2 citations · 2 across the 8 of their papers we have counts for
9 papers
Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT
Vishal V. Batchu, Michelangelo Conserva, Alex Wilson +5
Anthropogenic methane (CH4) point sources are critical drivers of near-term climate forcing, safety hazards, and system-inefficiencies. Space-based imaging spectroscopy is an emerg…
Exploration with Foundation Models: Capabilities, Limitations, and Hybrid Approaches
Remo Sasso, Michelangelo Conserva, Dominik Jeurissen +1
Exploration in reinforcement learning (RL) remains challenging, particularly in sparse-reward settings. While foundation models possess strong semantic priors, their capabilities a…
On the Limits of Tabular Hardness Metrics for Deep RL: A Study with the Pharos Benchmark
Michelangelo Conserva, Remo Sasso, Paulo Rauber
Principled evaluation is critical for progress in deep reinforcement learning (RL), yet it lags behind the theory-driven benchmarks of tabular RL. While tabular settings benefit fr…
Foundation Models as World Models: A Foundational Study in Text-Based GridWorlds
Remo Sasso, Michelangelo Conserva, Dominik Jeurissen +1
While reinforcement learning from scratch has shown impressive results in solving sequential decision-making tasks with efficient simulators, real-world applications with expensive…
Mapping Farmed Landscapes from Remote Sensing
Michelangelo Conserva, Alex Wilson, Charlotte Stanton +2
Effective management of agricultural landscapes is critical for meeting global biodiversity targets, but efforts are hampered by the absence of detailed, large-scale ecological map…
Heterogeneous graph neural networks for species distribution modeling
Lauren Harrell, Christine Kaeser-Chen, Burcu Karagol Ayan +7
Species distribution models (SDMs) are necessary for measuring and predicting occurrences and habitat suitability of species and their relationship with environmental factors. We i…
Posterior Sampling for Deep Reinforcement Learning
Remo Sasso, Michelangelo Conserva, Paulo Rauber
Despite remarkable successes, deep reinforcement learning algorithms remain sample inefficient: they require an enormous amount of trial and error to find good policies. Model-base…
Hardness in Markov Decision Processes: Theory and Practice
Michelangelo Conserva, Paulo Rauber
Meticulously analysing the empirical strengths and weaknesses of reinforcement learning methods in hard (challenging) environments is essential to inspire innovations and assess pr…
The Graph Cut Kernel for Ranked Data
Michelangelo Conserva, Marc Peter Deisenroth, K S Sesh Kumar
Many algorithms for ranked data become computationally intractable as the number of objects grows due to the complex geometric structure induced by rankings. An additional challeng…