39 citations · 41 across the 7 of their papers we have counts for
7 papers
Fast Revenue Maximization
Achraf Bahamou, Omar Besbes, Omar Mouchtaki
Problem definition: We study a data-driven pricing problem in which a seller sets a price for a single item based on demand observed at a limited number of historical prices. Our g…
Optimal Pricing with a Single Point
Amine Allouah, Achraf Bahamou, Omar Besbes
We study the following fundamental data-driven pricing problem. How can/should a decision-maker price its product based on data at a single historical price? How valuable is such d…
Kronecker-factored Quasi-Newton Methods for Deep Learning
Yi Ren, Achraf Bahamou, Donald Goldfarb
Second-order methods have the capability of accelerating optimization by using much richer curvature information than first-order methods. However, most are impractical for deep le…
Practical Quasi-Newton Methods for Training Deep Neural Networks
Donald Goldfarb, Yi Ren, Achraf Bahamou
We consider the development of practical stochastic quasi-Newton, and in particular Kronecker-factored block-diagonal BFGS and L-BFGS methods, for training deep neural networks (DN…
Stochastic Flows and Geometric Optimization on the Orthogonal Group
Krzysztof Choromanski, David Cheikhi, Jared Davis +12
We present a new class of stochastic, geometrically-driven optimization algorithms on the orthogonal group and naturally reductive homogeneous manifolds obtained from the ac…
A Dynamic Sampling Adaptive-SGD Method for Machine Learning
Achraf Bahamou, Donald Goldfarb
We propose a stochastic optimization method for minimizing loss functions, expressed as an expected value, that adaptively controls the batch size used in the computation of gradie…
Hawkes processes for credit indices time series analysis: How random are trades arrival times?
Achraf Bahamou, Maud Doumergue, Philippe Donnat
Targeting a better understanding of credit market dynamics, the authors have studied a stochastic model named the Hawkes process. Describing trades arrival times, this kind of mode…