OpenRaiser/Intern-Atlas huggingface dataset:
FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning
Authors: Weirui Kuang, Bingchen Qian, Zitao Li, Daoyuan Chen, Dawei Gao, Xuchen Pan, Yuexiang Xie, Yaliang Li, Bolin Ding, Jingren Zhou 0001
Venue: KDD
Year: 2024

Large language models (LLMs) have demonstrated great capabilities in various natural language understanding and generation tasks. These pre-trained LLMs can be further improved for specific downstream tasks by fine-tuning. However, the adoption of LLM in real-world applications can be hindered by privacy concerns and the resource-intensive nature of model training and fine-tuning. When multiple entities have similar interested tasks but cannot directly share their local data due to privacy regulations, federated learning (FL) is a mainstream solution to leverage the data of different entities. Besides avoiding direct data sharing, FL can also achieve rigorous data privacy protection, model intelligent property protection, and model customization via composition with different techniques. Despite the aforementioned advantages of FL, fine-tuning LLMs in FL settings still lacks adequate support from the existing frameworks and, therefore, faces challenges in optimizing the consumption of significant communication and computational resources, preparing various data for different tasks, and satisfying diverse information protection demands. In this paper, we discuss these challenges and introduce our package FederatedScope-LLM (FS-LLM) as a main contribution, which consists: (1) We build a complete end-to-end benchmarking pipeline under real-world scenarios, automizing the processes of dataset preprocessing, federated fine-tuning execution or simulation, and performance evaluation; (2) We provide comprehensive and off-the-shelf federated parameter-efficient fine-tuning (PEFT) algorithm implementations and versatile programming interfaces for future extension, enhancing the capabilities of LLMs in FL scenarios with low communication and computation costs, even without accessing the full model; (3) We adopt several accelerating and resource-efficient operators, and provide flexible pluggable sub-routines for interdisciplinary study. We conduct extensive and reproducible experiments to show the effectiveness of FS-LLM and benchmark advanced LLMs with PEFT algorithms in FL. We release FS-LLM at https://github.com/alibaba/FederatedScope/tree/llm.
Know, Grow, and Protect Net Worth: Using ML for Asset Protection by Preventing Overdraft Fees
Authors: Avishek Kumar, Tyson Silver
Venue: KDD
Year: 2024

When a customer overdraws their bank account and their balance is negative they are assessed an overdraft fee. Americans pay approximately $15 billion in unnecessary overdraft fees a year, often in $35 increments; users of the Mint personal finance app pay approximately $250 million in fees a year in particular. These overdraft fees are an excessive financial burden and lead to cascading overdraft fees trapping customers in financial hardship. To address this problem, we have created an ML-driven overdraft early warning system (ODEWS) that assesses a customer's risk of overdrafting within the next week using their banking and transaction data in the Mint app. At-risk customers are sent an alert so they can take steps to avoid the fee, ultimately changing their behavior and financial habits. The system deployed resulted in a $3 million savings in overdraft fees for Mint customers compared to a control group. Moreover, the methodology outlined here is part of a greater effort to provide ML-driven personalized financial advice to help our members know, grow, and protect their net worth, ultimately, achieving their financial goals.
Max-Min Diversification with Asymmetric Distances
Authors: Iiro Kumpulainen, Florian Adriaens, Nikolaj Tatti
Venue: KDD
Year: 2024

One of the most well-known and simplest models for diversity maximization is the Max-Min Diversification (MMD) model, which has been extensively studied in the data mining and database literature. In this paper, we initiate the study of the Asymmetric Max-Min Diversification (AMMD) problem. The input is a positive integer k and a complete digraph over n vertices, together with a nonnegative distance function over the edges obeying the directed triangle inequality. The objective is to select a set of k vertices, which maximizes the smallest pairwise distance between them. AMMD reduces to the well-studied MMD problem in case the distances are symmetric, and has natural applications to query result diversification, web search, and facility location problems. Although the MMD problem admits a simple 1/2-approximation by greedily selecting the next-furthest point, this strategy fails for AMMD and it remained unclear how to design good approximation algorithms for AMMD.
Compact Decomposition of Irregular Tensors for Data Compression: From Sparse to Dense to High-Order Tensors
Authors: Taehyung Kwon, Jihoon Ko, Jinhong Jung, Jun-Gi Jang, Kijung Shin
Venue: KDD
Year: 2024

An irregular tensor is a collection of matrices with different numbers of rows. Real-world data from diverse domains, including medical and stock data, are effectively represented as irregular tensors due to the inherent variations in data length. For their analysis, various tensor decomposition methods (e.g., PARAFAC2) have been devised. While they are expected to be effective in compressing large-scale irregular tensors, akin to regular tensor decomposition methods, our analysis reveals that their compression performance is limited due to the larger number of first mode factor matrices.
Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks
Authors: Yurui Lai, Xiaoyang Lin, Renchi Yang, Hongtao Wang
Venue: KDD
Year: 2024

In recent years, graph neural networks (GNNs) have emerged as a potent tool for learning on graph-structured data and won fruitful successes in varied fields. The majority of GNNs follow the message-passing paradigm, where representations of each node are learned by recursively aggregating features of its neighbors. However, this mechanism brings severe over-smoothing and efficiency issues over high-degree graphs (HDGs), wherein most nodes have dozens (or even hundreds) of neighbors, such as social networks, transaction graphs, power grids, etc. Additionally, such graphs usually encompass rich and complex structure semantics, which are hard to capture merely by feature aggregations in GNNs.Motivated by the above limitations, we propose TADA, an efficient and effective front-mounted data augmentation framework for GNNs on HDGs. Under the hood, TADA includes two key modules: (i) feature expansion with structure embeddings, and (ii) topology- and attribute-aware graph sparsification. The former obtains augmented node features and enhanced model capacity by encoding the graph structure into high-quality structure embeddings with our highly-efficient sketching method. Further, by exploiting task-relevant features extracted from graph structures and attributes, the second module enables the accurate identification and reduction of numerous redundant/noisy edges from the input graph, thereby alleviating over-smoothing and facilitating faster feature aggregations over HDGs. Empirically, \algo considerably improves the predictive performance of mainstream GNN models on 8 real homophilic/heterophilic HDGs in terms of node classification, while achieving efficient training and inference processes.
The First Workshop on AI Behavioral Science
Authors: Himabindu Lakkaraju, Qiaozhu Mei, Chenhao Tan, Jie Tang 0001, Yutong Xie 0007
Venue: KDD
Year: 2024

This workshop initiates a new study field which may be named AI behavioral science. It discusses recent findings, methodologies, applications, and potential societal impacts that are related to analyzing, understanding, and directing the behaviors of AI models, especially those built upon large language models. This half-day workshop includes several keynote and invited talks, a poster session, and a panel discussion.
ShapeFormer: Shapelet Transformer for Multivariate Time Series Classification
Authors: Xuan-May Le, Ling Luo 0002, Uwe Aickelin, Minh-Tuan Tran
Venue: KDD
Year: 2024

Multivariate time series classification (MTSC) has attracted significant research attention due to its diverse real-world applications. Recently, exploiting transformers for MTSC has achieved state-of-the-art performance. However, existing methods focus on generic features, providing a comprehensive understanding of data, but they ignore class-specific features crucial for learning the representative characteristics of each class. This leads to poor performance in the case of imbalanced datasets or datasets with similar overall patterns but differing in minor class-specific details. In this paper, we propose a novel Shapelet Transformer (ShapeFormer), which comprises class-specific and generic transformer modules to capture both of these features. In the class-specific module, we introduce the discovery method to extract the discriminative subsequences of each class (i.e. shapelets) from the training set. We then propose a Shapelet Filter to learn the difference features between these shapelets and the input time series. We found that the difference feature for each shapelet contains important class-specific features, as it shows a significant distinction between its class and others. In the generic module, convolution filters are used to extract generic features that contain information to distinguish among all classes. For each module, we employ the transformer encoder to capture the correlation between their features. As a result, the combination of two transformer modules allows our model to exploit the power of both types of features, thereby enhancing the classification performance. Our experiments on 30 UEA MTSC datasets demonstrate that ShapeFormer has achieved the highest accuracy ranking compared to state-of-the-art methods. The code is available at https://github.com/xuanmay2701/shapeformer.
Layer-Wise Adaptive Gradient Norm Penalizing Method for Efficient and Accurate Deep Learning
Authors: Sunwoo Lee 0001
Venue: KDD
Year: 2024

Sharpness-aware minimization (SAM) is known to improve the generalization performance of neural networks. However, it is not widely used in real-world applications yet due to its expensive model perturbation cost. A few variants of SAM have been proposed to tackle such an issue, but they commonly do not alleviate the cost noticeably. In this paper, we propose a lightweight layer-wise gradient norm penalizing method that tackles the expensive computational cost of SAM while maintaining its superior generalization performance. Our study empirically proves that the gradient norm of the whole model can be effectively suppressed by penalizing the gradient norm of only a few critical layers. We also theoretically show that such a partial model perturbation does not harm the convergence rate of SAM, allowing them to be safely adapted in real-world applications. To demonstrate the efficacy of the proposed method, we perform extensive experiments comparing the proposed method to mini-batch SGD and the conventional SAM using representative computer vision and language modeling benchmarks.
Continual Collaborative Distillation for Recommender System
Authors: Gyuseok Lee, SeongKu Kang, Wonbin Kweon, Hwanjo Yu
Venue: KDD
Year: 2024

Knowledge distillation (KD) has emerged as a promising technique for addressing the computational challenges associated with deploying large-scale recommender systems. KD transfers the knowledge of a massive teacher system to a compact student model, to reduce the huge computational burdens for inference while retaining high accuracy. The existing KD studies primarily focus on one-time distillation in static environments, leaving a substantial gap in their applicability to real-world scenarios dealing with continuously incoming users, items, and their interactions. In this work, we delve into a systematic approach to operating the teacher-student KD in a non-stationary data stream. Our goal is to enable efficient deployment through a compact student, which preserves the high performance of the massive teacher, while effectively adapting to continuously incoming data. We propose <u>C</u>ontinual <u>C</u>ollaborative <u>D</u>istillation (CCD) framework, where both the teacher and the student continually and collaboratively evolve along the data stream. CCD facilitates the student in effectively adapting to new data, while also enabling the teacher to fully leverage accumulated knowledge. We validate the effectiveness of CCD through extensive quantitative, ablative, and exploratory experiments on two real-world datasets. We expect this research direction to contribute to narrowing the gap between existing KD studies and practical applications, thereby enhancing the applicability of KD in real-world systems.
SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning
Authors: Jongha Lee, Sunwoo Kim 0006, Kijung Shin
Venue: KDD
Year: 2024

To detect anomalies in real-world graphs, such as social, email, and financial networks, various approaches have been developed. While they typically assume static input graphs, most real-world graphs grow over time, naturally represented as edge streams. In this context, we aim to achieve three goals: (a) instantly detecting anomalies as they occur, (b) adapting to dynamically changing states, and (c) handling the scarcity of dynamic anomaly labels.
2nd Workshop on Causal Inference and Machine Learning in Practice
Authors: Jeong-Yoon Lee, Yifeng Wu, Totte Harinen, Jing Pan, Paul Lo, Zhenyu Zhao, Huigang Chen, Zeyu Zheng 0002, Hasta Vanchinathan, Yingfei Wang, Roland Stevenson
Venue: KDD
Year: 2024

The workshop's rationale stems from the escalating interest in causal inference and machine learning methodologies within various industrial contexts. This surge in demand underscores the importance for both scholars and practitioners to exchange knowledge and best practices regarding the application of these techniques to tackle real-world challenges. Yet, applying causal machine learning techniques in real-world scenarios presents a range of challenges not addressed in the academic literature. This workshop aims to address the challenges for practical causal machine learning and explore new industry use cases. The workshop will provide a forum for practitioners and researchers to exchange ideas and explore new collaborations. Moreover, this workshop aims to capitalize on the success and achievements of the KDD 2023 Workshop titled "Causal Inference and Machine Learning in Practice".
Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed Graphs
Authors: Hao Li 0080, Hao Jiang 0010, Jiajun Fan, Dongsheng Ye, Liang Du
Venue: KDD
Year: 2024

Persistent homology, a fundamental technique within Topological Data Analysis (TDA), captures structural and shape characteristics of graphs, yet encounters computational difficulties when applied to dynamic directed graphs. This paper introduces the Dynamic Neural Dowker Network (DNDN), a novel framework specifically designed to approximate the results of dynamic Dowker filtration, aiming to capture the high-order topological features of dynamic directed graphs. Our approach creatively uses line graph transformations to produce both source and sink line graphs, highlighting the shared neighbor structures that Dowker complexes focus on. The DNDN incorporates a Source-Sink Line Graph Neural Network (SSLGNN) layer to effectively capture the neighborhood relationships among dynamic edges. Additionally, we introduce an innovative duality edge fusion mechanism, ensuring that the results for both the sink and source line graphs adhere to the duality principle intrinsic to Dowker complexes. Our approach is validated through comprehensive experiments on real-world datasets, demonstrating DNDN's capability not only to effectively approximate dynamic Dowker filtration results but also to perform exceptionally in dynamic graph classification tasks.
Causal Subgraph Learning for Generalizable Inductive Relation Prediction
Authors: Mei Li, Xiaoguang Liu 0001, Hua Ji, Shuangjia Zheng
Venue: KDD
Year: 2024

Inductive relation reasoning in knowledge graphs aims at predicting missing triplets involving unseen entities and/or unseen relations. While subgraph-based methods that reason about the local structure surrounding a candidate triplet have shown promise, they often fall short in accurately modeling the causal dependence between a triplet's subgraph and its ground-truth label. This limitation typically results in a susceptibility to spurious correlations caused by confounders, adversely affecting generalization capabilities. Herein, we introduce a novel front-door adjustment-based approach designed to learn the causal relationship between subgraphs and their ground-truth labels, specifically for inductive relation prediction. We conceptualize the semantic information of subgraphs as a mediator and employ a graph data augmentation mechanism to create augmented subgraphs. Furthermore, we integrate a fusion module and a decoder within the front-door adjustment framework, enabling the estimation of the mediator's combination with augmented subgraphs. We also introduce the reparameterization trick in the fusion model to enhance model robustness. Extensive experiments on widely recognized benchmark datasets demonstrate the proposed method's superiority in inductive relation prediction, particularly for tasks involving unseen entities and unseen relations. Additionally, the subgraphs reconstructed by our decoder offer valuable insights into the model's decision-making process, enhancing transparency and interpretability.
Privileged Knowledge State Distillation for Reinforcement Learning-based Educational Path Recommendation
Authors: Qingyao Li, Wei Xia 0001, Li&apos, ang Yin, Jiarui Jin, Yong Yu 0001
Venue: KDD
Year: 2024

Educational recommendation seeks to suggest knowledge concepts that match a learner's ability, thus facilitating a personalized learning experience. In recent years, reinforcement learning (RL) methods have achieved considerable results by taking the encoding of the learner's exercise log as the state and employing an RL-based agent to make suitable recommendations. However, these approaches suffer from handling the diverse and dynamic learner's knowledge states. In this paper, we introduce the privileged feature distillation technique and propose the P rivileged K nowledge S tate D istillation (PKSD ) framework, allowing the RL agent to leverage the "actual'' knowledge state as privileged information in the state encoding to help tailor recommendations to meet individual needs. Concretely, our PKSD takes the privileged knowledge states together with the representations of the exercise log for the state representations during training. And through distillation, we transfer the ability to adapt to learners to aknowledge state adapter. During inference, theknowledge state adapter would serve as the estimated privileged knowledge states instead of the real one since it is not accessible. Considering that there are strong connections among the knowledge concepts in education, we further propose to collaborate the graph structure learning for concepts into our PKSD framework. This new approach is termed GEPKSD (Graph-Enhanced PKSD). As our method is model-agnostic, we evaluate PKSD and GEPKSD by integrating them with five different RL bases on four public simulators, respectively. Our results verify that PKSD can consistently improve the recommendation performance with various RL methods, and our GEPKSD could further enhance the effectiveness of PKSD in all the simulations.
Rethinking Fair Graph Neural Networks from Re-balancing
Authors: Zhixun Li, Yushun Dong, Qiang Liu 0006, Jeffrey Xu Yu
Venue: KDD
Year: 2024

Driven by the powerful representation ability of Graph Neural Networks (GNNs), plentiful GNN models have been widely deployed in many real-world applications. Nevertheless, due to distribution disparities between different demographic groups, fairness in high-stake decision-making systems is receiving increasing attention. Although lots of recent works devoted to improving the fairness of GNNs and achieved considerable success, they all require significant architectural changes or additional loss functions requiring more hyper-parameter tuning. Surprisingly, we find that simple re-balancing methods can easily match or surpass existing fair GNN methods. We claim that the imbalance across different demographic groups is a significant source of unfairness, resulting in imbalanced contributions from each group to the parameters updating. However, these simple re-balancing methods have their own shortcomings during training. In this paper, we propose FairGB, <u>Fair</u> <u>G</u>raph Neural Network via re-<u>B</u>alancing, which mitigates the unfairness of GNNs by group balancing. Technically, FairGB consists of two modules: counterfactual node mixup and contribution alignment loss. Firstly, we select counterfactual pairs across inter-domain and inter-class, and interpolate the ego-networks to generate new samples. Guided by analysis, we can reveal the debiasing mechanism of our model by the causal view and prove that our strategy can make sensitive attributes statistically independent from target labels. Secondly, we reweigh the contribution of each group according to gradients. By combining these two modules, they can mutually promote each other. Experimental results on benchmark datasets show that our method can achieve state-of-the-art results concerning both utility and fairness metrics. Code is available at https://github.com/ZhixunLEE/FairGB.
SimDiff: Simple Denoising Probabilistic Latent Diffusion Model for Data Augmentation on Multi-modal Knowledge Graph
Authors: Ran Li, Shimin Di, Lei Chen 0002, Xiaofang Zhou 0001
Venue: KDD
Year: 2024

In this paper, we address the challenges of data augmentation in Multi-Modal Knowledge Graphs (MMKGs), a relatively under-explored area. We propose a novel diffusion-based generative model, the Simple Denoising Probabilistic Latent Diffusion Model (SimDiff). SimDiff is capable of handling different data modalities including the graph topology in a unified manner by the same diffusion model in the latent space. It enhances the utilization of multi-modal data and encourage the multi-modal fusion and reduces the dependency on limited training data. We validate our method in downstream Entity Alignment (EA) tasks in MMKGs, demonstrating that even when using only half of the seed entities in training, our methods can still achieve superior performance. This work contributes to the field by providing a new data generation or augmentation method for MMKGs, potentially paving the way for more effective use of MMKGs in various applications. Code is made available at https://github.com/ranlislz/SimDiff.
Self-Distilled Disentangled Learning for Counterfactual Prediction
Authors: Xinshu Li 0001, Mingming Gong, Lina Yao 0001
Venue: KDD
Year: 2024

The advancements in disentangled representation learning significantly enhance the accuracy of counterfactual predictions by granting precise control over instrumental variables, confounders, and adjustable variables. An appealing method for achieving the independent separation of these factors is mutual information minimization, a task that presents challenges in numerous machine learning scenarios, especially within high-dimensional spaces. To circumvent this challenge, we propose the Self-Distilled Disentanglement framework, referred to as SD2. Grounded in information theory, it ensures theoretically sound independent disentangled representations without intricate mutual information estimator designs for high-dimensional representations. Our comprehensive experiments, conducted on both synthetic and real-world datasets, confirms the effectiveness of our approach in facilitating counterfactual inference in the presence of both observed and unobserved confounders.