102 citations · 267 across the 17 of their papers we have counts for
4 papers · 1 filter
Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning
Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan +4
Although disentangled representations are often said to be beneficial for downstream tasks, current empirical and theoretical understanding is limited. In this work, we provide evi…
Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation
Divyat Mahajan, Ioannis Mitliagkas, Brady Neal +1
We study the problem of model selection in causal inference, specifically for conditional average treatment effect (CATE) estimation. Unlike machine learning, there is no perfect a…
Interventional Causal Representation Learning
Kartik Ahuja, Divyat Mahajan, Yixin Wang +1
Causal representation learning seeks to extract high-level latent factors from low-level sensory data. Most existing methods rely on observational data and structural assumptions (…
Towards efficient representation identification in supervised learning
Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis +1
Humans have a remarkable ability to disentangle complex sensory inputs (e.g., image, text) into simple factors of variation (e.g., shape, color) without much supervision. This abil…