GenAI4ELab/papercli-papers huggingface dataset:
Towards Precise Prediction Uncertainty in GNNs: Refining GNNs with Topology-grouping Strategy
Authors: Hyunjin Seo; Kyusung Seo; Joonhyung Park; Eunho Yang
Venue: AAAI 2025 (main)

Abstract: Recent advancements in graph neural networks (GNNs) have highlighted the critical need of calibrating model predictions, with neighborhood prediction similarity recognized as a pivotal component. Existing studies suggest that nodes with analogous neighborhood prediction similarity often exhibit similar calibration characteristics. Building on this insight, recent approaches incorporate neighborhood similarity into node-wise temperature scaling techniques. However, our analysis reveals that this assumption does not hold universally. Calibration errors can differ significantly even among nodes with comparable neighborhood similarity, depending on their confidence levels. This necessitates a re-evaluation of existing GNN calibration methods, as a single, unified approach may lead to sub-optimal calibration. In response, we introduce Simi-Mailbox, a novel approach that categorizes nodes by both neighborhood similarity and their own confidence, irrespective of proximity or connectivity. Our method allows fine-grained calibration by employing group-specific temperature scaling, with each temperature tailored to address the specific miscalibration level of affiliated nodes, rather than adhering to a uniform trend based on neighborhood similarity. Extensive experiments demonstrate the effectiveness of our Simi-Mailbox across diverse datasets on different GNN architectures, achieving up to 13.79% error reduction compared to uncalibrated GNN predictions.
Improving Model Probability Calibration by Integration of Large Data Sources with Biased Labels
Authors: Renat Sergazinov; Richard Chen; Cheng Ji; Jing Wu; Daniel Cociorva; Hakan Brunzell
Venue: AAAI 2025 (main)

Abstract: Probability calibration transforms raw output of a classification model into empirically interpretable probability. When the model is purposed to detect rare event and only a small expensive data source has clean labels, it becomes extraordinarily challenging to obtain accurate probability calibration. Utilizing an additional large cheap data source is very helpful, however, such data sources oftentimes suffer from biased labels. To this end, we introduce an approximate expectation-maximization (EM) algorithm to extract useful information from the large data sources. For a family of calibration methods based on the logistic likelihood, we derive closed-form updates and call the resulting iterative algorithm CalEM. We show that CalEM inherits convergence guarantees from the approximate EM algorithm. We test the proposed model in simulation and on the real marketing datasets, where it shows significant performance increases.
Know2Vec: A Black-Box Proxy for Neural Network Retrieval
Authors: Zhuoyi Shang; Yanwei Liu; Jinxia Liu; Xiaoyan Gu; Ying Ding; Xiangyang Ji
Venue: AAAI 2025 (main)

Abstract: For general users, training a neural network from scratch is usually challenging and labor-intensive. Fortunately, neural network zoos enable them to find a well-performing model for directly use or fine-tuning it in their local environments. Although current model retrieval solutions attempt to convert neural network models into vectors to avoid complex multiple inference processes required for model selection, it is still difficult to choose a suitable model due to inaccurate vectorization and biased correlation alignment between the query dataset and models. From the perspective of knowledge consistency, i.e., whether the knowledge possessed by the model can meet the needs of query tasks,  we propose a model retrieval scheme, named Know2Vec, that acts as a black-box retrieval proxy for model zoo. Know2Vec first accesses to models via a black-box interface in advance, capturing vital decision knowledge from models while ensuring their privacy. Next, it employs an effective encoding technique to transform the knowledge into precise model vectors.  Secondly, it maps the user's query task to a knowledge vector by probing the semantic relationships within query samples. 
Furthermore, the proxy ensures the knowledge-consistency between query vector and model vectors within their alignment space, which is optimized through the supervised learning with diverse loss functions, and finally it can identify the most suitable model for a given task during the inference stage. Extensive experiments show that our Know2Vec achieves superior retrieval accuracy against the state-of-the-art methods in diverse neural network retrieval tasks.
Linear Streaming Bandit: Regret Minimization and Fixed-Budget Epsilon-Best Arm Identification
Authors: Yuming Shao; Zhixuan Fang
Venue: AAAI 2025 (main)

Abstract: Recently, there has been a focus on the streaming setting in a line of works on the Multi-Armed Bandit (MAB). In this scenario, a large number of arms arrive in a streaming manner, and the algorithm scans through the stream and stores some arms in its limited processing memory. We advance this line of research by introducing the Linear Streaming Bandit setup, where the arriving arms have profile vectors observable to the algorithm. The profile of an arm has a linear correlation with the expected reward. This setup is motivated by real-world applications, such as when a company or a crowdsourcing platform hires a worker from many sequentially arriving applicants with their resumes. We address two problems in this setup: Regret Minimization and Fixed-Budget Epsilon-Best Arm Identification. For the former, we propose an algorithm whose regret is independent of the number of arms, thus it is able to handle arbitrarily long arm streams. For the latter, we present a multi-pass algorithm whose error probability is sub-linear w.r.t. the number of arms, and an algorithm identifying the exact best arm in only a single pass. We validate the effectiveness of all proposed algorithms through experiments on both synthetic and real-world datasets.
Offline-to-Online Hyperparameter Transfer for Stochastic Bandits
Authors: Dravyansh Sharma; Arun Suggala
Venue: AAAI 2025 (main)

Abstract: Classic algorithms for stochastic bandits typically use hyperparameters that govern their critical properties such as the trade-off between exploration and exploitation. Tuning these hyperparameters is a problem of great practical significance. However this is a challenging problem and in certain cases is information theoretically impossible. To address this challenge, we consider a practically relevant transfer learning setting where one has access to offline data collected from several bandit problems (tasks) coming from an unknown distribution over the tasks. Our aim is to use this offline data to set the hyperparameters for a new task drawn from the unknown distribution. We provide bounds on the inter-task (number of tasks) and intra-task (number of arm pulls for each task) sample complexity for learning near-optimal hyperparameters on unseen tasks drawn from the distribution. Our results apply to several classic algorithms, including tuning the exploration parameters in UCB and LinUCB and the noise parameter in GP-UCB. Our experiments indicate the significance and effectiveness of transfer of hyperparameters from offline problems in online learning with stochastic bandit feedback.
Boosting Test Performance with Importance Sampling--a Subpopulation Perspective
Authors: Hongyu Shen; Zhizhen Zhao
Venue: AAAI 2025 (main)

Abstract: Despite empirical risk minimization (ERM) is widely applied in the machine learning community, its performance is limited on data with spurious correlation or subpopulation that is introduced by hidden attributes. Existing literature proposed techniques to maximize group-balanced or worst-group accuracy when such correlation presents, yet, at the cost of lower average accuracy. In addition, many existing works conduct surveys on different subpopulation methods without revealing the inherent connection between these methods, which could hinder the technology advancement in this area. In this paper, we identify important sampling as a simple yet powerful tool for solving the subpopulation problem. On the theory side, we provide a new systematic formulation of the subpopulation problem, and explicitly identify the assumptions that are not clearly stated in the existing works. This helps to uncover the cause of the dropped average accuracy. We provide the first theoretical discussion on the connections of existing methods, revealing the core components that make them different. On the application side, we demonstrate a single estimator is enough to solve the subpopulation problem. In particular, we introduce the estimator in both attribute-known and -unknown scenarios in the subpopulation setup, offering flexibility in practical use cases. And empirically, we achieve state-of-the-art performance on commonly used benchmark datasets.
SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models
Authors: Shuaijie Shen; Chao Wang; Renzhuo Huang; Yan Zhong; Qinghai Guo; Zhichao Lu; Jianguo Zhang; Luziwei Leng
Venue: AAAI 2025 (main)

Abstract: Known as low energy consumption networks, spiking neural networks (SNNs) have gained a lot of attention within the past decades. While SNNs are increasing competitive with artificial neural networks (ANNs) for vision tasks, they are rarely used for long sequence tasks, despite their intrinsic temporal dynamics. In this work, we develop spiking state space models (SpikingSSMs) for long sequence learning by leveraging on the sequence learning abilities of state space models (SSMs). Inspired by dendritic neuron structure, we hierarchically integrate neuronal dynamics with the original SSM block, meanwhile realizing sparse synaptic computation. Furthermore, to solve the conflict of event-driven neuronal dynamics with parallel computing, we propose a light-weight surrogate dynamic network which accurately predicts the after-reset membrane potential and compatible to learnable thresholds, enabling orders of acceleration in training speed compared with conventional iterative methods. On the long range arena benchmark task, SpikingSSM achieves competitive performance to state-of-the-art SSMs meanwhile realizing on average 90% of network sparsity. On language modeling, our network significantly surpasses existing spiking large language models (spikingLLMs) on the WikiText-103 dataset with only a third of the model size, demonstrating its potential as backbone architecture for low computation cost LLMs.
Label-Free Backdoor Attacks in Vertical Federated Learning
Authors: Wei Shen; Wenke Huang; Guancheng Wan; Mang Ye
Venue: AAAI 2025 (main)

Abstract: Vertical Federated Learning (VFL) involves multiple clients collaborating to train a global model, with distributed features of shared samples. While it becomes a critical privacy-preserving learning paradigm, its security can be significantly compromised by backdoor attacks, where a malicious client injects a target backdoor by manipulating local data. Existing attack methods in VFL rely on the assumption that the malicious client can obtain additional knowledge about task labels, which is not applicable in VFL. In this work, we investigate a new backdoor attack paradigm in VFL, Label-Free Backdoor Attacks (LFBA), which does not require any additional task label information and is feasible in VFL settings. Specifically, while existing methods assume access to task labels or target-class samples, we demonstrate that the gradients of local embeddings reflect the semantic information of labels. It can be utilized to construct the target poison sample set. Besides, we uncover that backdoor triggers tend to be ignored and under-fitted due to the learning of original features, which hinders backdoor task optimization. To address this, we propose selectively switching poison samples to disrupt feature learning, promoting backdoor task learning while maintaining accuracy on clean data. Extensive experiments demonstrate the effectiveness of our method in various settings.
Open-Set Cross-Network Node Classification via Unknown-Excluded Adversarial Graph Domain Alignment
Authors: Xiao Shen; Zhihao Chen; Shirui Pan; Shuang Zhou; Laurence T. Yang; Xi Zhou
Venue: AAAI 2025 (main)

Abstract: Existing cross-network node classification methods are mainly proposed for closed-set setting, where the source network and the target network share exactly the same label space. Such a setting is restricted in real-world applications, since the target network might contain additional classes that are not present in the source. In this work, we study a more realistic open-set cross-network node classification (O-CNNC) problem, where the target network contains all the known classes in the source and further contains several target-private classes unseen in the source. Borrowing the concept from open-set domain adaptation, all target-private classes are defined as an additional “unknown” class. To address the challenging O-CNNC problem, we propose an unknown-excluded adversarial graph domain alignment (UAGA) model with a separate-adapt training strategy. Firstly, UAGA roughly separates known classes from unknown class, by training a graph neural network encoder and a neighborhood-aggregation node classifier in an adversarial framework. Then, unknown-excluded adversarial domain alignment is customized to align only target nodes from known classes with the source, while pushing target nodes from unknown class far away from the source, by assigning positive and negative domain adaptation coefficient to known class nodes and unknown class nodes. Extensive experiments on real-world datasets demonstrate significant outperformance of the proposed UAGA over state-of-the-art methods on O-CNNC.
LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers
Authors: Xuan Shen; Zhao Song; Yufa Zhou; Bo Chen; Yanyu Li; Yifan Gong; Kai Zhang; Hao Tan; Jason Kuen; Henghui Ding; Zhihao Shu; Wei Niu; Pu Zhao; Yanzhi Wang; Jiuxiang Gu
Venue: AAAI 2025 (main)

Abstract: Diffusion Transformers have emerged as the preeminent models for a wide array of generative tasks, demonstrating superior performance and efficacy across various applications. The promising results come at the cost of slow inference, as each denoising step requires running the whole transformer model with a large amount of parameters. In this paper, we show that performing the full computation of the model at each diffusion step is unnecessary, as some computations can be skipped by lazily reusing the results of previous steps. Furthermore, we show that the lower bound of similarity between outputs at consecutive steps is notably high, and this similarity can be linearly approximated using the inputs. To verify our demonstrations, we propose the **LazyDiT**, a lazy learning framework that efficiently leverages cached results from earlier steps to skip redundant computations. Specifically, we incorporate lazy learning layers into the model, effectively trained to maximize laziness, enabling dynamic skipping of redundant computations. Experimental results show that LazyDiT outperforms the DDIM sampler across multiple diffusion transformer models at various resolutions. Furthermore, we implement our method on mobile devices, achieving better performance than DDIM with similar latency.
Numerical Pruning for Efficient Autoregressive Models
Authors: Xuan Shen; Zhao Song; Yufa Zhou; Bo Chen; Jing Liu; Ruiyi Zhang; Ryan A. Rossi; Hao Tan; Tong Yu; Xiang Chen; Yufan Zhou; Tong Sun; Pu Zhao; Yanzhi Wang; Jiuxiang Gu
Venue: AAAI 2025 (main)

Abstract: Transformers have emerged as the leading architecture in deep learning, proving to be versatile and highly effective across diverse domains beyond language and image processing. However, their impressive performance often incurs high computational costs due to their substantial model size. This paper focuses on compressing decoder-only transformer-based autoregressive models through structural weight pruning to improve the model efficiency while preserving performance for both language and image generation tasks. Specifically, we propose a training-free pruning method that calculates a numerical score with Newton's method for the Attention and MLP modules, respectively. Besides, we further propose another compensation algorithm to recover the pruned model for better performance. To verify the effectiveness of our method, we provide both theoretical support and extensive experiments. Our experiments show that our method achieves state-of-the-art performance with reduced memory usage and faster generation speeds on GPUs.
Protecting Model Adaptation from Trojans in the Unlabeled Data
Authors: Lijun Sheng; Jian Liang; Ran He; Zilei Wang; Tieniu Tan
Venue: AAAI 2025 (main)

Abstract: Model adaptation tackles the distribution shift problem with a pre-trained model instead of raw data, which has become a popular paradigm due to its great privacy protection. Existing methods always assume adapting to a clean target domain, overlooking the security risks of unlabeled samples. This paper for the first time explores the potential trojan attacks on model adaptation launched by well-designed poisoning target data. Concretely, we provide two trigger patterns with two poisoning strategies for different prior knowledge owned by attackers. These attacks achieve a high success rate while maintaining the normal performance on clean samples in the test stage. To defend against such backdoor injection, we propose a plug-and-play method named  DiffAdapt, which can be seamlessly integrated with existing adaptation algorithms. Experiments across commonly used benchmarks and adaptation methods demonstrate the effectiveness of DiffAdapt. We hope this work will shed light on the safety of transfer learning with unlabeled data.
Stop Diverse OOD Attacks: Knowledge Ensemble for Reliable Defense
Authors: Zhenbo Shi; Xiaoman Liu; Yuxuan Zhang; Shuchang Wang; Rui Shu; Zhidong Yu; Wei Yang; Liusheng Huang
Venue: AAAI 2025 (main)

Abstract: Enhancing defense through model ensemble is an emerging trend, where the challenge lies in how to use ensemble knowledge to counter Out-of-Distribution (OOD) attacks. In this paper, we propose the Reliable Defense Ensemble (REE) to address this issue. REE optimizes the ensemble knowledge of models through aggregation and enhances multidimensional robust performance through collaboration. It employs the Dynamic Synergy Amplification for weight allocation and strategy adjustment. Furthermore, we design a new Kernel Anomaly Smoothing Detection Module, which detects anomalous attacks using a smoothing feature function based on Gaussian kernel mean embedding and a multi-layer feedback structure. Particularly, we build a framework that uses reinforcement learning to iteratively fine-tune the parameters of inter-model communication and consensus. Extensive experimental results show that REE outperforms current state-of-the-art methods by a large margin in defending against OOD attacks.
Learning from Summarized Data: Gaussian Process Regression with Sample Quasi-Likelihood
Authors: Yuta Shikuri
Venue: AAAI 2025 (main)

Abstract: Gaussian process regression is a powerful Bayesian nonlinear regression method. Recent research has enabled the capture of many types of observations using non-Gaussian likelihoods. To deal with various tasks in spatial modeling, we benefit from this development. Difficulties still arise when we can only access summarized data consisting of representative features, summary statistics, and data point counts. Such situations frequently occur primarily due to concerns about confidentiality and management costs associated with spatial data. This study tackles learning and inference using only summarized data within the framework of Gaussian process regression. To address this challenge, we analyze the approximation errors in the marginal likelihood and posterior distribution that arise from utilizing representative features. We also introduce the concept of sample quasi-likelihood, which facilitates learning and inference using only summarized data. Non-Gaussian likelihoods satisfying certain assumptions can be captured by specifying a variance function that characterizes a sample quasi-likelihood function. Theoretical and experimental results demonstrate that the approximation performance is influenced by the granularity of summarized data relative to the length scale of covariance functions. Experiments on a real-world dataset highlight the practicality of our method for spatial modeling.
Replication-proof Bandit Mechanism Design with Bayesian Agents
Authors: Suho Shin; Seyed A. Esmaeili; MohammadTaghi Hajiaghayi
Venue: AAAI 2025 (main)

Abstract: We study the problem of designing replication-proof bandit mechanisms when agents strategically register or replicate their own arms to maximize their payoff.
Specifically, we consider Bayesian agents who only know the distribution from which their own arms' mean rewards are sampled, unlike the original setting of by Shin, Lee, and Ok AISTATS'22.
Interestingly, with Bayesian agents in stark contrast to the previous work, analyzing the replication-proofness of an algorithm becomes significantly complicated even in a single-agent setting.
We provide sufficient and necessary conditions for an algorithm to be replication-proof in the single-agent setting, and present an algorithm that satisfies these properties.
These results center around several analytical theorems that focus on comparing the expected regret of multiple bandit instances, and therefore might be of independent interest since they have not been studied before to the best of our knowledge. 
We expand this result to the multi-agent setting, and provide a replication-proof algorithm for any problem instance.
We finalize our result by proving its sublinear regret upper bound which matches that of Shin, Lee, and Ok AISTATS'22.
Faithful and Accurate Self-Attention Attribution for Message Passing Neural Networks via the Computation Tree Viewpoint
Authors: Yong-Min Shin; Siqing Li; Xin Cao; Won-Yong Shin
Venue: AAAI 2025 (main)

Abstract: The self-attention mechanism has been adopted in various popular message passing neural networks (MPNNs), enabling the model to adaptively control the amount of information that flows along the edges of the underlying graph. Such attention-based MPNNs (Att-GNNs) have also been used as a baseline for multiple studies on explainable AI (XAI) since attention has steadily been seen as natural model interpretations, while being a viewpoint that has already been popularized in other domains (e.g., natural language processing and computer vision). However, existing studies often use naive calculations to derive attribution scores from attention, undermining the potential of attention as interpretations for Att-GNNs. In our study, we aim to fill the gap between the widespread usage of Att-GNNs and their potential explainability via attention. To this end, we propose GAtt, edge attribution calculation method for self-attention MPNNs based on the computation tree, a rooted tree that reflects the computation process of the underlying model. Despite its simplicity, we empirically demonstrate the effectiveness of GAtt in three aspects of model explanation: faithfulness, explanation accuracy, and case studies by using both synthetic and real-world benchmark datasets. In all cases, the results demonstrate that GAtt greatly improves edge attribution scores, especially compared to the previous naive approach.