Afshin Rostamizadeh
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Featured researches published by Afshin Rostamizadeh.
learning at scale | 2014
Arthur Asuncion; Jac de Haan; Mehryar Mohri; Kayur Patel; Afshin Rostamizadeh; Umar Syed; Lauren Wong
Google Research recently tested a massive online class model for an internal engineering education program, with machine learning as the topic, that blended theoretical concepts and Google-specific software tool tutorials. The goal of this training was to foster engineering capacity to leverage machine learning tools in future products. The course was delivered both synchronously and asynchronously, and students had the choice between studying independently or participating with a group. Since all students are company employees, unlike most publicly offered MOOCs we can continue to measure the students behavioral change long after the course is complete. This paper describes the course, outlines the available data set and presents directions for analysis.
economics and computation | 2016
Hamid Nazerzadeh; Renato Paes Leme; Afshin Rostamizadeh; Umar Syed
Ad exchange platforms connect online publishers and advertisers and facilitate the sale of billions of impressions every day. We study these environments from the perspective of a publisher who wants to find the profit-maximizing exchange in which to sell his inventory. Ideally, the publisher would run an auction among exchanges. However, this is not usually possible due to practical business considerations. Instead, the publisher must send each impression to only one of the exchanges, along with an asking price. We model the problem as a variation of the multi-armed bandits problem in which exchanges (arms) can behave strategically in order to maximizes their own profit. We propose e mechanisms that find the best exchange with sub-linear regret and have desirable incentive properties.
Social Science Research Network | 2016
Hamid Nazerzadeh; Renato Paes Leme; Afshin Rostamizadeh; Umar Syed
Ad Exchange platforms connect online publishers and advertisers and facilitate selling billions of impressions every day. We study these environments from the perspective of a publisher who wants to find the profit maximizing exchange to sell his inventory. Ideally, the publisher would run an auction among exchanges. However, this is not possible due to technological and other practical considerations. The publisher needs to send each impression to one of the exchanges with an asking price. We model the problem as a variation of multi-armed bandits where exchanges (arms) can behave strategically in order to maximizes their own profit. We propose a mechanism that finds the best exchange with sub-linear regret and has desirable incentive properties.
conference on information and knowledge management | 2015
Krzysztof Choromanski; Afshin Rostamizadeh; Umar Syed
We give the first Õ(1 over √ T)-error online algorithm for reconstructing noisy statistical databases, where T is the number of (online) sample queries received. The algorithm is optimal up to the poly(log(T)) factor in terms of the error and requires only O(log T) memory. It aims to learn a hidden database-vector w* Ε in ℜ D in order to accurately answer a stream of queries regarding the hidden database, which arrive in an online fashion from some unknown distribution D. We assume the distribution D is defined on the neighborhood of a low-dimensional manifold. The presented algorithm runs in O(dD)-time per query, where d is the dimensionality of the query-space. Contrary to the classical setting, there is no separate training set that is used by the algorithm to learn the database --- the stream on which the algorithm will be evaluated must also be used to learn the database-vector. The algorithm only has access to a binary oracle Ο that answers whether a particular linear function of the database-vector plus random noise is larger than a threshold, which is specified by the algorithm. We note that we allow for a significant O(D) amount of noise to be added while other works focused on the low noise o(√D)-setting. For a stream of T queries our algorithm achieves an average error Õ(1 over √T) by filtering out random noise, adapting threshold values given to the oracle based on its previous answers and, as a consequence, recovering with high precision a projection of a database-vector w* onto the manifold defining the query-space. Our algorithm may be also applied in the adversarial machine learning context to compromise machine learning engines by heavily exploiting the vulnerabilities of the systems that output only binary signal and in the presence of significant noise.
Archive | 2012
Mehryar Mohri; Afshin Rostamizadeh; Ameet Talwalkar
Journal of Machine Learning Research | 2012
Corinna Cortes; Mehryar Mohri; Afshin Rostamizadeh
neural information processing systems | 2013
Kareem Amin; Afshin Rostamizadeh; Umar Syed
conference on innovative data systems research | 2015
Sreeram Balakrishnan; Alon Y. Halevy; Boulos Harb; Hongrae Lee; Jayant Madhavan; Afshin Rostamizadeh; Warren Shen; Kenneth Wilder; Fei Wu; Cong Yu
neural information processing systems | 2014
Kareem Amin; Afshin Rostamizadeh; Umar Syed
international conference on machine learning | 2013
Corinna Cortes; Mehryar Mohri; Afshin Rostamizadeh