53 citations · 111 across the 16 of their papers we have counts for
16 papers
MADE: Benchmark Environments for Closed-Loop Materials Discovery
Shreshth A Malik, Tiarnan Doherty, Panagiotis Tigas +4
Existing benchmarks for computational materials discovery primarily evaluate static predictive tasks or isolated computational sub-tasks. While valuable, these evaluations neglect…
Challenges and Considerations in the Evaluation of Bayesian Causal Discovery
Amir Mohammad Karimi Mamaghan, Panagiotis Tigas, Karl Henrik Johansson +3
Representing uncertainty in causal discovery is a crucial component for experimental design, and more broadly, for safe and reliable causal decision making. Bayesian Causal Discove…
Deep Bayesian Active Learning for Preference Modeling in Large Language Models
Luckeciano C. Melo, Panagiotis Tigas, Alessandro Abate +1
Leveraging human preferences for steering the behavior of Large Language Models (LLMs) has demonstrated notable success in recent years. Nonetheless, data selection and labeling ar…
Amortized Active Causal Induction with Deep Reinforcement Learning
Yashas Annadani, Panagiotis Tigas, Stefan Bauer +1
We present Causal Amortized Active Structure Learning (CAASL), an active intervention design policy that can select interventions that are adaptive, real-time and that does not req…
Differentiable Multi-Target Causal Bayesian Experimental Design
Yashas Annadani, Panagiotis Tigas, Desi R. Ivanova +4
We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting -- a critical component for causal discover…
Modelling non-reinforced preferences using selective attention
Noor Sajid, Panagiotis Tigas, Zafeirios Fountas +3
How can artificial agents learn non-reinforced preferences to continuously adapt their behaviour to a changing environment? We decompose this question into two challenges: () en…
Global geomagnetic perturbation forecasting using Deep Learning
Vishal Upendran, Panagiotis Tigas, Banafsheh Ferdousi +6
Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field which arise from the interaction of the solar wind with Earth's magnetosphere,…
Interventions, Where and How? Experimental Design for Causal Models at Scale
Panagiotis Tigas, Yashas Annadani, Andrew Jesson +3
Causal discovery from observational and interventional data is challenging due to limited data and non-identifiability: factors that introduce uncertainty in estimating the underly…
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort +3
Estimating personalized treatment effects from high-dimensional observational data is essential in situations where experimental designs are infeasible, unethical, or expensive. Ex…
Exploration and preference satisfaction trade-off in reward-free learning
Noor Sajid, Panagiotis Tigas, Alexey Zakharov +2
Biological agents have meaningful interactions with their environment despite the absence of immediate reward signals. In such instances, the agent can learn preferred modes of beh…
Latent Mappings: Generating Open-Ended Expressive Mappings Using Variational Autoencoders
Tim Murray-Browne, Panagiotis Tigas
In many contexts, creating mappings for gestural interactions can form part of an artistic process. Creators seeking a mapping that is expressive, novel, and affords them a sense o…
Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition
Panagiotis Tigas, Téo Bloch, Vishal Upendran +6
Modeling and forecasting the solar wind-driven global magnetic field perturbations is an open challenge. Current approaches depend on simulations of computationally demanding model…
Spatial Assembly: Generative Architecture With Reinforcement Learning, Self Play and Tree Search
Panagiotis Tigas, Tyson Hosmer
With this work, we investigate the use of Reinforcement Learning (RL) for the generation of spatial assemblies, by combining ideas from Procedural Generation algorithms (Wave Funct…
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Angelos Filos, Panagiotis Tigas, Rowan McAllister +3
Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment, typically leading to arbitrary deductions and poorly-informed decisions. In pr…
Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning
Zain ul abi Din, Panagiotis Tigas, Samuel T. King +1
In this paper we present Percival, a browser-embedded, lightweight, deep learning-powered ad blocker. Percival embeds itself within the browser's image rendering pipeline, which ma…
Real-time jam-session support system
Panagiotis Tigas
We propose a method for the problem of real time chord accompaniment of improvised music. Our implementation can learn an underlying structure of the musical performance and predic…