Publications

127 entries « 1 of 3 »

2021

Terrance E. Boult, Przemyslaw A. Grabowicz, D. S. Prijatelj, R. Stern, L. Holder, J. Alspector, M. Jafarzadeh, T. Ahmad, A. R. Dhamija, C. Li, S. Cruz, A. Shrivastava, C. Vondrick, W. J. Scheirer

A Unifying Framework for Formal Theories of Novelty:Framework, Examples and Discussion Proceedings Article

In: AAAI'21 SMPT, 2021, ISSN: 23318422.

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Aarshee Mishra, Przemyslaw A. Grabowicz, Nicholas Perello

Towards Fair and Explainable Supervised Learning Proceedings Article

In: ICML Workshop on Socially Responsible Machine Learning, 2021.

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Amanda M Gentzel, Purva Pruthi, David Jensen

How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference Proceedings Article

In: International Conference on Machine Learning, pp. 3660–3671, PMLR 2021.

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David Jensen

Improving Causal Inference by Increasing Model Expressiveness Proceedings Article

In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 15053–15057, 2021.

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Akanksha Atrey, Prashant J. Shenoy, David Jensen

Preserving Privacy in Personalized Models for Distributed Mobile Services Miscellaneous

2021.

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Sam Witty, David Jensen, Vikash Mansinghka

A Simulation-Based Test of Identifiability for Bayesian Causal Inference Miscellaneous

2021.

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2020

David Ifeoluwa Adelani, Ryota Kobayashi, Ingmar Weber, Przemyslaw A. Grabowicz

Estimating community feedback effect on topic choice in social media with predictive modeling Journal Article

In: EPJ Data Science, vol. 9, no. 1, pp. 25, 2020, ISSN: 2193-1127.

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David Jensen, Javier Burroni, Matthew Rattigan

Object conditioning for causal inference Proceedings Article

In: Uncertainty in Artificial Intelligence, pp. 1072–1082, PMLR 2020.

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Akanksha Atrey, Kaleigh Clary, David Jensen

Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning Proceedings Article

In: International Conference on Learning Representations, 2020.

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Katherine A. Keith, David Jensen, Brendan O'Connor

Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates Proceedings Article

In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pp. 5332–5344, Association for Computational Linguistics, 2020.

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Sam Witty, Kenta Takatsu, David Jensen, Vikash Mansinghka

Causal Inference using Gaussian Processes with Structured Latent Confounders Proceedings Article

In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, pp. 10313–10323, PMLR, 2020.

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Amanda Gentzel, Justin Clarke, David Jensen

Using Experimental Data to Evaluate Methods for Observational Causal Inference Miscellaneous

2020.

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2019

Przemyslaw A. Grabowicz, Nicholas Perello, Kenta Takatsu

Resilience of Supervised Learning Algorithms to Discriminatory Data Perturbations Journal Article

In: 2019.

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David Jensen, others

Comment: Strengthening empirical evaluation of causal inference methods Journal Article

In: Statistical Science, vol. 34, no. 1, pp. 77–81, 2019.

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Emma Tosch, Eytan Bakshy, Emery D Berger, David Jensen, J Eliot B Moss

PlanAlyzer: assessing threats to the validity of online experiments Journal Article

In: Proceedings of the ACM on Programming Languages, vol. 3, no. OOPSLA, pp. 1–30, 2019.

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Huseyin Oktay, Akanksha Atrey, David Jensen

Identifying when effect restoration will improve estimates of causal effect Proceedings Article

In: Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 190–198, Society for Industrial and Applied Mathematics 2019.

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Amanda Gentzel, Dan Garant, David Jensen

The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data Proceedings Article

In: Advances in Neural Information Processing Systems, Curran Associates, Inc., 2019.

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Emma Tosch, Kaleigh Clary, John Foley, David Jensen

Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning Miscellaneous

2019.

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Sam Witty, Alexander Lew, David Jensen, Vikash Mansinghka

Bayesian causal inference via probabilistic program synthesis Miscellaneous

2019.

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2018

John Foley, Emma Tosch, Kaleigh Clary, David Jensen

Toybox: Better Atari Environments for Testing Reinforcement Learning Agents Proceedings Article

In: NeurIPS 2018 Workshop on Systems for ML, 2018.

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Sam Witty, David Jensen

Causal Graphs vs. Causal Programs: The Case of Conditional Branching Proceedings Article

In: First Conference on Probabilistic Programming (ProbProg), 2018.

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Sam Witty, Jun Ki Lee, Emma Tosch, Akanksha Atrey, Michael Littman, David Jensen

Measuring and characterizing generalization in deep reinforcement learning Miscellaneous

2018.

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Kaleigh Clary, Emma Tosch, John Foley, David Jensen

Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments Miscellaneous

2018.

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2017

Kaleigh Clary, David Jensen

A/B Testing in Networks with Adversarial Members Journal Article

In: 2017.

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Javier Burroni, Arjun Guha, David Jensen

INTERACTIVE WRITING AND DEBUGGING OF BAYESIAN PROBABILISTIC PROGRAMS Journal Article

In: 2017.

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Katerina Marazopoulou, David Arbour, David Jensen

On causal analysis for heterogeneous networks Proceedings Article

In: The 2017 ACM SIGKDD Workshop on Causal Discovery, 2017.

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Kaleigh Clary, Andrew McGregor, David Jensen

A/B Testing in Networks with Adversarial Nodes Proceedings Article

In: KDD Workshop on Mining and Learning with Graphs, 2017.

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2016

David Arbour, Dan Garant, David Jensen

Inferring Network Effects from Observational Data Proceedings Article

In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016, pp. 715–724, ACM, 2016.

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David Arbour, Katerina Marazopoulou, David Jensen

Inferring Causal Direction from Relational Data Proceedings Article

In: Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, UAI 2016, June 25-29, 2016, New York City, NY, USA, AUAI Press, 2016.

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Shiri Dori-Hacohen, David Jensen, James Allan

Controversy Detection in Wikipedia Using Collective Classification Proceedings Article

In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, July 17-21, 2016, pp. 797–800, ACM, 2016.

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Katerina Marazopoulou, Rumi Ghosh, Prasanth Lade, David Jensen

Causal Discovery for Manufacturing Domains Miscellaneous

2016.

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Dan Garant, David Jensen

Evaluating causal models by comparing interventional distributions Miscellaneous

2016.

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2015

Phillip B. Kirlin, David Jensen

Learning to Uncover Deep Musical Structure Proceedings Article

In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30, 2015, Austin, Texas, USA, pp. 1770–1776, AAAI Press, 2015.

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Jerod J. Weinman, David Jensen, David Lopatto

Teaching Computing as Science in a Research Experience Proceedings Article

In: Proceedings of the 46th ACM Technical Symposium on Computer Science Education, SIGCSE 2015, Kansas City, MO, USA, March 4-7, 2015, pp. 24–29, ACM, 2015.

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Katerina Marazopoulou, Marc Maier, David Jensen

Learning the Structure of Causal Models with Relational and Temporal Dependence Proceedings Article

In: Proceedings of the UAI 2015 Workshop on Advances in Causal Inference co-located with the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015), Amsterdam, The Netherlands, July 16, 2015, pp. 66–75, CEUR-WS.org, 2015.

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2014

Lisa Friedland, Amanda Gentzel, David Jensen

Classifier-adjusted density estimation for anomaly detection and one-class classification Proceedings Article

In: Proceedings of the 2014 SIAM International Conference on Data Mining, pp. 578–586, Society for Industrial and Applied Mathematics 2014.

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David Arbour, Katerina Marazopoulou, Dan Garant, David Jensen

Propensity Score Matching for Causal Inference with Relational Data Proceedings Article

In: Proceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction co-located with 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014), Quebec City, Canada, July 27, 2014, pp. 25–34, CEUR-WS.org, 2014.

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Katerina Marazopoulou, David Arbour, David Jensen

Refining the Semantics of Social Influence Miscellaneous

2014.

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2013

David Arbour, James Atwood, Ahmed El-Kishky, David Jensen

Agglomerative Clustering of Bagged Data Using Joint Distributions Journal Article

In: 2013.

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Lisa Friedland, David Jensen, Michael Lavine

Copy or Coincidence? A Model for Detecting Social Influence and Duplication Events Proceedings Article

In: Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA, 16-21 June 2013, pp. 1175–1183, JMLR.org, 2013.

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Marc Maier, Katerina Marazopoulou, David Arbour, David Jensen

A Sound and Complete Algorithm for Learning Causal Models from Relational Data Proceedings Article

In: Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, UAI 2013, Bellevue, WA, USA, August 11-15, 2013, AUAI Press, 2013.

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Marc Maier, Katerina Marazopoulou, David Arbour, David Jensen

Flattening network data for causal discovery: What could go wrong? Proceedings Article

In: Workshop on Information in Networks, 2013.

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Marc Maier, Katerina Marazopoulou, David Jensen

Reasoning about Independence in Probabilistic Models of Relational Data Miscellaneous

2013.

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2012

Matthew Rattigan

Leveraging Relational Representations for Causal Discovery PhD Thesis

2012, ISBN: 9781267786821, (AAI3545976).

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2011

Marc Maier, Matthew Rattigan, David Jensen

Indexing Network Structure with Shortest-Path Trees Journal Article

In: ACM Trans. Knowl. Discov. Data, vol. 5, no. 3, 2011, ISSN: 1556-4681.

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Matthew Rattigan, Marc Maier, David Jensen

Relational blocking for causal discovery Proceedings Article

In: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011.

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Phillip B Kirlin, David Jensen

Probabilistic Modeling of Hierarchical Music Analysis. Proceedings Article

In: Proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR, pp. 393–398, 2011.

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Huseyin Oktay, A Soner Balkir, Ian Foster, David Jensen

Distance estimation for very large networks using mapreduce and network structure indices Proceedings Article

In: Workshop on Information Networks, 2011.

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2010

Michael Hay, Gerome Miklau, David Jensen

Analyzing private network data Journal Article

In: Privacy-aware knowledge discovery: Novel applications and new techniques, pp. 459–498, 2010.

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Michael Hay, Gerome Miklau, David Jensen, Don Towsley, Chao Li

Resisting structural re-identification in anonymized social networks Journal Article

In: The VLDB Journal, vol. 19, no. 6, pp. 797–823, 2010.

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127 entries « 1 of 3 »