Masrour Zoghi
I work on machine learning and its applications to information retrieval. I am particularly interested in online learning.
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On the Value of Prior in Online Learning to Rank
Branislav Kveton
The 25th International Conference on Artificial Intelligence and Statistics (2022)
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This paper addresses the cold-start problem in online learning to rank (OLTR). We show both theoretically and empirically that priors improve the quality of ranked lists presented to users interactively based on user feedback. These priors can come in the form of unbiased estimates of the relevance of the ranked items, or more practically, can be obtained from offline-learned models. Our experiments show the effectiveness of priors in improving the short-term regret of tabular OLTR algorithms, based on Thompson sampling and BayesUCB.
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Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description
Akim Kumok
Chaitanya Kamath
Charlotte Stanton
Damien Desfontaines
Evgeniy Gabrilovich
Gerardo Flores
Gregory Alexander Wellenius
Ilya Eckstein
John S. Davis
Katie Everett
Krishna Kumar Gadepalli
Rayman Huang
Shailesh Bavadekar
Thomas Ludwig Roessler
Venky Ramachandran
Yael Mayer
Arxiv.org, N/A (2020)
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This report describes the aggregation and anonymization process applied to the initial version of COVID-19 Search Trends symptoms dataset, a publicly available dataset that shows aggregated, anonymized trends in Google searches for symptoms (and some related topics). The anonymization process is designed to protect the daily search activity of every user with \varepsilon-differential privacy for \varepsilon = 1.68.
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BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback
Chang Li
Branislav Kveton
Tor Lattimore
Ilya Markov
Maarten de Rijke
35th Conference on Uncertainty in Artificial Intelligence (2019)
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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 typically learned from relevance labels created by judges. This approach has generally become standard in industrial applications of ranking, such as search. However, this approach lacks exploration and thus is limited by the information content of the offline training data. In the online setting, an algorithm can experiment with lists and learn from feedback on them in a sequential fashion. Bandit algorithms are well-suited for this setting but they tend to learn user preferences from scratch, which results in a high initial cost of exploration. This poses an additional challenge of safe exploration in ranked lists. We propose BubbleRank, a bandit algorithm for safe re-ranking that combines the strengths of both the offline and online settings. The algorithm starts with an initial base list and improves it online by gradually exchanging higher-ranked less attractive items for lower-ranked more attractive items. We prove an upper bound on the n-step regret of BubbleRank that degrades gracefully with the quality of the initial base list. Our theoretical findings are supported by extensive experiments on a large-scale real-world click dataset.
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Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks
Sebastian Bruch
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '19) (2019), pp. 1241-1244
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Learning-to-Rank is a branch of supervised machine learning that seeks to produce an ordering of a list of items such that the utility of the ranked list is maximized. Unlike most machine learning techniques, however, the objective cannot be directly optimized using gradient descent methods as it is either discontinuous or flat everywhere. As such, learning-to-rank methods often optimize a loss function that either is loosely related to or upper-bounds a ranking utility instead. A notable exception is the approximation framework originally proposed by Qin et al. that facilitates a more direct approach to ranking metric optimization. We revisit that framework almost a decade later in light of recent advances in neural networks and demonstrate its superiority empirically. Through this study, we hope to show that the ideas from that work are more relevant than ever and can lay the foundation of learning-to-rank research in the age of deep neural networks.
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