structure of Causal Models with Relational and Temporal Dependence. UAI 2015: 572-581 - 2014
- [c61]Xiaoxi Xu, David D. Jensen, Edwina L. Rissland:
Strategy Mining. FLAIRS 2014 - [c60]Lisa Friedland, Amanda Gentzel, David D. Jensen:
Classifier-Adjusted Density Estimation for Anomaly Detection and One-Class Classification. SDM 2014: 578-586 - [c59]David T. Arbour, Katerina Marazopoulou, Dan Garant, David D. Jensen:
Propensity Score Matching for Causal Inference with Relational Data. CI@UAI 2014: 25-34 - [c58]Kun Tu, Bruno F. Ribeiro, David D. Jensen, Don Towsley, Benyuan Liu, Hua Jiang, Xiaodong Wang:
Online dating recommendations: matching markets and learning preferences. WWW (Companion Volume) 2014: 787-792 - [i5]Kun Tu, Bruno F. Ribeiro, Hua Jiang, Xiaodong Wang, David D. Jensen, Benyuan Liu, Don Towsley:
Online Dating Recommendations: Matching Markets and Learning Preferences. CoRR abs/1401.8042 (2014) - [i4]Katerina Marazopoulou, David T. Arbour, David D. Jensen:
Refining the Semantics of Social Influence. CoRR abs/1412.5238 (2014) - 2013
- [c57]Lisa Friedland, David D. Jensen, Michael Lavine:
Copy or Coincidence? A Model for Detecting Social Influence and Duplication Events. ICML (3) 2013: 1175-1183 - [c56]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:
Detecting insider threats in a real corporate database of computer usage activity. KDD 2013: 1393-1401 - [c55]Marc E. Maier, Katerina Marazopoulou, David T. Arbour, David D. Jensen:
A Sound and Complete Algorithm for Learning Causal Models from Relational Data. UAI 2013 - [i3]Marc E. Maier, Katerina Marazopoulou, David D. Jensen:
Reasoning about Independence in Probabilistic Models of Relational Data. CoRR abs/1302.4381 (2013) - [i2]Marc E. Maier, Katerina Marazopoulou, David T. Arbour, David D. Jensen:
A Sound and Complete Algorithm for Learning Causal Models from Relational Data. CoRR abs/1309.6843 (2013) - 2011
- [j13]Marc E. Maier, Matthew J. Rattigan, David D. Jensen:
Indexing Network Structure with Shortest-Path Trees. ACM Trans. Knowl. Discov. Data 5(3): 15:1-15:25 (2011) - [c54]Matthew J. Rattigan, Marc E. Maier, David D. Jensen:
Relational Blocking for Causal Discovery. AAAI 2011: 145-151 - [c53]Phillip B. Kirlin, David D. Jensen:
Probabilistic Modeling of Hierarchical Music Analysis. ISMIR 2011: 393-398 - 2010
- [j12]Michael Hay, Gerome Miklau, David D. Jensen, Donald F. Towsley, Chao Li:
Resisting structural re-identification in anonymized social networks. VLDB J. 19(6): 797-823 (2010) - [c52]Marc E. Maier, Brian J. Taylor, Hüseyin Oktay, David D. Jensen:
Learning Causal Models of Relational Domains. AAAI 2010: 531-538 - [c51]Matthew J. Rattigan, David D. Jensen:
Leveraging D-Separation for Relational Data Sets. ICDM 2010: 989-994 - [c50]Hüseyin Oktay, Brian J. Taylor, David D. Jensen:
Causal discovery in social media using quasi-experimental designs. SOMA@KDD 2010: 1-9 - [c49]Brian Delaney, Andrew S. Fast, William M. Campbell, Clifford J. Weinstein, David D. Jensen:
The Application of Statistical Relational Learning to a Database of Criminal and Terrorist Activity. SDM 2010: 409-417
2000 – 2009
- 2009
- [c48]David D. Jensen:
Knowledge Discovery by Design. KDIR 2009: 9-9 - [c47]Michael Hay, Chao Li, Gerome Miklau, David D. Jensen:
Accurate Estimation of the Degree Distribution of Private Networks. ICDM 2009: 169-178 - 2008
- [j11]Jennifer Neville, David D. Jensen:
A bias/variance decomposition for models using collective inference. Mach. Learn. 73(1): 87-106 (2008) - [j10]Özgür Simsek, David D. Jensen:
Navigating networks by using homophily and degree. Proc. Natl. Acad. Sci. USA 105(35): 12758-12762 (2008) - [j9]Amy McGovern, David D. Jensen:
Optimistic pruning for multiple instance learning. Pattern Recognit. Lett. 29(9): 1252-1260 (2008) - [j8]Michael Hay, Gerome Miklau, David D. Jensen, Donald F. Towsley, Philipp Weis:
Resisting structural re-identification in anonymized social networks. Proc. VLDB Endow. 1(1): 102-114 (2008) - [c46]David D. Jensen, Andrew S. Fast, Brian J. Taylor, Marc E. Maier, Matthew J. Rattigan:
Automatic Identification of Quasi-Experimental Designs for Scientific Discovery. AAAI Fall Symposium: Automated Scientific Discovery 2008: 24-25 - [c45]Andrew S. Fast, David D. Jensen:
Why Stacked Models Perform Effective Collective Classification. ICDM 2008: 785-790 - [c44]David D. Jensen, Andrew S. Fast, Brian J. Taylor, Marc E. Maier:
Automatic identification of quasi-experimental designs for discovering causal knowledge. KDD 2008: 372-380 - [c43]Ravi Kumar, Alexander Tuzhilin, Christos Faloutsos, David D. Jensen, Gueorgi Kossinets, Jure Leskovec, Andrew Tomkins:
Social networks: looking ahead. KDD 2008: 1060 - 2007
- [j7]Jennifer Neville, David D. Jensen:
Relational Dependency Networks. J. Mach. Learn. Res. 8: 653-692 (2007) - [c42]Matthew J. Rattigan, Marc E. Maier, David D. Jensen, Bin Wu, Xin Pei, Jianbin Tan, Yi Wang:
Exploiting Network Structure for Active Inference in Collective Classification. ICDM Workshops 2007: 429-434 - [c41]Matthew J. Rattigan, Marc E. Maier, David D. Jensen:
Graph clustering with network structure indices. ICML 2007: 783-790 - [c40]David D. Jensen:
Beyond Prediction: Directions for Probabilistic and Relational Learning. ILP 2007: 4-21 - [c39]Jennifer Neville, David D. Jensen:
Bias/Variance Analysis for Relational Domains. ILP 2007: 27-28 - [c38]Lisa Friedland, David D. Jensen:
Finding tribes: identifying close-knit individuals from employment patterns. KDD 2007: 290-299 - [c37]Andrew S. Fast, Lisa Friedland, Marc E. Maier, Brian J. Taylor, David D. Jensen, Henry G. Goldberg, John Komoroske:
Relational data pre-processing techniques for improved securities fraud detection. KDD 2007: 941-949 - [c36]Trevor Strohman, W. Bruce Croft, David D. Jensen:
Recommending citations for academic papers. SIGIR 2007: 705-706 - 2006
- [j6]Hendrik Blockeel, David D. Jensen, Stefan Kramer:
Introduction to the special issue on multi-relational data mining and statistical relational learning. Mach. Learn. 62(1-2): 3-5 (2006) - [c35]Andrew S. Fast, David D. Jensen:
The NFL Coaching Network: Analysis of the Social Network among Professional Football Coaches. AAAI Fall Symposium: Capturing and Using Patterns for Evidence Detection 2006: 112-119 - [c34]Chirag Shah, W. Bruce Croft, David D. Jensen:
Representing documents with named entities for story link detection (SLD). CIKM 2006: 868-869 - [c33]John Burgess, Brian Gallagher, David D. Jensen, Brian Neil Levine:
MaxProp: Routing for Vehicle-Based Disruption-Tolerant Networks. INFOCOM 2006 - [c32]Matthew J. Rattigan, Marc E. Maier, David D. Jensen:
Using structure indices for efficient approximation of network properties. KDD 2006: 357-366 - 2005
- [j5]Matthew J. Rattigan, David D. Jensen:
The case for anomalous link discovery. SIGKDD Explor. 7(2): 41-47 (2005) - [c31]Stephen Hart, Roderic A. Grupen, David D. Jensen:
A Relational Representation for Procedural Task Knowledge. AAAI 2005: 1280-1285 - [c30]Jennifer Neville, David D. Jensen:
Leveraging Relational Autocorrelation with Latent Group Models. ICDM 2005: 322-329 - [c29]Özgür Simsek, David D. Jensen:
Decentralized Search in Networks Using Homophily and Degree Disparity. IJCAI 2005: 304-310 - [c28]Jennifer Neville, Özgür Simsek, David D. Jensen, John Komoroske, Kelly Palmer, Henry G. Goldberg:
Using relational knowledge discovery to prevent securities fraud. KDD 2005: 449-458 - [c27]Andrew S. Fast, David D. Jensen, Brian Neil Levine:
Creating social networks to improve peer-to-peer networking. KDD 2005: 568-573 - [c26]George Dean Bissias, Marc Liberatore, David D. Jensen, Brian Neil Levine:
Privacy Vulnerabilities in Encrypted HTTP Streams. Privacy Enhancing Technologies 2005: 1-11 - [i1]Jennifer Neville, David D. Jensen:
Leveraging relational autocorrelation with latent group models. Probabilistic, Logical and Relational Learning 2005 - 2004
- [c25]Jennifer Neville, David D. Jensen:
Dependency Networks for Relational Data. ICDM 2004: 170-177 - [c24]David D. Jensen, Jennifer Neville, Brian Gallagher:
Why collective inference improves relational classification. KDD 2004: 593-598 - 2003
- [j4]Amy McGovern, Lisa Friedland, Michael Hay, Brian Gallagher, Andrew S. Fast, Jennifer Neville, David D. Jensen:
Exploiting relational structure to understand publication patterns in high-energy physics. SIGKDD Explor. 5(2): 165-172 (2003) - [c23]Jennifer Neville, David D. Jensen, Brian Gallagher:
Simple Estimators for Relational Bayesian Classifiers. ICDM 2003: 609-612 - [c22]David D. Jensen, Jennifer Neville, Michael Hay:
Avoiding Bias when Aggregating Relational Data with Degree Disparity. ICML 2003: 274-281 - [c21]Amy McGovern, David D. Jensen:
Identifying Predictive Structures in Relational Data Using Multiple Instance Learning. ICML 2003: 528-535 - [c20]David D. Jensen, Matthew J. Rattigan, Hannah Blau:
Information awareness: a prospective technical assessment. KDD 2003: 378-387 - [c19]Jennifer Neville, David D. Jensen, Lisa Friedland, Michael Hay:
Learning relational probability trees. KDD 2003: 625-630 - 2002
- [c18]David D. Jensen, Jennifer Neville:
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning. ICML 2002: 259-266 - [c17]David D. Jensen, Jennifer Neville:
Autocorrelation and Linkage Cause Bias in Evaluation of Relational Learners. ILP 2002: 101-116 - 2000
- [j3]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:
Reports on the AAAI Fall Symposia (November 1999 and November 1998). AI Mag. 21(2): 85-88 (2000) - [j2]David D. Jensen, Paul R. Cohen:
Multiple Comparisons in Induction Algorithms. Mach. Learn. 38(3): 309-338 (2000) - [j1]David D. Jensen:
Data Snooping, Dredging and Fishing: The Dark Side of Data Mining, A SIGKDD99 Panel Report. SIGKDD Explor. 1(2): 52-54 (2000) - [c16]Victor Lavrenko, Matthew D. Schmill, Dawn J. Lawrie, Paul Ogilvie, David D. Jensen, James Allan:
Language Models for Financial News Recommendation. CIKM 2000: 389-396 - [c15]David D. Jensen:
Knowledge Discovery from Graphs (Invited Talk). GD 2000: 170
1990 – 1999
- 1999
- [c14]David D. Jensen, Michael Atighetchi, Régis Vincent, Victor R. Lesser:
Learning Quantitative Knowledge for Multiagent Coordination. AAAI/IAAI 1999: 24-31 - [c13]Tim Oates, David D. Jensen:
Toward a Theoretical Understanding of Why and When Decision Tree Pruning Algorithms Fail. AAAI/IAAI 1999: 372-378 - [c12]David D. Jensen:
Statistical challenges to inductive inference in linked data. AISTATS 1999 - [c11]Foster J. Provost, David D. Jensen, Tim Oates:
Efficient Progressive Sampling. KDD 1999: 23-32 - [c10]David D. Jensen, Yulin Dong, Barbara Staudt Lerner, Eric K. McCall, Leon J. Osterweil, Stanley M. Sutton Jr., Alexander E. Wise:
Coordinating agent activities in knowledge discovery processes. WACC 1999: 137-146 - 1998
- [c9]Tim Oates, David D. Jensen:
Large Datasets Lead to Overly Complex Models: An Explanation and a Solution. KDD 1998: 294-298 - 1997
- [c8]Paul R. Cohen, David D. Jensen:
Overfitting Explained. AISTATS 1997: 115-122 - [c7]David D. Jensen:
Adjusting for Multiple Testing in Decision Tree Pruning. AISTATS 1997: 295-302 - [c6]Tim Oates, Matthew D. Schmill, David D. Jensen, Paul R. Cohen:
A Family of Algorithms for Finding Temporal Structure in Data. AISTATS 1997: 371-378 - [c5]Tim Oates, David D. Jensen:
The Effects of Training Set Size on Decision Tree Complexity. AISTATS 1997: 379-390 - [c4]Tim Oates, David D. Jensen:
The Effects of Training Set Size on Decision Tree Complexity. ICML 1997: 254-262 - [c3]David D. Jensen, Tim Oates, Paul R. Cohen:
Building Simple Models: A Case Study with Decision Trees. IDA 1997: 211-222 - [c2]David D. Jensen, Matthew D. Schmill:
Adjusting for Multiple Comparisons in Decision Tree Pruning. KDD 1997: 195-198 - 1996
- [c1]David D. Jensen, Todd M. La Porte:
Technology, language, and public decisions: finding common ground for experts and citizens. ISTAS 1996: 482-490
Coauthor Index
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