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ORCIDPanagiotis Tigas
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2020 – today
- 2024
[c9]Amir Mohammad Karimi-Mamaghan, Panagiotis Tigas, Karl Henrik Johansson, Yarin Gal, Yashas Annadani, Stefan Bauer:
Challenges and Considerations in the Evaluation of Bayesian Causal Discovery. ICML 2024- [i14]Yashas Annadani, Panagiotis Tigas, Stefan Bauer, Adam Foster:
Amortized Active Causal Induction with Deep Reinforcement Learning. CoRR abs/2405.16718 (2024) - [i13]Amir Mohammad Karimi-Mamaghan, Panagiotis Tigas, Karl Henrik Johansson, Yarin Gal, Yashas Annadani, Stefan Bauer:
Challenges and Considerations in the Evaluation of Bayesian Causal Discovery. CoRR abs/2406.03209 (2024) - [i12]Luckeciano C. Melo, Panagiotis Tigas, Alessandro Abate, Yarin Gal:
Deep Bayesian Active Learning for Preference Modeling in Large Language Models. CoRR abs/2406.10023 (2024) - 2023
- [c8]Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer:
Differentiable Multi-Target Causal Bayesian Experimental Design. ICML 2023: 34263-34279 - [i11]Yashas Annadani, Panagiotis Tigas, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer:
Differentiable Multi-Target Causal Bayesian Experimental Design. CoRR abs/2302.10607 (2023) - 2022
- [c7]Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer:
Interventions, Where and How? Experimental Design for Causal Models at Scale. NeurIPS 2022 - [i10]Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer:
Interventions, Where and How? Experimental Design for Causal Models at Scale. CoRR abs/2203.02016 (2022) - [i9]Vishal Upendran, Panagiotis Tigas, Banafsheh Ferdousi, Téo Bloch, Mark C. M. Cheung, Siddha Ganju, Asti Bhatt, Ryan M. McGranaghan, Yarin Gal:
Global geomagnetic perturbation forecasting using Deep Learning. CoRR abs/2205.12734 (2022) - [i8]Noor Sajid, Panagiotis Tigas, Zafeirios Fountas, Qinghai Guo, Alexey Zakharov, Lancelot Da Costa:
Modelling non-reinforced preferences using selective attention. CoRR abs/2207.13699 (2022) - 2021
- [c6]Tim Murray Browne, Panagiotis Tigas:
Latent Mappings: Generating Open-Ended Expressive Mappings Using Variational Autoencoders. NIME 2021 - [c5]Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal:
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. NeurIPS 2021: 30465-30478 - [c4]Andrey Malinin, Neil Band, Yarin Gal, Mark J. F. Gales, Alexander Ganshin, German Chesnokov, Alexey Noskov, Andrey Ploskonosov, Liudmila Prokhorenkova, Ivan Provilkov, Vatsal Raina, Vyas Raina, Denis Roginskiy, Mariya Shmatova, Panagiotis Tigas, Boris Yangel:
Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks. NeurIPS Datasets and Benchmarks 2021 - [i7]Panagiotis Tigas, Tyson Hosmer:
Spatial Assembly: Generative Architecture With Reinforcement Learning, Self Play and Tree Search. CoRR abs/2101.07579 (2021) - [i6]Panagiotis Tigas, Téo Bloch
, Vishal Upendran, Banafsheh Ferdoushi, Mark C. M. Cheung, Siddha Ganju, Ryan M. McGranaghan, Yarin Gal, Asti Bhatt:
Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition. CoRR abs/2102.01447 (2021) - [i5]Noor Sajid, Panagiotis Tigas, Alexey Zakharov, Zafeirios Fountas, Karl J. Friston:
Exploration and preference satisfaction trade-off in reward-free learning. CoRR abs/2106.04316 (2021) - [i4]Tim Murray Browne, Panagiotis Tigas:
Latent Mappings: Generating Open-Ended Expressive Mappings Using Variational Autoencoders. CoRR abs/2106.08867 (2021) - [i3]Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal:
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. CoRR abs/2111.02275 (2021) - 2020
- [c3]Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal:
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? ICML 2020: 3145-3153 - [c2]Zain ul Abi Din, Panagiotis Tigas, Samuel T. King, Benjamin Livshits:
PERCIVAL: Making In-Browser Perceptual Ad Blocking Practical with Deep Learning. USENIX ATC 2020: 387-400 - [i2]Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal:
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? CoRR abs/2006.14911 (2020)
2010 – 2019
- 2019
- [i1]Zain ul Abi Din, Panagiotis Tigas, Samuel T. King, Benjamin Livshits:
Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning. CoRR abs/1905.07444 (2019) - 2014
- [c1]Tim Murray Browne, Dom Aversano, Susanna Garcia, Wallace Hobbes, Daniel Lopez, Tadeo Sendon, Panagiotis Tigas, Kacper Ziemianin, Duncan Chapman:
The Cave of Sounds: An Interactive Installation Exploring How We Create Music Together. NIME 2014: 307-310
Coauthor Index
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last updated on 2024-09-04 01:26 CEST by the dblp team
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