GenAI4ELab/papercli-papers huggingface dataset:
PEARL: Input-Agnostic Prompt Enhancement with Negative Feedback Regulation for Class-Incremental Learning
Authors: Yongchun Qin; Pengfei Fang; Hui Xue
Venue: AAAI 2025 (main)

Abstract: Class-incremental learning (CIL) aims to continuously introduce novel categories into a classification system without forgetting previously learned ones, thus adapting to evolving data distributions. Researchers are currently focusing on leveraging the rich semantic information of pre-trained models (PTMs) in CIL tasks. Prompt learning has been adopted in CIL for its ability to adjust data distribution to better align with pre-trained knowledge. This paper critically examines the limitations of existing methods from the perspective of prompt learning, which heavily rely on input information. To address this issue, we propose a novel PTM-based CIL method called Input-Agnostic  Prompt Enhancement with NegAtive Feedback ReguLation (PEARL). In PEARL, we implement an input-agnostic global prompt coupled with an adaptive momentum update strategy to reduce the model's dependency on data distribution, thereby effectively mitigating catastrophic forgetting. Guided by negative feedback regulation, this adaptive momentum update addresses the parameter sensitivity inherent in fixed-weight momentum updates. Furthermore, it fosters the continuous enhancement of the prompt for new tasks by harnessing correlations between different tasks in CIL. Experiments on six benchmarks demonstrate that our method achieves state-of-the-art performance.
Multimodal Variational Autoencoder: A Barycentric View
Authors: Peijie Qiu; Wenhui Zhu; Sayantan Kumar; Xiwen Chen; Jin Yang; Xiaotong Sun; Abolfazl Razi; Yalin Wang; Aristeidis Sotiras
Venue: AAAI 2025 (main)

Abstract: Multiple signal modalities, such as vision and sounds, are naturally present in real-world phenomena. Recently, there has been growing interest in learning generative models, in particular variational autoencoder (VAE), to for multimodal representation learning especially in the case of missing modalities. The primary goal of these models is to learn a modality-invariant and modality-specific representation that characterizes information across multiple modalities. 
Previous attempts at multimodal VAEs approach this mainly through the lens of experts, aggregating unimodal inference distributions with a product of experts (PoE), a mixture of experts (MoE), or a combination of both. In this paper, we provide an alternative generic and theoretical formulation of multimodal VAE through the lens of barycenter. We first show that PoE and MoE are specific instances of barycenters, derived by minimizing the asymmetric weighted KL divergence to unimodal inference distributions. Our novel formulation extends these two barycenters to a more flexible choice by considering different types of divergences.
In particular, we explore the Wasserstein barycenter defined by the 2-Wasserstein distance, which better preserves the geometry of unimodal distributions by capturing both modality-specific and modality-invariant representations compared to KL divergence. Empirical studies on three multimodal benchmarks demonstrated the effectiveness of the proposed method.
PowerMLP: An Efficient Version of KAN
Authors: Ruichen Qiu; Yibo Miao; Shiwen Wang; Yifan Zhu; Lijia Yu; Xiao-Shan Gao
Venue: AAAI 2025 (main)

Abstract: The Kolmogorov-Arnold Network (KAN) is a new network architecture known for its high accuracy in several tasks such as function fitting and PDE solving. The superior expressive capability of KAN arises from the Kolmogorov-Arnold representation theorem and learnable spline functions. However, the computation of spline functions involves multiple iterations, which renders KAN significantly slower than MLP, thereby increasing the cost associated with model training and deployment. The authors of KAN also noted that "the biggest bottleneck of KANs lies in their slow training. KANs are usually 10x slower than MLPs, given the same number of parameters." To address this issue, we propose a novel MLP-type neural network PowerMLP that employs simpler non-iterative spline function representation, offering approximately the same training time as MLP while theoretically demonstrating stronger expressive power than KAN. Furthermore, we compare the FLOPs of KAN and PowerMLP, quantifying the faster computation speed of PowerMLP. Our comprehensive experiments demonstrate that PowerMLP generally achieves higher accuracy and a training speed about 40 times faster than KAN in various tasks.
Enhancing Masked Time-Series Modeling via Dropping Patches
Authors: Tianyu Qiu; Yi Xie; Hao Niu; Yun Xiong; Xiaofeng Gao
Venue: AAAI 2025 (main)

Abstract: This paper explores how to enhance existing masked time-series modeling by randomly dropping sub-sequence level patches of time series. On this basis, a simple yet effective method named DropPatch is proposed, which has two remarkable advantages: 1) It improves the pre-training efficiency by a square-level advantage; 2) It provides additional advantages for modeling in scenarios such as in-domain, cross-domain, few-shot learning and cold start. This paper conducts comprehensive experiments to verify the effectiveness of the method and analyze its internal mechanism. Empirically, DropPatch strengthens the attention mechanism, reduces information redundancy and serves as an efficient means of data augmentation. Theoretically, it is proved that DropPatch slows down the rate at which the Transformer representations collapse into the rank-1 linear subspace by randomly dropping patches, thus optimizing the quality of the learned representations.
Efficient 3D Recognition with Event-driven Spike Sparse Convolution
Authors: Xuerui Qiu; Man Yao; Jieyuan Zhang; Yuhong Chou; Ning Qiao; Shibo Zhou; Bo Xu; Guoqi Li
Venue: AAAI 2025 (main)

Abstract: Spiking Neural Networks (SNNs) provide an energy-efficient way to extract 3D spatio-temporal features. Point clouds are sparse 3D spatial data, which suggests that SNNs should be well-suited for processing them. However, when applying SNNs to point clouds, they often exhibit limited performance and fewer application scenarios. We attribute this to inappropriate preprocessing and feature extraction methods. To address this issue, we first introduce the Spike Voxel Coding (SVC) scheme, which encodes the 3D point clouds into a sparse spike train space, reducing the storage requirements
and saving time on point cloud preprocessing. Then, we propose a Spike Sparse Convolution (SSC) model for efficiently extracting 3D sparse point cloud features. Combining SVC and SSC, we design an efficient 3D SNN backbone  (E-3DSNN), which is friendly with neuromorphic hardware. For instance, SSC can be implemented on neuromorphic chips with only minor modifications to the addressing function of vanilla spike convolution. Experiments on ModelNet40, KITTI, and Semantic KITTI datasets demonstrate that E-3DSNN achieves state-of-the-art (SOTA) results with remarkable efficiency. 
Notably, our E-3DSNN (1.87M) obtained 91.7% top-1 accuracy on ModelNet40, surpassing the current best SNN baselines (14.3M) by 3.0%.  To our best knowledge, it is the first direct training 3D SNN backbone that can simultaneously handle various 3D computer vision tasks (e.g., classification, detection, and segmentation) with an event-driven nature.
Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement Learning
Authors: Yun Qu; Yuhang Jiang; Boyuan Wang; Yixiu Mao; Cheems Wang; Chang Liu; Xiangyang Ji
Venue: AAAI 2025 (main)

Abstract: Reinforcement learning (RL) often encounters delayed and sparse feedback in real-world applications, even with only episodic rewards. 
Previous approaches have made some progress in reward redistribution for credit assignment but still face challenges, including training difficulties due to redundancy and ambiguous attributions stemming from overlooking the multifaceted nature of mission performance evaluation. 
Hopefully, Large Language Model (LLM) encompasses fruitful decision-making knowledge and provides a plausible tool for reward redistribution.
Even so, deploying LLM in this case is non-trivial due to the misalignment between linguistic knowledge and the symbolic form requirement, together with inherent randomness and hallucinations in inference. 
To tackle these issues, we introduce LaRe, a novel LLM-empowered symbolic-based decision-making framework, to improve credit assignment. 
Key to LaRe is the concept of the Latent Reward, which works as a multi-dimensional performance evaluation, enabling more interpretable goal attainment from various perspectives and facilitating more effective reward redistribution. 
We examine that semantically generated code from LLM can bridge linguistic knowledge and symbolic latent rewards, as it is executable for symbolic objects.
Meanwhile, we design latent reward self-verification to increase the stability and reliability of LLM inference. 
Theoretically, reward-irrelevant redundancy elimination in the latent reward benefits RL performance from more accurate reward estimation.
Extensive experimental results witness that LaRe (i) achieves superior temporal credit assignment to SOTA methods, (ii) excels in allocating contributions among multiple agents, and (iii) outperforms policies trained with ground truth rewards for certain tasks.
Fair Federated Survival Analysis
Authors: Md Mahmudur Rahman; Sanjay Purushotham
Venue: AAAI 2025 (main)

Abstract: Federated Survival Analysis (FSA) is an emerging Federated Learning (FL) paradigm that enables training survival models on decentralized data while preserving privacy. However, existing FSA approaches largely overlook the potential risk of bias in predictions arising from demographic and censoring disparities across clients' datasets, which impacts the fairness and performance of federated survival models, especially for underrepresented groups. To address this gap, we introduce FairFSA, a novel FSA framework that adapts existing fair survival models to the federated setting. FairFSA jointly trains survival models using distributionally robust optimization, penalizing worst-case errors across subpopulations that exceed a specified probability threshold. Partially observed survival outcomes in clients are reconstructed with federated pseudo values (FPV) before model training to address censoring. Furthermore, we design a weight aggregation strategy by enhancing the FedAvg algorithm with a fairness-aware concordance index-based aggregation method to foster equitable performance distribution across clients. To the best of our knowledge, this is the first work to study and integrate fairness into Federated Survival Analysis. Comprehensive experiments on distributed non-IID datasets demonstrate FairFSA's superiority in fairness and accuracy over state-of-the-art FSA methods, establishing it as a robust FSA approach capable of handling censoring while providing equitable and accurate survival predictions for all subjects.
Kernel Learning for Sample Constrained Black-Box Optimization
Authors: Rajalaxmi Rajagopalan; Yu-Lin Wei; Romit Roy Choudhury
Venue: AAAI 2025 (main)

Abstract: Black box optimization (BBO) focuses on optimizing unknown functions in high-dimensional spaces. In many applications, sampling the unknown function is expensive, imposing a tight sample budget.Ongoing work is making progress on reducing the sample budget by learning the shape/structure of the function, known as kernel learning.
We propose a new method to learn the kernel of a Gaussian Process. Our idea is to create a continuous kernel space in the latent space of a variational autoencoder, and run an auxiliary optimization to identify the best kernel. Results show that the proposed method, Kernel Optimized Blackbox Optimization (KOBO), outperforms state of the art by estimating the optimal at considerably lower sample budgets.
Results hold not only across synthetic benchmark functions but also in real applications. We show that a hearing aid may be personalized with fewer audio queries to the user, or a generative model could converge to desirable images from limited user ratings.
Can Private Machine Learning Be Fair?
Authors: Joseph Rance; Filip Svoboda
Venue: AAAI 2025 (main)

Abstract: We show that current SOTA methods for privately and fairly training models are unreliable in many practical scenarios. Specifically, we (1) introduce a new type of adversarial attack that seeks to introduce unfairness into private model training, and (2) demonstrate that the use of methods for training on private data that are robust to adversarial attacks often leads to unfair models, regardless of the use of fairness-enhancing training methods. This leads to a dilemma when attempting to train fair models on private data: either (A) we use a robust training method which may introduce unfairness to the model itself, or (B) we train models which are vulnerable to adversarial attacks that introduce unfairness. This paper highlights flaws in robust learning methods when training fair models, yielding a new perspective for the design of robust and private learning systems.
Slice-and-Pack: Tailoring Deep Models for Customized Requirements
Authors: Ruice Rao; Dingwei Li; Ming Li
Venue: AAAI 2025 (main)

Abstract: The learnware paradigm aims to establish a learnware market such that users can build their own models by reusing appropriate existing models in the market without starting from scratch.
It is often the case that a single model is insufficient to fully satisfy the user's requirement.
Meanwhile,
offering multiple models can lead to higher costs for users
alongside an increase in hardware resource demands.
To address this challenge,
this paper proposes the ''Slice-and-Pack'' (S&P) framework to empower the market to provide users with only the required model fragments without having to offer entire abilities of all involved models.
Our framework first slices a set of models into small fragments and subsequently packs selected fragments according to user's specific requirement.
In the slicing stage,
we extract units layer by layer and connect these units to create numerous fragments.
In the packing stage,
an encoder-decoder mechanism is employed to assemble these fragments.
These processes are conducted within data-limited constraints due to privacy concerns.
Extensive experiments validate the effectiveness of our framework.
Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage
Authors: Md Rafi Ur Rashid; Jing Liu; Toshiaki Koike-Akino; Ye Wang; Shagufta Mehnaz
Venue: AAAI 2025 (main)

Abstract: Fine-tuning large language models on private data for downstream applications poses significant privacy risks in potentially exposing sensitive information. Several popular community platforms now offer convenient distribution of a large variety of pre-trained models, allowing anyone to publish without rigorous verification. This scenario creates a privacy threat, as pre-trained models can be intentionally crafted to compromise the privacy of fine-tuning datasets. In this study, we introduce a novel poisoning technique that uses model-unlearning as an attack tool. This approach manipulates a pre-trained language model to increase the leakage of private data during the fine-tuning process. Our method enhances both membership inference and data extraction attacks while preserving model utility. Experimental results across different models, datasets, and fine-tuning setups demonstrate that our attacks significantly surpass baseline performance. This work serves as a cautionary note for users who download pretrained models from unverified sources, highlighting the potential risks involved.
The VOROS: Lifting ROC Curves to 3D to Summarize Unbalanced Classifier Performance
Authors: Christopher Ratigan; Lenore Cowen
Venue: AAAI 2025 (main)

Abstract: While the area under the ROC curve is perhaps the most common measure that is  used to rank  relative performance of different binary classifiers, longstanding field folklore has noted that it can be a  measure that ill-captures the benefits of different classifiers when either the actual class values or misclassification costs are highly unbalanced between the two  classes. We introduce a new ROC surface, and the VOROS, a volume over this ROC surface, as a natural way to capture these costs, by lifting the  ROC curve to 3D. Compared to previous attempts to generalize the  ROC curve, our formulation provides also a simple and intuitive way to  model the scenario when only ranges, rather than exact values, are known for possible class imbalance and misclassification costs.
Error Bounds for Gaussian Process Regression Under Bounded Support Noise with Applications to Safety Certification
Authors: Robert Reed; Luca Laurenti; Morteza Lahijanian
Venue: AAAI 2025 (main)

Abstract: Gaussian Process Regression (GPR) is a powerful and elegant method for learning complex functions from noisy data with a wide range of applications, including in safety-critical domains. Such applications have two key features: (i) they require rigorous error quantification, and (ii) the noise is often bounded and non-Gaussian due to, e.g., physical constraints. While error bounds for applying GPR in the presence of non-Gaussian noise exist, they tend to be overly restrictive and conservative in practice. In this paper, we provide novel error bounds for GPR under bounded support noise. Specifically, by relying on concentration inequalities and assuming that the latent function has low complexity in the reproducing kernel Hilbert space (RKHS) corresponding to the GP kernel, we derive both probabilistic and deterministic bounds on the error of the GPR. We show that these errors are substantially tighter than existing state-of-the-art bounds and are particularly well-suited for GPR with neural network kernels, i.e., Deep Kernel Learning (DKL). Furthermore, motivated by applications in safety-critical domains, we illustrate how these bounds can be combined with stochastic barrier functions to successfully quantify the safety probability of an unknown dynamical system from finite data. We validate the efficacy of our approach through several benchmarks and comparisons against existing bounds. The results show that our bounds are consistently smaller, and that DKLs can produce error bounds tighter than sample noise, significantly improving the safety probability of control systems.
Improving Generalization for AI-Synthesized Voice Detection
Authors: Hainan Ren; Li Lin; Chun-Hao Liu; Xin Wang; Shu Hu
Venue: AAAI 2025 (main)

Abstract: AI-synthesized voice technology has the potential to create realistic human voices for beneficial applications, but it can also be misused for malicious purposes. While existing AI-synthesized voice detection models excel in intra-domain evaluation, they face challenges in generalizing across different domains, potentially becoming obsolete as new voice generators emerge. Current solutions use diverse data and advanced machine learning techniques (e.g., domain-invariant representation, self-supervised learning), but are limited by predefined vocoders and sensitivity to factors like background noise and speaker identity. In this work, we introduce an innovative disentanglement framework aimed at extracting domain-agnostic artifact features related to vocoders. Utilizing these features, we enhance model learning in a flat loss landscape, enabling escape from suboptimal solutions and improving generalization. Extensive experiments on benchmarks show our approach outperforms state-of-the-art methods, achieving up to 5.12% improvement in the equal error rate metric in intra-domain and 7.59% in cross-domain evaluations.
PrivDNFIS: Privacy-preserving and Efficient Deep Neuro-Fuzzy Inference System
Authors: Hao Ren; Xiao Lan; Rui Tang; Xingshu Chen
Venue: AAAI 2025 (main)

Abstract: Deep Neuro-Fuzzy Inference Systems (DNFIS) seamlessly fuse neural networks with the fuzzy inference system enabling intricate decision-making and knowledge representation, while upholding a commendable degree of adaptability and interpretability. However, the challenge of privacy-preserving inference (PI) over DNFIS has remained largely uncharted, with no prior research addressing this critical issue. In this paper, we embark on an exploration of this issue. We introduce an efficient and secure PI framework for DNFIS, named PrivDNFIS, which leverages the post-quantum lattice-based homomorphic encryption to implement secure computation protocols for PI over DNFIS. Our work incorporates several non-trivial performance enhancements. Firstly, it consolidates multiple elements of input feature vectors into a single message, reducing encryption/decryption overhead. Secondly, building upon this novel encoding approach, PrivDNFIS can perform ciphertext aggregation and vector-vector inner production without necessitating time-consuming ciphertext rotation operations. Thirdly, we replace the softmax function in the DNFIS layer with a quadratic function to further enhance inference efficiency, without compromising the inference accuracy. Under the given threat model, we provide formal security proof for PrivDNFIS. In comprehensive experimental results, PrivDNFIS demonstrates an approximately 1.9 to 4.4 times reduction in end-to-end time cost compared to the benchmark.
Monitoring Primitive Interactions During the Training of DNNs
Authors: Jie Ren; Xinhao Zheng; Jiyu Liu; Andrew Lizarraga; Ying Nian Wu; Liang Lin; Quanshi Zhang
Venue: AAAI 2025 (main)

Abstract: This paper focuses on the newly emerged research topic, i.e., whether the complex decision-making logic of a DNN can be mathematically summarized into a few simple logics. Beyond the explanation of a static DNN, in this paper, we hope to show that the seemingly complex learning dynamics of a DNN can be faithfully represented as the change of a few primitive interaction patterns encoded by the DNN. Therefore, we redefine the interaction of principal feature components in intermediate-layer features, which enables us to concisely summarize the highly complex dynamics of interactions throughout the learning of the DNN. The mathematical faithfulness of the new interaction is experimentally verified. From the perspective of learning efficiency, we find that the interactions naturally belong to five groups  (reliable, withdrawn, forgotten, betraying, and fluctuating interactions), each representing a distinct type of dynamics of an interaction being learned and/or being forgotten. This provides deep insights into the learning process of a DNN.