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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
- 2024
[j23]Alistair Knott
, Dino Pedreschi, Toshiya Jitsuzumi, Susan Leavy, David M. Eyers, Tapabrata Chakraborti, Andrew Trotman, Sundar Sundareswaran, Ricardo Baeza-Yates, Przemyslaw Biecek, Adrian Weller, Paul D. Teal, Subhadip Basu, Mehmet Haklidir, Virginia Morini, Stuart Russell, Yoshua Bengio:
AI content detection in the emerging information ecosystem: new obligations for media and tech companies. Ethics Inf. Technol. 26(4): 63 (2024)- [j22]Alistair Knott, Dino Pedreschi, Toshiya Jitsuzumi, Susan Leavy, David M. Eyers, Tapabrata Chakraborti, Andrew Trotman, Sundar Sundareswaran, Ricardo Baeza-Yates, Przemyslaw Biecek, Adrian Weller, Paul D. Teal, Subhadip Basu, Mehmet Haklidir, Virginia Morini, Stuart Russell, Yoshua Bengio:
Correction: AI content detection in the emerging information ecosystem: new obligations for media and tech companies. Ethics Inf. Technol. 26(4): 71 (2024) - [c164]Cassidy Laidlaw, Banghua Zhu, Stuart Russell, Anca D. Dragan:
The Effective Horizon Explains Deep RL Performance in Stochastic Environments. ICLR 2024 - [c163]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. ICLR 2024 - [c162]Hanlin Zhu, Baihe Huang, Stuart Russell:
On Representation Complexity of Model-based and Model-free Reinforcement Learning. ICLR 2024 - [c161]Luke Bailey, Euan Ong, Stuart Russell, Scott Emmons:
Image Hijacks: Adversarial Images can Control Generative Models at Runtime. ICML 2024 - [c160]Micah Carroll, Davis Foote, Anand Siththaranjan, Stuart Russell, Anca D. Dragan:
AI Alignment with Changing and Influenceable Reward Functions. ICML 2024 - [c159]Vincent Conitzer, Rachel Freedman, Jobst Heitzig, Wesley H. Holliday, Bob M. Jacobs, Nathan Lambert, Milan Mossé, Eric Pacuit, Stuart Russell, Hailey Schoelkopf, Emanuel Tewolde, William S. Zwicker:
Position: Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback. ICML 2024 - [c158]Evan Ellis, Gaurav R. Ghosal, Stuart J. Russell, Anca D. Dragan, Erdem Biyik:
A Generalized Acquisition Function for Preference-based Reward Learning. ICRA 2024: 2814-2821 - [c157]Qingyuan Lu, Justin Svegliato, Samer B. Nashed, Shlomo Zilberstein, Stuart Russell:
Ethically Compliant Autonomous Systems under Partial Observability. ICRA 2024: 16229-16235 - [i92]Benjamin Plaut, Hanlin Zhu, Stuart Russell:
Avoiding Catastrophe in Continuous Spaces by Asking for Help. CoRR abs/2402.08062 (2024) - [i91]Leon Lang, Davis Foote, Stuart Russell, Anca D. Dragan, Erik Jenner, Scott Emmons:
When Your AIs Deceive You: Challenges with Partial Observability of Human Evaluators in Reward Learning. CoRR abs/2402.17747 (2024) - [i90]Evan Ellis, Gaurav R. Ghosal, Stuart J. Russell, Anca D. Dragan, Erdem Biyik:
A Generalized Acquisition Function for Preference-based Reward Learning. CoRR abs/2403.06003 (2024) - [i89]Vincent Conitzer, Rachel Freedman, Jobst Heitzig, Wesley H. Holliday, Bob M. Jacobs, Nathan Lambert, Milan Mossé, Eric Pacuit, Stuart Russell, Hailey Schoelkopf, Emanuel Tewolde, William S. Zwicker:
Social Choice for AI Alignment: Dealing with Diverse Human Feedback. CoRR abs/2404.10271 (2024) - [i88]Hanlin Zhu, Baihe Huang, Shaolun Zhang, Michael I. Jordan, Jiantao Jiao, Yuandong Tian, Stuart Russell:
Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics. CoRR abs/2405.04669 (2024) - [i87]David Dalrymple, Joar Skalse, Yoshua Bengio, Stuart Russell, Max Tegmark, Sanjit Seshia, Steve Omohundro, Christian Szegedy, Ben Goldhaber, Nora Ammann, Alessandro Abate, Joe Halpern, Clark W. Barrett, Ding Zhao, Tan Zhi-Xuan, Jeannette Wing, Joshua B. Tenenbaum:
Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems. CoRR abs/2405.06624 (2024) - [i86]Micah Carroll, Davis Foote, Anand Siththaranjan, Stuart Russell, Anca D. Dragan:
AI Alignment with Changing and Influenceable Reward Functions. CoRR abs/2405.17713 (2024) - [i85]Shreyas Kapur, Erik Jenner, Stuart Russell:
Diffusion On Syntax Trees For Program Synthesis. CoRR abs/2405.20519 (2024) - [i84]Erik Jenner, Shreyas Kapur, Vasil Georgiev, Cameron Allen, Scott Emmons, Stuart Russell:
Evidence of Learned Look-Ahead in a Chess-Playing Neural Network. CoRR abs/2406.00877 (2024) - [i83]Jiahai Feng, Stuart Russell, Jacob Steinhardt:
Monitoring Latent World States in Language Models with Propositional Probes. CoRR abs/2406.19501 (2024) - [i82]Aly Lidayan, Michael Dennis, Stuart Russell:
BAMDP Shaping: a Unified Theoretical Framework for Intrinsic Motivation and Reward Shaping. CoRR abs/2409.05358 (2024) - 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) - [c156]Peter Barnett, Rachel Freedman, Justin Svegliato, Stuart Russell:
Active Reward Learning from Multiple Teachers. SafeAI@AAAI 2023 - [c155]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 - [c154]Paria Rashidinejad, Hanlin Zhu, Kunhe Yang, Stuart Russell, Jiantao Jiao:
Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian. ICLR 2023 - [c153]Niklas Lauffer, Ameesh Shah, Micah Carroll, Michael D. Dennis, Stuart Russell:
Who Needs to Know? Minimal Knowledge for Optimal Coordination. ICML 2023: 18599-18613 - [c152]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 - [c151]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 - [c150]Mason Nakamura, Justin Svegliato, Samer B. Nashed, Shlomo Zilberstein, Stuart Russell:
Formal Composition of Robotic Systems as Contract Programs. IROS 2023: 6727-6732 - [c149]Cassidy Laidlaw, Stuart J. Russell, Anca D. Dragan:
Bridging RL Theory and Practice with the Effective Horizon. NeurIPS 2023 - [i81]Peter Barnett, Rachel Freedman, Justin Svegliato, Stuart Russell:
Active Reward Learning from Multiple Teachers. CoRR abs/2303.00894 (2023) - [i80]Cassidy Laidlaw, Stuart Russell, Anca D. Dragan:
Bridging RL Theory and Practice with the Effective Horizon. CoRR abs/2304.09853 (2023) - [i79]Andrew Critch, Stuart Russell:
TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI. CoRR abs/2306.06924 (2023) - [i78]Niklas Lauffer, Ameesh Shah, Micah Carroll, Michael Dennis, Stuart Russell:
Who Needs to Know? Minimal Knowledge for Optimal Coordination. CoRR abs/2306.09309 (2023) - [i77]Luke Bailey, Euan Ong, Stuart Russell, Scott Emmons:
Image Hijacks: Adversarial Images can Control Generative Models at Runtime. CoRR abs/2309.00236 (2023) - [i76]Hanlin Zhu, Baihe Huang, Stuart Russell:
On Representation Complexity of Model-based and Model-free Reinforcement Learning. CoRR abs/2310.01706 (2023) - [i75]Rachel Freedman, Justin Svegliato, Kyle Hollins Wray, Stuart Russell:
Active teacher selection for reinforcement learning from human feedback. CoRR abs/2310.15288 (2023) - [i74]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) - [i73]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) - [i72]Cassidy Laidlaw, Banghua Zhu, Stuart Russell, Anca D. Dragan:
The Effective Horizon Explains Deep RL Performance in Stochastic Environments. CoRR abs/2312.08369 (2023) - [i71]Edmund Mills, Shiye Su, Stuart Russell, Scott Emmons:
ALMANACS: A Simulatability Benchmark for Language Model Explainability. CoRR abs/2312.12747 (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]Siddharth Srivastava, Stuart Russell, Alessandro Pinto:
Metaphysics of Planning Domain Descriptions. AAAI Fall Symposia 2015: 83-90 - [c105]David A. Moore, Stuart J. Russell:
Gaussian Process Random Fields. NIPS 2015: 3357-3365 - [c104]Dylan Hadfield-Menell, Stuart Russell:
Multitasking: Optimal Planning for Bandit Superprocesses. UAI 2015: 345-354 - [c103]Wei Wang, Stuart Russell:
A Smart-Dumb/Dumb-Smart Algorithm for Efficient Split-Merge MCMC. UAI 2015: 902-911 - [i24]David A. Moore, Stuart J. Russell:
Gaussian Process Random Fields. CoRR abs/1511.00054 (2015) - [i23]Hugh Chen, Yusuf Erol, Eric Shen, Stuart Russell:
Probabilistic Model-Based Approach for Heart Beat Detection. CoRR abs/1512.07931 (2015) - 2014
- [c102]Siddharth Srivastava, Eugene Fang, Lorenzo Riano, Rohan Chitnis, Stuart Russell, Pieter Abbeel:
Combined task and motion planning through an extensible planner-independent interface layer. ICRA 2014: 639-646 - [c101]Stuart Russell:
Unifying Logic and Probability: A New Dawn for AI? IPMU (1) 2014: 10-14 - [c100]Falk Lieder, Dillon Plunkett, Jessica B. Hamrick, Stuart J. Russell, Nicholas Hay, Thomas L. Griffiths:
Algorithm selection by rational metareasoning as a model of human strategy selection. NIPS 2014: 2870-2878 - [c99]David A. Moore, Stuart Russell:
Fast Gaussian Process Posteriors with Product Trees. UAI 2014: 613-622 - [c98]Siddharth Srivastava, Stuart Russell, Paul Ruan, Xiang Cheng:
First-Order Open-Universe POMDPs. UAI 2014: 742-751 - [i22]Nicholas Hay, Stuart Russell, David Tolpin, Solomon Eyal Shimony:
Selecting Computations: Theory and Applications. CoRR abs/1408.2048 (2014) - 2013
- [c97]Lei Li, Bharath Ramsundar, Stuart Russell:
Dynamic Scaled Sampling for Deterministic Constraints. AISTATS 2013: 397-405 - [c96]Sharad Vikram, Lei Li, Stuart Russell:
Writing and sketching in the air, recognizing and controlling on the fly. CHI Extended Abstracts 2013: 1179-1184 - [c95]Yusuf Erol, Lei Li, Bharath Ramsundar, Stuart Russell:
The Extended Parameter Filter. ICML (3) 2013: 1103-1111 - [c94]Mark Rogers, Lei Li, Stuart Russell:
Multilinear Dynamical Systems for Tensor Time Series. NIPS 2013: 2634-2642 - [c93]Stuart Russell:
Rationality and Intelligence: A Brief Update. PT-AI 2013: 7-28 - [c92]David A. Moore, Stuart Russell:
Product Trees for Gaussian Process Covariance in Sublinear Time. UAI Application Workshops 2013: 58-66 - [i21]Bhaskara Marthi, Hanna Pasula, Stuart Russell, Yuval Peres:
Decayed MCMC Filtering. CoRR abs/1301.0584 (2013) - [i20]Nando de Freitas, Pedro A. d. F. R. Højen-Sørensen, Michael I. Jordan, Stuart Russell:
Variational MCMC. CoRR abs/1301.2266 (2013) - [i19]Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, Stuart Russell:
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. CoRR abs/1301.3853 (2013) - [i18]Nir Friedman, Kevin P. Murphy, Stuart Russell:
Learning the Structure of Dynamic Probabilistic Networks. CoRR abs/1301.7374 (2013) - [i17]Nir Friedman, Stuart Russell:
Image Segmentation in Video Sequences: A Probabilistic Approach. CoRR abs/1302.1539 (2013) - [i16]Keiji Kanazawa, Daphne Koller, Stuart Russell:
Stochastic Simulation Algorithms for Dynamic Probabilistic Networks. CoRR abs/1302.4965 (2013) - [i15]Stuart Russell:
Fine-Grained Decision-Theoretic Search Control. CoRR abs/1304.1133 (2013) - [i14]Sampath Srinivas, Stuart Russell, Alice M. Agogino:
Automated Construction of Sparse Bayesian Networks from Unstructured Probabilistic Models and Domain Information. CoRR abs/1304.1530 (2013) - [i13]Yusuf Erol, Lei Li, Bharath Ramsundar, Stuart Russell:
The Extended Parameter Filter. CoRR abs/1305.1704 (2013) - 2012
- [c91]Siddharth Srivastava, Stuart Russell, Avi Pfeffer:
First-Order Models for POMDPs. StarAI@UAI 2012 - [c90]Shaunak Chatterjee, Stuart Russell:
Uncertain Observation Times. SUM 2012: 392-405 - [c89]Nicholas Hay, Stuart Russell, David Tolpin, Solomon Eyal Shimony:
Selecting Computations: Theory and Applications. UAI 2012: 346-355 - [i12]Shaunak Chatterjee, Stuart Russell:
A temporally abstracted Viterbi algorithm. CoRR abs/1202.3707 (2012) - [i11]Nimar S. Arora, Rodrigo de Salvo Braz, Erik B. Sudderth, Stuart Russell:
Gibbs Sampling in Open-Universe Stochastic Languages. CoRR abs/1203.3464 (2012) - [i10]Emma Brunskill, Stuart Russell:
RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains. CoRR abs/1203.3538 (2012) - [i9]Gregory Lawrence, Stuart Russell:
Improving Gradient Estimation by Incorporating Sensor Data. CoRR abs/1206.3272 (2012) - [i8]Brian Milch, Stuart Russell:
General-Purpose MCMC Inference over Relational Structures. CoRR abs/1206.6849 (2012) - [i7]Bhaskara Marthi, Stuart Russell, David Andre:
A compact, hierarchical Q-function decomposition. CoRR abs/1206.6851 (2012) - [i6]Eric P. Xing, Michael I. Jordan, Stuart Russell:
Graph partition strategies for generalized mean field inference. CoRR abs/1207.4156 (2012) - [i5]Nicholas Hay, Stuart Russell, David Tolpin, Solomon Eyal Shimony:
Selecting Computations: Theory and Applications. CoRR abs/1207.5879 (2012) - [i4]Gregory Lawrence, Noah J. Cowan, Stuart Russell:
Efficient Gradient Estimation for Motor Control Learning. CoRR abs/1212.2475 (2012) - [i3]Eric P. Xing, Michael I. Jordan, Stuart Russell:
A Generalized Mean Field Algorithm for Variational Inference in Exponential Families. CoRR abs/1212.2512 (2012) - 2011
- [c88]Nimar S. Arora, Stuart Russell, Paul Kidwell, Erik B. Sudderth:
Global Seismic Monitoring: A Bayesian Approach. AAAI 2011: 1533-1536 - [c87]Emma Brunskill, Stuart Russell:
Partially Observable Sequential Decision Making for Problem Selection in an Intelligent Tutoring System. EDM 2011: 327-328 - [c86]Jason Andrew Wolfe, Stuart Russell:
Bounded Intention Planning. IJCAI 2011: 2039-2045 - [c85]Shaunak Chatterjee, Stuart Russell:
A temporally abstracted Viterbi algorithm. UAI 2011: 96-104 - 2010
- [b5]Stuart J. Russell, Peter Norvig:
Artificial Intelligence - A Modern Approach, Third International Edition. Pearson Education 2010, ISBN 978-0-13-207148-2, pp. I-XVIII, 1-1132 - [j14]David W. Aha, Mark S. Boddy, Vadim Bulitko, Artur S. d'Avila Garcez, Prashant Doshi, Stefan Edelkamp, Christopher W. Geib, Piotr J. Gmytrasiewicz, Robert P. Goldman, Pascal Hitzler, Charles L. Isbell Jr., Darsana P. Josyula, Leslie Pack Kaelbling, Kristian Kersting, Maithilee Kunda, Luís C. Lamb, Bhaskara Marthi, Keith McGreggor, Vivi Nastase, Gregory M. Provan, Anita Raja, Ashwin Ram, Mark O. Riedl, Stuart Russell, Ashish Sabharwal, Jan-Georg Smaus, Gita Sukthankar, Karl Tuyls, Ron van der Meyden, Alon Y. Halevy, Lilyana Mihalkova, Sriraam Natarajan:
Reports of the AAAI 2010 Conference Workshops. AI Mag. 31(4): 95-108 (2010) - [c84]Nimar S. Arora, Stuart Russell, Erik B. Sudderth:
Automatic Inference in BLOG. StarAI@AAAI 2010 - [c83]Jason Andrew Wolfe, Bhaskara Marthi, Stuart Russell:
Hierarchical Planning for Mobile Manipulation. Bridging the Gap Between Task and Motion Planning 2010 - [c82]Jason Andrew Wolfe, Bhaskara Marthi, Stuart Russell:
Combined Task and Motion Planning for Mobile Manipulation. ICAPS 2010: 254-258 - [c81]Catherine G. Enright, Michael G. Madden, Stuart Russell, Norm Aleks, Geoffrey T. Manley, John G. Laffey, Brian Harte, Anne Mulvey, Niall Madden:
Modelling Glycaemia in ICU Patients - A Dynamic Bayesian Network Approach. BIOSIGNALS 2010: 452-459 - [c80]Nimar S. Arora, Stuart Russell, Paul Kidwell, Erik B. Sudderth:
Global seismic monitoring as probabilistic inference. NIPS 2010: 73-81 - [c79]Nimar S. Arora, Rodrigo de Salvo Braz, Erik B. Sudderth, Stuart Russell:
Gibbs Sampling in Open-Universe Stochastic Languages. UAI 2010: 30-39 - [c78]Emma Brunskill, Stuart Russell:
RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains. UAI 2010: 83-92 - [c77]Shaunak Chatterjee, Stuart Russell:
Why are DBNs sparse? AISTATS 2010: 81-88
2000 – 2009
- 2009
- [j13]Stuart Russell, Lawrence K. Saul:
Technical perspective - The ultimate pilot program. Commun. ACM 52(7): 96 (2009) - [j12]Songhwai Oh, Stuart Russell, Shankar Sastry:
Markov Chain Monte Carlo Data Association for Multi-Target Tracking. IEEE Trans. Autom. Control. 54(3): 481-497 (2009) - 2008
- [c76]Bhaskara Marthi, Stuart Russell, Jason Andrew Wolfe:
Angelic Hierarchical Planning: Optimal and Online Algorithms. ICAPS 2008: 222-231 - [c75]Norm Aleks, Stuart Russell, Michael G. Madden, Diane Morabito, Kristan Staudenmayer, Mitchell J. Cohen, Geoffrey T. Manley:
Probabilistic detection of short events, with application to critical care monitoring. NIPS 2008: 49-56 - [c74]Gregory Lawrence, Stuart Russell:
Improving Gradient Estimation by Incorporating Sensor Data. UAI 2008: 375-382 - 2007
- [c73]Bhaskara Marthi, Stuart Russell, Jason Andrew Wolfe:
Angelic Semantics for High-Level Actions. ICAPS 2007: 232-239 - 2006
- [c72]T. K. Satish Kumar, Stuart Russell:
On Some Tractable Cases of Logical Filtering. ICAPS 2006: 83-92 - [c71]Brian Milch, Stuart Russell:
First-Order Probabilistic Languages: Into the Unknown. ILP 2006: 10-24 - [c70]Bhaskara Marthi, Stuart Russell, David Andre:
A Compact, Hierarchical Q-function Decomposition. UAI 2006 - [c69]Brian Milch, Stuart Russell:
General-Purpose MCMC Inference over Relational Structures. UAI 2006 - 2005
- [c68]Brian Milch, Bhaskara Marthi, David A. Sontag, Stuart Russell, Daniel L. Ong, Andrey Kolobov:
Approximate Inference for Infinite Contingent Bayesian Networks. AISTATS 2005: 238-245 - [c67]Stuart Russell, Jason Andrew Wolfe:
Efficient belief-state AND-OR search, with application to Kriegspiel. IJCAI 2005: 278-285 - [c66]Bhaskara Marthi, Stuart Russell, David Latham, Carlos Guestrin:
Concurrent Hierarchical Reinforcement Learning. IJCAI 2005: 779-785 - [c65]Brian Milch, Bhaskara Marthi, Stuart Russell, David A. Sontag, Daniel L. Ong, Andrey Kolobov:
BLOG: Probabilistic Models with Unknown Objects. IJCAI 2005: 1352-1359 - [i2]Brian Milch, Bhaskara Marthi, Stuart Russell, David A. Sontag, Daniel L. Ong, Andrey Kolobov:
BLOG: Probabilistic Models with Unknown Objects. Probabilistic, Logical and Relational Learning 2005 - 2004
- [b4]Stuart Russell, Peter Norvig:
Künstliche Intelligenz - ein moderner Ansatz, 2. Auflage. Pearson Studium 2004, ISBN 978-3-8273-7089-1, pp. 1-1327 - [c64]Songhwai Oh, Stuart Russell, Shankar Sastry:
Markov chain Monte Carlo data association for general multiple-target tracking problems. CDC 2004: 735-742 - 2003
- [b3]Stuart Russell, Peter Norvig:
Artificial intelligence - a modern approach, 2nd Edition. Prentice Hall series in artificial intelligence, Prentice Hall 2003, ISBN 0130803022, pp. I-XXVIII, 1-1081 - [c63]Stuart Russell, Andrew Zimdars:
Q-Decomposition for Reinforcement Learning Agents. ICML 2003: 656-663 - [c62]Eyal Amir, Stuart Russell:
Logical Filtering. IJCAI 2003: 75-82 - [c61]Gregory Lawrence, Noah J. Cowan, Stuart Russell:
Efficient Gradient Estimation for Motor Control Learning. UAI 2003: 354-361 - [c60]Eric P. Xing, Michael I. Jordan, Stuart Russell:
A generalized mean field algorithm for variational inference in exponential families. UAI 2003: 583-591 - 2002
- [c59]David Andre, Stuart J. Russell:
State Abstraction for Programmable Reinforcement Learning Agents. AAAI/IAAI 2002: 119-125 - [c58]Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, Stuart Russell:
Distance Metric Learning with Application to Clustering with Side-Information. NIPS 2002: 505-512 - [c57]Hanna Pasula, Bhaskara Marthi, Brian Milch, Stuart Russell, Ilya Shpitser:
Identity Uncertainty and Citation Matching. NIPS 2002: 1401-1408 - [c56]Eric P. Xing, Michael I. Jordan, Richard M. Karp, Stuart Russell:
A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences. NIPS 2002: 1489-1496 - [c55]Bhaskara Marthi, Hanna Pasula, Stuart Russell, Yuval Peres:
Decayed MCMC Filtering. UAI 2002: 319-326 - 2001
- [c54]Nikunj C. Oza, Stuart Russell:
Online Bagging and Boosting. AISTATS 2001: 229-236 - [c53]Hanna Pasula, Stuart Russell:
Approximate inference for first-order probabilistic languages. IJCAI 2001: 741-748 - [c52]Nikunj C. Oza, Stuart Russell:
Experimental comparisons of online and batch versions of bagging and boosting. KDD 2001: 359-364 - [c51]Nando de Freitas, Pedro A. d. F. R. Højen-Sørensen, Stuart Russell:
Variational MCMC. UAI 2001: 120-127 - [p2]Kevin Murphy, Stuart Russell:
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. Sequential Monte Carlo Methods in Practice 2001: 499-515 - 2000
- [c50]Andrew Y. Ng, Stuart Russell:
Algorithms for Inverse Reinforcement Learning. ICML 2000: 663-670 - [c49]David Andre, Stuart J. Russell:
Programmable Reinforcement Learning Agents. NIPS 2000: 1019-1025 - [c48]Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, Stuart Russell:
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. UAI 2000: 176-183
1990 – 1999
- 1999
- [c47]Stuart Russell:
Expressive Probability Models in Science. Discovery Science 1999: 13-16 - [c46]Andrew Y. Ng, Daishi Harada, Stuart Russell:
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping. ICML 1999: 278-287 - [c45]Vassilis A. Papavassiliou, Stuart Russell:
Convergence of Reinforcement Learning with General Function Approximators. IJCAI 1999: 748-757 - [c44]Hanna Pasula, Stuart Russell, Michael Ostland, Yaacov Ritov:
Tracking Many Objects with Many Sensors. IJCAI 1999: 1160-1171 - 1998
- [j11]Timothy Huang, Stuart Russell:
Object Identification: A Bayesian Analysis with Application to Traffic Surveillance. Artif. Intell. 103(1-2): 77-93 (1998) - [j10]Prasad Tadepalli, Stuart Russell:
Learning from Examples and Membership Queries with Structured Determinations. Mach. Learn. 32(3): 245-295 (1998) - [c43]Geoffrey Zweig, Stuart Russell:
Speech Recognition with Dynamic Bayesian Networks. AAAI/IAAI 1998: 173-180 - [c42]Richard Dearden, Nir Friedman, Stuart Russell:
Bayesian Q-Learning. AAAI/IAAI 1998: 761-768 - [c41]Stuart Russell:
Learning Agents for Uncertain Environments (Extended Abstract). COLT 1998: 101-103 - [c40]Geoffrey Zweig, Stuart Russell:
Probabilistic modeling with Bayesian networks for automatic speech recognition. ICSLP 1998 - [c39]Nir Friedman, Kevin P. Murphy, Stuart Russell:
Learning the Structure of Dynamic Probabilistic Networks. UAI 1998: 139-147 - 1997
- [j9]Stuart J. Russell:
Rationality and Intelligence. Artif. Intell. 94(1-2): 57-77 (1997) - [j8]Stuart Russell, Lewis Stiller, Othar Hansson:
PNPACK: Computing with Probabilities in Java. Concurr. Pract. Exp. 9(11): 1333-1339 (1997) - [j7]John Binder, Daphne Koller, Stuart Russell, Keiji Kanazawa:
Adaptive Probabilistic Networks with Hidden Variables. Mach. Learn. 29(2-3): 213-244 (1997) - [c38]Stuart Russell:
Uncertain Learning Agents (Abstract). ECML 1997: 3 - [c37]Nir Friedman, Moisés Goldszmidt, David Heckerman, Stuart Russell:
Challenge: What is the Impact of Bayesian Networks on Learning? IJCAI (1) 1997: 10-15 - [c36]Timothy Huang, Stuart Russell:
Object Identification in a Bayesian Context. IJCAI 1997: 1276-1283 - [c35]John Binder, Kevin P. Murphy, Stuart Russell:
Space-Efficient Inference in Dynamic Probabilistic Networks. IJCAI 1997: 1292-1296 - [c34]Ronald Parr, Stuart Russell:
Reinforcement Learning with Hierarchies of Machines. NIPS 1997: 1043-1049 - [c33]Stuart Russell:
Learning in Rational Agents. NIPS 1997 - [c32]Nir Friedman, Stuart Russell:
Image Segmentation in Video Sequences: A Probabilistic Approach. UAI 1997: 175-181 - 1996
- [j6]Shlomo Zilberstein, Stuart Russell:
Optimal Composition of Real-Time Systems. Artif. Intell. 82(1-2): 181-213 (1996) - [c31]Stuart Russell:
Tools for Autonomous Agents (Abstract). KI 1996: 331 - [p1]Stuart Russell:
Machine Learning. Artificial Intelligence 1996: 89-133 - 1995
- [b2]Stuart J. Russell, Peter Norvig:
Artificial intelligence - a modern approach: the intelligent agent book. Prentice Hall series in artificial intelligence, Prentice Hall 1995, ISBN 978-0-13-103805-9, pp. I-XXVIII, 1-932 - [j5]Stuart J. Russell, Devika Subramanian:
Provably Bounded-Optimal Agents. J. Artif. Intell. Res. 2: 575-609 (1995) - [j4]Stuart Russell, Peter Norvig:
A Modern, Agent-Oriented Approach to Introductory Artificial Intelligence. SIGART Bull. 6(2): 24-26 (1995) - [c30]Stuart Russell:
Rationality and Intelligence. IJCAI (1) 1995: 950-960 - [c29]Ronald Parr, Stuart Russell:
Approximating Optimal Policies for Partially Observable Stochastic Domains. IJCAI 1995: 1088-1095 - [c28]Stuart Russell, John Binder, Daphne Koller, Keiji Kanazawa:
Local Learning in Probabilistic Networks with Hidden Variables. IJCAI 1995: 1146-1152 - [c27]Jeff Forbes, Timothy Huang, Keiji Kanazawa, Stuart Russell:
The BATmobile: Towards a Bayesian Automated Taxi. IJCAI 1995: 1878-1885 - [c26]Keiji Kanazawa, Daphne Koller, Stuart Russell:
Stochastic simulation algorithms for dynamic probabilistic networks. UAI 1995: 346-351 - [e1]Armand Prieditis, Stuart Russell:
Machine Learning, Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, USA, July 9-12, 1995. Morgan Kaufmann 1995, ISBN 1-55860-377-8 [contents] - [i1]Stuart J. Russell, Devika Subramanian:
Provably Bounded-Optimal Agents. CoRR cs.AI/9505103 (1995) - 1994
- [c25]Timothy Huang, Daphne Koller, Jitendra Malik, Gary H. Ogasawara, Bobby S. Rao, Stuart Russell, Joseph Weber:
Automatic Symbolic Traffic Scene Analysis Using Belief Networks. AAAI 1994: 966-972 - [c24]Jonathan Tash, Stuart Russell:
Control Strategies for a Stochastic Planner. AAAI 1994: 1079-1085 - [c23]Daphne Koller, Joseph Weber, Timothy Huang, Jitendra Malik, Gary H. Ogasawara, Stuart Russell, Bobby S. Rao:
Towards robust automatic traffic scene analysis in real-time. ICPR (1) 1994: 126-131 - 1993
- [c22]Saso Dzeroski, Stephen H. Muggleton, Stuart Russell:
Learnability of Constrained Logic Programs. ECML 1993: 342-347 - [c21]Ron Musick, Jason Catlett, Stuart Russell:
Decision Theoretic Subsampling for Induction on Large Databases. ICML 1993: 212-219 - [c20]Stuart J. Russell, Devika Subramanian, Ronald Parr:
Provably Bounded Optimal Agents. IJCAI 1993: 338-345 - [c19]Gary H. Ogasawara, Stuart Russell:
Planning Using Multiple Execution Architectures. IJCAI 1993: 1394-1401 - [c18]Shlomo Zilberstein, Stuart Russell:
Anytime Sensing Planning and Action: A Practical Model for Robot Control. IJCAI 1993: 1402-1407 - 1992
- [c17]Ron Musick, Stuart Russell:
How Long Will It Take? AAAI 1992: 466-471 - [c16]Saso Dzeroski, Stephen H. Muggleton, Stuart Russell:
PAC-Learnability of Determinate Logic Programs. COLT 1992: 128-135 - [c15]Stuart Russell:
Efficient Memory-Bounded Search Methods. ECAI 1992: 1-5 - 1991
- [b1]Stuart J. Russell, Eric Wefald:
Do the right thing - studies in limited rationality. MIT Press 1991, ISBN 978-0-262-18144-0, pp. I-XX, 1-200 - [j3]Stuart Russell, Eric Wefald:
Principles of Metareasoning. Artif. Intell. 49(1-3): 361-395 (1991) - [j2]Stuart J. Russell:
Prior knowledge and autonomous learning. Robotics Auton. Syst. 8(1-2): 145-159 (1991) - [j1]Stuart J. Russell:
An Architecture for Bounded Rationality. SIGART Bull. 2(4): 146-150 (1991) - [c14]Stuart J. Russell, Shlomo Zilberstein:
Composing Real-Time Systems. IJCAI 1991: 212-217
1980 – 1989
- 1989
- [c13]Eric Wefald, Stuart J. Russell:
Adaptive Learning of Decision-Theoretic Search Control Knowledge. ML 1989: 408-411 - [c12]Benjamin N. Grosof, Stuart J. Russell:
Declarative Bias for Structural Domains. ML 1989: 480-482 - [c11]Stuart J. Russell:
Execution Architectures and Compilation. IJCAI 1989: 15-22 - [c10]Stuart Russell, Eric Wefald:
On Optimal Game-Tree Search using Rational Meta-Reasoning. IJCAI 1989: 334-340 - [c9]Stuart J. Russell, Eric Wefald:
Principles of Metareasoning. KR 1989: 400-411 - [c8]Sampath Srinivas, Stuart Russell, Alice M. Agogino:
Automated Construction of Sparse Bayesian Networks from Unstructured Probabilistic Models and Domain Information. UAI 1989: 295-308 - 1988
- [c7]Michael S. Braverman, Stuart J. Russell:
IMEX: Overcoming Intactability In Explanation Based Learning. AAAI 1988: 575-579 - [c6]Stuart J. Russell:
Tree-Structured Bias. AAAI 1988: 641-645 - [c5]Michael S. Braverman, Stuart J. Russell:
Boundaries of Operationality. ML 1988: 221-234 - 1987
- [c4]Stuart J. Russell, Benjamin N. Grosof:
A Declarative Approach to Bias in Concept Learning. AAAI 1987: 505-510 - [c3]Todd R. Davies, Stuart J. Russell:
A Logical Approach to Reasoning by Analogy. IJCAI 1987: 264-270 - 1986
- [c2]Stuart J. Russell:
Quantitative Analysis of Analogy. AAAI 1986: 284-288 - [c1]Stuart Russell:
Preliminary Steps Toward the Automation of Induction. AAAI 1986: 477-484
Coauthor Index
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