Masrour Zoghi
I work on machine learning and its applications to information retrieval. I am particularly interested in online learning.
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Density-based User Representation through Gaussian Process Regression for Multi-interest Personalized Retrieval
Haolun Wu
Xue Liu
Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS-24), Vancouver (2024)
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Personalized recommendation systems are increasingly essential in our information-rich society, aiding users in navigating the expansive online realm. However, accurately modeling the diverse and dynamic interests of the users remains a formidable challenge. Existing user modeling methods, like Single-point User Representation (SUR) and Multi-point User Representation (MUR), have their limitations in terms of accuracy, diversity, computation cost, and adaptability. To overcome these challenges, we introduce a novel model, the Density-based User Representation (DUR), leveraging Gaussian Process Regression (GPR), which has not been extensively explored in multi-interest recommendation and retrieval. Our approach inherently captures user interest dynamics without manual tuning, provides uncertainty-awareness, and is more efficient than point-based representation methods. This paper outlines the development and implementation of GPR4DUR, details its evaluation protocols, and presents extensive analysis demonstrating its effectiveness and efficiency. Experiments on real-world offline datasets confirm our method’s adaptability and efficiency. Further online experiments simulating user behavior illuminate the benefits of our method in the exploration-exploitation trade-off by effectively utilizing model uncertainty.
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Overcoming Prior Misspecification in Online Learning to Rank
Mohammadjavad Azizi
International Conference on Artificial Intelligence and Statistics, PMLR (2023), pp. 594-614
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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 the true prior. In this paper, we propose and analyze adaptive algorithms that address this issue and additionally extend these results to the linear and generalized linear models. We also consider scalar relevance feedback on top of click feedback. Moreover, we demonstrate the efficacy of our algorithms using both synthetic and real-world experiments.
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Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation
Li Ding
NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World (2023)
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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 using pertinent evaluation methods, and then adapt the model based on the optimal updates and previous progress history. EV3 offers substantial flexibility without imposing stringent constraints like differentiability on the key objectives relevant to the tasks of interest, allowing for exploratory updates with intentionally-biased gradients and through a diversity of losses and optimizers.
Additionally, the assessment phase provides reliable safety controls to ensure robust generalization, and can dynamically prioritize tasks in scenarios with multiple objectives. With inspiration drawn from evolutionary algorithms, meta-learning, and neural architecture search, we investigate an application of EV3 to knowledge distillation. Our experimental results illustrate EV3’s capability to safely explore the modeling landscape, while hinting at its potential applicability across numerous domains due to its inherent flexibility and adaptability. Finally, we provide a JAX implementation of EV3, along with source code for experiments, available
at: https://github.com/google-research/google-research/tree/master/ev3.
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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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