Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Bahamou, Achraf"'
Problem definition: We study a data-driven pricing problem in which a seller offers a price for a single item based on demand observed at a small number of historical prices. Our goal is to derive precise evaluation procedures of the value of the his
Externí odkaz:
http://arxiv.org/abs/2407.07316
Autor:
Bahamou, Achraf, Goldfarb, Donald
We propose a new per-layer adaptive step-size procedure for stochastic first-order optimization methods for minimizing empirical loss functions in deep learning, eliminating the need for the user to tune the learning rate (LR). The proposed approach
Externí odkaz:
http://arxiv.org/abs/2305.13664
Deep neural networks (DNNs) are currently predominantly trained using first-order methods. Some of these methods (e.g., Adam, AdaGrad, and RMSprop, and their variants) incorporate a small amount of curvature information by using a diagonal matrix to
Externí odkaz:
http://arxiv.org/abs/2202.04124
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 data? We consider a decision-maker who optimizes over (potentially rand
Externí odkaz:
http://arxiv.org/abs/2103.05611
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 learning, where the number of training parameters is huge. In Goldfarb e
Externí odkaz:
http://arxiv.org/abs/2102.06737
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 (DNNs). In DNN training, the number of variables and components of the gr
Externí odkaz:
http://arxiv.org/abs/2006.08877
Autor:
Choromanski, Krzysztof, Cheikhi, David, Davis, Jared, Likhosherstov, Valerii, Nazaret, Achille, Bahamou, Achraf, Song, Xingyou, Akarte, Mrugank, Parker-Holder, Jack, Bergquist, Jacob, Gao, Yuan, Pacchiano, Aldo, Sarlos, Tamas, Weller, Adrian, Sindhwani, Vikas
We present a new class of stochastic, geometrically-driven optimization algorithms on the orthogonal group $O(d)$ and naturally reductive homogeneous manifolds obtained from the action of the rotation group $SO(d)$. We theoretically and experimentall
Externí odkaz:
http://arxiv.org/abs/2003.13563
Autor:
Bahamou, Achraf, Goldfarb, Donald
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 gradient approximations and the step size used to move along such directions
Externí odkaz:
http://arxiv.org/abs/1912.13357
Publikováno v:
Proceedings - International Conference on Time Series and Forecasting, ITISE 2018. Granada: University of Granada, pp. 1178-1192
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 model allows for the capture of self-excitement and mutual interactions ph
Externí odkaz:
http://arxiv.org/abs/1902.03714
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