Eric Horvitz: Selected references

People, Machines, and Intelligence

Recent

• B. Wilder, E. Horvitz, E. Kamar. Learning to Complement Humans, IJCAI 2020.
  
  
• E. Kamar, S. Hacker, E. Horvitz. Combining Human and Machine Intelligence in Large-scale Crowdsourcing, AAMAS 2012.
  
  
• R. Ramakrishnan, E. Kamar, B. Nushi, D. Dey, J. Shah, E. Horvitz. Overcoming Blind Spots in the Real World: Leveraging Complementary Abilities for Joint Execution, AAAI 2019.
  
  
• G. Bansal, B. Nushi, E. Kamar, D.S. Weld, W.S. Lasescki, E. Horvitz. Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff, AAAI 2019.
  
  
• S. Amershi, D. Weld, M. Vorvoreanu, A. Fourney, et al. Guidelines for Human-AI Interaction, CHI 2019.
  
  
• B. Nushi, E. Kamar, E. Horvitz. Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure, HCOMP 2018.
  
  
• D. Bohus, C.W. Saw, E. Horvitz. Directions Robot: In-the-Wild Experiences and Lessons Learned, AAMAS 2014.
  
  
• S. Jegelka, A. Kapoor, and E. Horvitz. An Interactive Approach to Solving Correspondence Problems, Intl Journal of Computer Vision, 2013.
  
  
• A. Kapoor, B. Lee, D. Tan, E. Horvitz. Performance and Preferences: Interactive Refinement of Machine Learning Procedures, AAAI 2012.
  
  
• D. Bohus and E. Horvitz. Decisions about Turns in Multiparty Conversation: From Perception to Action, ICMI 2011. (video)
  
  
• A. Kapoor, B. Lee, D. Tan, E. Horvitz. Interactive Optimization for Steering Classification, CHI 2010.
  
  
• D. Bohus and E. Horvitz. Models for Multiparty Engagement in Open-World Dialog, Sigdial 2009. video
  

Earlier

• E. Horvitz. Principles of Mixed-Initiative User Interfaces. CHI 1999 video, 1999 tv debut
  
  
• E. Horvitz and M Barry. Display of Information for Time-Critical Decision Making. UAI 1995.
  
  
• E. Horvitz, J. Breese, D. Heckerman, D. Hovel, and K. Rommelse. The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. UAI 1998, pp. 256-265.   video   more info.
  
  
• E. Horvitz, A. Jacobs, D. Hovel. Attention-Sensitive Alerting, UAI 1999, pp. 305-313. pdf, video
  
  
• E. Horvitz, C. M. Kadie, T. Paek, D. Hovel. Models of Attention in Computing and Communications: From Principles to Applications, CACM 46(3):52-59, March 2003.   NYTimes article   Video: Directions & futures   Video: Priorities & Notification Platform   photo.
  
  
• E. Horvitz and T. Paek. Complementary Computing: Policies for Transferring Callers from Dialog Systems to Human Receptionists. User Modeling and User Adapted Interaction 17 (2007).
  
  
• E. Horvitz, S. Dumais, P. Koch. Learning Predictive Models of Memory Landmarks, Cognitive Science 2004 (CogSci). video
  
  
• E. Horvitz and J. Apacible. Learning and Reasoning about Interruption. ICMI 2003.
  
  
• E. Horvitz, D. Heckerman, B. Nathwani, L.M. Fagan, The Use of a Heuristic Problem-Solving Hierarchy to Facilitate the Explanation of Hypothesis-Directed Reasoning, Medinfo 1986, pp. 27-31.
  
  
• E. Horvitz. Reflections on Challenges and Promises of Mixed-Initiative Interaction, AI Magazine 28 (2007).
  
  
• N. Oliver and E. Horvitz. Selective Perception Policies for Limiting Computation in Multimodal Systems: A Comparative Analysis, ICMI 2003 (also appears: Lectures Notes in Computer Science. Springer Verlag, Jan. 2005).
  
  
• N. Oliver, A. Garg, and Eric Horvitz. Layered Representations for Learning and Inferring Office Activity from Multiple Sensory Channels, Computer Vision and Image Understanding (CVIU), 96(2004), pp. 163-180. Presentation at IJCAI 2001.

AI Principles, Methods, and Systems

Recent

• S.J. Gershman, E.J. Horvitz, J.B. Tenenbaum. Computational Rationality: A Converging Paradigm for Intelligence in Brains, Minds, and Machines, 16 July 2015, Science 349. 273-278 (2015).
  
  
• A. Kolobov, Y. Peres, C. Lu, E. Horvitz. Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling, NeurIPS 2019.
  
  
• E. Kamar and E. Horvitz. Light at the End of the Tunnel: A Monte Carlo Approach to Computing Value of Information, AAMAS 2013.
  
  
• H. Aly, J. Krumm, G. Ranade, E. Horvitz. Computing Value of Spatiotemporal Information, CACM 2020.
  
  
• M. Srivastava, B. Nushi, E. Kamar, S. Shah, E. Horvitz. Backward Compatibility in Machine Learning Systems, KDD 2020.
  
  
• A. Modi, D. Dey, A. Agarwal, A. Swaminathan, B. Nushi, S. Andrist, E. Horvitz. Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations, AAAI 2020.
  
  
• A. Grover, A. Kapoor, E. Horvitz. A Deep Hybrid Model for Weather Forecasting. KDD 2015.
  
  
• R. Ramakrishnan, E. Kamar, D. Dey, J. Shah, E. Horvitz. Discovering Blind Spots in Reinforcement Learning, AAMAS 2018.
  
  
• J. Krumm and E. Horvitz. Traffic Updates: Saying a Lot While Revealing a Little, AAAI 2019.
  
  
• Y. Azar, E. Horvitz, E. Lubetzky, Y. Peres, D. Shahaf. Tractable near-optimal policies for crawling, PNAS 2018. (pdf)
  
  
• H. Lakkaraju, E. Kamar, R. Caruana, E. Horvitz. Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration, AAAI 2017.
  
  
• J.C. Krumm and E. Horvitz. Risk-Aware Planning: Methods and Case Study for Safe Driving Routes, IAAI 2017.
  
  
• A. Kapoor and E. Horvitz. Breaking Boundaries: Active Information Acquisition Across Learning and Diagnosis, NIPS 2009.
  

Earlier

• E. Horvitz. Principles and Applications of Continual Computation, AI Journal, 126:159-196 (2001).
  
  
• E. Horvitz, Y. Ruan, C. Gomes, H. Kautz, B. Selman, D. M. Chickering. A Bayesian Approach to Tackling Hard Computational Problems. UAI 2001, pp. 235-244. Slides.
  
  
• P. Dagum and E. Horvitz. A Bayesian Analysis of Simulation Algorithms for Inference in Belief Networks. Networks, 23:499-516, 1993.
  
  
• D. Shahaf and E. Horvitz. Investigations of Continual Computation, IJCAI 2009.
  
  
• F. Bach, D. Heckerman, E. Horvitz. Considering Cost Asymmetry in Learning Classifiers. Journal of Machine Learning Research, 7 (2006) 1713–1741.
  
  
• E. Horvitz and A. Klein, Utility-Based Abstraction and Categorization. UAI 1993, pp. 128-135.
  
  
• P. Dagum, A. Galper, and E. Horvitz. Dynamic Network Models for Forecasting, UAI 1992.
  
  
• E.J. Horvitz, J.S. Breese, M. Henrion, Decision Theory in Expert Systems and Artificial Intelligence, Journal of Approximate Reasoning, 2:247-302, 1988.
  
  
• E.J. Horvitz, G.F. Cooper, D.E. Heckerman, Reflection and action under scarce resources: Theoretical principles and empirical study. IJCAI 1989, pp. 1121-1127.
  
  
• D. Heckerman, J.S. Breese, E. Horvitz, The Compilation of Decision Models, UAI 1989, pp. 162-173.
  
  
• E.J. Horvitz, Reasoning under varying and uncertain resource constraints. AAAI 1988, pp. 111-116.
  
  
• E. Horvitz, Reasoning about Beliefs and Actions under Computational Resource Constraints, In: L. Kanal, et al. ed., Uncertainty in Artificial Intelligence 3, Elsevier, 1989, pp. 301-324. (Conference version: E. Horvitz, Reasoning about Beliefs and Actions under Computational Resource Constraints, UAI 1987, pp. 429-444.
  
  
• E. Horvitz, J. Apacible, R. Sarin, and L. Liao. Prediction, Expectation, and Surprise: Methods, Designs, and Study of a Deployed Traffic Forecasting Service, UAI 2005.
  
  
• D. Azari, E. Horvitz, S. Dumais, E. Brill. Actions, Answers, and Uncertainty: A Decision Making Perspective on Web-Based Question Asking, Information Processing and Management, 40(5), 2004, pp. 849-868.
  
  
• P. N. Bennett, S. T. Dumais, and E. Horvitz. The Combination of Text Classifiers using Reliability Indicators. Information Retrieval.
  

Biomedical Informatics

Recent

• U. Singer, K. Radinsky, E. Horvitz. On Biases of Attention in Scientific Discovery, J. Bioinformatics 2020.
  
  
• J. Wiens, J. Guttag, and E. Horvitz. Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach. JMLR, April 2016.
  
  
• D.H. Lee, M. Yetisgen, L. Vanderwende, E. Horvitz. Predicting Severe Clinical Events by Learning about Life-Saving Actions and Outcomes using Distant Supervision, J. Biomedical Informatics 2020.
  
  
• M. Bayati, M. Braverman, M. Gillam, K.M. Mack, G. Ruiz, M.S. Smith, E. Horvitz. Data-Driven Decisions for Reducing Readmissions for Heart Failure: General Methodology and Case Study. PLOS One Medicine. October 2014.
  
  
• J. Wiens, Wayne N. Campbell, Ella S. Franklin, J. Guttag, E. Horvitz. Learning Data-Driven Patient Risk Stratification Models for Clostridium difficile. Open Forum Infectious Diseases Advance Access, June 2014.
  
  
• J. Wiens, J. Guttag, and E. Horvitz. A Study in Transfer Learning: Leveraging Data from Multiple Hospitals to Enhance Hospital-Specific Predictions, JAMIA: 0:1–8 (2014).
  
  
• R.W. White, N.P. Tatonetti, N. H. Shah, R.B. Altman, E. Horvitz. Web-Scale Pharmacovigilance: Listening to Signals from the Crowd. JAMIA, March 2013.
  
  
• J. Wiens, J. Guttag, E. Horvitz. Patient Risk Stratification for Hospital-Associated C. Diff as a Time-Series Classification Task, NIPS 2012.
  
  
• R.W. White and E. Horvitz. Cyberchondria: Studies of the Escalation of Medical Concerns in Web Search. ACM Transactions on Information Systems, 27(4), Article 23, November 2009, DOI 101145/1629096.1629101.
  
  
• R.W. White, P. Murali Doraiswamy, and E. Horvitz. Detecting Neurodegenerative Disorders from Web Search Signals. Nature Digital Medicine, April 2018.
  
  
• E. Horvitz, Perspective: The Future of Biomedical Informatics: Bottlenecks and Opportunities, In: E.H. Shortliffe, J.J. Cimino, et. al, Biomedical Informatics: Computer Applications in Health Care and Biomedicine, Springer, 2021.
  
  
• R. W. White and E. Horvitz. Early Signs of Lung Carcinoma in Web Search Logs: Findings and Implications, JAMA Oncology, November 2016.
  
  
• T. Althoff, E. Horvitz, R.W. White, J. Zeitzer, Population-Scale Study of Sleep and Performance, WWW 2017.
  
  
• J. Paparrizos, R.W. White, E. Horvitz. Screening for Pancreatic Adenocarcinoma using Signals from Web Search Logs: Feasibility Study and Results, J. Oncology Practice, June 7, 2016. doi: 10.1200/JOP.2015.010504.
  
  
• M.J. Paul, R.W. White, E. Horvitz. Search and Breast Cancer: On Episodic Shifts of Attention over Life Histories of an Illness, ACM Transactions on the Web, 2016.
  
  
• R.W. White, S. Wang, A. Pant, R. Harpaz, P. Shukla, W. Sun, W. DuMouchel, E. Horvitz. Early Identification of Adverse Drug Reactions from Search Log Data, J. Biomedical Informatics. 59:42–48, 2015.
  
  

Earlier

• E. Horvitz and A. Seiver. Time-Critical Action: Representations and Application. UAI 1997.
  
  
• E. Horvitz and G. Rutledge. Time-Dependent Utility and Action Under Uncertainty. UAI 1991, pp. 151-158.
  
  
• D. E. Heckerman, E. J. Horvitz, and B. N. Nathwani. Toward Normative Expert Systems: Part I The Pathfinder Project. Methods of Information in Medicine, 31:90-105 (1992).
  
  
• E. Horvitz, D. Heckerman, B. Nathwani, L.M. Fagan, The use of a heuristic problem-solving hierarchy to facilitate the explanation of hypothesis-directed reasoning, Medinfo 1986, pp. 27-31.
  
  
• E.J. Horvitz, D.E. Heckerman, B.N. Nathwani, L.M. Fagan, Diagnostic Strategies in the Hypothesis-Directed Pathfinder System, In: First IEEE Conference on Artificial Intelligence Applications, December 1984, pp. 630-636.
  

Computing, People, and Society

• E. Horvitz. AI, People, and Society, Science 357:6346, pp. 7. (2017) DOI: 10.1126/science.aao2466
  
  
• E. Horvitz and D. Mulligan. Data, privacy, and the greater good, 16 July 2015, Science 349. pp. 253-255 (2015).
  
  
• T.G. Dietterich and E.J. Horvitz, Rise of Concerns about AI: Reflections and Directions. CACM 58:10, pp. 38-40 10.1145/2770869
  
  
• E. Fast and E. Horvitz. Long-Term Trends in the Public Perception of Artificial Intelligence, AAAI 2017.
  
  
• A. Howard, C. Zhang, E. Horvitz. Addressing Bias in Machine Learning Algorithms: A Pilot Study on Emotion Recognition for Intelligent Systems. IEEE Workshop on Advanced Robotics and its Social Impacts, 2017 DOI: 10.1109/ARSO.2017.8025197
  
  
• J. Suh, E. Horvitz, R.W. White, T. Althoff, Population-Scale Study of Human Needs During the COVID-19 Pandemic: Analysis and Implications, WSDM 2021.
  
  
• A. Krause and E. Horvitz. A Utility-theoretic Approach to Privacy in Online Services, JAIR, 39 (2010) 633-662.
  
  
• A. Singla, E. Horvitz, E. Kamar, R.W. White. Stochastic Privacy, AAAI 2014.
  
  
• J. Benaloh, M. Chase, E. Horvitz, K. Lauter. Patient Controlled Encryption: Ensuring Privacy in Electronic Medical Records, ACM CCSW 2009, Chicago, IL, November, 2009.
  
  
• A. Krause, E. Horvitz, A. Kansal, F. Zhao. Toward Community Sensing, IPSN 2008.
  
  
• J. Aythora, et al., Multi-stakeholder Media Provenance Management to Counter Synthetic Media Risks in News Publishing, IBC 2020 (video.)
  
  
• P. England, H.S. Malvar, E. Horvitz, J.W. Stokes, C. Fournet, et al., AMP: Authentication of Media via Provenance, Jan. 2020.
  
  
• A. Spangher, G. Ranade, B. Nushi, A. Fourney, E. Horvitz. Characterizing Search-Engine Traffic to Internet Research Agency Web Properties, Web Conf. 2020.
  
  
• R. Boyd, A. Spangher, A. Fourney, B. Nushi, G. Ranade, J. Pennebaker, E. Horvitz. Characterizing the Internet Research Agency's Social Media Operations During the 2016 U.S. Presidential Election using Linguistic Analyses, PsyArXiv Preprints 2018. doi: 10.31234/osf.io/ajh2q
  
  
• A. Fourney, M.Z. Racz, G. Ranade, M. Mobius, E. Horvitz. Geographic and Temporal Trends in Fake News Consumption During the 2016 US Presidential Election, CIKM 2017.
  
  
• Eric Horvitz, One-Hundred Year Study on Artificial Intelligence: Reflections and Framing, One Hundred Year Study on Artifical Intelligence, Stanford University 2014.
  
  
• AAAI Presidential Panel on Long-Term AI Futures, Association for the Advancement of AI (AAAI), 2008-2009.