Explainable AI

  • Exploratory not Explanatory: Counterfactual Analysis of Saliency Maps for Deep RL — A. Atrey, K. Clary, and D. Jensen (ICLR 2020)
  • Generalization in Deep Reinforcement Learning — S. Witty, J. Lee, E. Tosch, A. Atrey, M. Littman, and D. Jensen (NeurIPS CRACT 2018)
  • Let’s Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments — K. Clary, E. Tosch, J. Foley, and D. Jensen (NeurIPS CRACT 2018)
  • Toybox: Better Atari Environments for Reinforcement Learning — J. Foley, E. Tosch, K. Clary, and D. Jensen (NeurIPS Systems4ML 2018)

Causal Modeling

  • Causal Inference using Gaussian Processes with Structured Latent Confounders — S. Witty, K. Takatsu, D. Jensen, and V. Mansinghka (ICML 2020) [PDF]
  • The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data — A. Gentzel, D. Garant, and D. Jensen (NeurIPS 2019) [pdf] [video] [poster]
  • A/B Testing in Networks with Adversarial Members — K. Clary and D. Jensen (KDD 2017) [PDF]
  • Evaluating causal models by comparing interventional distributions — D. Garant and D. Jensen (SIGKDD Workshop on Causation 2016) [PDF]
  • Inferring causal direction from relational data — D. Arbour, K. Marazopoulou, and D. Jensen (UAI 2016) [PDF]
  • Inferring network effects in relational data — D. Arbour, D. Garant, and D. Jensen (KDD 2016) [PDF]
  • Learning the Structure of Causal Models with Relational and Temporal Dependence — K. Marazopoulou, M. Maier, and D. Jensen (UAI 2015) [PDF]
  • Propensity score matching for causal inference with relational data — D. Arbour, K. Marazopoulou, D. Garant, and D. Jensen (Causal Inference: Learning and Prediction Workshop at UAI 2014) [PDF]
  • Reasoning about independence in probabilistic models of relational data — M. Maier, K. Marazopoulou, and D. Jensen (arXiv 2014) [PDF]
  • A sound and complete algorithm for learning causal models from relational data — M. Maier, K. Marazopoulou, D. Arbour, and D. Jensen (UAI 2013) [PDF]
  • Relational blocking for causal discovery — M. Rattigan, M. Maier, and D. Jensen (AAAI 2011) [PDF]
  • Causal discovery in social media using quasi-experimental designs — H. Oktay, B. Taylor, and D. Jensen (SIGKDD Workshop on Social Media Analytics 2010) [PDF]
  • Learning causal models of relational domains — M. Maier, B. Taylor, H. Oktay, and D. Jensen (AAAI 2010) [PDF]
  • Automatic identification of quasi-experimental designs for discovering causal knowledge — D. Jensen, A. Fast, B. Taylor, and M. Maier (KDD 2008) [PDF]

Probabilistic Programming

  • Causal Graphs vs. Causal Programs: The Case of Conditional Branching — S. Witty and D. Jensen (ProbProg 2018) [PDF]

Statistical Relational Learning

  • Refining the semantics of social influence — K. Marazopoulou, D. Arbour, and D. Jensen (NIPS Workshop 2014) [PDF]
  • Why stacked models perform effective collective classification — A. Fast and D. Jensen (ICDM 2008) [PDF]
  • Relational dependency networks — J. Neville and D. Jensen (JMLR 2007) [PDF]
  • Leveraging relational autocorrelation with latent group models — J. Neville and D. Jensen (ICDM 2005) [PDF]
  • Why collective inference improves relational classification — D. Jensen, J. Neville, and B. Gallagher (KIDD 2004) [PDF]
  • Learning relational probability trees — J. Neville, D. Jensen, L. Friedland, and M. Hay (KDD 2003) [PDF]
  • Simple estimators for relational Bayesian classifiers — J. Neville, D. Jensen, and B. Gallagher (ICDM 2003) [PDF]
  • Linkage and autocorrelation cause feature selection bias in relational learning — D. Jensen and J. Neville (ICML 2002) [PDF]

Navigation and Routing in Networks

  • Indexing network structure with shortest-path trees — M. Maier, M. Rattigan, and D. Jensen (ACM TKDD 2011) [PDF]
  • Navigating networks by using homophily and degree — Ö. Şimşek and D. Jensen (PNAS 2008) [PDF]
  • MaxProp: Routing for vehicle-based disruption-tolerant networks — J. Burgess, B. Gallagher, D. Jensen, and B. Levine (INFOCOM 2006) [PDF]
  • Using structure indices for efficient approximation of network properties — M. Rattigan, M. Maier, and D. Jensen (KDD 2006) [PDF]
  • Creating social networks to improve peer-to-peer networking — A. Fast, D. Jensen, and B. Levine (KDD 2005) [PDF]

Privacy and Networks

  • Resisting structural re-identification in anonymized social networks — M. Hay, G. Miklau, D. Jensen, D. Towsley, and L. Chao. (The VLDB Journal 2010) [PDF]
  • Accurate estimation of the degree distribution of private networks — M. Hay, L. Chao, G. Miklau, and D. Jensen (ICDM 2009) [PDF]
  • Privacy vulnerabilities in encrypted HTTP streams — G. Bissias, M. Liberatore, D. Jensen, and B. Levine (PET 2006) [PDF]

Fraud Detection and Security

  • Detecting Insider Threats in a Real Corporate Database of Computer Usage Activity — T. Senator, et al. (KDD 2013) [PDF]
  • Using relational knowledge discovery to prevent securities fraud — J. Neville, Ö. Şimşek, D. Jensen, J. Komoroske, K. Palmer, and H. Goldberg (Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2005) [PDF]