OpenRaiser/Intern-Atlas huggingface dataset:
Fair Column Subset Selection
Authors: Antonis Matakos, Bruno Ordozgoiti, Suhas Thejaswi
Venue: KDD
Year: 2024

The problem of column subset selection asks for a subset of columns from an input matrix such that the matrix can be reconstructed as accurately as possible within the span of the selected columns. A natural extension is to consider a setting where the matrix rows are partitioned into two groups, and the goal is to choose a subset of columns that minimizes the maximum reconstruction error of both groups, relative to their respective best rank-k approximation. Extending the known results of column subset selection to this fair setting is not straightforward: in certain scenarios it is unavoidable to choose columns separately for each group, resulting in double the expected column count. We propose a deterministic leverage-score sampling strategy for the fair setting and show that sampling a column subset of minimum size becomes NP-hard in the presence of two groups. Despite these negative results, we give an approximation algorithm that guarantees a solution within 1.5 times the optimal solution size. We also present practical heuristic algorithms based on rank-revealing QR factorization. Finally, we validate our methods through an extensive set of experiments using real-world data.
Explainable Artificial Intelligence on Biosignals for Clinical Decision Support
Authors: Miriam Cindy Maurer, Jacqueline Michelle Metsch, Philip Hempel, Theresa Bender, Nicolai Spicher, Anne-Christin Hauschild
Venue: KDD
Year: 2024

Deep learning has proven effective in several areas, including computer vision, natural language processing, and disease prediction, which can support clinicians in making decisions along the clinical pathway. However, in order to successfully integrate these algorithms into clinical practice, it is important that their decision-making processes are transparent, explainable, and interpretable. Firstly, this tutorial will introduce targeted eXplainable Artificial Intelligence (XAI) methods to address the urgent need for explainability of deep learning in healthcare applications. In particular, it focuses on algorithms for raw biosignals without prior feature extraction that enable medical diagnoses, specifically electrocardiograms (ECG) -- stemming from the heart -- and electroencephalograms (EEG) representing the electrical activity of the brain. Secondly, participants are provided with a comprehensive workflow that includes both data processing and an introduction to relevant network architectures. Subsequently, various XAI methods are described and it is shown, how the resulting relevance attributions can be visualized on biosignals. Finally, two compelling real-world use cases are presented that demonstrate the effectiveness of XAI in analyzing ECG and EEG signals for disease prediction and sleep classification, respectively. In summary, the tutorial will provide the skills required for gaining insight into the decision process of deep neural networks processing authentic clinical biosignal data.
Scaling Training Data with Lossy Image Compression
Authors: Katherine L. Mentzer, Andrea Montanari
Venue: KDD
Year: 2024

Empirically-determined scaling laws have been broadly successful in predicting the evolution of large machine learning models with training data and number of parameters. As a consequence, they have been useful for optimizing the allocation of limited resources, most notably compute time.In certain applications, storage space is an important constraint, and data format needs to be chosen carefully as a consequence. Computer vision is a prominent example: images are inherently analog, but are always stored in a digital format using a finite number of bits. Given a dataset of digital images, the number of bits L to store each of them can be further reduced using lossy data compression. This, however, can degrade the quality of the model trained on such images, since each example has lower resolution.In order to capture this trade-off and optimize storage of training data, we propose a 'storage scaling law' that describes the joint evolution of test error with sample size and number of bits per image. We prove that this law holds within a stylized model for image compression, and verify it empirically on two computer vision tasks, extracting the relevant parameters. We then show that this law can be used to optimize the lossy compression level. At given storage, models trained on optimally compressed images present a significantly smaller test error with respect to models trained on the original data. Finally, we investigate the potential benefits of randomizing the compression level.
Learning Causal Networks from Episodic Data
Authors: Osman Mian, Sarah Mameche, Jilles Vreeken
Venue: KDD
Year: 2024

In numerous real-world domains, spanning from environmental monitoring to long-term medical studies, observations do not arrive in a single batch but rather over time in episodes. This challenges the traditional assumption in causal discovery of a single, observational dataset, not only because each episode may be a biased sample of the population but also because multiple episodes could differ in the causal interactions underlying the observed variables. We address these issues using notions of context switches and episodic selection bias, and introduce a framework for causal modeling of episodic data. We show under which conditions we can apply information-theoretic scoring criteria for causal discovery while preserving consistency. To in practice discover the causal model progressively over time, we propose the CONTINENT algorithm which, taking inspiration from continual learning, discovers the causal model in an online fashion without having to re-learn the model upon arrival of each new episode. Our experiments over a variety of settings including selection bias, unknown interventions, and network changes showcase that CONTINENT works well in practice and outperforms the baselines by a clear margin.
European Data Science Day: KDD-2024 Special Day
Authors: Dunja Mladenic, Dumitru Roman
Venue: KDD
Year: 2024

The European Data Science Day offers a full day focused exclusively on innovative KDD-relevant research and development projects from national and regional funding programs, as well as corporate, start-up, and nonprofit channels. The idea is to bring together a diverse community of researchers in Data Science, Machine Learning, Language Technologies, and Knowledge Discovery, as well as partnerships in the social and physical sciences/arts, to showcase the state-of-the-art in research and applications.
Quantifying and Estimating the Predictability Upper Bound of Univariate Numeric Time Series
Authors: Jamal Mohammed, Michael H. Böhlen, Sven Helmer
Venue: KDD
Year: 2024

The intrinsic predictability of a given time series indicates how well an (ideal) algorithm could potentially predict it when trained on the time series data. Being able to compute the intrinsic predictability helps the developers of prediction algorithms immensely in deciding whether there is further optimization potential, as it tells them how close they are to what is (theoretically) achievable. We call the intrinsic predictability the predictability upper bound ¶imax and propose a novel method for quantifying and estimating it for univariate numeric time series. So far, this has only been done for symbolic time series, even though most real-world time series are numeric by nature. We base our technique on the close relationship between entropy and predictability, utilizing the entropy rate of a time series to compute ¶imax . Since existing entropy rate estimators, such as those based on the Lempel-Ziv compression algorithm, only work for symbolic data, we develop new estimators using tolerance thresholds for matching numeric values. We demonstrate that ¶imax is an effective upper bound that characterizes the intrinsic predictability of a time series. We give formal proofs and we validate our arguments experimentally by comparing ¶imax with the prediction accuracy of different state-of-the-art models on various real-world datasets from different domains.
Valuing an Engagement Surface using a Large Scale Dynamic Causal Model
Authors: Abhimanyu Mukerji, Sushant More, Ashwin Viswanathan Kannan, Lakshmi Ravi, Hua Chen, Naman Kohli, Chris Khawand, Dinesh Mandalapu
Venue: KDD
Year: 2024

With recent rapid growth in online shopping, AI-powered Engagement Surfaces (ES) have become ubiquitous across retail services. These engagement surfaces perform an increasing range of functions, including recommending new products for purchase, reminding customers of their orders and providing delivery notifications. Understanding the causal effect of engagement surfaces on value driven for customers and businesses remains an open scientific question. In this paper, we develop a dynamic causal model at scale to disentangle value attributable to an ES, and to assess its effectiveness. We demonstrate the application of this model to inform business decision-making by understanding returns on investment in the ES, and identifying product lines and features where the ES adds the most value.
DARE to Diversify: DAta Driven and Diverse LLM REd Teaming
Authors: Manish Nagireddy, Bernat Guillen Pegueroles, Ioana Baldini
Venue: KDD
Year: 2024

Large language models (LLMs) have been rapidly adopted, as showcased by ChatGPT's overnight popularity, and are integrated in products used by millions of people every day, such as search engines and productivity suites. Yet the societal impact of LLMs, encompassing both benefits and harms, is not well understood. Inspired by cybersecurity practices, red-teaming is emerging as a technique to uncover model vulnerabilities. Despite increasing attention from industry, academia, and government centered around red-teaming LLMs, such efforts are still limited in the diversity of the red-teaming focus, approaches and participants. Importantly, given that LLMs are becoming ubiquitous, it is imperative that red-teaming efforts are scaled out to include large segments of the research, practitioners and the people whom are directly affected by the deployment of these systems. The goal of this tutorial is two fold. First, we introduce the topic of LLM red-teaming by reviewing the state of the art for red-teaming practices, from participatory events to automatic AI-focused approaches, exposing the gaps in both the techniques and coverage of the targeted harms. Second, we plan to engage the audience in a hands-on and interactive exercise in LLM red-teaming to showcase the ease (or difficulty) of exposing model vulnerabilities, contingent on both the targeted harm and model capabilities. We believe that the KDD community of researchers and practitioners are in a unique position to address the existing gaps in red-teaming approaches, given their longstanding research and practice of extracting knowledge from data.
Privacy-Preserving Federated Learning using Flower Framework
Authors: Mohammad Naseri, Javier Fernández-Marqués, Yan Gao 0016, Heng Pan
Venue: KDD
Year: 2024

AI projects often face the challenge of limited access to meaningful amounts of training data. In traditional approaches, collecting data in a central location can be problematic, especially in industry settings with sensitive and distributed data. However, there is a solution -"moving the computation to the data" through Federated Learning.
Money Never Sleeps: Maximizing Liquidity Mining Yields in Decentralized Finance
Authors: Wangze Ni, Yiwei Zhao, Weijie Sun 0002, Lei Chen 0002, Peng Cheng 0003, Chen Jason Zhang, Xuemin Lin 0001
Venue: KDD
Year: 2024

The popularity of decentralized finance has drawn attention to liquidity mining (LM). In LM, a user deposits her cryptocurrencies into liquidity pools to provide liquidity for exchanges and earn yields. Different liquidity pools offer varying yields and require different pairs of cryptocurrencies. A user can exchange a cryptocurrency for another with some exchange costs. Thus, an LM solution consists of exchange transactions and deposit transactions, guaranteeing (1) each exchange transaction must exchange one cryptocurrency for another at a specific rate (i.e., the exchange constraint); (2) the amounts of cryptocurrencies deposited in a liquidity pool must exceed the required threshold (i.e., the minimum constraint); (3) each deposit transaction must deposit a specific pair of cryptocurrencies at a certain rate in a liquidity pool (i.e., the deposit constraint); and (4) the cryptocurrencies used in the solution do not exceed the cryptocurrencies that the user has (i.e., the budget constraint). Selecting the most profitable LM solution is challenging due to the vast number of candidate solutions. To address this challenge, we define the yield maximization liquidity mining (YMLM) problem. Given a set of liquidity pools, a set of the user's cryptocurrencies, a set of exchange rates, and an evaluation function, YMLM aims to find an LM solution with maximal yields, satisfying the minimum, exchange, deposit, and budget constraints. We prove that YMLM is NP-hard and cannot be solved by algorithms with constant approximation ratios. To tackle YMLM, we propose two algorithms, namely YMLM\_GD and YMLM\_SK, with parameterized approximation ratios. Extensive experiments on both real and synthetic datasets show that our approaches outperform the baselines in yields.
Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series
Authors: Kohei Obata, Koki Kawabata, Yasuko Matsubara, Yasushi Sakurai
Venue: KDD
Year: 2024

Multivariate time series data suffer from the problem of missing values, which hinders the application of many analytical methods. To achieve the accurate imputation of these missing values, exploiting inter-correlation by employing the relationships between sequences (i.e., a network) is as important as the use of temporal dependency, since a sequence normally correlates with other sequences. Moreover, exploiting an adequate network depending on time is also necessary since the network varies over time. However, in real-world scenarios, we normally know neither the network structure nor when the network changes beforehand. Here, we propose a missing value imputation method for multivariate time series, namely MissNet, that is designed to exploit temporal dependency with a state-space model and inter-correlation by switching sparse networks. The network encodes conditional independence between features, which helps us understand the important relationships for imputation visually. Our algorithm, which scales linearly with reference to the length of the data, alternatively infers networks and fills in missing values using the networks while discovering the switching of the networks. Extensive experiments demonstrate that MissNet outperforms the state-of-the-art algorithms for multivariate time series imputation and provides interpretable results.
Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I
Authors: Harrie Oosterhuis, Rolf Jagerman, Zhen Qin 0001, Xuanhui Wang, Michael Bendersky
Venue: KDD
Year: 2024

The traditional evaluation of information retrieval (IR) systems is generally very costly as it requires manual relevance annotation from human experts. Recent advancements in generative artificial intelligence -specifically large language models (LLMs)- can generate relevance annotations at an enormous scale with relatively small computational costs. Potentially, this could alleviate the costs traditionally associated with IR evaluation and make it applicable to numerous low-resource applications. However, generated relevance annotations are not immune to (systematic) errors, and as a result, directly using them for evaluation produces unreliable results.
Ontology Enrichment for Effective Fine-grained Entity Typing
Authors: Siru Ouyang, Jiaxin Huang 0001, Pranav Pillai, Yunyi Zhang 0001, Yu Zhang 0044, Jiawei Han 0001
Venue: KDD
Year: 2024

Fine-grained entity typing (FET) is the task of identifying specific entity types at a fine-grained level for entity mentions based on their contextual information. Conventional methods for FET require extensive human annotation, which is time-consuming and costly given the massive scale of data. Recent studies have been developing weakly supervised or zero-shot approaches. We study the setting of zero-shot FET where only an ontology is provided. However, most existing ontology structures lack rich supporting information and even contain ambiguous relations, making them ineffective in guiding FET. Recently developed language models, though promising in various few-shot and zero-shot NLP tasks, may face challenges in zero-shot FET due to their lack of interaction with task-specific ontology. In this study, we propose øurs, where we (1) enrich each node in the ontology structure with two categories of extra information:instance information for training sample augmentation andtopic information to relate types with contexts, and (2) develop a coarse-to-fine typing algorithm that exploits the enriched information by training an entailment model with contrasting topics and instance-based augmented training samples. Our experiments show that øurs achieves high-quality fine-grained entity typing without human annotation, outperforming existing zero-shot methods by a large margin and rivaling supervised methods. øurs also enjoys strong transferability to unseen and finer-grained types. We will open source this work upon acceptance.
Inference Optimization of Foundation Models on AI Accelerators
Authors: Youngsuk Park, Kailash Budhathoki, Liangfu Chen, Jonas M. Kübler, Jiaji Huang, Matthäus Kleindessner, Jun Huan, Volkan Cevher, Yida Wang 0003, George Karypis
Venue: KDD
Year: 2024

Powerful foundation models, including large language models (LLMs), with Transformer architectures have ushered in a new era of Generative AI across various industries. Industry and research community have witnessed a large number of new applications, based on those foundation models. Such applications include question and answer, customer services, image and video generation, and code completions, among others. However, as the number of model parameters reaches to hundreds of billions, their deployment incurs prohibitive inference costs and high latency in real-world scenarios. As a result, the demand for cost-effective and fast inference using AI accelerators is ever more higher. To this end, our tutorial offers a comprehensive discussion on complementary inference optimization techniques using AI accelerators. Beginning with an overview of basic Transformer architectures and deep learning system frameworks, we deep dive into system optimization techniques for fast and memory-efficient attention computations and discuss how they can be implemented efficiently on AI accelerators. Next, we describe architectural elements that are key for fast transformer inference. Finally, we examine various model compression and fast decoding strategies in the same context.
BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization
Authors: Zhongyi Pei, Zhiyao Cen, Yipeng Huang 0006, Chen Wang 0018, Lin Liu 0001, Philip S. Yu, Mingsheng Long, Jianmin Wang 0001
Venue: KDD
Year: 2024

Hyperparameter optimization (HPO) is known to be costly in deep learning, especially when leveraging automated approaches. Most of the existing automated HPO methods are accuracy-based, i.e., accuracy metrics are used to guide the trials of different hyperparameter configurations amongst a specific search space. However, many trials may encounter severe training problems, such as vanishing gradients and insufficient convergence, which can hardly be reflected by accuracy metrics in the early stages of the training and often result in poor performance. This leads to an inefficient optimization trajectory because the bad trials occupy considerable computation resources and reduce the probability of finding excellent hyperparameter configurations within a time limitation. In this paper, we propose Bad Trial Tackler (BTTackler), a novel HPO framework that introduces training diagnosis to identify training problems automatically and hence tackles bad trials. BTTackler diagnoses each trial by calculating a set of carefully designed quantified indicators and triggers early termination if any training problems are detected. Evaluations are performed on representative HPO tasks consisting of three classical deep neural networks (DNN) and four widely used HPO methods. To better quantify the effectiveness of an automated HPO method, we propose two new measurements based on accuracy and time consumption. Results show the advantage of BTTackler on two-fold: (1) it reduces 40.33% of time consumption to achieve the same accuracy comparable to baseline methods on average and (2) it conducts 44.5% more top-10 trials than baseline methods on average within a given time budget. We also released an open-source Python library that allows users to easily apply BTTackler to automated HPO processes with minimal code changes\footnotehttps://github.com/thuml/BTTackler.
Scalable Rule Lists Learning with Sampling
Authors: Leonardo Pellegrina, Fabio Vandin
Venue: KDD
Year: 2024

Learning interpretable models has become a major focus of machine learning research, given the increasing prominence of machine learning in socially important decision-making. Among interpretable models, rule lists are among the best-known and easily interpretable ones. However, finding optimal rule lists is computationally challenging, and current approaches are impractical for large datasets.
TSC: A Simple Two-Sided Constraint against Over-Smoothing
Authors: Furong Peng, Kang Liu, Xuan Lu 0001, Yuhua Qian, HongRen Yan, Chao Ma 0002
Venue: KDD
Year: 2024

Graph Convolutional Neural Network (GCN), a widely adopted method for analyzing relational data, enhances node discriminability through the aggregation of neighboring information. Usually, stacking multiple layers can improve the performance of GCN by leveraging information from high-order neighbors. However, the increase of the network depth will induce the over-smoothing problem, which can be attributed to the quality and quantity of neighbors changing: (a) neighbor quality, node's neighbors become overlapping in high order, leading to aggregated information becoming indistinguishable, (b) neighbor quantity, the exponentially growing aggregated neighbors submerges the node's initial feature by recursively aggregating operations. Current solutions mainly focus on one of the above causes and seldom consider both at once. Aiming at tackling both causes of over-smoothing in one shot, we introduce a simple Two-Sided Constraint (TSC) for GCNs, comprising two straightforward yet potent techniques: random masking and contrastive constraint. The random masking acts on the representation matrix's columns to regulate the degree of information aggregation from neighbors, thus preventing the convergence of node representations. Meanwhile, the contrastive constraint, applied to the representation matrix's rows, enhances the discriminability of the nodes. Designed as a plug-in module, TSC can be easily coupled with GCN or SGC architectures. Experimental analyses on diverse real-world graph datasets verify that our approach markedly reduces the convergence of node's representation and the performance degradation in deeper GCN.