2019/05/13 by Daniyar Chumbalov, Chumbalov, Daniyar, Lucas Maystre +3
Computer Science · #Advanced Image and Video Retrieval Techniques #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms
paper · pdf · doi:10.48550/arxiv.1905.05049
openalex publication_date 2019/05/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider the problem of finding a target object t using pairwise comparisons, by asking an oracle questions of the form "Which object from the pair (i,j) is more similar to t?". Objects live in a space of latent features, from which the oracle generates noisy answers. First, we consider the \em non-blind setting where these features are accessible. We propose a new Bayesian comparison-based search algorithm with noisy answers; it has low computational complexity yet is efficient in the number of queries. We provide theoretical guarantees, deriving the form of the optimal query and proving almost sure convergence to the target t. Second, we consider the blind setting, where the object features are hidden from the search algorithm. In this setting, we combine our search method and a new distributional triplet embedding algorithm into one scalable learning framework called Learn2Search. We show that the query complexity of our approach on two real-world datasets is on par with the non-blind setting, which is not achievable using any of the current state-of-the-art embedding methods. Finally, we demonstrate the efficacy of our framework by conducting an experiment with users searching for movie actors.