
Emma Brunskill
Associate Professor of Computer Science and, by courtesy, of Education
Academic Appointments
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Associate Professor, Computer Science
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Associate Professor (By courtesy), Graduate School of Education
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Faculty Affiliate, Institute for Human-Centered Artificial Intelligence (HAI)
Program Affiliations
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Symbolic Systems Program
2020-21 Courses
- Counterfactuals: The Science of What Ifs?
CS 31N (Spr) - Reinforcement Learning
CS 234 (Win) -
Independent Studies (17)
- Advanced Reading and Research
CS 499 (Aut, Win, Spr, Sum) - Advanced Reading and Research
CS 499P (Aut, Win, Spr, Sum) - Computer Laboratory
CS 393 (Spr, Sum) - Curricular Practical Training
CS 390A (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390B (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390C (Spr, Sum) - Independent Database Project
CS 395 (Spr, Sum) - Independent Project
CS 399 (Aut, Win, Spr) - Independent Project
CS 399P (Win, Spr, Sum) - Independent Work
CS 199 (Aut, Win, Spr, Sum) - Independent Work
CS 199P (Aut, Win, Spr, Sum) - Master's Research
CME 291 (Aut, Win) - Part-time Curricular Practical Training
CS 390D (Win, Spr, Sum) - Programming Service Project
CS 192 (Spr) - Senior Project
CS 191 (Win, Spr, Sum) - Supervised Undergraduate Research
CS 195 (Spr, Sum) - Writing Intensive Senior Project (WIM)
CS 191W (Aut, Win, Spr)
- Advanced Reading and Research
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Prior Year Courses
2019-20 Courses
- Reinforcement Learning
CS 234 (Win)
2018-19 Courses
- Advanced Survey of Reinforcement Learning
CS 332 (Aut) - Reinforcement Learning
CS 234 (Win)
2017-18 Courses
- Advanced Survey of Reinforcement Learning
CS 332 (Aut) - Reinforcement Learning
CS 234 (Win)
- Reinforcement Learning
Stanford Advisees
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Doctoral Dissertation Reader (AC)
Erdem Biyik, Vikranth Dwaracherla, James Harrison, Zhiyuan Jerry Lin, Andrea Zanette -
Doctoral Dissertation Advisor (AC)
Ramtin Keramati, Yao Liu, Tong Mu -
Doctoral Dissertation Co-Advisor (AC)
Jean-Raymond Betterton, Scott Fleming, Ayush Kanodia, Xinkun Nie -
Master's Program Advisor
Kaidi Cao, Antonio Ferris, Daniel Guillen, Zhe Han, Minnie Ho, Emily Huang, Tyler Layden, Kazuki Mogi, Kelly Ndombe, Jason Zhao -
Doctoral (Program)
Jonathan Lee, Yao Liu, Allen Nie
All Publications
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Where's the Reward?: A Review of Reinforcement Learning for Instructional Sequencing
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION
2019; 29 (4): 568–620
View details for DOI 10.1007/s40593-019-00187-x
View details for Web of Science ID 000504748200005
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Preventing undesirable behavior of intelligent machines.
Science (New York, N.Y.)
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
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Fairer but Not Fair Enough On the Equitability of Knowledge Tracing
ASSOC COMPUTING MACHINERY. 2019: 335–39
View details for DOI 10.1145/3303772.3303838
View details for Web of Science ID 000473277300044
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BookBuddy: Turning Digital Materials Into Interactive Foreign Language Lessons Through a Voice Chatbot
ASSOC COMPUTING MACHINERY. 2019
View details for DOI 10.1145/3330430.3333643
View details for Web of Science ID 000507611000030
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Key Phrase Extraction for Generating Educational Question-Answer Pairs
ASSOC COMPUTING MACHINERY. 2019
View details for DOI 10.1145/3330430.3333636
View details for Web of Science ID 000507611000020
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Value Driven Representation for Human-in-the-Loop Reinforcement Learning
ASSOC COMPUTING MACHINERY. 2019: 176–80
View details for DOI 10.1145/3320435.3320471
View details for Web of Science ID 000482185300025
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PLOTS: Procedure Learning from Observations using Subtask Structure
ASSOC COMPUTING MACHINERY. 2019: 1007–15
View details for Web of Science ID 000474345000116
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QuizBot: A Dialogue-based Adaptive Learning System for Factual Knowledge
ASSOC COMPUTING MACHINERY. 2019
View details for DOI 10.1145/3290605.3300587
View details for Web of Science ID 000474467904049
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Representation Balancing MDPs for Off-Policy Policy Evaluation
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
View details for Web of Science ID 000461823302064
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Shared Autonomy for an Interactive AI System
ASSOC COMPUTING MACHINERY. 2018: 20–22
View details for DOI 10.1145/3266037.3266088
View details for Web of Science ID 000494261200007
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Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017
View details for Web of Science ID 000452649405077
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Regret Minimization in MDPs with Options without Prior Knowledge
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017
View details for Web of Science ID 000452649403023
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Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017
View details for Web of Science ID 000452649402053