270 citations · 669 across the 40 of their papers we have counts for
7 papers · 1 filter
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…
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective
Kartik Ahuja, Jun Wang, Amit Dhurandhar +2
Recently, invariant risk minimization (IRM) was proposed as a promising solution to address out-of-distribution (OOD) generalization. However, it is unclear when IRM should be pref…
Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making
Charvi Rastogi, Yunfeng Zhang, Dennis Wei +3
Several strands of research have aimed to bridge the gap between artificial intelligence (AI) and human decision-makers in AI-assisted decision-making, where humans are the consume…
Invariant Risk Minimization Games
Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney +1
The standard risk minimization paradigm of machine learning is brittle when operating in environments whose test distributions are different from the training distribution due to s…
Model Agnostic Multilevel Explanations
Karthikeyan Natesan Ramamurthy, Bhanukiran Vinzamuri, Yunfeng Zhang +1
In recent years, post-hoc local instance-level and global dataset-level explainability of black-box models has received a lot of attention. Much less attention has been given to ob…
Learning Global Transparent Models Consistent with Local Contrastive Explanations
Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam +1
There is a rich and growing literature on producing local contrastive/counterfactual explanations for black-box models (e.g. neural networks). In these methods, for an input, an ex…