23 citations · 62 across the 50 of their papers we have counts for
5 papers · 1 filter
On the Power of Differentiable Learning versus PAC and SQ Learning
Emmanuel Abbe, Pritish Kamath, Eran Malach +2
We study the power of learning via mini-batch stochastic gradient descent (SGD) on the population loss, and batch Gradient Descent (GD) on the empirical loss, of a differentiable m…
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…
Understanding the Eluder Dimension
Gene Li, Pritish Kamath, Dylan J. Foster +1
We provide new insights on eluder dimension, a complexity measure that has been extensively used to bound the regret of algorithms for online bandits and reinforcement learning wit…
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…