GenAI4ELab/papercli-papers huggingface dataset:
Genomics Data Lossless Compression with (S, K)-Mer Encoding and Deep Neural Networks
Authors: Hui Sun; Liping Yi; Huidong Ma; Yongxia Sun; Yingfeng Zheng; Wenwen Cui; Meng Yan; Gang Wang; Xiaoguang Liu
Venue: AAAI 2025 (main)

Abstract: Learning-based compression shows competitive compression ratios for genomics data. It often includes three types of compressors: static, adaptive and semi-adaptive.  However, these existing compressors suffer from inferior compression ratios or throughput, and adaptive compressors also faces model cold-start problems. To address these issues, we propose DeepGeCo, a novel genomics data lossless adaptive compression framework with (s,k)-mer encoding and deep neural networks, involving three compression modes (MINI for static, PLUS for adaptive, ULTRA for semi-adaptive) for flexible requirements of compression ratios or throughput. In DeepGeCo, (1) we develop BiGRU and Transformer as the backbone to build Warm-Start and Supporter models in terms of cold-start problems. (2) We introduce (s,k)-mer encoding to pre-process genomics data before feeding it into the DNN model for improve model throughput, and we propose a new metric - Ranking of Throughput and Compression Ratio (RTCR) for effective encoding parameters selection.  (3) We design a threshold controller and a probabilistic mixer within the backbone to balance compression ratios and model throughput. Experiments on 10 real-world datasets show that DeepGeCo's three compression modes improve up to a 22.949X average throughput and up to a 31.095% average compression ratio improvement while occupying low CPU or GPU memory.
Pioneer: Physics-informed Riemannian Graph ODE for Entropy-increasing Dynamics
Authors: Li Sun; Ziheng Zhang; Zixi Wang; Yujie Wang; Qiqi Wan; Hao Li; Hao Peng; Philip S. Yu
Venue: AAAI 2025 (main)

Abstract: Dynamic interacting system modeling is important for understanding and simulating real world systems, e.g., meteorology and the spread of COVID. The system is typically described as a graph, where multiple objects dynamically interact with each other and evolve over time. In recent years, graph Ordinary Differential Equations (ODE) receive increasing research attentions. While achieving encouraging results, existing solutions prioritize the traditional Euclidean space, and neglect the intrinsic geometry of the system and physics laws, e.g., the principle of entropy increasing. The aforementioned limitations motivate us to rethink the system dynamics from a fresh perspective of Riemannian geometry, and pose a more realistic problem of physics-informed dynamic system modeling, considering the underlying geometry and physics law for the first time. In this paper, we present a novel physics-informed Riemannian graph ODE for a wide range of entropy-increasing dynamic systems (termed as Pioneer). In particular, we formulate a differential system on the Riemannian manifold, where a manifold-valued graph ODE is governed by the proposed constrained Ricci flow, and a manifold preserving Gyro-transform aware of system geometry. Theoretically, we report the provable entropy non-decreasing of our formulation, obeying the physics laws. Empirical results show the superiority of Pioneer on real datasets.
GDiffRetro: Retrosynthesis Prediction with Dual Graph Enhanced Molecular Representation and Diffusion Generation
Authors: Shengyin Sun; Wenhao Yu; Yuxiang Ren; Weitao Du; Liwei Liu; Xuecang Zhang; Ying Hu; Chen Ma
Venue: AAAI 2025 (main)

Abstract: Retrosynthesis prediction focuses on identifying reactants capable of synthesizing a target product. Typically, the retrosynthesis prediction involves two phases: Reaction Center Identification and Reactant Generation. However, we argue that most existing methods suffer from two limitations in the two phases: (i) Existing models do not adequately capture the ``face'' information in molecular graphs for the reaction center identification. (ii) Current approaches for the reactant generation predominantly use sequence generation in a 2D space, which lacks versatility in generating reasonable distributions for completed reactive groups and overlooks molecules' inherent 3D properties. To overcome the above limitations, we propose GDiffRetro. For the reaction center identification, GDiffRetro uniquely integrates the original graph with its corresponding dual graph to represent molecular structures, which helps guide the model to focus more on the faces in the graph. For the reactant generation, GDiffRetro employs a conditional diffusion model in 3D to further transform the obtained synthon into a complete reactant. Our experimental findings reveal that GDiffRetro outperforms state-of-the-art semi-template models across various evaluative metrics.
LLM4RSR: Large Language Models as Data Correctors for Robust Sequential Recommendation
Authors: Yatong Sun; Xiaochun Yang; Zhu Sun; Yan Wang; Bin Wang; Xinghua Qu
Venue: AAAI 2025 (main)

Abstract: Sequential Recommenders (SRs) are trained to predict the next item as the target given its preceding items as the input, assuming every input-target pair is matched and is reliable for training. However, users can be induced by external distractions to click on items inconsistent with their true preferences, resulting in unreliable training instances with mismatched input-target pairs. To resist unreliable data, researchers attempt to develop Robust SRs (RSRs). However, our data analysis unveils that existing RSRs are data-driven. That is, for most instances formed by infrequently co-occurred items, existing RSRs are uncertain about their reliability. 
To fill this gap, we propose a generic framework -- LLM4RSR (Large Language Models for Robust Sequential Recommendation) to semantically complement data-driven RSRs by correcting uncertain instances into reliable ones based on LLMs' semantic comprehension of items beyond co-occurrence. In this way, RSRs can be re-trained with the corrected data for better accuracy. This is a selective knowledge distillation procedure, where the LLM acts as a teacher guiding student RSRs via uncertain instances. To align LLMs with the data correction task and mitigate inherent hallucinations, we equip the LLM with profile, plan, and memory modules, which are automatically optimized via textual gradient descent, eliminating the need for human effort and expertise. Experiments on four real-world datasets spanning eight backbones verify the generality, effectiveness, and efficiency of LLM4RSR.
EDGE: Unknown-aware Multi-label Learning by Energy Distribution Gap Expansion
Authors: Yuchen Sun; Qianqian Xu; Zitai Wang; Zhiyong Yang; Junwei He
Venue: AAAI 2025 (main)

Abstract: Multi-label Out-Of-Distribution (OOD) detection aims to discriminate the OOD samples from the multi-label In-Distribution (ID) ones. Compared with its multiclass counterpart, it is crucial to model the joint information among classes. To this end, JointEnergy, which is a representative multi-label OOD inference criterion, summarizes the logits of all the classes. However, we find that JointEnergy can produce an imbalance problem in OOD detection, especially when the model lacks enough discrimination ability. Specifically, we find that the samples only related to minority classes tend to be classified as OOD samples due to the ambiguous energy decision boundary. Besides, imbalanced multi-label learning methods, originally designed for ID ones, would not make sense for OOD detection scenarios, even producing a serious negative transfer effect. In this paper, we resort to auxiliary outlier exposure (OE) and propose an unknown-aware multi-label learning framework to reshape the uncertainty energy space layout. In this framework, the energy score is separately optimized for tail ID samples and unknown samples, and the energy distribution gap between them is expanded, such that the tail ID samples can have a significantly larger energy score than the OOD ones. What's more, a simple yet effective measure is designed to select more informative OE datasets. Finally, comprehensive experimental results on multiple multi-label and OOD datasets reveal the effectiveness of the proposed method.
Hubness Change Point Detection
Authors: Ikumi Suzuki; Kazuo Hara; Eiji Murakami
Venue: AAAI 2025 (main)

Abstract: This study proposes a new change detection method that leverages hubness. 
Hubness is a phenomenon that occurs in high-dimensional spaces, where certain special data points, known as hub data, tend to be closer to other data points. 
Hubness is known to degrade the accuracy of methods based on nearest neighbor search. 
Therefore, many studies in the past have focused on reducing hubness to improve accuracy. 
In contrast, this study utilizes hubness to detect changes. 
Specifically, if there is no change, suppressing the hubness occurring in the two datasets obtained by dividing the time series data will result in a uniform data distribution. 
However, if there is a change, even if we try to reduce the hubness in the two datasets obtained by dividing the time series data before and after the change, the hubness will not be reduced, and the data distribution will not become uniform. 
We use this finding to detect changes. 
Experiments with synthetic data show that the proposed method achieves accuracy comparable to or exceeding that of existing methods. Additionally, the proposed method achieves good accuracy with real-world data from hydraulic systems and gas sensors, along with excellent runtime performance.
Language Pre-training Guided Masking Representation Learning for Time Series Classification
Authors: Liaoyuan Tang; Zheng Wang; Jie Wang; Guanxiong He; Zhezheng Hao; Rong Wang; Feiping Nie
Venue: AAAI 2025 (main)

Abstract: The representation learning of time series has a wide range of downstream tasks and applications in many practical scenarios. However, due to the complexity, spatiotemporality, and continuity of sequential stream data, compared with the representation learning of structural data such as images/videos, the time series self-supervised representation learning is even more challenging. Besides, the direct application of existing contrastive learning and masked autoencoder based approaches to time series representation learning encounters inherent theoretical limitations, such as ineffective augmentation and masking strategies. To this end, we propose a Language Pre-training guided Masking Representation Learning (LPMRL) for times series classification. Specifically, we first propose a novel language pre-training guided masking encoder for adaptively sampling semantic spatiotemporal patches via natural language descriptions and improving the discriminability of latent representations. Furthermore, we present the dual-information contrastive learning mechanism to explore both local and global information by meticulously designing high-quality hard negative samples of time series data samples. As a result, we also design various experiments, such as visualization of masking position and distribution and reconstruction error to verify the reasonability of proposed language guided masking technique. Last, we evaluate the performance of proposed representation learning via classification task conducted on 106 time series datasets, which demonstrates the effectiveness of proposed method.
Unlocking the Power of Patch: Patch-Based MLP for Long-Term Time Series Forecasting
Authors: Peiwang Tang; Weitai Zhang
Venue: AAAI 2025 (main)

Abstract: Recent studies have attempted to refine the Transformer architecture to demonstrate its effectiveness in Long-Term Time Series Forecasting (LTSF) tasks. Despite surpassing many linear forecasting models with ever-improving performance, we remain skeptical of Transformers as a solution for LTSF. We attribute the effectiveness of these models largely to the adopted Patch mechanism, which enhances sequence locality to an extent yet fails to fully address the loss of temporal information inherent to the permutation-invariant self-attention mechanism. Further investigation suggests that simple linear layers augmented with the Patch mechanism may outperform complex Transformer-based LTSF models. Moreover, diverging from models that use channel independence, our research underscores the importance of cross-variable interactions in enhancing the performance of multivariate time series forecasting. The interaction information between variables is highly valuable but has been misapplied in past studies, leading to suboptimal cross-variable models. Based on these insights, we propose a novel and simple Patch-based MLP (PatchMLP) for LTSF tasks. Specifically, we employ simple moving averages to extract smooth components and noise-containing residuals from time series data, engaging in semantic information interchange through channel mixing and specializing in random noise with channel independence processing. The PatchMLP model consistently achieves state-of-the-art results on several real-world datasets. We hope this surprising finding will spur new research directions in the LTSF field and pave the way for more efficient and concise solutions.
CoDeR: Counterfactual Demand Reasoning for Sequential Recommendation
Authors: Shuai Tang; Sitao Lin; Jianghong Ma; Xiaofeng Zhang
Venue: AAAI 2025 (main)

Abstract: Sequential recommendation systems aim to predict the next item based on users' historical interactions. While traditional methods focus on learning feature representations or user preferences, they often struggle with detecting subtle demand shifts in short sequences, especially when these shifts are obscured by noise or biases. To address these issues, we propose CoDeR (Counterfactual Demand Reasoning), a novel framework designed to handle demand shifts in sequential recommendations with greater precision. CoDeR features two key modules: (1) the User Demand Extraction module, which utilizes self-attention mechanisms and demand graphs to identify and model demand shifts from minimal user interactions; and (2) the Counterfactual Demand Reasoning module, which employs causal effect analysis and backdoor adjustment techniques to distinguish true demand shifts from noisy or biased signals. Our approach represents the first application of counterfactual reasoning to sequential recommendation systems. Comprehensive experiments on three real-world datasets demonstrate that CoDeR significantly outperforms existing baselines.
Adapting to Non-Stationary Environments: Multi-Armed Bandit Enhanced Retrieval-Augmented Generation on Knowledge Graphs
Authors: Xiaqiang Tang; Jian Li; Nan Du; Sihong Xie
Venue: AAAI 2025 (main)

Abstract: Despite the superior performance of Large language models on many NLP tasks, they still face significant limitations in memorizing extensive world knowledge. 
Recent studies have demonstrated that leveraging the Retrieval-Augmented Generation (RAG) framework, combined with Knowledge Graphs that encapsulate extensive factual data in a structured format, robustly enhances the reasoning capabilities of LLMs.
However, deploying such systems in real-world scenarios presents challenges: the continuous evolution of non-stationary environments may lead to performance degradation and user satisfaction requires a careful balance of performance and responsiveness.
To address these challenges, we introduce a Multi-objective Multi-Armed Bandit enhanced RAG framework, supported by multiple retrieval methods with diverse capabilities under rich and evolving retrieval contexts in practice.
Within this framework, each retrieval method is treated as a distinct "arm''. The system utilizes real-time user feedback to adapt to dynamic environments, by selecting the appropriate retrieval method based on input queries and the historical multi-objective performance of each arm.
Extensive experiments conducted on two benchmark KGQA datasets demonstrate that our method significantly outperforms baseline methods in non-stationary settings while achieving state-of-the-art performance in station environments.
ScoreNet: Consistency-driven Framework with Multi-side Information Fusion for Session-based Recommendation
Authors: Piao Tong; Qiao Liu; Zhipeng Zhang; Yuke Wang; Tian Lan
Venue: AAAI 2025 (main)

Abstract: Fusing side information in session-based recommendation is crucial for improving the performance of next-item prediction by providing additional context.  Recent methods optimize attention weights by combining item and side information embeddings. However, semantic heterogeneity between item IDs and side information introduces computational noise in attention calculation, leading to inconsistencies in user interest modeling and reducing the accuracy of candidate item scores. These methods also often fail to leverage session-based re-interaction patterns, limiting improvements in score prediction during the decoding phase.
To address these challenges, we propose ScoreNet, a consistency-driven framework with multi-side information fusion for session-based recommendation. ScoreNet explicitly models users' persistent preferences, generating consistent decoding scores for candidate items within a unified framework. It incorporates a multi-path re-engagement network to capture re-interaction behavior patterns in a semantic-agnostic manner, enhancing side information fusion while avoiding semantic interference. Additionally, a position-enhanced consistent scoring network redistributes attention scores within sessions, improving prediction accuracy, especially for items with limited interactions. Extensive experiments on three real-world datasets demonstrate that ScoreNet outperforms state-of-the-art models.
POI Recommendation via Multi-Objective Adversarial Imitation Learning
Authors: Zhenglin Wan; Anjun Gao; Xingrui Yu; Pingfu Chao; Jun Song; Maohao Ran
Venue: AAAI 2025 (main)

Abstract: Point-of-Interest (POI) recommendation aims to predict users' future locations based on their historical check-ins. Despite the success of recent deep learning approaches in capturing POI semantics and user behavior, they continue to face the persistent problem of data sparsity and incompleteness. In this paper, we introduce Multi-Objective Adversarial Imitation Recommender (MOAIR), a novel framework that integrates Generative Adversarial Imitation Learning with multi-objective to address this issue. MOAIR effectively captures user behavior patterns and spatial-temporal contextual information via graph-enhanced self-supervised state encoder and overcomes data sparsity by robustly learning from limited data and generating diverse samples. By accommodating diverse user patterns in the training data, the framework also mitigates the typical mode-collapse issue in generative adversarial learning and thus enhances the overall performance. MOAIR employs a multi-objective imitation learning architecture where the imitation learning agent (IL agent) explores the POI space and receives multifaceted reward signals. Utilizing the Paralleled Proximal Policy Optimization (3PO) framework to optimize multi-objectives, the IL agent ensures efficient and stable policy updates. Additionally, to address the issue of high noise in POI recommendation scenarios, we use a novel generative way to define our policy net and incorporate a variational bottleneck for regularization to enhance the stability of adversarial learning. Comprehensive experiments reveal the superior performance for MOAIR compared to other baseline approaches, especially with sparse training data.
Explicit and Implicit Examinee-Question Relation Exploiting for Efficient Computerized Adaptive Testing
Authors: Changqian Wang; Shangshang Yang; Siyu Song; Ziwen Wang; Haiping Ma; Xingyi Zhang; Bo Jin
Venue: AAAI 2025 (main)

Abstract: Computerized adaptive testing(CAT) is a crucial task in computer-aided education, which aims to adaptively select suitable question to diagnose examinees' ability status. Existing CAT approaches enhance selection performance by exploring examinee-question(E-Q) relation. These approaches either exclusively utilize explicit E-Q relation. For instance, policy-based approaches determine question selection based on predefined criteria. While effective in adapting to changes in question banks, these methods often entail significant computational costs in searching for suitable questions. Conversely, some studies focus solely on implicit E-Q relation. For example, learning-based approaches train agents to efficiently select questions by learning from large-scale datasets. However, they may struggle with newly introduced questions. Additionally, most of these existing question selectors are based on greedy strategies, which potentially overlooks promising quuestions. To bridge the above two types of approaches, we propose a novel framework named Relation Exploiting-based CAT(RECAT) by exploring and exploiting the implicit and explicit examinee-question relation. Specifically, we first define an examinee true ability-oriented selection objective to select more suitable questions. Then, to learn the implicit E-Q relation, we design a question selector, which explores the examinee ability and generates best-fitting questions for specific examinee ability from two aspects, including generation consistency and knowledge matching. The former aims to maximize the likelihood estimation of the implicit E-Q relation learning process, while the latter is employed to fit the distribution of real questions. To fully exploit explicit E-Q relation, we generate a high-quality candidate set for the given examinee's ability using implicit E-Q relation, which streamlines the search process, minimizing selection latency. We demonstrate the effectiveness and efficiency of our framework through comprehensive experiments on real-world datasets.
ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data
Authors: Chengsen Wang; Qi Qi; Jingyu Wang; Haifeng Sun; Zirui Zhuang; Jinming Wu; Lei Zhang; Jianxin Liao
Venue: AAAI 2025 (main)

Abstract: Human experts typically integrate numerical and textual multimodal information to analyze time series. However, most traditional deep learning predictors rely solely on unimodal numerical data, using a fixed-length window for training and prediction on a single dataset, and cannot adapt to different scenarios. The powered pre-trained large language model has introduced new opportunities for time series analysis. Yet, existing methods are either inefficient in training, incapable of handling textual information, or lack zero-shot forecasting capability. In this paper, we innovatively model time series as a foreign language and construct ChatTime, a unified framework for time series and text processing. As an out-of-the-box multimodal time series foundation model, ChatTime provides zero-shot forecasting capability and supports bimodal input/output for both time series and text. We design a series of experiments to verify the superior performance of ChatTime across multiple tasks and scenarios, and create four multimodal datasets to address data gaps. The experimental results demonstrate the potential and utility of ChatTime.