Tuvalu's foreign minister, Simon Kofe, told the COP27 climate summit yesterday that Tuvalu would look to the metaverse to preserve its culture and history. With global temperatures expected to rise as much as 2.8 degrees Celsius by the end of the century, the Pacific island nation is particularly vulnerable to rising sea levels. At last year's COP26 summit, Kofe addressed the conference while standing knee-deep in seawater to highlight the climate change threat. Climate scientists anticipate the entire country will be underwater by the end of the 21st century. Addressing the climate summit, Kofe said: "As our land disappears, we have no choice but to become the world's first digital nation. Our land, our ocean, our culture are the most precious assets of our people. And to keep them safe from harm, no matter what happens in the physical world, we'll move them to the cloud."

In 2015, I visited Fiji, Kiribati, and Tuvalu, which had just been hit by a cyclone. There, I learned a slogan -- "1.5 to stay alive" -- which refers to the 1.5 degrees Celsius (2.7 degrees Fahrenheit) threshold for global warming that, in theory, would avoid disastrous consequences. People living on the Pacific islands are well aware of the grave threat to humanity posed by climate change. Six months later, I met these new comrades again at climate negotiations in Paris. While speaking at an event, I referred to "1.5 to stay alive".

Relational triple extraction is challenging for its difficulty in capturing rich correlations between entities and relations. Existing works suffer from 1) heterogeneous representations of entities and relations, and 2) heterogeneous modeling of entity-entity interactions and entity-relation interactions. Therefore, the rich correlations are not fully exploited by existing works. In this paper, we propose UniRel to address these challenges. Specifically, we unify the representations of entities and relations by jointly encoding them within a concatenated natural language sequence, and unify the modeling of interactions with a proposed Interaction Map, which is built upon the off-the-shelf self-attention mechanism within any Transformer block. With comprehensive experiments on two popular relational triple extraction datasets, we demonstrate that UniRel is more effective and computationally efficient. The source code is available at https://github.com/wtangdev/UniRel.

Point clouds are a widely available and canonical data modality which convey the 3D geometry of a scene. Despite significant progress in classification and segmentation from point clouds, policy learning from such a modality remains challenging, and most prior works in imitation learning focus on learning policies from images or state information. In this paper, we propose a novel framework for learning policies from point clouds for robotic manipulation with tools. We use a novel neural network, ToolFlowNet, which predicts dense per-point flow on the tool that the robot controls, and then uses the flow to derive the transformation that the robot should execute. We apply this framework to imitation learning of challenging deformable object manipulation tasks with continuous movement of tools, including scooping and pouring, and demonstrate significantly improved performance over baselines which do not use flow. We perform 50 physical scooping experiments with ToolFlowNet and attain 82% scooping success. See https://tinyurl.com/toolflownet for supplementary material.

The advancement of deep learning has led to the development of neural decoders for low latency communications. However, neural decoders can be very complex which can lead to increased computation and latency. We consider iterative pruning approaches (such as the lottery ticket hypothesis algorithm) to prune weights in neural decoders. Decoders with fewer number of weights can have lower latency and lower complexity while retaining the accuracy of the original model. This will make neural decoders more suitable for mobile and other edge devices with limited computational power. We also propose semi-soft decision decoding for neural decoders which can be used to improve the bit error rate performance of the pruned network.

Bayesian optimization (BO) is a class of popular methods for expensive black-box optimization, and has been widely applied to many scenarios. However, BO suffers from the curse of dimensionality, and scaling it to high-dimensional problems is still a challenge. In this paper, we propose a variable selection method MCTS-VS based on Monte Carlo tree search (MCTS), to iteratively select and optimize a subset of variables. That is, MCTS-VS constructs a low-dimensional subspace via MCTS and optimizes in the subspace with any BO algorithm. We give a theoretical analysis of the general variable selection method to reveal how it can work. Experiments on high-dimensional synthetic functions and real-world problems (i.e., NAS-bench problems and MuJoCo locomotion tasks) show that MCTS-VS equipped with a proper BO optimizer can achieve state-of-the-art performance.

Amazon, Nimble, Plus One, Waymo, and Zoox use remote human supervision of robot fleets in applications ranging from self-driving taxis to automated warehouse fulfillment [1, 2, 3, 4, 5]. These robots intermittently cede control during task execution to remote human supervisors for corrective interventions. The interventions take place either during learning, when they are used to improve the robot policy, or during execution, when the policy is no longer updated but robots can still request human assistance when needed to improve reliability. In the continual learning setting, these occur simultaneously: the robot policy has been deployed but continues to be updated indefinitely with additional intervention data. Furthermore, any individual robot can share its intervention data with the rest of the fleet. As opposed to robot swarms that must coordinate with each other to achieve a common objective, a robot fleet is a set of independent robots simultaneously executing the same control policy in parallel environments. We refer to the setting of a robot fleet learning via interactive requests for human supervision (see Figure 1) as Interactive Fleet Learning (IFL). Of central importance in IFL is the supervisor allocation problem: how should limited human supervision be allocated to robots in a manner that maximizes the throughput of the fleet?

In recent years, the machine learning research landscape has been reshaped by the growth of Chinese AI research. China now consistently stands as the second-largest country in terms of total publications at NeurIPS, after the United States. In 2020, papers from Chinese institutions represented 13.6% of all NeurIPS publications [6]. The following year, this share increased to 17.5%, representing a relative increase of 28.7% [13].

We study a general multi-dueling bandit problem, where an agent compares multiple options simultaneously and aims to minimize the regret due to selecting suboptimal arms. This setting generalizes the traditional two-dueling bandit problem and finds many real-world applications involving subjective feedback on multiple options. We start with the two-dueling bandit setting and propose two efficient algorithms, DoublerBAI and MultiSBM-Feedback. DoublerBAI provides a generic schema for translating known results on best arm identification algorithms to the dueling bandit problem, and achieves a regret bound of $O(\ln T)$. MultiSBM-Feedback not only has an optimal $O(\ln T)$ regret, but also reduces the constant factor by almost a half compared to benchmark results. Then, we consider the general multi-dueling case and develop an efficient algorithm MultiRUCB. Using a novel finite-time regret analysis for the general multi-dueling bandit problem, we show that MultiRUCB also achieves an $O(\ln T)$ regret bound and the bound tightens as the capacity of the comparison set increases. Based on both synthetic and real-world datasets, we empirically demonstrate that our algorithms outperform existing algorithms.

Systems engineering, in particular in the automotive domain, needs to cope with the massively increasing numbers of requirements that arise during the development process. To guarantee a high product quality and make sure that functional safety standards such as ISO26262 are fulfilled, the exploitation of potentials of model-driven systems engineering in the form of automatic analyses, consistency checks, and tracing mechanisms is indispensable. However, the language in which requirements are written, and the tools needed to operate on them, are highly individual and require domain-specific tailoring. This hinders automated processing of requirements as well as the linking of requirements to models. Introducing formal requirement notations in existing projects leads to the challenge of translating masses of requirements and process changes on the one hand and to the necessity of the corresponding training for the requirements engineers. In this paper, based on the analysis of an open-source set of automotive requirements, we derive domain-specific language constructs helping us to avoid ambiguities in requirements and increase the level of formality. The main contribution is the adoption and evaluation of few-shot learning with large pretrained language models for the automated translation of informal requirements to structured languages such as a requirement DSL. We show that support sets of less than ten translation examples can suffice to few-shot train a language model to incorporate keywords and implement syntactic rules into informal natural language requirements.