Search Results for author:
Found 10 papers, 5 papers with code
Differentiable Multi-Target Causal Bayesian Experimental Design
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 discovery from finite data where interventions can be costly or risky.
Modelling non-reinforced preferences using selective attention
These memories are selectively attended to, using attention and gating blocks, to update agent's preferences.
Global geomagnetic perturbation forecasting using Deep Learning
The model summarizes 2 hours of solar wind measurement using a Gated Recurrent Unit, and generates forecasts of coefficients which are folded with a spherical harmonic basis to enable global forecasts.
Interventions, Where and How? Experimental Design for Causal Models at Scale
Existing methods in experimental design for causal discovery from limited data either rely on linear assumptions for the SCM or select only the intervention target.
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
We introduce causal, Bayesian acquisition functions grounded in information theory that bias data acquisition towards regions with overlapping support to maximize sample efficiency for learning personalized treatment effects.
Exploration and preference satisfaction trade-off in reward-free learning
In this paper, we pursue the notion that this learnt behaviour can be a consequence of reward-free preference learning that ensures an appropriate trade-off between exploration and preference satisfaction.
Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition
Modeling and forecasting the solar wind-driven global magnetic field perturbations is an open challenge.
Spatial Assembly: Generative Architecture With Reinforcement Learning, Self Play and Tree Search
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 Function Collapse algorithm (WFC)) and RL for Game Solving.
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment, typically leading to arbitrary deductions and poorly-informed decisions.
Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning
In this paper we present Percival, a browser-embedded, lightweight, deep learning-powered ad blocker.
