22 citations · 44 across the 7 of their papers we have counts for
12 papers
Properties from Mechanisms: An Equivariance Perspective on Identifiable Representation Learning
Kartik Ahuja, Jason Hartford, Yoshua Bengio
A key goal of unsupervised representation learning is "inverting" a data generating process to recover its latent properties. Existing work that provably achieves this goal relies…
Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?
Dinghuai Zhang, Kartik Ahuja, Yilun Xu +2
Can models with particular structure avoid being biased towards spurious correlation in out-of-distribution (OOD) generalization? Peters et al. (2016) provides a positive answer fo…
SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization
Soroosh Shahtalebi, Jean-Christophe Gagnon-Audet, Touraj Laleh +3
A major bottleneck in the real-world applications of machine learning models is their failure in generalizing to unseen domains whose data distribution is not i.i.d to the training…
Treatment Effect Estimation using Invariant Risk Minimization
Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam +3
Inferring causal individual treatment effect (ITE) from observational data is a challenging problem whose difficulty is exacerbated by the presence of treatment assignment bias. In…
Learning to Initialize Gradient Descent Using Gradient Descent
Kartik Ahuja, Amit Dhurandhar, Kush R. Varshney
Non-convex optimization problems are challenging to solve; the success and computational expense of a gradient descent algorithm or variant depend heavily on the initialization str…
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions
Kartik Ahuja, Karthikeyan Shanmugam, Amit Dhurandhar
Recently, invariant risk minimization (IRM) (Arjovsky et al.) was proposed as a promising solution to address out-of-distribution (OOD) generalization. In Ahuja et al., it was show…