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Learning to Initialize Gradient Descent Using Gradient Descent
Non-convex optimization problems are challenging to solve; the success a...
12/22/2020 ∙ by Kartik Ahuja, et al. ∙
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Empirical or Invariant Risk Minimization? A Sample Complexity Perspective
Recently, invariant risk minimization (IRM) was proposed as a promising ...
10/30/2020 ∙ by Kartik Ahuja, et al. ∙
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Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions
Recently, invariant risk minimization (IRM) (Arjovsky et al.) was propos...
10/28/2020 ∙ by Kartik Ahuja, et al. ∙
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Invariant Risk Minimization Games
The standard risk minimization paradigm of machine learning is brittle w...
02/11/2020 ∙ by Kartik Ahuja, et al. ∙
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Estimating Kullback-Leibler Divergence Using Kernel Machines
Recently, a method called the Mutual Information Neural Estimator (MINE)...
05/02/2019 ∙ by Kartik Ahuja, et al. ∙
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Risk-Stratify: Confident Stratification Of Patients Based On Risk
A clinician desires to use a risk-stratification method that achieves co...
11/02/2018 ∙ by Kartik Ahuja, et al. ∙
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Generalized Concordance for Competing Risks
Existing metrics in competing risks survival analysis such as concordanc...
10/26/2018 ∙ by Kartik Ahuja, et al. ∙
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Piecewise Approximations of Black Box Models for Model Interpretation
Machine Learning models have proved extremely successful for a wide vari...
06/27/2018 ∙ by Kartik Ahuja, et al. ∙
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