公平感知机器学习的文献知道很多不同的公平概念。然而，众所周知，不可能满足所有人，因为某些概念相互矛盾。在本文中，我们仔细研究了学业绩效预测（APP）系统，并尝试提出最适合这项任务的公平性。为此，我们扫描了最近的文献提出了有关使用哪种公平概念并将这些准则应用于应用程序的准则。我们的发现表明，基于App的Wysiwyg Worldview以及对人群的潜在长期改善，均等的赔率是最适合APP的概念。

In this work, we propose a new approach that combines data from multiple sensors for reliable obstacle avoidance. The sensors include two depth cameras and a LiDAR arranged so that they can capture the whole 3D area in front of the robot and a 2D slide around it. To fuse the data from these sensors, we first use an external camera as a reference to combine data from two depth cameras. A projection technique is then introduced to convert the 3D point cloud data of the cameras to its 2D correspondence. An obstacle avoidance algorithm is then developed based on the dynamic window approach. A number of experiments have been conducted to evaluate our proposed approach. The results show that the robot can effectively avoid static and dynamic obstacles of different shapes and sizes in different environments.

This study proposes an approach for establishing an optimal multihop ad-hoc network using multiple unmanned aerial vehicles (UAVs) to provide emergency communication in disaster areas. The approach includes two stages, one uses particle swarm optimization (PSO) to find optimal positions to deploy UAVs, and the other uses a behavior-based controller to navigate the UAVs to their assigned positions without colliding with obstacles in an unknown environment. Several constraints related to the UAVs' sensing and communication ranges have been imposed to ensure the applicability of the proposed approach in real-world scenarios. A number of simulation experiments with data loaded from real environments have been conducted. The results show that our proposed approach is not only successful in establishing multihop ad-hoc routes but also meets the requirements for real-time deployment of UAVs.

在这项工作中，我们建议使用分布式样本，即来自目标类别外部的未标记样本，以改善几乎没有记录的学习。具体而言，我们利用易于可用的分布样品来驱动分类器，以避免通过最大化原型到分布样品的距离，同时最大程度地减少分布样品的距离（即支持，查询数据），以避免使用分类器。。我们的方法易于实施，不可知论的是提取器，轻量级，而没有任何额外的预训练费用，并且适用于归纳和跨传输设置。对各种标准基准测试的广泛实验表明，所提出的方法始终提高具有不同架构的预审计网络的性能。

RTE is a significant problem and is a reasonably active research community. The proposed research works on the approach to this problem are pretty diverse with many different directions. For Vietnamese, the RTE problem is moderately new, but this problem plays a vital role in natural language understanding systems. Currently, methods to solve this problem based on contextual word representation learning models have given outstanding results. However, Vietnamese is a semantically rich language. Therefore, in this paper, we want to present an experiment combining semantic word representation through the SRL task with context representation of BERT relative models for the RTE problem. The experimental results give conclusions about the influence and role of semantic representation on Vietnamese in understanding natural language. The experimental results show that the semantic-aware contextual representation model has about 1% higher performance than the model that does not incorporate semantic representation. In addition, the effects on the data domain in Vietnamese are also higher than those in English. This result also shows the positive influence of SRL on RTE problem in Vietnamese.

Self-Supervised Learning (SSL) is crucial for real-world applications, especially in data-hungry domains such as healthcare and self-driving cars. In addition to a lack of labeled data, these applications also suffer from distributional shifts. Therefore, an SSL method should provide robust generalization and uncertainty estimation in the test dataset to be considered a reliable model in such high-stakes domains. However, existing approaches often focus on generalization, without evaluating the model's uncertainty. The ability to compare SSL techniques for improving these estimates is therefore critical for research on the reliability of self-supervision models. In this paper, we explore variants of SSL methods, including Jigsaw Puzzles, Context, Rotation, Geometric Transformations Prediction for vision, as well as BERT and GPT for language tasks. We train SSL in auxiliary learning for vision and pre-training for language model, then evaluate the generalization (in-out classification accuracy) and uncertainty (expected calibration error) across different distribution covariate shift datasets, including MNIST-C, CIFAR-10-C, CIFAR-10.1, and MNLI. Our goal is to create a benchmark with outputs from experiments, providing a starting point for new SSL methods in Reliable Machine Learning. All source code to reproduce results is available at https://github.com/hamanhbui/reliable_ssl_baselines.

We introduce an approach for the answer-aware question generation problem. Instead of only relying on the capability of strong pre-trained language models, we observe that the information of answers and questions can be found in some relevant sentences in the context. Based on that, we design a model which includes two modules: a selector and a generator. The selector forces the model to more focus on relevant sentences regarding an answer to provide implicit local information. The generator generates questions by implicitly combining local information from the selector and global information from the whole context encoded by the encoder. The model is trained jointly to take advantage of latent interactions between the two modules. Experimental results on two benchmark datasets show that our model is better than strong pre-trained models for the question generation task. The code is also available (shorturl.at/lV567).

Monte-Carlo Tree Search (MCTS) is an adversarial search paradigm that first found prominence with its success in the domain of computer Go. Early theoretical work established the game-theoretic soundness and convergence bounds for Upper Confidence bounds applied to Trees (UCT), the most popular instantiation of MCTS; however, there remain notable gaps in our understanding of how UCT behaves in practice. In this work, we address one such gap by considering the question of whether UCT can exhibit lookahead pathology -- a paradoxical phenomenon first observed in Minimax search where greater search effort leads to worse decision-making. We introduce a novel family of synthetic games that offer rich modeling possibilities while remaining amenable to mathematical analysis. Our theoretical and experimental results suggest that UCT is indeed susceptible to pathological behavior in a range of games drawn from this family.

Out-of-distribution (OOD) generalisation aims to build a model that can well generalise its learnt knowledge from source domains to an unseen target domain. However, current image classification models often perform poorly in the OOD setting due to statistically spurious correlations learning from model training. From causality-based perspective, we formulate the data generation process in OOD image classification using a causal graph. On this graph, we show that prediction P(Y|X) of a label Y given an image X in statistical learning is formed by both causal effect P(Y|do(X)) and spurious effects caused by confounding features (e.g., background). Since the spurious features are domain-variant, the prediction P(Y|X) becomes unstable on unseen domains. In this paper, we propose to mitigate the spurious effect of confounders using front-door adjustment. In our method, the mediator variable is hypothesized as semantic features that are essential to determine a label for an image. Inspired by capability of style transfer in image generation, we interpret the combination of the mediator variable with different generated images in the front-door formula and propose novel algorithms to estimate it. Extensive experimental results on widely used benchmark datasets verify the effectiveness of our method.

Incorporating prior knowledge of physics laws and structural properties of dynamical systems into the design of deep learning architectures has proven to be a powerful technique for improving their computational efficiency and generalization capacity. Learning accurate models of robot dynamics is critical for safe and stable control. Autonomous mobile robots, including wheeled, aerial, and underwater vehicles, can be modeled as controlled Lagrangian or Hamiltonian rigid-body systems evolving on matrix Lie groups. In this paper, we introduce a new structure-preserving deep learning architecture, the Lie group Forced Variational Integrator Network (LieFVIN), capable of learning controlled Lagrangian or Hamiltonian dynamics on Lie groups, either from position-velocity or position-only data. By design, LieFVINs preserve both the Lie group structure on which the dynamics evolve and the symplectic structure underlying the Hamiltonian or Lagrangian systems of interest. The proposed architecture learns surrogate discrete-time flow maps instead of surrogate vector fields, which allows better and faster prediction without requiring the use of a numerical integrator, neural ODE, or adjoint techniques. Furthermore, the learnt discrete-time dynamics can be combined seamlessly with computationally scalable discrete-time (optimal) control strategies.