GenAI4ELab/papercli-papers huggingface dataset:
RoPaSS: Robust Watermarking for Partial Screen-Shooting Scenarios
Authors: Zehua Ma; Han Fang; Xi Yang; Kejiang Chen; Weiming Zhang
Venue: AAAI 2025 (main)

Abstract: Screen-shooting robust watermarking is an effective means of preventing screen content leakage from unauthorized camera shooting, as it can trace the leaked source through the watermark extraction thereby providing an effective deterrent. However, current screen-shooting resilient watermarking schemes rely on the image's contours to synchronize and then extract the watermark. While in practical applications, it's common for only a portion of the image to be captured, resulting in a limited performance of the previous watermarking schemes. To address this problem, we propose the RoPaSS: a robust watermarking scheme for partial screen-shooting scenarios, which effectively constructs symmetric characteristics on the embedding watermark to handle the sticky re-synchronization issue. Specifically, RoPaSS consists of a watermark encoder, a decoder, and three estimators, which are trained in two stages. In the first training stage, RoPaSS integrates the flipping operation into the watermark encoder and decoder training to increase the redundancy of watermark messages and artificially guide the generation of symmetric watermarks. In the second stage, estimators utilize the watermark symmetry as an additional reference to estimate the restoration parameters to resynchronize the partially captured watermarked image. Experiments have demonstrated the excellent performance of RoPaSS in partial screen-shooting traceability, with extraction accuracy of above 93% in frontal shooting and above 86% in 30° shooting even if only 50% of the image content is captured.
FedSum: Data-Efficient Federated Learning Under Data Scarcity Scenario for Text Summarization
Authors: Zhiyong Ma; Zhengping Li; Yuanjie Shi; Jian Chen
Venue: AAAI 2025 (main)

Abstract: Text summarization task extracts salient information from a large amount of text for productivity enhancement.
However, most existing methods heavily rely on training models from ample and centrally stored data which is infeasible to collect in practice, due to privacy concerns and data scarcity nature under several settings (e.g., edge computing or cold starting).
The main challenge lies in constructing the privacy-preserving and well-behaved summarization model under the data scarcity scenario, where the data scarcity nature will lead to the knowledge shortage of the model while magnifying the impact of data bias, causing performance degeneration.
To tackle this challenge, previous studies attempt to complement samples or improve the efficiency of data.
The former is usually associated with high computing costs or has a large dependence on empirical settings, while the latter might not effective due to the lack of consideration of data bias.
In this work, we propose FedSum which extends the standard FL framework from depth and breadth to further extract prime and diversified knowledge from limited resources for text summarization.
For depth extension, we introduce a Data Partition method to cooperatively recognize the prime samples that are more significant and unbiased, and the Data skip mechanism is introduced to help the model further focus on those prime samples during the local training process.
For breadth extension, FedSum extends the source of knowledge and develops the summarization model by extracting knowledge from the data samples, hidden spaces, and globally received parameters.
Extensive experiments on four benchmark datasets verify the promising improvement of FedSum compared to baselines, and show its generalizability, scalability, and robustness.
Local Causal Discovery for Structural Evidence of Direct Discrimination
Authors: Jacqueline Maasch; Kyra Gan; Violet Chen; Agni Orfanoudaki; Nil-Jana Akpinar; Fei Wang
Venue: AAAI 2025 (main)

Abstract: Identifying the causal pathways of unfairness is a critical objective for improving policy design and algorithmic decision-making. Prior work in causal fairness analysis often requires knowledge of the causal graph, hindering practical applications in complex or low-knowledge domains. Moreover, global discovery methods that learn causal structure from data can display unstable performance on finite  samples, preventing robust fairness conclusions. To mitigate these challenges, we introduce local discovery for direct discrimination (LD3): a method that uncovers structural evidence of direct unfairness by identifying the causal parents of an outcome variable. LD3 performs a linear number of conditional independence tests relative to variable set size, and allows for latent confounding under the sufficient condition that all parents of the outcome are observed. We show that LD3 returns a valid adjustment set (VAS) under a new graphical criterion for the weighted controlled direct effect, a qualitative indicator of direct discrimination. LD3 limits unnecessary adjustment, providing interpretable VAS for assessing unfairness. We use LD3 to analyze causal fairness in two complex decision systems: criminal recidivism prediction and liver transplant allocation. LD3 was more time-efficient and returned more plausible results on real-world data than baselines, which took 46× to 5870× longer to execute.
Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness
Authors: Agathe Fernandes Machado; Arthur Charpentier; Ewen Gallic
Venue: AAAI 2025 (main)

Abstract: In this paper, we link two existing approaches to derive counterfactuals: adaptations based on a causal graph, and optimal transport. We extend  "Knothe's rearrangement" and "triangular transport" to probabilistic graphical models, and use this counterfactual approach, referred to as sequential transport, to discuss fairness at the individual level. After establishing the theoretical foundations of the proposed method, we demonstrate its application through numerical experiments on both synthetic and real datasets.
The Gradient of Algebraic Model Counting
Authors: Jaron Maene; Luc De Raedt
Venue: AAAI 2025 (main)

Abstract: Algebraic model counting unifies many inference tasks on logic formulas by exploiting semirings. Rather than focusing on inference, we consider learning, especially in statistical-relational and neurosymbolic AI, which combine logical, probabilistic and neural representations. Concretely, we show that the very same semiring perspective of algebraic model counting also applies to learning.  This allows us to unify various learning algorithms by generalizing gradients and backpropagation to different semirings. Furthermore, we show how cancellation and ordering properties of a semiring can be exploited for more memory-efficient backpropagation. This allows us to obtain some interesting variations of state-of-the-art gradient-based optimisation methods for probabilistic logical models. We also discuss why algebraic model counting on tractable circuits does not lead to more efficient second-order optimization. Empirically, our algebraic backpropagation exhibits considerable speed-ups as compared to existing approaches.
PALM: Pushing Adaptive Learning Rate Mechanisms for Continual Test-Time Adaptation
Authors: Sarthak Kumar Maharana; Baoming Zhang; Yunhui Guo
Venue: AAAI 2025 (main)

Abstract: Real-world vision models in dynamic environments face rapid shifts in domain distributions, leading to decreased recognition performance. Using unlabeled test data, continuous test-time adaptation (CTTA) directly adjusts a pre-trained source discriminative model to these changing domains. A highly effective CTTA method involves applying layer-wise adaptive learning rates for selectively adapting pre-trained layers. However, it suffers from the poor estimation of domain shift and the inaccuracies arising from the pseudo-labels. This work aims to overcome these limitations by identifying layers for adaptation via quantifying model prediction uncertainty without relying on pseudo-labels. We utilize the magnitude of gradients as a metric, calculated by backpropagating the KL divergence between the softmax output and a uniform distribution, to select layers for further adaptation. Subsequently, for the parameters exclusively belonging to these selected layers, with the remaining ones frozen, we evaluate their sensitivity to approximate the domain shift and adjust their learning rates accordingly. We conduct extensive image classification experiments on CIFAR-10C, CIFAR-100C, and ImageNet-C, demonstrating the superior efficacy of our method compared to prior approaches.
TabGLM: Tabular Graph Language Model for Learning Transferable Representations Through Multi-Modal Consistency Minimization
Authors: Anay Majee; Maria Xenochristou; Wei-Peng Chen
Venue: AAAI 2025 (main)

Abstract: Handling heterogeneous data in tabular datasets poses a significant challenge for deep learning models. While attention-based architectures and self-supervised learning have achieved notable success, their application to tabular data remains less effective over linear and tree based models. Although several breakthroughs have been achieved by models which transform tables into uni-modal transformations like image, language and graph, these models often underperform in the presence of feature heterogeneity. To address this gap, we introduce TabGLM (Tabular Graph Language Model), a novel multi-modal architecture designed to model both structural and semantic information from a table. TabGLM transforms each row of a table into a fully connected graph and serialized text, which are then encoded using a graph neural network (GNN) and a text encoder, respectively. By aligning these representations through a joint, multi-modal, self-supervised learning objective, TabGLM leverages complementary information from both modalities, thereby enhancing feature learning. TabGLM's flexible graph-text pipeline efficiently processes heterogeneous datasets with significantly fewer parameters over existing Deep Learning approaches. Evaluations across 25 benchmark datasets demonstrate substantial performance gains, with TabGLM achieving an average AUC-ROC improvement of up to 5.56% over State-of-the-Art (SoTA) tabular learning methods.
HyperDefender: A Robust Framework for Hyperbolic GNNs
Authors: Nikita Malik; Rahul Gupta; Sandeep Kumar
Venue: AAAI 2025 (main)

Abstract: Graph neural networks for hyperbolic space has emerged as a powerful tool for embedding datasets exhibiting a highly non-Euclidean latent anatomy e.g., graphs with hierarchical structures. While several Hyperbolic Graph Neural Networks (Hy-GNNs) have been developed to enhance the representation of hierarchical datasets, they remain susceptible to noise and adversarial attacks, posing serious risks in critical applications. The absence of robust Hy-GNN frameworks underscores a pressing problem. This research addresses this challenge by introducing HyperDefender—a robust and flexible approach designed to fortify Hy-GNNs against adversarial attacks and noises. HyperDefender aims to secure the reliability of applications that depend on the integrity of hierarchical graph-structured data in real-world scenarios. Experimental results demonstrate that HyperDefender significantly improves node classification accuracy across various attacks, effectively mitigating the performance degradation typically observed in Hy-GNNs when the hierarchy in original datasets is compromised.
SPACETIME: Causal Discovery from Non-Stationary Time Series
Authors: Sarah Mameche; Lénaïg Cornanguer; Urmi Ninad; Jilles Vreeken
Venue: AAAI 2025 (main)

Abstract: Understanding causality is challenging and often complicated by changing causal relationships over time and across environments. Climate patterns, for example, shift over time with recurring seasonal trends, while also depending on geographical characteristics such as ecosystem variability. Existing methods for discovering causal graphs from time series either assume stationarity, do not permit both temporal and spatial distribution changes, or are unaware of locations with the same causal relationships. In this work, we therefore unify the three tasks of causal graph discovery in the non-stationary multi-context setting, of reconstructing temporal regimes, and of partitioning datasets and time intervals into those where invariant causal relationships hold. To construct a consistent score that forms the basis of our method, we employ the Minimum Description Length principle. Our resulting algorithm SPACETIME simultaneously accounts for heterogeneity across space and non-stationarity over time. Given multiple time series, it discovers regime changepoints and a temporal causal graph using non-parametric functional modeling and kernelized discrepancy testing. We also show that our method provides insights into real-world phenomena such as river-runoff measured at different catchments and biosphere-atmosphere interactions across ecosystems.
Implicit Relative Labeling-Importance Aware Multi-Label Metric Learning
Authors: Jun-Xiang Mao; Yong Rui; Min-Ling Zhang
Venue: AAAI 2025 (main)

Abstract: Multi-label metric learning, as an extension of metric learning to multi-label scenarios, aims to learn better similarity metrics for objects with rich semantics. Existing multi-label metric learning approaches employ the common assumption of equal labeling-importance, i.e., all associated labels are considered relevant to the training instance, while there is no differentiation in the relative importance of their semantics. However, this common assumption does not reflect the fact that the importance of each relevant label is generally different, even though such importance information is not directly accessible from the training examples. In this paper, we claim that it is beneficial to leverage the implicit Relative LabelingImportance (RLI) information to facilitate multi-label metric learning. Specifically, the manifold structure within the feature space is exploited by local linear reconstruction, and then the RLIs are recovered by transferring such structure to the label space. Subsequently, a discrimiative multi-label metric learning framework is introduced to align the predictive modeling outputs with the recovered RLIs, under which instances with similar RLI are implicitly pulled closer to each other, while those with dissimilar RLI are pushed further apart. Comprehensive experiments on benchmark multi-label datasets validate the superiority of our proposed approach in learning effective similarity metrics between multi-label examples.
Harnessing Event Sensory Data for Error Pattern Prediction in Vehicles: A Language Model Approach
Authors: Hugo Math; Rainer Lienhart; Robin Schön
Venue: AAAI 2025 (main)

Abstract: In this paper, we draw an analogy between processing natural languages and processing multivariate event streams from vehicles in order to predict when and what error pattern is most likely to occur in the future for a given car. Our approach leverages the temporal dynamics and contextual relationships of our event data from a fleet of cars. Event data is composed of discrete values of error codes as well as continuous values such as time and mileage. Modelled by two causal Transformers, we can anticipate vehicle failures and malfunctions before they happen. Thus, we introduce CarFormer, a Transformer model trained via a new self-supervised learning strategy, and EPredictor, an autoregressive Transformer decoder model capable of predicting when and what error pattern will most likely occur after some error code apparition. Despite the challenges of high cardinality of event types, their unbalanced frequency of appearance and limited labelled data, our experimental results demonstrate the excellent predictive ability of our novel model. Specifically, with sequences of 160 error codes on average, our model is able with only half of the error codes to achieve 80% F1 score for predicting what error pattern will occur and achieves an average absolute error of 58.4 ± 13.2h when forecasting the time of occurrence, thus enabling confident predictive maintenance and enhancing vehicle safety.
Eco Search: A No-delay Best-First Search Algorithm for Program Synthesis
Authors: Théo Matricon; Nathanaël Fijalkow; Guillaume Lagarde
Venue: AAAI 2025 (main)

Abstract: Many approaches to program synthesis perform a combinatorial search within a large space of programs to find one that satisfies a given specification. To tame the search space blowup, previous works introduced probabilistic and neural approaches to guide this combinatorial search by inducing heuristic cost functions. Best-first search algorithms ensure to search in the exact order induced by the cost function, significantly reducing the portion of the program space to be explored. We present a new best-first search algorithm called Eco Search, which is the first no-delay algorithm for pre-generation cost function: the amount of compute required between outputting two programs is constant, and in particular does not increase over time. This key property yields important speedups: we observe that Eco Search outperforms its predecessors on two classical domains.
CohEx: A Generalized Framework for Cohort Explanation
Authors: Fanyu Meng; Xin Liu; Zhaodan Kong; Xin Chen
Venue: AAAI 2025 (main)

Abstract: eXplainable Artificial Intelligence (XAI) has garnered significant attention for enhancing transparency and trust in machine learning models. However, the scopes of most existing explanation techniques focus either on offering a holistic view of the explainee model (global explanation) or on individual instances (local explanation), while the middle ground, i.e., cohort-based explanation, is less explored. Cohort explanations offer insights into the explainee's behavior on a specific group or cohort of instances, enabling a deeper understanding of model decisions within a defined context. In this paper, we discuss the unique challenges and opportunities associated with measuring cohort explanations, define their desired properties, and create a generalized framework for generating cohort explanations based on supervised clustering.
Causal Inference over Visual-Semantic-Aligned Graph for Image Classification
Authors: Lei Meng; Xiangxian Li; Xiaoshuo Yan; Haokai Ma; Zhuang Qi; Wei Wu; Xiangxu Meng
Venue: AAAI 2025 (main)

Abstract: Incorporating tagging information to regularize the representation learning of images usually leads to improved performance in image classification by aligning the visual features with the textual ones of higher discriminative power. Existing methods typically follow the predictive approach, which uses tags as the semantic labels for visual input to make predictions. However, they typically face the problem of handling the heterogeneity between modalities. In order to learn accurate visual-semantic mapping, this paper presents a visual-semantic causal association modeling framework termed VSCNet. It aligns visual regions with tags, uses a pre-learned hierarchy of visual and semantic exemplars to refine tag predictions and constructs an augmented heterogeneous graph to perform causal intervention. Specifically, the fine-grained visual-semantic alignment (FVA) module adaptively locates the semantic-intensive regions corresponding to tags. The heterogeneous association refinement (HAR) module associates the visual regions, semantic elements and pre-learned visual prototypes in a heterogeneous graph to filter the error predictions and enrich the information. The causal inference with graphical masking (CIM) module applies self-learned masks to discover the causal nodes and edges in the heterogeneous graph to address the spurious association, forming robust causal representations. Experimental results from two benchmarking datasets show that VSCNet effectively builds the visual-semantic associations from images and leads to better performance than the state-of-the-art methods with enriched predictive information.
ID-GMLM: Intelligent Decision-Making with Integrated Graph Models and Large Language Models
Authors: Zhenhua Meng; Fanshen Meng; Rongheng Lin; Budan Wu
Venue: AAAI 2025 (main)

Abstract: Multi-criteria decision making (MCDM) and preference learning (PL) are crucial subfields of intelligent decision-making, both aiming to aid decision-makers (DMs) in selecting, classifying, or ranking alternatives. While MCDM and PL can complement each other to some extent, existing approaches combining MCDM and PL often struggle with large data volumes and complex relational information. To address this, we propose a novel approach called ID-GMLM that integrates graph models and large language models (LLMs) for intelligent decision-making. It reformulates decision-making as a high-parallelism ranking function in the graph domain, using graph neural networks (GNNs) to learn and understand complex relationships between alternatives or criteria, and LLMs to parse and quantify the preferences of DMs. ID-GMLM features a multi-task learning framework that optimizes the primary task of predicting alternative rankings while modeling criterion interactions through the auxiliary task. Additionally, ID-GMLM incorporates a parameter tuning network based on criterion weights and an attention network, allowing the model to adaptively adjust to the context of the current task and the evolving preferences of DMs. Experiments on benchmark datasets demonstrate that ID-GMLM achieves significant performance improvements, inheriting the interpretability and intuitive appeal of MCDM while leveraging the computational efficiency and high accuracy of PL.
Heterogeneous Multi-Agent Bandits with Parsimonious Hints
Authors: Amirmahdi Mirfakhar; Xuchuang Wang; Jinhang Zuo; Yair Zick; Mohammad Hajiesmaili
Venue: AAAI 2025 (main)

Abstract: We study a hinted heterogeneous multi-agent multi-armed bandits problem (HMA2B), where agents can query low-cost observations (hints) in addition to pulling arms. In this framework, each of the M agents has a unique reward distribution over K arms, and in T rounds, they can observe the reward of the arm they pull only if no other agent pulls that arm. The goal is to maximize the total utility by querying the minimal necessary hints without pulling arms, achieving time-independent regret. We study HMA2B in both centralized and decentralized setups. Our main centralized algorithm, GP-HCLA, which is an extension of HCLA, uses a central decision-maker for arm-pulling and hint queries, achieving O(M^4 K) regret with O(M K log T) adaptive hints. In decentralized setups, we propose two algorithms, HD-ETC and EBHD-ETC, that allow agents to choose actions independently through collision-based communication and query hints uniformly until stopping, yielding O(M^3 K^2) regret with O(M^3 K log T) hints, where the former requires knowledge of the minimum gap and the latter does not. Finally, we establish lower bounds to prove the optimality of our results and verify them through numerical simulations.