no code implementations • 8 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.
no code implementations • 20 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.
no code implementations • 15 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.
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.
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.
