Search Results for author: Pratik Worah

Found 5 papers, 0 papers with code

Learning to Price Against a Moving Target

no code implementations8 Jun 2021 Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng, Pratik Worah

In the Learning to Price setting, a seller posts prices over time with the goal of maximizing revenue while learning the buyer's valuation.

Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems

no code implementations20 Oct 2020 Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab Mirrokni

Unlike nonconvex optimization, where gradient descent is guaranteed to converge to a local optimizer, algorithms for nonconvex-nonconcave minimax optimization can have topologically different solution paths: sometimes converging to a solution, sometimes never converging and instead following a limit cycle, and sometimes diverging.

The Landscape of the Proximal Point Method for Nonconvex-Nonconcave Minimax Optimization

no code implementations15 Jun 2020 Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab Mirrokni

Critically, we show this envelope not only smooths the objective but can convexify and concavify it based on the level of interaction present between the minimizing and maximizing variables.

The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network

no code implementations NeurIPS 2018 Jeffrey Pennington, Pratik Worah

An important factor contributing to the success of deep learning has been the remarkable ability to optimize large neural networks using simple first-order optimization algorithms like stochastic gradient descent.

Nonlinear random matrix theory for deep learning

no code implementations NeurIPS 2017 Jeffrey Pennington, Pratik Worah

Neural network configurations with random weights play an important role in the analysis of deep learning.

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