Academic Appointments


Program Affiliations


  • Symbolic Systems Program

2019-20 Courses


Stanford Advisees


All Publications


  • Where's the Reward?: A Review of Reinforcement Learning for Instructional Sequencing INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION Doroudi, S., Aleven, V., Brunskill, E. 2019; 29 (4): 568–620
  • Preventing undesirable behavior of intelligent machines. Science (New York, N.Y.) Thomas, P. S., Castro da Silva, B., Barto, A. G., Giguere, S., Brun, Y., Brunskill, E. 2019; 366 (6468): 999–1004

    Abstract

    Intelligent machines using machine learning algorithms are ubiquitous, ranging from simple data analysis and pattern recognition tools to complex systems that achieve superhuman performance on various tasks. Ensuring that they do not exhibit undesirable behavior-that they do not, for example, cause harm to humans-is therefore a pressing problem. We propose a general and flexible framework for designing machine learning algorithms. This framework simplifies the problem of specifying and regulating undesirable behavior. To show the viability of this framework, we used it to create machine learning algorithms that precluded the dangerous behavior caused by standard machine learning algorithms in our experiments. Our framework for designing machine learning algorithms simplifies the safe and responsible application of machine learning.

    View details for DOI 10.1126/science.aag3311

    View details for PubMedID 31754000

  • Fairer but Not Fair Enough On the Equitability of Knowledge Tracing Doroudi, S., Brunskill, E., Azcona, D., Chung, R. ASSOC COMPUTING MACHINERY. 2019: 335–39
  • Key Phrase Extraction for Generating Educational Question-Answer Pairs Willis, A., Davis, G., Ruan, S., Manoharan, L., Landay, J., Brunskill, E., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019
  • PLOTS: Procedure Learning from Observations using Subtask Structure Mu, T., Goel, K., Brunskill, E., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019: 1007–15
  • Value Driven Representation for Human-in-the-Loop Reinforcement Learning Keramati, R., Brunskill, E., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019: 176–80
  • QuizBot: A Dialogue-based Adaptive Learning System for Factual Knowledge Ruan, S., Jiang, L., Xu, J., Tham, B., Qiu, Z., Zhu, Y., Murnane, E. L., Brunskill, E., Landay, J. A., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019
  • BookBuddy: Turning Digital Materials Into Interactive Foreign Language Lessons Through a Voice Chatbot Ruan, S., Willis, A., Xu, Q., Davis, G. M., Jiang, L., Brunskill, E., Landay, J. A., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019
  • Shared Autonomy for an Interactive AI System Zhou, S., Mu, T., Goel, K., Bernstein, M., Brunskill, E., ACM ASSOC COMPUTING MACHINERY. 2018: 20–22
  • Representation Balancing MDPs for Off-Policy Policy Evaluation Liu, Y., Gottesman, O., Raghu, A., Komorowski, M., Faisal, A., Doshi-Velez, F., Brunskill, E., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
  • Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning Dann, C., Lattimore, T., Brunskill, E., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017
  • Regret Minimization in MDPs with Options without Prior Knowledge Fruit, R., Pirotta, M., Lazaric, A., Brunskill, E., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017
  • Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation Guo, Z., Thomas, P. S., Brunskill, E., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017