15 research outputs found
Online Product Quantization
Approximate nearest neighbor (ANN) search has achieved great success in many
tasks. However, existing popular methods for ANN search, such as hashing and
quantization methods, are designed for static databases only. They cannot
handle well the database with data distribution evolving dynamically, due to
the high computational effort for retraining the model based on the new
database. In this paper, we address the problem by developing an online product
quantization (online PQ) model and incrementally updating the quantization
codebook that accommodates to the incoming streaming data. Moreover, to further
alleviate the issue of large scale computation for the online PQ update, we
design two budget constraints for the model to update partial PQ codebook
instead of all. We derive a loss bound which guarantees the performance of our
online PQ model. Furthermore, we develop an online PQ model over a sliding
window with both data insertion and deletion supported, to reflect the
real-time behaviour of the data. The experiments demonstrate that our online PQ
model is both time-efficient and effective for ANN search in dynamic large
scale databases compared with baseline methods and the idea of partial PQ
codebook update further reduces the update cost.Comment: To appear in IEEE Transactions on Knowledge and Data Engineering
(DOI: 10.1109/TKDE.2018.2817526
Randomized Exploration in Generalized Linear Bandits
We study two randomized algorithms for generalized linear bandits, GLM-TSL
and GLM-FPL. GLM-TSL samples a generalized linear model (GLM) from the Laplace
approximation to the posterior distribution. GLM-FPL fits a GLM to a randomly
perturbed history of past rewards. We prove
bounds on the -round regret of GLM-TSL and GLM-FPL, where is the number
of features and is the number of arms. The regret bound of GLM-TSL improves
upon prior work and the regret bound of GLM-FPL is the first of its kind. We
apply both GLM-TSL and GLM-FPL to logistic and neural network bandits, and show
that they perform well empirically. In more complex models, GLM-FPL is
significantly faster. Our results showcase the role of randomization, beyond
sampling from the posterior, in exploration