It is a longstanding problem that Q-learning suffers from the overestimation bias. This issue originates from the fact that Q-learning uses the expectation of maximum Q-value to approximate the maximum expected Q-value. A number of algorithms, such as ...

Asynchronous stochastic gradient descent (ASGD) is a computationally efficient algorithm, which speeds up deep learning training and plays an important role in distributed deep learning. However, ASGD suffers from the stale gradient problem, i.e.,...

Reposting plays an essential role in boosting visibility on online social networks (OSNs). In this paper, we study the problem of designing “reposting service” in an OSN to incentivize “transactions” between requesters (users who seek to enhance ...

A variety of algorithms have been proposed to address the long-standing overestimation bias problem of Q-learning. Reducing this overestimation bias may lead to an underestimation bias, such as double Q-learning. However, it is still unclear how ...

Traumatic experiences have the potential to give rise to posttraumatic stress disorder (PTSD), a debilitating psychiatric condition associated with impairments in both social and occupational functioning. There has been great interest in utilizing ...$\mathrm{}$

Negative sampling plays a key role in implicit feedback collaborative filtering. It draws high-quality negative samples from a large number of uninteracted samples. Existing methods primarily focus on hard negative samples, while overlooking the ...

Conversational contextual bandit is one of the notable variants of contextual bandit and it is shown to have superior performance in recommendation applications. The core idea of conversational contextual bandits utilizing is conversational ...

A variety of algorithms has been proposed to mitigate the overestimation bias of Q-learning. These algorithms reduce the estimation of maximum Q-value, i.e., homogeneous update. As a result, some of these algorithms such as Double Q-learning ...

Off-policy learning, referring to the procedure of policy optimization with access only to logged feedback data, has shown importance in various real-world applications, such as search engines and recommender systems. While the ground-truth logging ...

While personalization increases the utility of item recommendation, it also suffers from the issue of popularity bias. However, previous methods emphasize adopting supervised learning models to relieve popularity bias in the static recommendation, ...

In recent years, exploring integrated heterogeneous features architecture has become one of the hot spots in 3D point cloud understanding. However, the efficacy of end-to-end training in enhancing the precision of multi-view fusion for point cloud ...$$$$$$$$

lidar enables fast reconstruction of the real world using high-precision point cloud maps. It usually requires the pose information (also called trajectory) of point clouds obtained by lidar at different times so that all scans are unified in the ...

Contrastive learning has been widely applied in sequential recommendation to improve the recommendation performance. Existing contrastive learning methods focus on adjusting the views number of positive and negative samples to enhance the item ...

User abandonment behaviors are quite common in recommendation applications such as online shopping recommendation and news recommendation. To maximize its total “reward” under the risk of user abandonment, the online platform needs to carefully ...

• Model the user abandonment behavior in recommendation systems via Markov decision processes.
• Transfer other similar users’ information to optimize future decisions.
• An algorithmic framework with two components and theoretical ...

Posttraumatic stress disorder (PTSD) is a mental health condition that may develop following exposure to trauma, with diverse and complex longitudinal trajectories of symptoms during the days to months after a traumatic event. To supplement ...

Online rating system serves as an indispensable building block for many web applications. Previous studies showed that due to assimilate-contrast effects, historical ratings could significantly distort users’ ratings, leading to low accuracy of ...

Registration is a basic yet crucial task in point cloud processing. In correspondence-based point cloud registration, matching correspondences by point feature techniques may lead to an extremely high outlier (false correspondence) ratio. Current outlier ...

Multi-armed bandits (MAB) are widely applied to optimize networking applications such as crowdsensing and mobile edge computing. Additional feedbacks (or partial feedbacks) on some arms are usually possible to be collected in many networking ...

Offline logged data is quite common in many web applications such as recommendation, Internet advertising, etc., which offers great potentials to improve online decision making. It is a non-trivial task to utilize offline logged data for online ...

Motivated by distributed selection problems, we formulate a new variant of multi-player multi-armed bandit (MAB) model, which captures stochastic arrival of requests to each arm and the policy of allocating requests to players. The challenge is ...