$Q$ learning is a popular model free reinforcement learning method. Most of
existing works focus on analyzing $Q$ learning for finite state and action
spaces. If the state space is continuous, then the original $Q$ learning method
can not be directly used. A modification of the original $Q$ learning method
was proposed in (Shah and Xie, 2018), which estimates $Q$ values with nearest
neighbors. Such modification makes $Q$ learning suitable for continuous state
space. (Shah and Xie, 2018) shows that the convergence rate of estimated $Q$
function is $\tilde{O}(T^{-1/(d+3)})$, which is slower than the minimax lower
bound $\tilde{\Omega}(T^{-1/(d+2)})$, indicating that this method is not
efficient. This paper proposes two new $Q$ learning methods to bridge the gap
of convergence rates in (Shah and Xie, 2018), with one of them being offline,
while the other is online. Despite that we still use nearest neighbor approach
to estimate $Q$ function, the algorithms are crucially different from (Shah and
Xie, 2018). In particular, we replace the kernel nearest neighbor in
discretized region with a direct nearest neighbor approach. Consequently, our
approach significantly improves the convergence rate. Moreover, the time
complexity is also significantly improved in high dimensional state spaces. Our
analysis shows that both offline and online methods are minimax rate optimal.

该研究提出两种新的 Q 学习方法，以弥补 (Shah and Xie, 2018) 中收敛速率差异，其中一种是脱机方法，另一种是在线方法。尽管我们仍然使用最近邻方法估计 Q 函数，但算法与 (Shah and Xie, 2018) 有着关键的不同之处。特别地，在离散区域中，我们将核最近邻方法替换为直接最近邻方法。因此，我们的方法显著提高了收敛速率。此外，在高维状态空间中，时间复杂度也显著改善。我们的分析表明，脱机和在线方法都是极小极大速率最优的。

最小化差值的最优 $Q$ 学习与最近邻

Minimax Optimal $Q$ Learning with Nearest Neighbors

We present an instance-based nearest neighbor approach to entity linking. In
contrast to most prior entity retrieval systems which represent each entity
with a single vector, we build a contextualized mention-encoder that learns to
place similar mentions of the same entity closer in vector space than mentions
of different entities. This approach allows all mentions of an entity to serve
as "class prototypes" as inference involves retrieving from the full set of
labeled entity mentions in the training set and applying the nearest mention
neighbor's entity label. Our model is trained on a large multilingual corpus of
mention pairs derived from Wikipedia hyperlinks, and performs nearest neighbor
inference on an index of 700 million mentions. It is simpler to train, gives
more interpretable predictions, and outperforms all other systems on two
multilingual entity linking benchmarks.

提出了一种基于最近邻法的实体链接方法，该方法通过建立一个上下文相关的提及编码器，学习将相似的提及放置在比不同实体的提及更接近的向量空间中，从而利用所有实体的提及作为 “类原型”，通过对训练集中标记的实体的提及集合进行检索，并应用最近的提及邻居实体标签进行推理。通过对维基百科超链接的提及对的大型多语言语料库进行训练，该模型在一个包含 7 亿个提及的索引上进行最近邻推理。该方法训练简单，预测可解释性更强，并在两个多语言实体链接基准测试中优于所有其他现有方法。

MOLEMAN: 基于实体提及注释网络的实体提及链接

MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation  Network

Zero-shot learning (ZSL) aims to recognize objects of novel classes without
any training samples of specific classes, which is achieved by exploiting the
semantic information and auxiliary datasets. Recently most ZSL approaches focus
on learning visual-semantic embeddings to transfer knowledge from the auxiliary
datasets to the novel classes. However, few works study whether the semantic
information is discriminative or not for the recognition task. To tackle such
problem, we propose a coupled dictionary learning approach to align the
visual-semantic structures using the class prototypes, where the discriminative
information lying in the visual space is utilized to improve the less
discriminative semantic space. Then, zero-shot recognition can be performed in
different spaces by the simple nearest neighbor approach using the learned
class prototypes. Extensive experiments on four benchmark datasets show the
effectiveness of the proposed approach.

本文提出了一种基于耦合字典学习的零样本学习方法，该方法通过利用种类原型中的区分性信息来对齐视觉 - 语义结构，进而提高不太具有区分性的语义空间的表现，并通过简单的最近邻方法在不同空间上执行零样本识别。在四个基准数据集上的实验证明了该方法的有效性。