GenAI4ELab/papercli-papers huggingface dataset:
Generalizing Constraint Models in Constraint Acquisition
Authors: Dimos Tsouros; Senne Berden; Steven Prestwich; Tias Guns
Venue: AAAI 2025 (main)

Abstract: Constraint Acquisition (CA) aims to widen the use of constraint programming by assisting users in the modeling process. However, most CA methods suffer from a significant drawback: they learn a single set of individual constraints for a specific problem instance, but cannot generalize these constraints to the parameterized constraint specifications of the problem. In this paper, we address this limitation by proposing GenCon, a novel approach to learn parameterized constraint models capable of modeling varying instances of the same problem.
To achieve this generalization, we make use of statistical learning techniques at the level of individual constraints.
Specifically, we propose to train a classifier to predict, for any possible constraint and parameterization, whether the constraint belongs to the problem.
We then show how, for some classes of classifiers, we can extract decision rules to construct interpretable constraint specifications. This enables the generation of ground constraints for any parameter instantiation.
Additionally, we present a generate-and-test approach that can be used with any classifier, to generate the ground constraints on the fly.
Our empirical results demonstrate that our approach achieves high accuracy and is robust to noise in the input instances.
Solving Higher-Order Quantified Boolean Satisfiability via Higher-Order Model Checking
Authors: Hiroshi Unno; Takeshi Tsukada; Jie-Hong Roland Jiang
Venue: AAAI 2025 (main)

Abstract: The satisfiability (SAT) problem of higher-order quantified Boolean formula (HOQBF) emerged as a natural generalization of SAT, quantified SAT, and second-order quantified SAT. 
 It allows succinct encoding of k-EXPTIME problems beyond the reach of prior Boolean satisfiability formulations, but its application was hampered by the lack of solvers.  In this paper, we present the first HOQBF solver that leverages techniques from the model-checking community.  Our HOQBF solver is based on reduction to higher-order model checking, which is a generalization from model checking of while-programs to that of higher-order functional programs.  The ability of a higher-order model checker to deal with higher-order functions in a program is used to reason about higher-order quantifiers in HOQBF.
On the Modelling of Constraints with Tractable Logical Operators
Authors: Ruiwei Wang; Roland H. C. Yap
Venue: AAAI 2025 (main)

Abstract: Solving a Constraint Satisfaction Problem (CSP) usually requires a model typically using existing basic constraints. The most flexible form of constraint, ad-hoc (generic) constraints defined with certain constraint representations, such as binary constraint tree (BCT) and decision diagrams, have been proposed where basic constraints in intensional form are insufficient. A modeller may wish to combine basic constraints using logic operators (and, or, negation). However, negation, a key logical operator for expressivity is not tractable in many existing constraint representations. This creates a dilemma, for modelling, we would desire more flexibility, but a model whose operations are intractable may in turn be impractical.

In this paper, we give a framework which allows for a tractable negation operator on constraint representations. We apply the framework on the BCT and ordered decision diagram constraints, giving new subforms. These subforms can be strictly more succinct than ordered multi-valued decision diagrams (OMDD), while being as tractable as OMDD for logical combinations. We give applications to show effective propagators from logical combinations and in building large constraint models for configuration problems.
Prediction-Based Adaptive Variable Ordering Heuristics for Constraint Satisfaction Problems
Authors: Jitao Xu; Yaling Wu; Hongbo Li; Minghao Yin
Venue: AAAI 2025 (main)

Abstract: Variable ordering heuristics (VOH) play a central role in solving Constraint Satisfaction Problems (CSP). The performance of different VOHs may vary greatly when solving the same CSP instance, so identifying an efficient candidate VOH for a given CSP has been a key issue in the community. In this study, we propose a prediction-based approach to adaptively select efficient VOHs for different CSPs from a set of candidates. Our work demonstrates that efficient candidate VOHs can be identified by learning from the topology of search trees. Specifically, we propose to represent the topology of a binary search tree by the sequence of the Numbers of Positive Decisions (NPD) made before each failure occurs. Based on the representation, we predict the total failure number of a search tree from its beginning part. When solving a CSP, we run a probing procedure to obtain the NPD sequences generated by candidate VOHs and select an efficient one for the resolution according to the prediction results. Our experiments show that the Long Short Term Memory model and Gradient Boosting Decision Tree models trained with the search trees sampled from easy instances are effective in identifying efficient VOHs for hard instances. The models capture some common structure properties hidden in the search trees of different problems. Our approach outperforms the state-of-the-art adaptive VOHs in terms of the number of solved instances and the PAR2 score of runtime.
Towards Projected and Incremental Pseudo-Boolean Model Counting
Authors: Suwei Yang; Kuldeep S. Meel
Venue: AAAI 2025 (main)

Abstract: Model counting is a fundamental task that involves determining the number of satisfying assignments to a logical formula, typically in conjunctive normal form (CNF). While CNF model counting has received extensive attention over recent decades, interest in Pseudo-Boolean (PB) model counting is just emerging partly due to the greater flexibility of PB formulas. As such, we observed feature gaps in existing PB counters such as a lack of support for projected and incremental settings, which could hinder adoption.

In this work, our main contribution is the introduction of the PB model counter PBCount2, the first exact PB model counter with support for projected and incremental model counting. Our counter, PBCount2, uses our Least Occurrence Weighted Min Degree (LOW-MD) computation ordering heuristic to support projected model counting and a cache mechanism to enable incremental model counting. In our evaluations, PBCount2 completed at least 1.40x the number of benchmarks of competing methods for projected model counting and at least 1.18x of competing methods in incremental model counting.
Motif-aware Graph Neural Networks for Networked Time Series Imputation
Authors: Nourhan Ahmed; Vijaya Krishna Yalavarthi; Lars Schmidt-Thieme
Venue: AAAI 2025 (main)

Abstract: Networked time series are time series on a graph, one for each node, with applications in traffic and weather monitoring. Graph neural networks are natural candidates for networked time series imputation and have recently outperformed existing alternatives such as recurrent and generative models for time series imputation as they utilize a relational inductive bias for imputation. However, existing GNN-based approaches fail to capture the higher-order topological structure between sensors, which are shaped by recurring substructures in the graph, referred to as temporal motifs. In addition, it remains uncertain which motifs are the most pivotal motifs guiding the imputation task in networked time series. In this paper, we fill in this gap by proposing a graph neural network designed to leverage motif structures within the network by employing weighted motif adjacency matrices to capture higher-order neighborhood information. In particular, (1) we design a motif-wise multi-view attention module that explicitly captures various higher-order structures along with an attention mechanism that automatically assigns high weights to informative ones in order to maximize the use of higher-order information. (2) We introduce a gated fusion module by merging gated recurrent networks and graph convolutional networks to capture the spatial and temporal dependency in order to reflect the intricate impacts of temporal and spatial influence. Experimental results demonstrate that when compared to state-of-the-art models for time-series imputation tasks, our proposed model can reduce the error by around 19%.
Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum
Authors: Wei Ai; Fuchen Zhang; Yuntao Shou; Tao Meng; Haowen Chen; Keqin Li
Venue: AAAI 2025 (main)

Abstract: Efficiently capturing consistent and complementary semantic features in context is crucial for Multimodal Emotion Recognition in Conversations (MERC). However, limited by the over-smoothing or low-pass filtering characteristics of spatial graph neural networks, are insufficient to accurately capture the long-distance consistency low-frequency information and complementarity high-frequency information of the utterances. To this end, this paper revisits the task of MERC from the perspective of the graph spectrum and proposes a Graph-Spectrum-based Multimodal Consistency and Complementary collaborative learning framework GS-MCC. First, GS-MCC uses a sliding window to construct a multimodal interaction graph to model conversational relationships and designs efficient Fourier graph operators (FGO) to extract long-distance high-frequency and low-frequency information, respectively. FGO can be stacked in multiple layers, which can effectively alleviate the over-smoothing problem. Then, GS-MCC uses contrastive learning to construct self-supervised signals that reflect complementarity and consistent semantic collaboration with high and low-frequency signals, thereby improving the ability of high and low-frequency information to reflect genuine emotions. Finally, GS-MCC inputs the coordinated high and low-frequency information into the MLP network and softmax function for emotion prediction. Extensive experiments have proven the superiority of the GS-MCC architecture proposed in this paper on two benchmark data sets.
GeoPro-Net: Learning Interpretable Spatiotemporal Prediction Models Through Statistically-Guided Geo-Prototyping
Authors: Bang An; Xun Zhou; Zirui Zhou; Ronilo Ragodos; Zenglin Xu; Jun Luo
Venue: AAAI 2025 (main)

Abstract: The problem of forecasting spatiotemporal events such as crimes and accidents is crucial to public safety and city management. Besides accuracy, interpretability is also a key requirement for spatiotemporal forecasting models to justify the decisions. Merely presenting predicted scores fails to convince the public and does not contribute to future urban planning. Interpretation of the spatiotemporal forecasting mechanism is, however, challenging due to the complexity of multi-source spatiotemporal features, the non-intuitive nature of spatiotemporal patterns for non-expert users, and the presence of spatial heterogeneity in the data. Currently, no existing deep learning model intrinsically interprets the complex predictive process learned from multi-source spatiotemporal features. To bridge the gap, we propose GeoPro-Net, an intrinsically interpretable spatiotemporal model for spatiotemporal event forecasting problems. GeoPro-Net introduces a novel Geo-concept convolution operation, which employs statistical tests to extract predictive patterns in the input as "Geo-concepts'', and condenses the "Geo-concept-encoded'' input through interpretable channel fusion and geographic-based pooling. In addition, GeoPro-Net learns different sets of prototypes of concepts inherently, and projects them to real-world cases for interpretation. Comprehensive experiments and case studies on four real-world datasets demonstrate that GeoPro-Net provides better interpretability while still achieving competitive prediction performance compared with state-of-the-art baselines.
Alleviating Performance Disparity in Adversarial Spatiotemporal Graph Learning Under Zero-Inflated Distribution
Authors: Songran Bai; Yuheng Ji; Yue Liu; Xingwei Zhang; Xiaolong Zheng; Daniel Dajun Zeng
Venue: AAAI 2025 (main)

Abstract: Spatiotemporal Graph Learning (SGL) under Zero-Inflated Distribution (ZID) is crucial for urban risk management tasks, including crime prediction and traffic accident profiling. However, SGL models are vulnerable to adversarial attacks, compromising their practical utility. While adversarial training (AT) has been widely used to bolster model robustness, our study finds that traditional AT exacerbates performance disparities between majority and minority classes under ZID, potentially leading to irreparable losses due to underreporting critical risk events. In this paper, we first demonstrate the smaller top-k gradients and lower separability of minority class are key factors contributing to this disparity. To address these issues, we propose MinGRE, a framework for Minority Class Gradients and Representations Enhancement. MinGRE employs a multi-dimensional attention mechanism to reweight spatiotemporal gradients, minimizing the gradient distribution discrepancies across classes. Additionally, we introduce an uncertainty-guided contrastive loss to improve the inter-class separability and intra-class compactness of minority representations with higher uncertainty. Extensive experiments demonstrate that the MinGRE framework not only significantly reduces the performance disparity across classes but also achieves enhanced robustness compared to existing baselines. These findings underscore the potential of our method in fostering the development of more equitable and robust models.
CUGF: A Reliable and Fair Recommendation Framework
Authors: Nitin Bisht; Xiuwen Gong; Guandong Xu
Venue: AAAI 2025 (main)

Abstract: Recommendation systems (RS) play a crucial role in assisting decision-making but often suffer from either a lack of
credibility or unfairness problems. A few recommendation
models have endeavored to address the problem from only
one aspect, and approaches to solving both problems remain
to be explored. This paper aims to construct a generalized
fairness-based recommendation framework that can also provide the credibility of recommendation models. Generally,
we propose a reliable and fair recommendation framework
called Conformalized User Group Fairness (CUGF) based
on the inspiration of conformal prediction. Specifically, we
construct dynamic prediction sets that are guaranteed to cover
the true item with a user pre-specified probability to ensure
credibility while designing novel fairness metrics based on
empirical risks to guarantee the fairness of users across different groups. Furthermore, we design a novel CUGF Algorithm
to optimize the parameter γ that dominates the prediction sets
and also the fairness. Besides, we conduct extensive experiments by applying CUGF on top of various recommendation
models and representative datasets to validate its effectiveness with respect to recommendation performance (in terms
of average set size) and fairness (in terms of the two defined
fairness metrics), the results of which demonstrate the validity of the proposed framework.
Cross-modal Multi-task Learning for Multimedia Event Extraction
Authors: Jianwei Cao; Yanli Hu; Zhen Tan; Xiang Zhao
Venue: AAAI 2025 (main)

Abstract: Multimedia event extraction aims to jointly extract event structural knowledge from multiple modalities, thus improving the comprehension and utilization of events in the growing multimedia content (e.g., multimedia news). A key challenge in multimedia event extraction is to establish cross-modal correlations during training without multimedia event annotations. Considering the complexity and cost of annotation across modalities, the multimedia event extraction task only provides parallel annotated data for evaluation. Previous works attempt to learn implicit correlations directly from unlabeled image-text pairs, but do not yield substantially better performance for event-centric tasks. To address this problem, we propose a cross-modal multi-task learning framework X-MTL to establish cross-modal correlations at the task level, which can simultaneously address four key tasks of multimedia event extraction: trigger detection, argument extraction, verb classification, and role classification. Specifically, to process inputs from different modalities and tasks, we utilize two separate modality-specific encoders and a modality-shared encoder to learn joint task representations, and introduce textual and visual prompt learning methods to enrich and unify task inputs. To resolve task conflict in cross-modal multi-task learning, we propose a pseudo label based knowledge distillation method, combined with dynamic weight adjustment method, which can effectively lift the performance to surpass the separately-trained models. On the Multimedia Event Extraction benchmark M2E2, experimental results show that X-MTL surpasses the current state-of-the-art (SOTA) methods by 4.1% for multimedia event mention and 8.2% for multimedia argument role.
Spatiotemporal-aware Trend-Seasonality Decomposition Network for Traffic Flow Forecasting
Authors: Lingxiao Cao; Bin Wang; Guiyuan Jiang; Yanwei Yu; Junyu Dong
Venue: AAAI 2025 (main)

Abstract: Traffic prediction is critical for optimizing travel scheduling and enhancing public safety, yet the complex spatial and temporal dynamics within traffic data present significant challenges for accurate forecasting. In this paper, we introduce a novel model, the Spatiotemporal-aware Trend-Seasonality Decomposition Network (STDN). This model begins by constructing a dynamic graph structure to represent traffic flow and incorporates novel spatio-temporal embeddings to jointly capture global traffic dynamics. The  representations learned are further refined by a specially designed trend-seasonality decomposition module, which disentangles the trend-cyclical component and seasonal component for each traffic node at different times within the graph. These components are subsequently processed through an encoder-decoder network to generate the final predictions. Extensive experiments conducted on real-world traffic datasets demonstrate that STDN achieves superior performance with remarkable computation cost. Furthermore, we have released a new traffic dataset named JiNan, which features unique inner-city dynamics, thereby enriching the scenario comprehensiveness in traffic prediction evaluation.
Enhancing Long-and Short-Term Representations for Next POI Recommendations via Frequency and Hierarchical Contrastive Learning
Authors: Jiajie Chen; Yu Sang; Peng-Fei Zhang; Jiaan Wang; Jianfeng Qu; Zhixu Li
Venue: AAAI 2025 (main)

Abstract: Next POI recommendation aids users in predicting their destinations of interest and plays an increasingly vital role in location-based social services. Recent works focus on analyzing both long-term and short-term interests in POI recommendation to gain a deeper understanding of user profiles. However, these methods for modeling long-term user’s sequences primarily rely on the Transformer model, which functions as a low-pass filter, often leading to the loss of high-frequency information. Additionally, long-term and short-term sequences are typically modeled independently, with short-term sequences often defined solely by the most recent check-ins, overlooking their interactions and dependencies. Therefore, we propose Enhancing Long-and Short-Term Representations for Next POI Recommendations via Frequency and Hierarchical Contrastive Learning (FHCRec). FHCRec captures both high-frequency and low-frequency information in long-term sequences to model richer long-term user’s preference representations. Moreover, it harnesses the characteristics of the short-term subsequences embedded within long-term sequences to enhance short-term preference characterization via local and global hierarchical contrastive learning, resulting in more personalized short-term preferences. The enhanced long-term and short-term preferences are integrated to improve model recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of our method.
LLM-DR: A Novel LLM-Aided Diffusion Model for Rule Generation on Temporal Knowledge Graphs
Authors: Kai Chen; Xin Song; Ye Wang; Liqun Gao; Aiping Li; Xiaojuan Zhao; Bin Zhou; Yalong Xie
Venue: AAAI 2025 (main)

Abstract: Among various temporal knowledge graph (TKG) extrapolation methods, rule-based approaches stand out for their explicit rules and transparent reasoning paths. However, the vast search space for rule extraction poses a challenge in identifying high-quality logic rules. To navigate this challenge, we explore the use of generation models to generate new rules, thereby enriching our rule base and enhancing our reasoning capabilities. In this paper, we introduce LLM-DR, an innovative rule-based method for TKG extrapolation, which harnesses diffusion models to generate rules that are consistent with the distribution of the source data, while also amalgamating the rich semantic insights of Large Language Models (LLMs). Specifically, our LLM-DR generates semantically relevant and high-quality rules, employing conditional diffusion models in a classifier-free guidance fashion and refining them with LLM-based constraints. To assess rule efficacy, we meticulously design a coarse-to-fine evaluation strategy that initiates with coarse-grained filtering to eliminate less plausible rules and proceeds with fine-grained scoring to quantify the reliability of the retained. Extensive experiments demonstrate the promising capacity of our LLM-DR.
DHMoE: Diffusion Generated Hierarchical Multi-Granular Expertise for Stock Prediction
Authors: Weijun Chen; Yanze Wang
Venue: AAAI 2025 (main)

Abstract: Stock prediction stands as a pivotal research objective within the Fintech. Existing deep learning research revolves around the development and scaling of one individual neural network predictor. However, in the dynamic and noisy landscape of the stock market, reliance solely on a single predictor poses risks of limited adaptability to diverse market conditions and challenges in effectively integrating multi-source information. Besides, top-down teaching and bottom-up hierarchical decision-making paradigms are critical for robust and accurate stock prediction within successful quantitative firms. Nonetheless, there is scarcely any research that integrates this workflow into stock prediction. To this end, we propose Diffusion Generated Hierarchical Mixture-of-Experts (DHMoE) to emulate such workflow in stock prediction. Specifically, DHMoE is crafted as a three-layer tree structure, where each expert functions as a node within the tree and their parameters are generated in a top-down, recursive manner. Recognizing the leading role of the top-level root expert, we harness the robust capabilities of diffusion models for generating and introduce the Diffusion Inverted Transformer (DIT) as the root expert. The DIT is tailored to receive information from various modalities as conditional inputs and allocate parameters to bottom-level experts. These bottom-level experts are responsible for performing predictions specific to their respective input modalities. The prediction results are then synthesized in a bottom-up manner, culminating in the final prediction outcomes. Experiments on three stock trading datasets reveal that DHMoE outperforms state-of-the-art methods in terms of both cumulative and risk-adjusted returns.