Panagiotis Tigas

27 papers A* 7Journal 16Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Multilevel Low-Rank Newton Method with Super-linear Convergence Rate and its Application to Non-convex Problems. Trans. Mach. Learn. Res. Nick Tsipinakis, Panagiotis Tigas, Panos Parpas
2026	J	`jnl`	MADE: Benchmark Environments for Closed-Loop Materials Discovery. CoRR Shreshth A. Malik, Tiarnan A. S. Doherty, Panagiotis Tigas, Muhammed Razzak, Stephen J. Roberts, Aron Walsh, Yarin Gal
2024	A*	`conf`	Amortized Active Causal Induction with Deep Reinforcement Learning. NeurIPS Yashas Annadani, Panagiotis Tigas, Stefan Bauer, Adam Foster
2024	J	`jnl`	Amortized Active Causal Induction with Deep Reinforcement Learning. CoRR Yashas Annadani, Panagiotis Tigas, Stefan Bauer, Adam Foster
2024	A*	`conf`	Challenges and Considerations in the Evaluation of Bayesian Causal Discovery. ICML Amir Mohammad Karimi-Mamaghan, Panagiotis Tigas, Karl Henrik Johansson, Yarin Gal, Yashas Annadani, Stefan Bauer
2024	J	`jnl`	Challenges and Considerations in the Evaluation of Bayesian Causal Discovery. CoRR Amir Mohammad Karimi-Mamaghan, Panagiotis Tigas, Karl Henrik Johansson, Yarin Gal, Yashas Annadani, Stefan Bauer
2024	A*	`conf`	Deep Bayesian Active Learning for Preference Modeling in Large Language Models. NeurIPS Luckeciano Carvalho Melo, Panagiotis Tigas, Alessandro Abate, Yarin Gal
2024	J	`jnl`	Deep Bayesian Active Learning for Preference Modeling in Large Language Models. CoRR Luckeciano C. Melo, Panagiotis Tigas, Alessandro Abate, Yarin Gal
2023	A*	`conf`	Differentiable Multi-Target Causal Bayesian Experimental Design. ICML Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer
2023	J	`jnl`	Differentiable Multi-Target Causal Bayesian Experimental Design. CoRR Yashas Annadani, Panagiotis Tigas, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer
2022	J	`jnl`	Global geomagnetic perturbation forecasting using Deep Learning. CoRR Vishal Upendran, Panagiotis Tigas, Banafsheh Ferdousi, Téo Bloch, Mark C. M. Cheung, Siddha Ganju, Asti Bhatt, Ryan M. McGranaghan, Yarin Gal
2022	A*	`conf`	Interventions, Where and How? Experimental Design for Causal Models at Scale. NeurIPS Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer
2022	J	`jnl`	Interventions, Where and How? Experimental Design for Causal Models at Scale. CoRR Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer
2022	J	`jnl`	Modelling non-reinforced preferences using selective attention. CoRR Noor Sajid, Panagiotis Tigas, Zafeirios Fountas, Qinghai Guo, Alexey Zakharov, Lancelot Da Costa
2021	A*	`conf`	Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. NeurIPS Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
2021	J	`jnl`	Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. CoRR Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
2021	J	`jnl`	Exploration and preference satisfaction trade-off in reward-free learning. CoRR Noor Sajid, Panagiotis Tigas, Alexey Zakharov, Zafeirios Fountas, Karl J. Friston
2021	J	`jnl`	Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition. CoRR Panagiotis Tigas, Téo Bloch, Vishal Upendran, Banafsheh Ferdoushi, Mark C. M. Cheung, Siddha Ganju, Ryan M. McGranaghan, Yarin Gal, Asti Bhatt
2021	—	`conf`	Latent Mappings: Generating Open-Ended Expressive Mappings Using Variational Autoencoders. NIME Tim Murray Browne, Panagiotis Tigas
2021	J	`jnl`	Latent Mappings: Generating Open-Ended Expressive Mappings Using Variational Autoencoders. CoRR Tim Murray Browne, Panagiotis Tigas
2021	—	`conf`	Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks. NeurIPS Datasets and Benchmarks 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
2021	J	`jnl`	Spatial Assembly: Generative Architecture With Reinforcement Learning, Self Play and Tree Search. CoRR Panagiotis Tigas, Tyson Hosmer
2020	A*	`conf`	Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? ICML Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal
2020	J	`jnl`	Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? CoRR Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal
2020	—	`conf`	PERCIVAL: Making In-Browser Perceptual Ad Blocking Practical with Deep Learning. USENIX ATC Zain ul Abi Din, Panagiotis Tigas, Samuel T. King, Benjamin Livshits
2019	J	`jnl`	Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning. CoRR Zain ul Abi Din, Panagiotis Tigas, Samuel T. King, Benjamin Livshits
2014	—	`conf`	The Cave of Sounds: An Interactive Installation Exploring How We Create Music Together. NIME Tim Murray Browne, Dom Aversano, Susanna Garcia, Wallace Hobbes, Daniel Lopez, Tadeo Sendon, Panagiotis Tigas, Kacper Ziemianin, Duncan Chapman