Sam Toyer

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Pieter AbbeelUC Berkeley | CovariantVerified email at cs.berkeley.edu
Stuart RussellProfessor of Computer Science, University of California, BerkeleyVerified email at cs.berkeley.edu
Sylvie ThiebauxThe Australian National University & LAAS-CNRS, University of ToulouseVerified email at anu.edu.au
Felipe TrevizanAustralian National UniversityVerified email at anu.edu.au
Lexing XieAustralian National UniversityVerified email at anu.edu.au
Adam GleaveCEO at FAR AIVerified email at far.ai
Scott EmmonsUC BerkeleyVerified email at berkeley.edu
Steven H. WangETH ZürichVerified email at berkeley.edu
Stephen GouldProfessor at Australian National UniversityVerified email at anu.edu.au
Justin SvegliatoSenior Research Scientist, Microsoft, UC BerkeleyVerified email at microsoft.com
Sergey LevineUC Berkeley, Physical IntelligenceVerified email at eecs.berkeley.edu
Angjoo KanazawaUC BerkeleyVerified email at berkeley.edu
Xue Bin PengAssistant Professor at Simon Fraser University, Research Scientist at NVIDIAVerified email at sfu.ca
Olivia WatkinsUC BerkeleyVerified email at berkeley.edu
Rohin ShahResearch Scientist, Google DeepMindVerified email at deepmind.com
Xin ChenETH ZurichVerified email at ethz.ch
Tengda HanGoogle DeepMind | VGG, University of OxfordVerified email at robots.ox.ac.uk
Anoop CherianMitsubishi Electric Research Labs (MERL), Australian National UniversityVerified email at merl.com
Andrew CritchUC Berkeley, Department of Electrical Engineering and Computer SciencesVerified email at eecs.berkeley.edu
Dillon BowenThe Wharton School of BusinessVerified email at wharton.upenn.edu

Sam Toyer

UC Berkeley

Verified email at berkeley.edu - Homepage

Artificial intelligence


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Variational discriminator bottleneck: Improving imitation learning, inverse RL, and GANs by constraining information flow XB Peng, A Kanazawa, S Toyer, P Abbeel, S Levine ICLR 2019, 2018	254	2018
Action Schema Networks: Generalised Policies with Deep Learning S Toyer, F Trevizan, S Thiebaux, L Xie AAAI Conference on Artificial Intelligence (AAAI), 2018	119	2018
Asnets: Deep learning for generalised planning S Toyer, S Thiébaux, F Trevizan, L Xie Journal of Artificial Intelligence Research 68, 1-68, 2020	83	2020
Human pose forecasting via deep Markov models S Toyer, A Cherian, T Han, S Gould International Conference on Digital Image Computing: Techniques and …, 2017	56	2017
The MAGICAL Benchmark for Robust Imitation S Toyer, R Shah, A Critch, S Russell NeurIPS 2020, 2020	49	2020
imitation: Clean imitation learning implementations A Gleave, M Taufeeque, J Rocamonde, E Jenner, SH Wang, S Toyer, ... arXiv preprint arXiv:2211.11972, 2022	48	2022
Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game S Toyer, O Watkins, EA Mendes, J Svegliato, L Bailey, T Wang, I Ong, ... ICLR 2024, 2023	33	2023
An Empirical Investigation of Representation Learning for Imitation X Chen, S Toyer, C Wild, S Emmons, I Fischer, KH Lee, N Alex, SH Wang, ... NeurIPS 2021, Datasets and Benchmarks Track, 2021	30	2021
The imitation library for imitation learning and inverse reinforcement learning S Wang, S Toyer, A Gleave, S Emmons	25	2020
A strongreject for empty jailbreaks A Souly, Q Lu, D Bowen, T Trinh, E Hsieh, S Pandey, P Abbeel, ... arXiv preprint arXiv:2402.10260, 2024	21	2024
Publishing and Using Earth Observation Data with the RDF Data Cube and the Discrete Global Grid System D Brizhinev, S Toyer, K Taylor https://www.w3.org/TR/eo-qb/, 2017	20	2017
A primer on maximum causal entropy inverse reinforcement learning A Gleave, S Toyer arXiv preprint arXiv:2203.11409, 2022	19	2022
Guiding search with generalized policies for probabilistic planning W Shen, F Trevizan, S Toyer, S Thiébaux, L Xie Proceedings of the International Symposium on Combinatorial Search 10 (1 …, 2019	16	2019
Derail: Diagnostic environments for reward and imitation learning P Freire, A Gleave, S Toyer, S Russell arXiv preprint arXiv:2012.01365, 2020	8	2020
Computer vision training using paired image data S Gould, S Toyer, D Reiner US Patent App. 16/360,954, 2019	6	2019
Variational discriminator bottleneck: improving imitation learning XB Peng, A Kanazawa, S Toyer, P Abbeel, S Levine Inverse RL, and GANs by Constraining Information Flow.[(accessed on 29 …, 2018	5	2018
seals: Suite of environments for algorithms that learn specifications A Gleave, P Freire, S Wang, S Toyer	4	2020
Generalised policies for probabilistic planning with deep learning S Toyer Research and development, honours thesis, Research School of Computer …, 2017	4	2017
Action Schema Networks–IPC Version M Hao, S Toyer, R Wang, S Thiébaux, F Trevizan Tenth International Planning Competition (IPC-10) Learning Track: Planner …, 2023	3	2023
Single image completion from retrieved image collections S Gould, S Toyer, D Reiner US Patent 10,885,628, 2021	2	2021

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