25,843 research outputs found
Near-Optimal Target Learning With Stochastic Binary Signals
We study learning in a noisy bisection model: specifically, Bayesian
algorithms to learn a target value V given access only to noisy realizations of
whether V is less than or greater than a threshold theta. At step t = 0, 1, 2,
..., the learner sets threshold theta t and observes a noisy realization of
sign(V - theta t). After T steps, the goal is to output an estimate V^ which is
within an eta-tolerance of V . This problem has been studied, predominantly in
environments with a fixed error probability q < 1/2 for the noisy realization
of sign(V - theta t). In practice, it is often the case that q can approach
1/2, especially as theta -> V, and there is little known when this happens. We
give a pseudo-Bayesian algorithm which provably converges to V. When the true
prior matches our algorithm's Gaussian prior, we show near-optimal expected
performance. Our methods extend to the general multiple-threshold setting where
the observation noisily indicates which of k >= 2 regions V belongs to
Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition
Product reviews and ratings on e-commerce websites provide customers with
detailed insights about various aspects of the product such as quality,
usefulness, etc. Since they influence customers' buying decisions, product
reviews have become a fertile ground for abuse by sellers (colluding with
reviewers) to promote their own products or to tarnish the reputation of
competitor's products. In this paper, our focus is on detecting such abusive
entities (both sellers and reviewers) by applying tensor decomposition on the
product reviews data. While tensor decomposition is mostly unsupervised, we
formulate our problem as a semi-supervised binary multi-target tensor
decomposition, to take advantage of currently known abusive entities. We
empirically show that our multi-target semi-supervised model achieves higher
precision and recall in detecting abusive entities as compared to unsupervised
techniques. Finally, we show that our proposed stochastic partial natural
gradient inference for our model empirically achieves faster convergence than
stochastic gradient and Online-EM with sufficient statistics.Comment: Accepted to the 25th ACM SIGKDD Conference on Knowledge Discovery and
Data Mining, 2019. Contains supplementary material. arXiv admin note: text
overlap with arXiv:1804.0383
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Towards Informed Exploration for Deep Reinforcement Learning
In this thesis, we discuss various techniques for improving exploration for deep reinforcement learning. We begin with a brief review of reinforcement learning (RL) and the fundamental v.s. exploitation trade-off. Then we review how deep RL has improved upon classical and summarize six categories of the latest exploration methods for deep RL, in the order increasing usage of prior information. We then explore representative works in three categories discuss their strengths and weaknesses. The first category, represented by Soft Q-learning, uses regularization to encourage exploration. The second category, represented by count-based via hashing, maps states to hash codes for counting and assigns higher exploration to less-encountered states. The third category utilizes hierarchy and is represented by modular architecture for RL agents to play StarCraft II. Finally, we conclude that exploration by prior knowledge is a promising research direction and suggest topics of potentially impact
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