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ORCIDStuart Russell 0001
Stuart J. Russell
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- affiliation: University of California, Berkeley, Department of Electrical Engineering and Computer Sciences, CA, USA
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2020 – today
- 2023
[j21]Alistair Knott
, Dino Pedreschi, Raja Chatila, Tapabrata Chakraborti, Susan Leavy, Ricardo Baeza-Yates, David M. Eyers, Andrew Trotman, Paul D. Teal, Przemyslaw Biecek, Stuart Russell, Yoshua Bengio:
Generative AI models should include detection mechanisms as a condition for public release. Ethics Inf. Technol. 25(4): 55 (2023)- [c154]Peter Barnett, Rachel Freedman, Justin Svegliato, Stuart Russell:
Active Reward Learning from Multiple Teachers. SafeAI@AAAI 2023 - [c153]Alexander K. Lew, George Matheos, Tan Zhi-Xuan, Matin Ghavamizadeh, Nishad Gothoskar, Stuart Russell, Vikash K. Mansinghka:
SMCP3: Sequential Monte Carlo with Probabilistic Program Proposals. AISTATS 2023: 7061-7088 - [c152]Paria Rashidinejad, Hanlin Zhu, Kunhe Yang, Stuart Russell, Jiantao Jiao:
Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian. ICLR 2023 - [c151]Niklas Lauffer, Ameesh Shah, Micah Carroll, Michael D. Dennis, Stuart Russell:
Who Needs to Know? Minimal Knowledge for Optimal Coordination. ICML 2023: 18599-18613 - [c150]Joar Max Viktor Skalse, Matthew Farrugia-Roberts, Stuart Russell, Alessandro Abate, Adam Gleave:
Invariance in Policy Optimisation and Partial Identifiability in Reward Learning. ICML 2023: 32033-32058 - [c149]Tony Tong Wang, Adam Gleave, Tom Tseng, Kellin Pelrine, Nora Belrose, Joseph Miller, Michael D. Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, Stuart Russell:
Adversarial Policies Beat Superhuman Go AIs. ICML 2023: 35655-35739 - [i79]Peter Barnett, Rachel Freedman, Justin Svegliato, Stuart Russell:
Active Reward Learning from Multiple Teachers. CoRR abs/2303.00894 (2023) - [i78]Cassidy Laidlaw, Stuart Russell, Anca D. Dragan:
Bridging RL Theory and Practice with the Effective Horizon. CoRR abs/2304.09853 (2023) - [i77]Andrew Critch, Stuart Russell:
TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI. CoRR abs/2306.06924 (2023) - [i76]Niklas Lauffer, Ameesh Shah, Micah Carroll, Michael Dennis, Stuart Russell:
Who Needs to Know? Minimal Knowledge for Optimal Coordination. CoRR abs/2306.09309 (2023) - [i75]Luke Bailey, Euan Ong, Stuart Russell, Scott Emmons:
Image Hijacks: Adversarial Images can Control Generative Models at Runtime. CoRR abs/2309.00236 (2023) - [i74]Hanlin Zhu, Baihe Huang, Stuart Russell:
On Representation Complexity of Model-based and Model-free Reinforcement Learning. CoRR abs/2310.01706 (2023) - [i73]Rachel Freedman, Justin Svegliato, Kyle Hollins Wray, Stuart Russell:
Active teacher selection for reinforcement learning from human feedback. CoRR abs/2310.15288 (2023) - [i72]Yoshua Bengio, Geoffrey E. Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, Gillian K. Hadfield, Jeff Clune, Tegan Maharaj, Frank Hutter, Atilim Günes Baydin, Sheila A. McIlraith, Qiqi Gao, Ashwin Acharya, David Krueger, Anca D. Dragan, Philip H. S. Torr, Stuart Russell, Daniel Kahneman, Jan Brauner, Sören Mindermann:
Managing AI Risks in an Era of Rapid Progress. CoRR abs/2310.17688 (2023) - [i71]Sam Toyer, Olivia Watkins, Ethan Adrian Mendes, Justin Svegliato, Luke Bailey, Tiffany Wang, Isaac Ong, Karim Elmaaroufi, Pieter Abbeel, Trevor Darrell, Alan Ritter, Stuart Russell:
Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game. CoRR abs/2311.01011 (2023) - 2022
- [j20]Kenji Doya, Arisa Ema, Hiroaki Kitano, Masamichi Sakagami, Stuart Russell:
Social impact and governance of AI and neurotechnologies. Neural Networks 152: 542-554 (2022) - [j19]Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, Stuart Russell:
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism. IEEE Trans. Inf. Theory 68(12): 8156-8196 (2022) - [c148]Arnaud Fickinger, Samuel Cohen, Stuart Russell, Brandon Amos:
Cross-Domain Imitation Learning via Optimal Transport. ICLR 2022 - [c147]Micah D. Carroll, Anca D. Dragan, Stuart Russell, Dylan Hadfield-Menell:
Estimating and Penalizing Induced Preference Shifts in Recommender Systems. ICML 2022: 2686-2708 - [c146]Scott Emmons, Caspar Oesterheld, Andrew Critch, Vincent Conitzer, Stuart Russell:
For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria. ICML 2022: 5924-5943 - [c145]Samer B. Nashed, Justin Svegliato, Abhinav Bhatia, Stuart Russell, Shlomo Zilberstein:
Selecting the Partial State Abstractions of MDPs: A Metareasoning Approach with Deep Reinforcement Learning. IROS 2022: 11665-11670 - [c144]Stuart Russell:
Provably Beneficial Artificial Intelligence. IUI 2022: 3 - [p7]Stuart J. Russell:
Biography of Judea Pearl. Probabilistic and Causal Inference 2022: 1-10 - [p6]Stuart Russell:
Human-Compatible Artificial Intelligence. Human-Like Machine Intelligence 2022: 3-23 - [p5]Stuart Russell:
Artificial Intelligence and the Problem of Control. Perspectives on Digital Humanism 2022: 19-24 - [i70]Joar Skalse, Matthew Farrugia-Roberts, Stuart Russell, Alessandro Abate, Adam Gleave:
Invariance in Policy Optimisation and Partial Identifiability in Reward Learning. CoRR abs/2203.07475 (2022) - [i69]Micah Carroll, Dylan Hadfield-Menell, Stuart Russell, Anca D. Dragan:
Estimating and Penalizing Induced Preference Shifts in Recommender Systems. CoRR abs/2204.11966 (2022) - [i68]Xin Chen, Sam Toyer, Cody Wild, Scott Emmons, Ian Fischer, Kuang-Huei Lee, Neel Alex, Steven H. Wang, Ping Luo, Stuart Russell, Pieter Abbeel, Rohin Shah:
An Empirical Investigation of Representation Learning for Imitation. CoRR abs/2205.07886 (2022) - [i67]Scott Emmons, Caspar Oesterheld, Andrew Critch, Vincent Conitzer, Stuart Russell:
For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria. CoRR abs/2207.03470 (2022) - [i66]Andrew Critch, Michael Dennis, Stuart Russell:
Cooperative and uncooperative institution designs: Surprises and problems in open-source game theory. CoRR abs/2208.07006 (2022) - [i65]Tony Tong Wang, Adam Gleave, Nora Belrose, Tom Tseng, Joseph Miller, Michael D. Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, Stuart Russell:
Adversarial Policies Beat Professional-Level Go AIs. CoRR abs/2211.00241 (2022) - [i64]Paria Rashidinejad, Hanlin Zhu, Kunhe Yang, Stuart Russell, Jiantao Jiao:
Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian. CoRR abs/2211.00716 (2022) - [i63]Adam Gleave, Mohammad Taufeeque, Juan Rocamonde, Erik Jenner, Steven H. Wang, Sam Toyer, Maximilian Ernestus, Nora Belrose, Scott Emmons, Stuart Russell:
imitation: Clean Imitation Learning Implementations. CoRR abs/2211.11972 (2022) - 2021
- [c143]Prasad Tadepalli, Stuart J. Russell:
PAC Learning of Causal Trees with Latent Variables. AAAI 2021: 9774-9781 - [c142]Charlotte Roman, Michael Dennis, Andrew Critch, Stuart Russell:
Accumulating Risk Capital Through Investing in Cooperation. AAMAS 2021: 1073-1081 - [c141]Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, Jan Leike:
Quantifying Differences in Reward Functions. ICLR 2021 - [c140]Cynthia Chen, Xin Chen, Sam Toyer, Cody Wild, Scott Emmons, Ian Fischer, Kuang-Huei Lee, Neel Alex, Steven H. Wang, Ping Luo, Stuart Russell, Pieter Abbeel, Rohin Shah:
An Empirical Investigation of Representation Learning for Imitation. NeurIPS Datasets and Benchmarks 2021 - [c139]Tianjun Zhang, Paria Rashidinejad, Jiantao Jiao, Yuandong Tian, Joseph E. Gonzalez, Stuart Russell:
MADE: Exploration via Maximizing Deviation from Explored Regions. NeurIPS 2021: 9663-9680 - [c138]Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, Stuart Russell:
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism. NeurIPS 2021: 11702-11716 - [c137]Cassidy Laidlaw, Stuart Russell:
Uncertain Decisions Facilitate Better Preference Learning. NeurIPS 2021: 15070-15083 - [c136]Arnaud Fickinger, Hengyuan Hu, Brandon Amos, Stuart J. Russell, Noam Brown:
Scalable Online Planning via Reinforcement Learning Fine-Tuning. NeurIPS 2021: 16951-16963 - [c135]Micah Carroll, Dylan Hadfield-Menell, Stuart Russell, Anca D. Dragan:
Estimating and Penalizing Preference Shift in Recommender Systems. RecSys 2021: 661-667 - [p4]Raja Chatila, Virginia Dignum, Michael Fisher, Fosca Giannotti, Katharina Morik, Stuart Russell, Karen Yeung:
Trustworthy AI. Reflections on Artificial Intelligence for Humanity 2021: 13-39 - [p3]Jocelyn Maclure, Stuart Russell:
AI for Humanity: The Global Challenges. Reflections on Artificial Intelligence for Humanity 2021: 116-126 - [i62]Charlotte Roman, Michael Dennis, Andrew Critch, Stuart Russell:
Accumulating Risk Capital Through Investing in Cooperation. CoRR abs/2101.10305 (2021) - [i61]Daniel Filan, Stephen Casper, Shlomi Hod, Cody Wild, Andrew Critch, Stuart Russell:
Clusterability in Neural Networks. CoRR abs/2103.03386 (2021) - [i60]Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, Stuart Russell:
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism. CoRR abs/2103.12021 (2021) - [i59]Tianjun Zhang, Paria Rashidinejad, Jiantao Jiao, Yuandong Tian, Joseph Gonzalez, Stuart Russell:
MADE: Exploration via Maximizing Deviation from Explored Regions. CoRR abs/2106.10268 (2021) - [i58]Cassidy Laidlaw, Stuart Russell:
Learning the Preferences of Uncertain Humans with Inverse Decision Theory. CoRR abs/2106.10394 (2021) - [i57]Rohin Shah, Cody Wild, Steven H. Wang, Neel Alex, Brandon Houghton, William H. Guss, Sharada P. Mohanty, Anssi Kanervisto, Stephanie Milani, Nicholay Topin, Pieter Abbeel, Stuart Russell, Anca D. Dragan:
The MineRL BASALT Competition on Learning from Human Feedback. CoRR abs/2107.01969 (2021) - [i56]Arnaud Fickinger, Natasha Jaques, Samyak Parajuli, Michael Chang, Nicholas Rhinehart, Glen Berseth, Stuart Russell, Sergey Levine:
Explore and Control with Adversarial Surprise. CoRR abs/2107.07394 (2021) - [i55]Arnaud Fickinger, Hengyuan Hu, Brandon Amos, Stuart Russell, Noam Brown:
Scalable Online Planning via Reinforcement Learning Fine-Tuning. CoRR abs/2109.15316 (2021) - [i54]Arnaud Fickinger, Samuel Cohen, Stuart Russell, Brandon Amos:
Cross-Domain Imitation Learning via Optimal Transport. CoRR abs/2110.03684 (2021) - [i53]Shlomi Hod, Stephen Casper, Daniel Filan, Cody Wild, Andrew Critch, Stuart Russell:
Detecting Modularity in Deep Neural Networks. CoRR abs/2110.08058 (2021) - 2020
- [b6]Stuart Russell, Peter Norvig:
Artificial Intelligence: A Modern Approach (4th Edition). Pearson 2020, ISBN 9780134610993 - [c134]Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, Stuart Russell:
Adversarial Policies: Attacking Deep Reinforcement Learning. ICLR 2020 - [c133]Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre M. Bayen, Stuart Russell, Andrew Critch, Sergey Levine:
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design. NeurIPS 2020 - [c132]Paria Rashidinejad, Jiantao Jiao, Stuart Russell:
SLIP: Learning to predict in unknown dynamical systems with long-term memory. NeurIPS 2020 - [c131]Sam Toyer, Rohin Shah, Andrew Critch, Stuart Russell:
The MAGICAL Benchmark for Robust Imitation. NeurIPS 2020 - [i52]Daniel Filan, Shlomi Hod, Cody Wild, Andrew Critch, Stuart Russell:
Neural Networks are Surprisingly Modular. CoRR abs/2003.04881 (2020) - [i51]Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, Jan Leike:
Quantifying Differences in Reward Functions. CoRR abs/2006.13900 (2020) - [i50]Arnaud Fickinger, Simon Zhuang, Dylan Hadfield-Menell, Stuart Russell:
Multi-Principal Assistance Games. CoRR abs/2007.09540 (2020) - [i49]Paria Rashidinejad, Jiantao Jiao, Stuart Russell:
SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory. CoRR abs/2010.05899 (2020) - [i48]Sam Toyer, Rohin Shah, Andrew Critch, Stuart Russell:
The MAGICAL Benchmark for Robust Imitation. CoRR abs/2011.00401 (2020) - [i47]Pedro Freire, Adam Gleave, Sam Toyer, Stuart Russell:
DERAIL: Diagnostic Environments for Reward And Imitation Learning. CoRR abs/2012.01365 (2020) - [i46]Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre M. Bayen, Stuart Russell, Andrew Critch, Sergey Levine:
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design. CoRR abs/2012.02096 (2020) - [i45]Eric J. Michaud, Adam Gleave, Stuart Russell:
Understanding Learned Reward Functions. CoRR abs/2012.05862 (2020) - [i44]Arnaud Fickinger, Simon Zhuang, Andrew Critch, Dylan Hadfield-Menell, Stuart Russell:
Multi-Principal Assistance Games: Definition and Collegial Mechanisms. CoRR abs/2012.14536 (2020)
2010 – 2019
- 2019
- [c130]Shihui Li, Yi Wu, Xinyue Cui, Honghua Dong, Fei Fang, Stuart Russell:
Robust Multi-Agent Reinforcement Learning via Minimax Deep Deterministic Policy Gradient. AAAI 2019: 4213-4220 - [c129]Yi Wu, Yuxin Wu, Aviv Tamar, Stuart Russell, Georgia Gkioxari, Yuandong Tian:
Bayesian Relational Memory for Semantic Visual Navigation. ICCV 2019: 2769-2779 - [c128]David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Stuart J. Russell, Thomas L. Griffiths:
Cognitive model priors for predicting human decisions. ICML 2019: 5133-5141 - [i43]Ori Plonsky, Reut Apel, Eyal Ert, Moshe Tennenholtz, David Bourgin, Joshua C. Peterson, Daniel Reichman, Thomas L. Griffiths, Stuart J. Russell, Evan C. Carter, James F. Cavanagh, Ido Erev:
Predicting human decisions with behavioral theories and machine learning. CoRR abs/1904.06866 (2019) - [i42]David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Thomas L. Griffiths, Stuart J. Russell:
Cognitive Model Priors for Predicting Human Decisions. CoRR abs/1905.09397 (2019) - [i41]Adam Gleave, Michael Dennis, Neel Kant, Cody Wild, Sergey Levine, Stuart Russell:
Adversarial Policies: Attacking Deep Reinforcement Learning. CoRR abs/1905.10615 (2019) - [i40]Yi Wu, Yuxin Wu, Aviv Tamar, Stuart Russell, Georgia Gkioxari, Yuandong Tian:
Bayesian Relational Memory for Semantic Visual Navigation. CoRR abs/1909.04306 (2019) - 2018
- [c127]Dhruv Malik, Malayandi Palaniappan, Jaime F. Fisac, Dylan Hadfield-Menell, Stuart Russell, Anca D. Dragan:
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning. ICML 2018: 3391-3399 - [c126]Yi Wu, Siddharth Srivastava, Nicholas Hay, Simon S. Du, Stuart Russell:
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms. ICML 2018: 5339-5348 - [c125]Tongzhou Wang, Yi Wu, Dave Moore, Stuart J. Russell:
Meta-Learning MCMC Proposals. NeurIPS 2018: 4150-4160 - [c124]Nishant Desai, Andrew Critch, Stuart J. Russell:
Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making. NeurIPS 2018: 4717-4725 - [c123]Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart J. Russell, Pieter Abbeel:
Learning Plannable Representations with Causal InfoGAN. NeurIPS 2018: 8747-8758 - [i39]Yi Wu, Siddharth Srivastava, Nicholas Hay, Simon S. Du, Stuart Russell:
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms. CoRR abs/1806.02027 (2018) - [i38]Dhruv Malik, Malayandi Palaniappan, Jaime F. Fisac, Dylan Hadfield-Menell, Stuart Russell, Anca D. Dragan:
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning. CoRR abs/1806.03820 (2018) - [i37]Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart Russell, Pieter Abbeel:
Learning Plannable Representations with Causal InfoGAN. CoRR abs/1807.09341 (2018) - [i36]Yi Wu, Yuxin Wu, Aviv Tamar, Stuart Russell, Georgia Gkioxari, Yuandong Tian:
Learning and Planning with a Semantic Model. CoRR abs/1809.10842 (2018) - [i35]Aaron Tucker, Adam Gleave, Stuart Russell:
Inverse reinforcement learning for video games. CoRR abs/1810.10593 (2018) - 2017
- [c122]Yusuf Bugra Erol, Yi Wu, Lei Li, Stuart Russell:
A Nearly-Black-Box Online Algorithm for Joint Parameter and State Estimation in Temporal Models. AAAI 2017: 1861-1869 - [c121]Dylan Hadfield-Menell, Anca D. Dragan, Pieter Abbeel, Stuart Russell:
The Off-Switch Game. AAAI Workshops 2017 - [c120]David A. Moore, Stuart Russell:
Signal-based Bayesian Seismic Monitoring. AISTATS 2017: 1293-1301 - [c119]Yi Wu, David Bamman, Stuart Russell:
Adversarial Training for Relation Extraction. EMNLP 2017: 1778-1783 - [c118]Dylan Hadfield-Menell, Anca D. Dragan, Pieter Abbeel, Stuart Russell:
The Off-Switch Game. IJCAI 2017: 220-227 - [c117]Aijun Bai, Stuart Russell:
Efficient Reinforcement Learning with Hierarchies of Machines by Leveraging Internal Transitions. IJCAI 2017: 1418-1424 - [c116]Smitha Milli, Dylan Hadfield-Menell, Anca D. Dragan, Stuart Russell:
Should Robots be Obedient? IJCAI 2017: 4754-4760 - [c115]Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart J. Russell, Anca D. Dragan:
Inverse Reward Design. NIPS 2017: 6765-6774 - [c114]Aijun Bai, Stuart Russell, Xiaoping Chen:
Concurrent Hierarchical Reinforcement Learning for RoboCup Keepaway. RoboCup 2017: 190-203 - [i34]David A. Moore, Stuart J. Russell:
Signal-based Bayesian Seismic Monitoring. CoRR abs/1703.00561 (2017) - [i33]Smitha Milli, Dylan Hadfield-Menell, Anca D. Dragan, Stuart Russell:
Should Robots be Obedient? CoRR abs/1705.09990 (2017) - [i32]Tongzhou Wang, Yi Wu, David A. Moore, Stuart J. Russell:
Neural Block Sampling. CoRR abs/1708.06040 (2017) - [i31]Andrew Critch, Stuart Russell:
Servant of Many Masters: Shifting priorities in Pareto-optimal sequential decision-making. CoRR abs/1711.00363 (2017) - [i30]Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart Russell, Anca D. Dragan:
Inverse Reward Design. CoRR abs/1711.02827 (2017) - 2016
- [c113]Siddharth Srivastava, Stuart Russell, Alessandro Pinto:
Metaphysics of Planning Domain Descriptions. AAAI 2016: 1074-1080 - [c112]Stuart Russell, Ole Torp Lassen, Justin Uang, Wei Wang:
The Physics of Text: Ontological Realism in Information Extraction. AKBC@NAACL-HLT 2016: 51-56 - [c111]Aijun Bai, Siddharth Srivastava, Stuart Russell:
Markovian State and Action Abstractions for MDPs via Hierarchical MCTS. IJCAI 2016: 3029-3039 - [c110]Yi Wu, Lei Li, Stuart Russell, Rastislav Bodík:
Swift: Compiled Inference for Probabilistic Programming Languages. IJCAI 2016: 3637-3645 - [c109]Dylan Hadfield-Menell, Christopher Lin, Rohan Chitnis, Stuart Russell, Pieter Abbeel:
Sequential quadratic programming for task plan optimization. IROS 2016: 5040-5047 - [c108]Dylan Hadfield-Menell, Stuart Russell, Pieter Abbeel, Anca D. Dragan:
Cooperative Inverse Reinforcement Learning. NIPS 2016: 3909-3917 - [e2]Blai Bonet, Sven Koenig, Benjamin Kuipers, Illah R. Nourbakhsh, Stuart Russell, Moshe Y. Vardi, Toby Walsh:
AI, Ethics, and Society, Papers from the 2016 AAAI Workshop, Phoenix, Arizona, USA, February 13, 2016. AAAI Technical Report WS-16-02, AAAI Press 2016 [contents] - [i29]Stuart Russell, Daniel Dewey, Max Tegmark:
Research Priorities for Robust and Beneficial Artificial Intelligence. CoRR abs/1602.03506 (2016) - [i28]Yusuf Bugra Erol, Yi Wu, Lei Li, Stuart Russell:
Towards Practical Bayesian Parameter and State Estimation. CoRR abs/1603.08988 (2016) - [i27]Dylan Hadfield-Menell, Anca D. Dragan, Pieter Abbeel, Stuart Russell:
Cooperative Inverse Reinforcement Learning. CoRR abs/1606.03137 (2016) - [i26]Yi Wu, Lei Li, Stuart Russell, Rastislav Bodík:
Swift: Compiled Inference for Probabilistic Programming Languages. CoRR abs/1606.09242 (2016) - [i25]Dylan Hadfield-Menell, Anca D. Dragan, Pieter Abbeel, Stuart Russell:
The Off-Switch Game. CoRR abs/1611.08219 (2016) - 2015
- [j18]Stuart Russell, Tom Dietterich, Eric Horvitz, Bart Selman, Francesca Rossi, Demis Hassabis, Shane Legg, Mustafa Suleyman, Dileep George, D. Scott Phoenix:
Letter to the Editor: Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter. AI Mag. 36(4): 3-4 (2015) - [j17]Stuart Russell, Daniel Dewey, Max Tegmark:
Research Priorities for Robust and Beneficial Artificial Intelligence. AI Mag. 36(4): 105-114 (2015) - [j16]Eric Eaton, Tom Dietterich, Maria L. Gini, Barbara J. Grosz, Charles L. Isbell Jr., Subbarao Kambhampati, Michael L. Littman, Francesca Rossi, Stuart Russell, Peter Stone, Toby Walsh, Michael J. Wooldridge:
Who speaks for AI? AI Matters 2(2): 4-14 (2015) - [j15]Stuart Russell:
Unifying logic and probability. Commun. ACM 58(7): 88-97 (2015) - [c107]Siddharth Srivastava, Shlomo Zilberstein, Abhishek Gupta, Pieter Abbeel, Stuart Russell:
Tractability of Planning with Loops. AAAI 2015: 3393-3401 - [c106]