Monte Carlo Tree Search (MCTS) algorithms such as AlphaGo and MuZero have
achieved superhuman performance in many challenging tasks. However, the
computational complexity of MCTS-based algorithms is influenced by the size of
the search space. To address this issue, we propose a novel probability tree
state abstraction (PTSA) algorithm to improve the search efficiency of MCTS. A
general tree state abstraction with path transitivity is defined. In addition,
the probability tree state abstraction is proposed for fewer mistakes during
the aggregation step. Furthermore, the theoretical guarantees of the
transitivity and aggregation error bound are justified. To evaluate the
effectiveness of the PTSA algorithm, we integrate it with state-of-the-art
MCTS-based algorithms, such as Sampled MuZero and Gumbel MuZero. Experimental
results on different tasks demonstrate that our method can accelerate the
training process of state-of-the-art algorithms with 10%-45% search space
reduction.

通过引入概率树状态抽象（PTSA）算法，改善了常见的蒙特卡罗树搜索（MCTS）算法的搜索效率，并且与最先进的算法结合使用，降低了 10% 至 45% 的搜索空间。

用概率树状态抽象加速蒙特卡罗树搜索

Accelerating Monte Carlo Tree Search with Probability Tree State  Abstraction

Feature construction can contribute to comprehensibility and performance of
machine learning models. Unfortunately, it usually requires exhaustive search
in the attribute space or time-consuming human involvement to generate
meaningful features. We propose a novel heuristic approach for reducing the
search space based on aggregation of instance-based explanations of predictive
models. The proposed Explainable Feature Construction (EFC) methodology
identifies groups of co-occurring attributes exposed by popular explanation
methods, such as IME and SHAP. We empirically show that reducing the search to
these groups significantly reduces the time of feature construction using
logical, relational, Cartesian, numerical, and threshold num-of-N and X-of-N
constructive operators. An analysis on 10 transparent synthetic datasets shows
that EFC effectively identifies informative groups of attributes and constructs
relevant features. Using 30 real-world classification datasets, we show
significant improvements in classification accuracy for several classifiers and
demonstrate the feasibility of the proposed feature construction even for large
datasets. Finally, EFC generated interpretable features on a real-world problem
from the financial industry, which were confirmed by a domain expert.

提出了一种可解释的特征构造 (EFC) 方法，该方法通过聚合实例解释的预测模型，识别了 IME 和 SHAP 等流行解释方法所揭示的共同属性组，并使用逻辑，关系，笛卡尔，数字，阈值 num-of-N 和 X-of-N 构造算子，将搜索空间减少到这些共同属性组中，最终在 30 个真实世界分类数据集上证明了 EFC 在提高分类准确性和构造可解释性特征方面的有效性。

使用单个预测解释的特征构建

Feature construction using explanations of individual predictions

This paper presents F-Siamese Tracker, a novel approach for single object
tracking prominently characterized by more robustly integrating 2D and 3D
information to reduce redundant search space. A main challenge in 3D single
object tracking is how to reduce search space for generating appropriate 3D
candidates. Instead of solely relying on 3D proposals, firstly, our method
leverages the Siamese network applied on RGB images to produce 2D region
proposals which are then extruded into 3D viewing frustums. Besides, we perform
an online accuracy validation on the 3D frustum to generate refined point cloud
searching space, which can be embedded directly into the existing 3D tracking
backbone. For efficiency, our approach gains better performance with fewer
candidates by reducing search space. In addition, benefited from introducing
the online accuracy validation, for occasional cases with strong occlusions or
very sparse points, our approach can still achieve high precision, even when
the 2D Siamese tracker loses the target. This approach allows us to set a new
state-of-the-art in 3D single object tracking by a significant margin on a
sparse outdoor dataset (KITTI tracking). Moreover, experiments on 2D single
object tracking show that our framework boosts 2D tracking performance as well.

本文介绍了一种新型的 F-Siamese Tracker 方法，它通过更加稳健地整合 2D 和 3D 信息来减少冗余搜索空间，从而在 KITTI 跟踪数据集上显著提高了 3D 单目标跟踪和 2D 单目标跟踪性能。