Pritish Kamath | DeepAI

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∙ 05/03/2022

Do More Negative Samples Necessarily Hurt in Contrastive Learning?

Recent investigations in noise contrastive estimation suggest, both empi...

0 Pranjal Awasthi, et al. ∙

∙ 08/09/2021

On the Power of Differentiable Learning versus PAC and SQ Learning

We study the power of learning via mini-batch stochastic gradient descen...

0 Emmanuel Abbe, et al. ∙

∙ 04/14/2021

Eluder Dimension and Generalized Rank

We study the relationship between the eluder dimension for a function cl...

0 Gene Li, et al. ∙

∙ 03/01/2021

Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels

We study the relative power of learning with gradient descent on differe...

0 Eran Malach, et al. ∙

∙ 01/04/2021

Does Invariant Risk Minimization Capture Invariance?

We show that the Invariant Risk Minimization (IRM) formulation of Arjovs...

0 Pritish Kamath, et al. ∙

∙ 03/09/2020

Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity

We present and study approximate notions of dimensional and margin compl...

0 Pritish Kamath, et al. ∙

∙ 12/10/2019

On the Complexity of Modulo-q Arguments and the Chevalley-Warning Theorem

We study the search problem class PPA_q defined as a modulo-q analog of ...

0 Mika Göös, et al. ∙

∙ 08/12/2017

Dimension Reduction for Polynomials over Gaussian Space and Applications

We introduce a new technique for reducing the dimension of the ambient s...

0 Badih Ghazi, et al. ∙

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