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David 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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