Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Zoghi, Masrour"'
Autor:
Wu, Haolun, Meshi, Ofer, Zoghi, Masrour, Diaz, Fernando, Liu, Xue, Boutilier, Craig, Karimzadehgan, Maryam
Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, like single-point and multi-point representations, have limitations w.r
Externí odkaz:
http://arxiv.org/abs/2310.20091
We introduce EV3, a novel meta-optimization framework designed to efficiently train scalable machine learning models through an intuitive explore-assess-adapt protocol. In each iteration of EV3, we explore various model parameter updates, assess them
Externí odkaz:
http://arxiv.org/abs/2310.18893
The recent literature on online learning to rank (LTR) has established the utility of prior knowledge to Bayesian ranking bandit algorithms. However, a major limitation of existing work is the requirement for the prior used by the algorithm to match
Externí odkaz:
http://arxiv.org/abs/2301.10651
Autor:
Bavadekar, Shailesh, Dai, Andrew, Davis, John, Desfontaines, Damien, Eckstein, Ilya, Everett, Katie, Fabrikant, Alex, Flores, Gerardo, Gabrilovich, Evgeniy, Gadepalli, Krishna, Glass, Shane, Huang, Rayman, Kamath, Chaitanya, Kraft, Dennis, Kumok, Akim, Marfatia, Hinali, Mayer, Yael, Miller, Benjamin, Pearce, Adam, Perera, Irippuge Milinda, Ramachandran, Venky, Raman, Karthik, Roessler, Thomas, Shafran, Izhak, Shekel, Tomer, Stanton, Charlotte, Stimes, Jacob, Sun, Mimi, Wellenius, Gregory, Zoghi, Masrour
This report describes the aggregation and anonymization process applied to the initial version of COVID-19 Search Trends symptoms dataset (published at https://goo.gle/covid19symptomdataset on September 2, 2020), a publicly available dataset that sho
Externí odkaz:
http://arxiv.org/abs/2009.01265
Online ranker evaluation is one of the key challenges in information retrieval. While the preferences of rankers can be inferred by interleaving methods, the problem of how to effectively choose the ranker pair that generates the interleaved list wit
Externí odkaz:
http://arxiv.org/abs/1812.04412
Autor:
Li, Chang, Kveton, Branislav, Lattimore, Tor, Markov, Ilya, de Rijke, Maarten, Szepesvari, Csaba, Zoghi, Masrour
In this paper, we study the problem of safe online learning to re-rank, where user feedback is used to improve the quality of displayed lists. Learning to rank has traditionally been studied in two settings. In the offline setting, rankers are typica
Externí odkaz:
http://arxiv.org/abs/1806.05819
Autor:
Zoghi, Masrour, Tunys, Tomas, Ghavamzadeh, Mohammad, Kveton, Branislav, Szepesvari, Csaba, Wen, Zheng
Online learning to rank is a core problem in information retrieval and machine learning. Many provably efficient algorithms have been recently proposed for this problem in specific click models. The click model is a model of how the user interacts wi
Externí odkaz:
http://arxiv.org/abs/1703.02527
Autor:
Zoghi, Masrour
This thesis presents some statistical refinements of the bandits approach presented in [11] in the situation where there is no observation noise. We give an improved bound on the cumulative regret of the samples chosen by an algorithm that is related
Externí odkaz:
http://hdl.handle.net/2429/42865
Autor:
Zoghi, Masrour
The first part of this thesis investigates the Gromov width of maximal dimensional coadjoint orbits of compact simple Lie groups. An upper bound for the Gromov width is provided for all compact simple Lie groups but only for those coadjoint orbits t
Externí odkaz:
http://hdl.handle.net/1807/26269
A version of the dueling bandit problem is addressed in which a Condorcet winner may not exist. Two algorithms are proposed that instead seek to minimize regret with respect to the Copeland winner, which, unlike the Condorcet winner, is guaranteed to
Externí odkaz:
http://arxiv.org/abs/1506.00312