44 citations · 183 across the 34 of their papers we have counts for
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Multi-Domain Causal Representation Learning via Weak Distributional Invariances
Kartik Ahuja, Amin Mansouri, Yixin Wang
Causal representation learning has emerged as the center of action in causal machine learning research. In particular, multi-domain datasets present a natural opportunity for showc…
On the Identifiability of Quantized Factors
Vitória Barin-Pacela, Kartik Ahuja, Simon Lacoste-Julien +1
Disentanglement aims to recover meaningful latent ground-truth factors from the observed distribution solely, and is formalized through the theory of identifiability. The identifia…
A Closer Look at In-Context Learning under Distribution Shifts
Kartik Ahuja, David Lopez-Paz
In-context learning, a capability that enables a model to learn from input examples on the fly without necessitating weight updates, is a defining characteristic of large language…
Reusable Slotwise Mechanisms
Trang Nguyen, Amin Mansouri, Kanika Madan +4
Agents with the ability to comprehend and reason about the dynamics of objects would be expected to exhibit improved robustness and generalization in novel scenarios. However, achi…