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Found 4 papers, 2 papers with code
Deep Backtracking Counterfactuals for Causally Compliant Explanations
Counterfactuals answer questions of what would have been observed under altered circumstances and can therefore offer valuable insights.
Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimators
We study causal effect estimation from a mixture of observational and interventional data in a confounded linear regression model with multivariate treatments.
Deep learning for satellite image forecasting of vegetation greenness
Here, we target the spatial and temporal variations of land surface reflectance and vegetation greenness, measuring the density of green vegetation and active foliage area, conditioned on current and past climate and the local topography.
Ranked #3 on
Earth Surface Forecasting
on EarthNet2021 OOD Track
Multi-agent Actor-Critic with Time Dynamical Opponent Model
In this paper, we propose to exploit the fact that the agents seek to improve their expected cumulative reward and introduce a novel \textit{Time Dynamical Opponent Model} (TDOM) to encode the knowledge that the opponent policies tend to improve over time.
