23 citations · 28 across the 7 of their papers we have counts for
8 papers
Supervised Bayesian Specification Inference from Demonstrations
Ankit Shah, Pritish Kamath, Shen Li +4
When observing task demonstrations, human apprentices are able to identify whether a given task is executed correctly long before they gain expertise in actually performing that ta…
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels
Eran Malach, Pritish Kamath, Emmanuel Abbe +1
We study the relative power of learning with gradient descent on differentiable models, such as neural networks, versus using the corresponding tangent kernels. We show that under…
Does Invariant Risk Minimization Capture Invariance?
Pritish Kamath, Akilesh Tangella, Danica J. Sutherland +1
We show that the Invariant Risk Minimization (IRM) formulation of Arjovsky et al. (2019) can fail to capture "natural" invariances, at least when used in its practical "linear" for…
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity
Pritish Kamath, Omar Montasser, Nathan Srebro
We present and study approximate notions of dimensional and margin complexity, which correspond to the minimal dimension or norm of an embedding required to approximate, rather the…
On the Complexity of Modulo-q Arguments and the Chevalley-Warning Theorem
Mika Göös, Pritish Kamath, Katerina Sotiraki +1
We study the search problem class defined as a modulo- analog of the well-known class introduced by Papadim…
Dimension Reduction for Polynomials over Gaussian Space and Applications
Badih Ghazi, Pritish Kamath, Prasad Raghavendra
We introduce a new technique for reducing the dimension of the ambient space of low-degree polynomials in the Gaussian space while preserving their relative correlation structure,…