AAAI 2024 Papers

More than ever, air transport players (i.e., airline and airport companies) in an intensely competitive climate need to benefit from a carefully optimized management of airport resources to improve the quality of service and control the induced costs. In this paper, we investigate the Airport Check-in Desk Assignment Problem. We propose a Constraint Programming (CP) model for this problem, and present some promising experimental results from data coming from ADP (Aéroport de Paris). Our works are deployed in a preprod environment since 1 year.

Deep reinforcement learning (DRL) has gained immense success in many applications, including gaming AI, robotics, and system scheduling. Distributed algorithms and architectures have been vastly proposed (e.g., actor-learner architecture) to accelerate DRL training with large-scale server-based clusters. However, training on-policy algorithms with the actor-learner architecture unavoidably induces resource wasting due to synchronization between learners and actors, thus resulting in significantly extra billing. As a promising alternative, serverless computing naturally fits on-policy synchronization and alleviates resource wasting in distributed DRL training with pay-as-you-go pricing. Yet, none has leveraged serverless computing to facilitate DRL training. This paper proposes MinionsRL, the first serverless distributed DRL training framework that aims to accelerate DRL training- and cost-efficiency with dynamic actor scaling. We prototype MinionsRL on top of Microsoft Azure Container Instances and evaluate it with popular DRL tasks from OpenAI Gym. Extensive experiments show that MinionsRL reduces total training time by up to 52% and training cost by 86% compared to latest solutions.

In the era of large language models like Chatgpt, maintaining academic integrity in programming education has become challenging due to potential misuse. There's a pressing need for reliable detectors to identify Chatgpt-generated code. While previous studies have tackled model-generated text detection, identifying such code remains uncharted territory. In this paper, we introduce a novel method to discern Chatgpt-generated code. We employ targeted masking perturbation, emphasizing code sections with high perplexity. Fine-tuned CodeBERT is utilized to replace these masked sections, generating subtly perturbed samples. Our scoring system amalgamates overall perplexity, variations in code line perplexity, and burstiness. In this scoring scheme, a higher rank for the original code suggests it's more likely to be chatgpt-generated. The underlying principle is that code generated by models typically exhibits consistent, low perplexity and reduced burstiness, with its ranking remaining relatively stable even after subtle modifications. In contrast, human-written code, when perturbed, is more likely to produce samples that the model prefers. Our approach significantly outperforms current detectors, especially against OpenAI's text-davinci-003 model, with the average AUC rising from 0.56 (GPTZero baseline) to 0.87.

There has been significant progress in improving the performance of graph neural networks (GNNs) through enhancements in graph data, model architecture design, and training strategies. For fairness in graphs, recent studies achieve fair representations and predictions through either graph data pre-processing (e.g., node feature masking, and topology rewiring) or fair training strategies (e.g., regularization, adversarial debiasing, and fair contrastive learning). How to achieve fairness in graphs from the model architecture perspective is less explored. More importantly, GNNs exhibit worse fairness performance compared to multilayer perception since their model architecture (i.e., neighbor aggregation) amplifies biases. To this end, we aim to achieve fairness via a new GNN architecture. We propose Fair Message Passing (FMP) designed within a unified optimization framework for GNNs. Notably, FMP explicitly renders sensitive attribute usage in forward propagation for node classification task using cross-entropy loss without data pre-processing. In FMP, the aggregation is first adopted to utilize neighbors' information and then the bias mitigation step explicitly pushes demographic group node presentation centers together. In this way, FMP scheme can aggregate useful information from neighbors and mitigate bias to achieve better fairness and prediction tradeoff performance. Experiments on node classification tasks demonstrate that the proposed FMP outperforms several baselines in terms of fairness and accuracy on three real-world datasets. The code is available at https://github.com/zhimengj0326/FMP.

Social media sites have become a popular platform for individuals to seek and share health information. Despite the progress in natural language processing for social media mining, a gap remains in analyzing health-related texts on social discourse in the context of events. Event-driven analysis can offer insights into different facets of healthcare at an individual and collective level, including treatment options, misconceptions, knowledge gaps, etc. This paper presents a paradigm to characterize health-related information-seeking in social discourse through the lens of events. Events here are board categories defined with domain experts that capture the trajectory of the treatment/medication. To illustrate the value of this approach, we analyze Reddit posts regarding medications for Opioid Use Disorder (OUD), a critical global health concern. To the best of our knowledge, this is the first attempt to define event categories for characterizing information-seeking in OUD social discourse. Guided by domain experts, we develop TREAT-ISE, a novel multilabel treatment information-seeking event dataset to analyze online discourse on an event-based framework. This dataset contains Reddit posts on information-seeking events related to recovery from OUD, where each post is annotated based on the type of events. We also establish a strong performance benchmark (77.4% F1 score) for the task by employing several machine learning and deep learning classifiers. Finally, we thoroughly investigate the performance and errors of ChatGPT on this task, providing valuable insights into the LLM's capabilities and ongoing characterization efforts.

Previous studies disclose that Large Language Models (LLMs) suffer from hallucinations when generating texts, bringing a novel and challenging research topic to the public, which centers on enabling LLMs to generate texts with citations. Existing work exposes two limitations when using LLMs to generate answers to questions with provided documents: unsatisfactory answer correctness and poor citation quality. To tackle the above issues, we investigate using Chain-of-Thought (CoT) to elicit LLMs’ ability to synthesize correct answers from multiple documents, as well as properly cite these documents. Moreover, we propose a Citation Insurance Mechanism, which enables LLMs to detect and cite those missing citations. We conduct experiments on the ALCE benchmark with six open-source LLMs. Experimental results demonstrate that: (1) the CoT prompting strategy significantly improves the quality of text generation with citations; (2) the Citation Insurance Mechanism delivers impressive gains in citation quality at a low cost; (3) our best approach performs comparably as previous best ChatGPT-based baselines. Extensive analyses further validate the effectiveness of the proposed approach.

This study aims to improve the generation of 3D gestures by utilizing multimodal information from human speech. Previous studies have focused on incorporating additional modalities to enhance the quality of generated gestures. However, these methods perform poorly when certain modalities are missing during inference. To address this problem, we suggest using speech-derived multimodal priors to improve gesture generation. We introduce a novel method that separates priors from speech and employs multimodal priors as constraints for generating gestures. Our approach utilizes a chain-like modeling method to generate facial blendshapes, body movements, and hand gestures sequentially. Specifically, we incorporate rhythm cues derived from facial deformation and stylization prior based on speech emotions, into the process of generating gestures. By incorporating multimodal priors, our method improves the quality of generated gestures and eliminate the need for expensive setup preparation during inference. Extensive experiments and user studies confirm that our proposed approach achieves state-of-the-art performance.

Nuclei classification is a critical step in computer-aided diagnosis with histopathology images. In the past, various methods have employed graph neural networks (GNN) to analyze cell graphs that model inter-cell relationships by considering nuclei as vertices. However, they are limited by the GNN mechanism that only passes messages among local nodes via fixed edges. To address the issue, we develop a cell graph transformer (CGT) that treats nodes and edges as input tokens to enable learnable adjacency and information exchange among all nodes. Nevertheless, training the transformer with a cell graph presents another challenge. Poorly initialized features can lead to noisy self-attention scores and inferior convergence, particularly when processing the cell graphs with numerous connections. Thus, we further propose a novel topology-aware pretraining method that leverages a graph convolutional network (GCN) to learn a feature extractor. The pre-trained features may suppress unreasonable correlations and hence ease the finetuning of CGT. Experimental results suggest that the proposed cell graph transformer with topology-aware pretraining significantly improves the nuclei classification results, and achieves the state-of-the-art performance. Code and models are available at https://github.com/lhaof/CGT

Rendering photorealistic dynamic scenes has been a focus of recent research, with applications in virtual and augmented reality. While the Neural Radiance Field (NeRF) has shown remarkable rendering quality for static scenes, achieving real-time rendering of dynamic scenes remains challenging due to expansive computation for the time dimension. The incorporation of explicit-based methods, specifically voxel grids, has been proposed to accelerate the training and rendering of neural radiance fields with hybrid representation. However, employing a hybrid representation for dynamic scenes results in overfitting due to fast convergence, which can result in artifacts (e.g., floaters, noisy geometric) on novel views. To address this, we propose a compact and efficient method for dynamic neural radiance fields, namely Ced-NeRF which only require a small number of additional parameters to construct a hybrid representation of dynamic NeRF. Evaluation of dynamic scene datasets shows that our Ced-NeRF achieves fast rendering speeds while maintaining high-quality rendering results. Our method outperforms the current state-of-the-art methods in terms of quality, training and rendering speed.

Engineering design methods aim to generate new designs that meet desired performance requirements. Past work has directly introduced conditional Generative Adversarial Networks (cGANs) into this field and achieved promising results in single-point design problems(one performance requirement under one working condition). However, these methods assume that the performance requirements are distributed in categorical space, which is not reasonable in these scenarios. Although Continuous conditional GANs (CcGANs) introduce Vicinal Risk Minimization (VRM) to reduce the performance loss caused by this assumption, they still face the following challenges: 1) CcGANs can not handle multi-point design problems (multiple performance requirements under multiple working conditions). 2) Their training process is time-consuming due to the high computational complexity of the vicinal loss. To address these issues, A Continuous conditional Diffusion Probabilistic Model (CcDPM) is proposed, which the first time introduces the diffusion model into the engineering design area and VRM into the diffusion model. CcDPM adopts a novel sampling method called multi-point design sampling to deal with multi-point design problems. Moreover, the k-d tree is used in the training process of CcDPM to shorten the calculation time of vicinal loss and speed up the training process by 2-300 times in our experiments. Experiments on a synthetic problem and three real-world design problems demonstrate that CcDPM outperforms the state-of-the-art GAN models.

While decentralized training is attractive in multi-agent reinforcement learning (MARL) for its excellent scalability and robustness, its inherent coordination challenges in collaborative tasks result in numerous interactions for agents to learn good policies. To alleviate this problem, action advising methods make experienced agents share their knowledge about what to do, while less experienced agents strictly follow the received advice. However, this method of sharing and utilizing knowledge may hinder the team's exploration of better states, as agents can be unduly influenced by suboptimal or even adverse advice, especially in the early stages of learning. Inspired by the fact that humans can learn not only from the success but also from the failure of others, this paper proposes a novel knowledge sharing framework called Cautiously-Optimistic kNowledge Sharing (CONS). CONS enables each agent to share both positive and negative knowledge and cautiously assimilate knowledge from others, thereby enhancing the efficiency of early-stage exploration and the agents' robustness to adverse advice. Moreover, considering the continuous improvement of policies, agents value negative knowledge more in the early stages of learning and shift their focus to positive knowledge in the later stages. Our framework can be easily integrated into existing Q-learning based methods without introducing additional training costs. We evaluate CONS in several challenging multi-agent tasks and find it excels in environments where optimal behavioral patterns are difficult to discover, surpassing the baselines in terms of convergence rate and final performance.

Artificial light plays an integral role in modern cities, significantly enhancing human productivity and the efficiency of civilization. However, excessive illumination can lead to light pollution, posing non-negligible threats to economic burdens, ecosystems, and human health. Despite its critical importance, the exploration of its causes remains relatively limited within the field of artificial intelligence, leaving an incomplete understanding of the factors contributing to light pollution and sustainable illumination planning distant. To address this gap, we introduce a novel framework named Causally Aware Generative Adversarial Networks (CAGAN). This innovative approach aims to uncover the fundamental drivers of light pollution within cities and offer intelligent solutions for optimal illumination resource allocation in the context of sustainable urban development. We commence by examining light pollution across 33,593 residential areas in seven global metropolises. Our findings reveal substantial influences on light pollution levels from various building types, notably grasslands, commercial centers and residential buildings as significant contributors. These discovered causal relationships are seamlessly integrated into the generative modeling framework, guiding the process of generating light pollution maps for diverse residential areas. Extensive experiments showcase CAGAN’s potential to inform and guide the implementation of effective strategies to mitigate light pollution. Our code and data are publicly available at https://github.com/zhangyuuao/Light_Pollution_CAGAN.

Text-based Person Retrieval (TPR) aims to retrieve relevant images of specific pedestrians based on the given textual query. The mainstream approaches primarily leverage pretrained deep neural networks to learn the mapping of visual and textual modalities into a common latent space for cross-modality matching. Despite their remarkable achievements, existing efforts mainly focus on learning the statistical cross-modality correlation found in training data, other than the intrinsic causal correlation. As a result, they often struggle to retrieve accurately in the face of environmental changes such as illumination, pose, and occlusion, or when encountering images with similar attributes. In this regard, we pioneer the observation of TPR from a causal view. Specifically, we assume that each image is composed of a mixture of causal factors (which are semantically consistent with text descriptions) and non-causal factors (retrieval-irrelevant, e.g., background), and only the former can lead to reliable retrieval judgments. Our goal is to extract text-critical robust visual representation (i.e., causal factors) and establish domain invariant cross-modality correlations for accurate and reliable retrieval. However, causal/non-causal factors are unobserved, so we emphasize that ideal causal factors that can simulate causal scenes should satisfy two basic principles:1） Independence: being independent of non-causal factors, and 2）Sufficiency: being causally sufficient for TPR across different environments. Building on that, we propose an Invariant Representation Learning method for TPR (IRLT), that enforces the visual representations to satisfy the two aforementioned critical properties. Extensive experiments on three datasets clearly demonstrate the advantages of IRLT over leading baselines in terms of accuracy and generalization.

Knowing a prerequisite structure among skills in a subject domain effectively enables several educational applications, including intelligent tutoring systems and curriculum planning. Traditionally, educators or domain experts use intuition to determine the skills' prerequisite relationships, which is time-consuming and prone to fall into the trap of blind spots. In this paper, we focus on inferring the prerequisite structure given access to students' performance on exercises in a subject. Nevertheless, it is challenging since students' mastery of skills can not be directly observed, but can only be estimated, i.e., its latency in nature. To tackle this problem, we propose a causal-driven skill prerequisite structure discovery (CSPS) method in a two-stage learning framework. In the first stage, we learn the skills' correlation relationships presented in the covariance matrix from the student performance data while, through the predicted covariance matrix in the second stage, we consider a heuristic method based on conditional independence tests and standardized partial variance to discover the prerequisite structure. We demonstrate the performance of the new approach with both simulated and real-world data. The experimental results show the effectiveness of the proposed model for identifying the skills' prerequisite structure.

Multi-hop fact verification aims to detect the veracity of the given claim by integrating and reasoning over multiple pieces of evidence. Conventional multi-hop fact verification models are prone to rely on spurious correlations from the annotation artifacts, leading to an obvious performance decline on unbiased datasets. Among the various debiasing works, the causal inference-based methods become popular by performing theoretically guaranteed debiasing such as casual intervention or counterfactual reasoning. However, existing causal inference-based debiasing methods, which mainly formulate fact verification as a single-hop reasoning task to tackle shallow bias patterns, cannot deal with the complicated bias patterns hidden in multiple hops of evidence. To address the challenge, we propose Causal Walk, a novel method for debiasing multi-hop fact verification from a causal perspective with front-door adjustment. Specifically, in the structural causal model, the reasoning path between the treatment (the input claim-evidence graph) and the outcome (the veracity label) is introduced as the mediator to block the confounder. With the front-door adjustment, the causal effect between the treatment and the outcome is decomposed into the causal effect between the treatment and the mediator, which is estimated by applying the idea of random walk, and the causal effect between the mediator and the outcome, which is estimated with normalized weighted geometric mean approximation. To investigate the effectiveness of the proposed method, an adversarial multi-hop fact verification dataset and a symmetric multi-hop fact verification dataset are proposed with the help of the large language model. Experimental results show that Causal Walk outperforms some previous debiasing methods on both existing datasets and the newly constructed datasets. Code and data will be released at https://github.com/zcccccz/CausalWalk.

We study the problem of agent selection in causal strategic learning under multiple decision makers and address two key challenges that come with it. Firstly, while much of prior work focuses on studying a fixed pool of agents that remains static regardless of their evaluations, we consider the impact of selection procedure by which agents are not only evaluated, but also selected. When each decision maker unilaterally selects agents by maximising their own utility, we show that the optimal selection rule is a trade-off between selecting the best agents and providing incentives to maximise the agents' improvement. Furthermore, this optimal selection rule relies on incorrect predictions of agents' outcomes. Hence, we study the conditions under which a decision maker's optimal selection rule will not lead to deterioration of agents' outcome nor cause unjust reduction in agents' selection chance. To that end, we provide an analytical form of the optimal selection rule and a mechanism to retrieve the causal parameters from observational data, under certain assumptions on agents' behaviour. Secondly, when there are multiple decision makers, the interference between selection rules introduces another source of biases in estimating the underlying causal parameters. To address this problem, we provide a cooperative protocol which all decision makers must collectively adopt to recover the true causal parameters. Lastly, we complement our theoretical results with simulation studies. Our results highlight not only the importance of causal modeling as a strategy to mitigate the effect of gaming, as suggested by previous work, but also the need of a benevolent regulator to enable it.

Existing causal representation learning methods are based on the causal graph they build. However, due to the omission of bias within the causal graph, they essentially encourage models to learn biased causal effects in latent space. In this paper, we propose a novel causally disentangling framework that aims to learn unbiased causal effects. We first introduce inductive and dataset biases into traditional causal graph for the physical concepts of interest. Then, we eliminate the negative effects from these two biases by counterfactual intervention with reweighted loss function for learning unbiased causal effects. Finally, we employ the causal effects into the VAE to endow the latent representations with causality. In particular, we highlight that removing biases in this paper is regarded as a part of learning process for unbiased causal effects, which is crucial for causal disentanglement performance improvement. Through extensive experiments on real-world and synthetic datasets, we show that our method outperforms different baselines and obtains the state-of-the-art results for achieving causal representation learning.

Count data naturally arise in many fields, such as finance, neuroscience, and epidemiology, and discovering causal structure among count data is a crucial task in various scientific and industrial scenarios. One of the most common characteristics of count data is the inherent branching structure described by a binomial thinning operator and an independent Poisson distribution that captures both branching and noise. For instance, in a population count scenario, mortality and immigration contribute to the count, where survival follows a Bernoulli distribution, and immigration follows a Poisson distribution. However, causal discovery from such data is challenging due to the non-identifiability issue: a single causal pair is Markov equivalent, i.e., X->Y and Y->X are distributed equivalent. Fortunately, in this work, we found that the causal order from X to its child Y is identifiable if X is a root vertex and has at least two directed paths to Y, or the ancestor of X with the most directed path to X has a directed path to Y without passing X. Specifically, we propose a Poisson Branching Structure Causal Model (PB-SCM) and perform a path analysis on PB-SCM using high-order cumulants. Theoretical results establish the connection between the path and cumulant and demonstrate that the path information can be obtained from the cumulant. With the path information, causal order is identifiable under some graphical conditions. A practical algorithm for learning causal structure under PB-SCM is proposed and the experiments demonstrate and verify the effectiveness of the proposed method.

As responsible AI gains importance in machine learning algorithms, properties like fairness, adversarial robustness, and causality have received considerable attention in recent years. However, despite their individual significance, there remains a critical gap in simultaneously exploring and integrating these properties. In this paper, we propose a novel approach that examines the relationship between individual fairness, adversarial robustness, and structural causal models (SCMs) in heterogeneous data spaces, particularly when dealing with discrete sensitive attributes. We use SCMs and sensitive attributes to create a fair metric and apply it to measure semantic similarity among individuals. By introducing a novel causal adversarial perturbation (CAP) and applying adversarial training, we create a new regularizer that combines individual fairness, causality, and robustness in the classifier. Our method is evaluated on both real-world and synthetic datasets, demonstrating its effectiveness in achieving an accurate classifier that simultaneously exhibits fairness, adversarial robustness, and causal awareness.