We develop optimal algorithms for learning undirected Gaussian trees and directed Gaussian polytrees from data. We consider both problems of distribution learning (i.e. in KL distance) and structure learning (i.e. exact recovery). The first approach is based on the Chow-Liu algorithm, and learns an optimal tree-structured distribution efficiently. The second approach is a modification of the PC algorithm for polytrees that uses partial correlation as a conditional independence tester for constraint-based structure learning. We derive explicit finite-sample guarantees for both approaches, and show that both approaches are optimal by deriving matching lower bounds. Additionally, we conduct numerical experiments to compare the performance of various algorithms, providing further insights and empirical evidence.

In academia, a growing body of research investigates the role of human values in designing ethical AI [12, 31, 74, 90]. Indeed, one of our leading AI research luminaries, Stuart Russell, believes the overarching goal of AI should change from "intelligence" to "intelligence provably aligned with human values" [74]. This call to arms gave birth to the value alignment problem. This challenge of engineering values into AI in response to the value alignment problem has resulted in a range of research areas: how human values can be learnt [43, 44, 45, 91]; how individual values can be aggregated to the level of groups [41]; how arguments that explicitly reference values can be made [7]; how decision making can be value-driven [14, 17, 21]; how online institutions can ensure value-aligned behaviours in hybrid communities [56, 57]; and how norms are selected or synthesised to maximise value-alignment [55, 80, 83]. Yet despite these efforts, no formal model of values exists today that provides a concrete foundational platform from which data structures and algorithms can be designed to build AI architectures that address the valuealignment problem. In response, we propose such a model built on the following guiding principles: 1) we employ a formal language to be precise about modelling values and related concepts [23, 47]; 2) we construct the formal components of this model to provide the foundations for the data structures and algorithmic design that will enable value-based reasoning; 3) we design the model to be agnostic on any specific implementation of values, though we do provide example implementation scenarios to illustrate the model's ubiquity and practical applicability; 4) we set out the model to subsume and relate to established concepts in AI research as much as possible; 5) we provide illustrative examples of building data structures and algorithms enabling value-based reasoning taken from our ongoing research applied to real-world use cases; 6) we ensure the model draws upon the wealth of work from within social psychology and explicitly demonstrate the grounding of our model within this research; and

Standard context-aware neural machine translation (NMT) typically relies on parallel document-level data, exploiting both source and target contexts. Concatenation-based approaches in particular, still a strong baseline for document-level NMT, prepend source and/or target context sentences to the sentences to be translated, with model variants that exploit equal amounts of source and target data on each side achieving state-of-the-art results. In this work, we investigate whether target data should be further promoted within standard concatenation-based approaches, as most document-level phenomena rely on information that is present on the target language side. We evaluate novel concatenation-based variants where the target context is prepended to the source language, either in isolation or in combination with the source context. Experimental results in English-Russian and Basque-Spanish show that including target context in the source leads to large improvements on target language phenomena. On source-dependent phenomena, using only target language context in the source achieves parity with state-of-the-art concatenation approaches, or slightly underperforms, whereas combining source and target context on the source side leads to significant gains across the board.

Multi-objective reinforcement learning (MORL) algorithms extend conventional reinforcement learning (RL) to the more general case of problems with multiple, conflicting objectives, represented by vector-valued rewards. Widely-used scalar RL methods such as Q-learning can be modified to handle multiple objectives by (1) learning vector-valued value functions, and (2) performing action selection using a scalarisation or ordering operator which reflects the user's utility with respect to the different objectives. However, as we demonstrate here, if the user's utility function maps widely varying vector-values to similar levels of utility, this can lead to interference in the value-function learned by the agent, leading to convergence to sub-optimal policies. This will be most prevalent in stochastic environments when optimising for the Expected Scalarised Return criterion, but we present a simple example showing that interference can also arise in deterministic environments. We demonstrate empirically that avoiding the use of random tie-breaking when identifying greedy actions can ameliorate, but not fully overcome, the problems caused by value function interference.

Art appreciation is vital in nurturing critical thinking and emotional intelligence among learners. However, traditional art appreciation education has often been hindered by limited access to art resources, especially for disadvantaged students, and an imbalanced emphasis on STEM subjects in mainstream education. In response to these challenges, recent technological advancements have paved the way for innovative solutions. This study explores the application of multi-modal large language models (MLLMs) in art appreciation education, focusing on developing LLaVA-Docent, a model that leverages these advancements. Our approach involved a comprehensive literature review and consultations with experts in the field, leading to developing a robust data framework. Utilizing this framework, we generated a virtual dialogue dataset that was leveraged by GPT-4. This dataset was instrumental in training the MLLM, named LLaVA-Docent. Six researchers conducted quantitative and qualitative evaluations of LLaVA-Docent to assess its effectiveness, benchmarking it against the GPT-4 model in a few-shot setting. The evaluation process revealed distinct strengths and weaknesses of the LLaVA-Docent model. Our findings highlight the efficacy of LLaVA-Docent in enhancing the accessibility and engagement of art appreciation education. By harnessing the potential of MLLMs, this study makes a significant contribution to the field of art education, proposing a novel methodology that reimagines the way art appreciation is taught and experienced.

The widespread availability of Large Language Models (LLMs) within Integrated Development Environments (IDEs) has led to their speedy adoption. Conversational interactions with LLMs enable programmers to obtain natural language explanations for various software development tasks. However, LLMs often leap to action without sufficient context, giving rise to implicit assumptions and inaccurate responses. Conversations between developers and LLMs are primarily structured as question-answer pairs, where the developer is responsible for asking the the right questions and sustaining conversations across multiple turns. In this paper, we draw inspiration from interaction patterns and conversation analysis -- to design Robin, an enhanced conversational AI-assistant for debugging. Through a within-subjects user study with 12 industry professionals, we find that equipping the LLM to -- (1) leverage the insert expansion interaction pattern, (2) facilitate turn-taking, and (3) utilize debugging workflows -- leads to lowered conversation barriers, effective fault localization, and 5x improvement in bug resolution rates.

One promising methods have proven successful across a range strategy in this context is to use data from one modality, e.g., of domains, partly due to their ability to generate text data, as a supervision signal in the interpretation of another, meaningful shared representations of complex e.g., image data (Mori et al., 1999; Wang et al., 2009; phenomena. To enhance the depth of analysis Ramanathan et al., 2013; He & Peng, 2017; Radford et al., and understanding of these acquired representations, 2021). The primary approach for achieving this is known we introduce a unified causal model specifically as multimodal contrastive representation learning, which designed for multimodal data. By examining focuses on optimizing a symmetric contrastive loss (Zhang this model, we show that multimodal contrastive et al., 2022; Radford et al., 2021), e.g., a symmetric adaptation representation learning excels at identifying latent of the standard contrastive loss (Wu et al., 2018; Tian coupled variables within the proposed unified et al., 2020; He et al., 2020; Chen et al., 2020). The learned model, up to linear or permutation transformations representations, guided by the symmetric contrastive loss, resulting from different assumptions. Our have been applied in a variety of applications, including findings illuminate the potential of pre-trained zero/few-shot learning (Radford et al., 2021; Zhou et al., multimodal models, e.g., CLIP, in learning disentangled 2022a), domain generalization (Zhou et al., 2022a;b), and representations through a surprisingly robustness to adversarial examples (Ban & Dong, 2022).

Instruction tuning on a mixture of tasks has improved zero-shot capabilities in natural language processing (NLP). Nevertheless, existing methods often learn features that exhibit correlations between instruction-formatted samples and target labels, rather than causal relationships. Termed as ``spurious correlation'' in statistics, such a correlation may change drastically in a new task, making the effect from the learned features to be misleading. To this end, we develop a meta Structural Causal Model (meta-SCM) to integrate different NLP tasks under a single causal structure of the data. Specifically, the meta-SCM introduces multiple latent factors that represent properties of source context, only some of which causally influence the target labels for a specific task. The key idea is to learn task-required causal factors and only use those to make predictions for a given task. Theoretically, we prove the causal factor can be identified without mixing information from others. Guided by the identifiability, we propose a Structural Instruction Tuning (SIT) method to learn the task-required causal representations that can mimic the causal factors for each task. The utility of our approach is verified by improvements of zero-shot ability on a range of unseen datasets and tasks.

Graph-based environments pose unique challenges to multi-agent reinforcement learning. In decentralized approaches, agents operate within a given graph and make decisions based on partial or outdated observations. The size of the observed neighborhood limits the generalizability to different graphs and affects the reactivity of agents, the quality of the selected actions, and the communication overhead. This work focuses on generalizability and resolves the trade-off in observed neighborhood size with a continuous information flow in the whole graph. We propose a recurrent message-passing model that iterates with the environment's steps and allows nodes to create a global representation of the graph by exchanging messages with their neighbors. Agents receive the resulting learned graph observations based on their location in the graph. Our approach can be used in a decentralized manner at runtime and in combination with a reinforcement learning algorithm of choice. We evaluate our method across 1000 diverse graphs in the context of routing in communication networks and find that it enables agents to generalize and adapt to changes in the graph.

Relevant combinatorial optimization problems (COPs) are often NP-hard. While they have been tackled mainly via handcrafted heuristics in the past, advances in neural networks have motivated the development of general methods to learn heuristics from data. Many approaches utilize a neural network to directly construct a solution, but are limited in further improving based on already constructed solutions at inference time. Our approach, Moco, learns a graph neural network that updates the solution construction procedure based on features extracted from the current search state. This meta training procedure targets the overall best solution found during the search procedure given information such as the search budget. This allows Moco to adapt to varying circumstances such as different computational budgets. Moco is a fully learnable meta optimizer that does not utilize any problem specific local search or decomposition. We test Moco on the Traveling Salesman Problem (TSP) and Maximum Independent Set (MIS) and show that it outperforms other approaches on MIS and is overall competitive on the TSP, especially outperforming related approaches, partially even if they use additional local search.