19 research outputs found
<News> Bush Fire Control Using Arbors in Green Corridor Project at Bossou
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Safe Collaborative Filtering
Excellent tail performance is crucial for modern machine learning tasks, such
as algorithmic fairness, class imbalance, and risk-sensitive decision making,
as it ensures the effective handling of challenging samples within a dataset.
Tail performance is also a vital determinant of success for personalised
recommender systems to reduce the risk of losing users with low satisfaction.
This study introduces a "safe" collaborative filtering method that prioritises
recommendation quality for less-satisfied users rather than focusing on the
average performance. Our approach minimises the conditional value at risk
(CVaR), which represents the average risk over the tails of users' loss. To
overcome computational challenges for web-scale recommender systems, we develop
a robust yet practical algorithm that extends the most scalable method,
implicit alternating least squares (iALS). Empirical evaluation on real-world
datasets demonstrates the excellent tail performance of our approach while
maintaining competitive computational efficiency
Why Guided Dialog Policy Learning performs well? Understanding the role of adversarial learning and its alternative
Dialog policies, which determine a system's action based on the current state
at each dialog turn, are crucial to the success of the dialog. In recent years,
reinforcement learning (RL) has emerged as a promising option for dialog policy
learning (DPL). In RL-based DPL, dialog policies are updated according to
rewards. The manual construction of fine-grained rewards, such as
state-action-based ones, to effectively guide the dialog policy is challenging
in multi-domain task-oriented dialog scenarios with numerous state-action pair
combinations. One way to estimate rewards from collected data is to train the
reward estimator and dialog policy simultaneously using adversarial learning
(AL). Although this method has demonstrated superior performance
experimentally, it is fraught with the inherent problems of AL, such as mode
collapse. This paper first identifies the role of AL in DPL through detailed
analyses of the objective functions of dialog policy and reward estimator.
Next, based on these analyses, we propose a method that eliminates AL from
reward estimation and DPL while retaining its advantages. We evaluate our
method using MultiWOZ, a multi-domain task-oriented dialog corpus
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博士(Doctor)工学(Engineering)奈良先端科学技術大学院大学博第742号甲第742号博士(工学)奈良先端科学技術大学院大
Mixing-Time Regularized Policy Gradient
Policy gradient reinforcement learning (PGRL) has been receiving substantial attention as a mean for seeking stochastic policies that maximize cumulative reward. However, the learning speed of PGRL is known to decrease substantially when PGRL explores the policies that give the Markov chains having long mixing time. We study a new approach of regularizing how the PGRL explores the policies by the use of the hitting time of the Markov chains. The hitting time gives an upper bound on the mixing time, and the proposed approach improves the learning efficiency by keeping the mixing time of the Markov chains short. In particular, we propose a method of temporal-difference learning for estimating the gradient of the hitting time. Numerical experiments show that the proposed method outperforms conventional methods of PGRL