Combinatorial optimization problems have aroused extensive research interests
due to its huge application potential. In practice, there are highly redundant
patterns and characteristics during solving the combinatorial optimization
problem, which can be captured by machine learning models. Thus, the 2021
NeurIPS Machine Learning for Combinatorial Optimization (ML4CO) competition is
proposed with the goal of improving state-of-the-art combinatorial optimization
solvers by replacing key heuristic components with machine learning techniques.
This work presents our solution and insights gained by team qqy in the dual
task of the competition. Our solution is a highly efficient imitation learning
framework for performance improvement of Branch and Bound (B&B), named Yordle.
It employs a hybrid sampling method and an efficient data selection method,
which not only accelerates the model training but also improves the decision
quality during branching variable selection. In our experiments, Yordle greatly
outperforms the baseline algorithm adopted by the competition while requiring
significantly less time and amounts of data to train the decision model.
Specifically, we use only 1/4 of the amount of data compared to that required
for the baseline algorithm, to achieve around 50% higher score than baseline
algorithm. The proposed framework Yordle won the championship of the student
leaderboard.

本文介绍了在 2021 年 NeurIPS 机器学习求解组合优化问题比赛中，qqy 团队的胜出方案 Yordle。该方案是一种高效的模仿学习框架，用于改进分支定界算法的性能，并通过数据采样和筛选提高准确率。在实验中，Yordle 的性能显著优于竞赛所采用的基准算法，且需要更少的数据和时间来训练决策模型。

Yordle: 分支定界高效模仿学习

Yordle: An Efficient Imitation Learning for Branch and Bound

Non-autoregressive translation models (NAT) have achieved impressive
inference speedup. A potential issue of the existing NAT algorithms, however,
is that the decoding is conducted in parallel, without directly considering
previous context. In this paper, we propose an imitation learning framework for
non-autoregressive machine translation, which still enjoys the fast translation
speed but gives comparable translation performance compared to its
auto-regressive counterpart. We conduct experiments on the IWSLT16, WMT14 and
WMT16 datasets. Our proposed model achieves a significant speedup over the
autoregressive models, while keeping the translation quality comparable to the
autoregressive models. By sampling sentence length in parallel at inference
time, we achieve the performance of 31.85 BLEU on WMT16 Ro$\rightarrow$En and
30.68 BLEU on IWSLT16 En$\rightarrow$De.

本文提出了基于仿真学习的非自回归机器翻译框架，该框架仍然具有快速翻译速度但与其自回归对应方法相比具有可比较的翻译性能。通过对 IWSLT16、WMT14 和 WMT16 数据集进行实验，发现我们提出的模型在保持翻译质量可比的同时，比自回归模型有更快的翻译速度。在推断时并行采样句子长度，WMT16 Ro→En 的 BLEU 值达到 31.85，而 IWSLT16 En→De 的 BLEU 值为 30.68。

非自回归神经机器翻译的模仿学习

Imitation Learning for Non-Autoregressive Neural Machine Translation

The budgeted information gathering problem - where a robot with a fixed fuel
budget is required to maximize the amount of information gathered from the
world - appears in practice across a wide range of applications in autonomous
exploration and inspection with mobile robots. Although there is an extensive
amount of prior work investigating effective approximations of the problem,
these methods do not address the fact that their performance is heavily
dependent on distribution of objects in the world. In this paper, we attempt to
address this issue by proposing a novel data-driven imitation learning
framework.
We present an efficient algorithm, EXPLORE, that trains a policy on the
target distribution to imitate a clairvoyant oracle - an oracle that has full
information about the world and computes non-myopic solutions to maximize
information gathered. We validate the approach on a spectrum of results on a
number of 2D and 3D exploration problems that demonstrates the ability of
EXPLORE to adapt to different object distributions. Additionally, our analysis
provides theoretical insight into the behavior of EXPLORE. Our approach paves
the way forward for efficiently applying data-driven methods to the domain of
information gathering.

本篇论文研究的是一个应用于移动机器人自主探索和检查的问题，即在燃料预算有限的情况下最大化从世界中获取的信息量的问题。作者提出了一种新颖的数据驱动的模仿学习框架，并通过 EXPLORE 算法来训练一个策略以模仿一个有完整信息并计算非简约解来最大化获取信息的先知，验证了这一方法在不同对象分布上的适应能力。