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We propose a new benchmark and library to fit neural fields at scale.

We introduce DECAF, a framework that is a first step towards adapting and composing causal representations.

We introduce the problem of hierarchical causal representation learning and show how our method, HERCULES, can generalize across environments with much fewer fine-grained interventions.

PDE-Refiner is an iterative refinement process that enables neural operator training for accurate and stable predictions over long time horizons. Published at NeurIPS 2023 (**Spotlight**).

BISCUIT identifies causal variables from videos using binary interactions between an external system (e.g. robot) and the causal variables. Published at UAI 2023 (**Spotlight**).

We present iCITRIS, a causal representation learning method that can identify causal variables with instantaneous effects and their graph from temporal sequences. Published at ICLR 2023.

We learn object-centric representations in an unsupervised manner by differentiably parameterizing and solving a min-cut problem to partition the image. Published at ICLR 2023.

We learn sampling subsets within a neural network by relaxing conditional poisson sampling. Published at ICLR 2023.

We derive a minimal bound of experiments that guarantee identifiability of causal variables, opening up new opportunities for using intervention design for causal representation learning.

We present CITRIS, a causal representation learning algorithm for multidimensional causal factors identified from videos with interventions. Published at ICML 2022 (**Spotlight**).