| Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? A Filos, P Tigas, R McAllister, N Rhinehart, S Levine, Y Gal ICML 2020, 2020 | 86 | 2020 |
| Shifts: A dataset of real distributional shift across multiple large-scale tasks A Malinin, N Band, G Chesnokov, Y Gal, MJF Gales, A Noskov, ... arXiv preprint arXiv:2107.07455, 2021 | 25 | 2021 |
| PERCIVAL: Making In-Browser Perceptual Ad Blocking Practical with Deep Learning Z Abi Din, P Tigas, ST King, B Livshits 2020 USENIX Annual Technical Conference (USENIX ATC 20), 387-400, 2020 | 24* | 2020 |
| Exploration and preference satisfaction trade-off in reward-free learning N Sajid, P Tigas, A Zakharov, Z Fountas, K Friston arXiv preprint arXiv:2106.04316, 2021 | 8 | 2021 |
| The Cave Of Sounds: An Interactive Installation Exploring How We Create Music Together T Murray-Browne, D Aversano, S Garcia, W Hobbes, D Lopez, T Sendon, ... NIME, 2014 | 8 | 2014 |
| Latent Mappings: Generating Open-Ended Expressive Mappings Using Variational Autoencoders T Murray-Browne, P Tigas arXiv preprint arXiv:2106.08867, 2021 | 4 | 2021 |
| Causal-BALD: Deep Bayesian active learning of outcomes to infer treatment-effects from observational data A Jesson, P Tigas, J van Amersfoort, A Kirsch, U Shalit, Y Gal Advances in Neural Information Processing Systems 34, 30465-30478, 2021 | 3 | 2021 |
| Interventions, where and how? experimental design for causal models at scale P Tigas, Y Annadani, A Jesson, B Schölkopf, Y Gal, S Bauer arXiv preprint arXiv:2203.02016, 2022 | 2 | 2022 |
| Spatial Assembly: Generative Architecture With Reinforcement Learning, Self Play and Tree Search P Tigas, T Hosmer Workshop on Machine Learning for Creativity and Design at the 34rd …, 2020 | 2* | 2020 |
| Unravelling A Regulation Machine: Fake News, Toxic Comments And'Illegitimate'Culture D Mylonaki, P Tigas A Peer-Reviewed Journal About 7 (1), 86-98, 2018 | 2 | 2018 |
| Global geomagnetic perturbation forecasting using Deep Learning V Upendran, P Tigas, B Ferdousi, T Bloch, MCM Cheung, S Ganju, ... Space Weather, e2022SW003045, 2022 | 1 | 2022 |
| Emergent Interfaces: Vague, Complex, Bespoke and Embodied Interaction between Humans and Computers T Murray-Browne, P Tigas Applied Sciences 11 (18), 8531, 2021 | 1 | 2021 |
| Modelling non-reinforced preferences using selective attention N Sajid, P Tigas, Z Fountas, Q Guo, A Zakharov, L Da Costa arXiv preprint arXiv:2207.13699, 2022 | | 2022 |
| Global geomagnetic perturbation forecasting using Deep Learning V Upendran, P Tigas, B Ferdousi, T Bloch, MCM Cheung, S Ganju, ... Space Weather, e2022SW003045, 2022 | | 2022 |
| Ground magnetic field perturbation forecasting based on deep learning on spherical harmonics decomposition B Ferdousi, V Upendran, P Tigas, T Bloch, A Bhatt, S Ganju, ... AGU Fall Meeting Abstracts 2021, SM35B-1966, 2021 | | 2021 |
| Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition P Tigas, T Bloch, V Upendran, B Ferdoushi, M Cheung, S Ganju, ... arXiv preprint arXiv:2102.01447, 2021 | | 2021 |
| Determining new representations of" Geoeffectiveness" using deep learning V Upendran, B Ferdousi, T Bloch, P Tigas, Y Gal, A Bhatt, ... AGU Fall Meeting Abstracts 2020, NG004-0011, 2020 | | 2020 |
| Real2sim: Automatic Generation of Open Street Map Towns For Autonomous Driving Benchmarks A Mondal, P Tigas, Y Gal | | 2020 |
| Deep Reinforcement Learning for Autonomous Robotic Tensegrity (ART) T Hosmer, P Tigas 39th Annual Conference of the Association for Computer Aided Design in …, 2019 | | 2019 |
| DOWN TO EARTH: A framework for empathizing with scientific data and invisible threats P Tigas, P Selinas, D Mylonaki International Astronautical Congress, 2015 | | 2015 |
Yarin GalAssociate Professor, University of OxfordVerified email at cs.ox.ac.uk
