OpenRaiser/Intern-Atlas huggingface dataset:
LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions
Authors: Anand Brahmbhatt, Mohith Pokala, Rishi Saket, Aravindan Raghuveer
Venue: CIKM
Year: 2024

With large neural models becoming increasingly accurate and powerful, they have raised privacy and transparency concerns on data usage. Therefore, data platforms, regulations and user expectations are rapidly evolving leading to enforcing privacy via aggregation. We focus on the use case of online advertising where the emergence of aggregate data is imminent and can significantly impact the multi-billion dollar industry. In aggregated datasets, labels are assigned to groups of data points rather than individual data points. This leads to a formulation of a weakly supervised task - Learning from Label Proportions where a model is trained on groups (a.k.a bags) of instances and their corresponding label proportions to predict labels for individual instances. While learning on aggregate data due to privacy concerns is becoming increasingly popular there is no large scale benchmark for measuring performance and guiding improvements on this important task. We propose LLP-Bench - a web scale benchmark with ~ 70 datasets and 45 million datapoints. To the best of our knowledge, LLP-Bench is the first large scale tabular LLP benchmark with an extensive diversity in constituent datasets, realistic in terms of the sponsored search datasets used and aggregation mechanisms followed. Through more than 3000 experiments we compare the performance of 9 SOTA methods in detail. To the best of our knowledge, this is the first study that compares diverse approaches in such depth.
Facets of Disparate Impact: Evaluating Legally Consistent Bias in Machine Learning
Authors: Jarren Briscoe, Assefaw H. Gebremedhin
Venue: CIKM
Year: 2024

Leveraging current legal standards, we define bias through the lens of marginal benefits and objective testing with the novel metric "Objective Fairness Index". This index combines the contextual nuances of objective testing with metric stability, providing a legally consistent and reliable measure. Utilizing the Objective Fairness Index, we provide fresh insights into sensitive machine learning applications, such as COMPAS (recidivism prediction), highlighting the metric's practical and theoretical significance. The Objective Fairness Index allows one to differentiate between discriminatory tests and systemic disparities.
Pairing Clustered Inverted Indexes with κ-NN Graphs for Fast Approximate Retrieval over Learned Sparse Representations
Authors: Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli, Rossano Venturini
Venue: CIKM
Year: 2024

Learned sparse representations form an effective and interpretable class of embeddings for text retrieval. While exact top-k retrieval over such embeddings faces efficiency challenges, a recent algorithm called Seismic has enabled remarkably fast, highly-accurate approximate retrieval. Seismic statically prunes inverted lists, organizes each list into geometrically-cohesive blocks, and augments each block with a summary vector. At query time, each inverted list associated with a query term is traversed one block at a time in an arbitrary order, with the inner product between the query and summaries determining if a block must be evaluated. When a block is deemed promising, its documents are fully evaluated with a forward index. Seismic is one to two orders of magnitude faster than state-of-the-art inverted index-based solutions and significantly outperforms the winning graph-based submissions to the BigANN 2023 Challenge. In this work, we speed up Seismic further by introducing two innovations to its query processing subroutine. First, we traverse blocks in order of importance, rather than arbitrarily. Second, we take the list of documents retrieved by Seismic and expand it to include the neighbors of each document using an offline k-regular nearest neighbor graph; the expanded list is then ranked to produce the final top-k set. Experiments on two public datasets show that our extension, named SeismicWave, can reach almost-exact accuracy levels and is up to 2.2x faster than Seismic.
Wise Fusion: Group Fairness Enhanced Rank Fusion
Authors: Kathleen Cachel, Elke A. Rundensteiner
Venue: CIKM
Year: 2024

Rank fusion is a technique for combining multiple rankings into a single aggregated ranking, commonly used in high-stakes applications. For hiring decisions, a fused ranking might combine evaluations of different candidates from various job boards into one list. Ideally, such fused rankings are fair. Meaning they do not withhold opportunities or resources from marginalized groups of candidates, even if such biases may be present in the to-be-fused rankings. Prior work fairly aggregating rankings is limited to ensuring proportional (not addressing equality) fairness when combining ranked lists containing the same candidate items. Yet, real-world fusion tasks often combine rankings of varying candidate sets, may also contain relevance scores, or are better suited to equal representation. To address fairness in these settings, we present a new plug-and-play fairness-aware fusion strategy: WISE fusion. WISE works in fusion settings where we have closed-box access to a score-powered rank fusion (SRF) method, making it possible to fairness-enhance existing fusion pipelines with little added cost. WISE uses existing evaluations of candidates from an as-is SRF method to achieve proportional or equal rank fairness in the final fused ranking. Our experimental study demonstrates that WISE beats the fairness and utility performance of state-of-the-art methods applied to these new fair rank fusion settings.
FairRankTune: A Python Toolkit for Fair Ranking Tasks
Authors: Kathleen Cachel, Elke A. Rundensteiner
Venue: CIKM
Year: 2024

We present FairRankTune, a multi-purpose open-source Python toolkit offering three primary services: quantifying fairness-related harms, leveraging bias mitigation algorithms, and constructing custom fairness-relevant datasets. FairRankTune provides researchers and practitioners with a self-contained resource for fairness auditing, experimentation, and advancing research. The central piece of FairRankTune is a novel fairness-tunable ranked data generator, RankTune, that streamlines the creation of custom fairness-relevant ranked datasets. FairRankTune also offers numerous fair ranking metrics and fairness-aware ranking algorithms within the same plug-and-play package. We demonstrate the key innovations of FairRankTune, focusing on features that are valuable to stakeholders via use cases highlighting workflows in the end-to-end process of mitigating bias in ranking systems. FairRankTune addresses the gap of limited publicly available datasets, auditing tools, and implementations for fair ranking.
Bubble Sketch: A High-performance and Memory-efficient Sketch for Finding Top-k Items in Data Streams
Authors: Lu Cao, Qilong Shi, Yuxi Liu, Hanyue Zheng, Yao Xin, Wenjun Li, Tong Yang, Yangyang Wang, Yang Xu, Weizhe Zhang, Mingwei Xu
Venue: CIKM
Year: 2024

Sketch algorithms are crucial for identifying top-k items in large-scale data streams. Existing methods often compromise between performance and accuracy, unable to efficiently handle increasing data volumes with limited memory. We present Bubble Sketch, a compact algorithm that excels in both performance and accuracy. Bubble Sketch achieves this by (1) Recording only full keys of hot items, significantly reducing memory usage, and (2) Using threshold relocation to resolve conflicts, enhancing detection accuracy. Unlike traditional methods, Bubble Sketch eliminates the need for a Min-Heap, ensuring fast processing speeds. Experiments show Bubble Sketch outperforms the other seven algorithms compared, with the highest throughput and precision, and surpasses HeavyKeeper in accuracy by up to two orders of magnitude.
MATCC: A Novel Approach for Robust Stock Price Prediction Incorporating Market Trends and Cross-time Correlations
Authors: Zhiyuan Cao, Jiayu Xu, Chengqi Dong, Peiwen Yu, Tian Bai
Venue: CIKM
Year: 2024

Stock price prediction has been a challenging problem due to non-stationary dynamics and complex market dependencies. Existing work has two limitations: 1. Previous studies have underestimated the importance of market trends, relying solely on stock data to learn patterns and capture market regularities implicitly. However, due to random stock fluctuations and trading noise caused by market sentiment, it is difficult to learn underlying market trends, resulting in poor model performance. 2. Prior research has predominantly concentrated on time-aligned feature correlations, with limited exploration of cross-time stock correlations. To address these issues, we propose a novel framework, MATCC (Market Trend and Cross-time Correlation model). It explicitly extracts market trends as guiding information, decomposes stock data into trend and fluctuation components, and employs a carefully designed structure for mining cross-time correlation. Extensive experiments demonstrate that MATCC significantly outperforms previous works in both ranking and portfolio-based metrics. Additionally, we illustrate the influence of trends and correlations on stock prediction through visualization. We publish our code at https://github.com/caozhiy/MATCC.
Scalable Unsupervised Feature Selection with Reconstruction Error Guarantees via QMR Decomposition
Authors: Ciwan Ceylan, Kambiz Ghoorchian, Danica Kragic
Venue: CIKM
Year: 2024

Unsupervised feature selection (UFS) methods have garnered significant attention for their capability to eliminate redundant features without relying on class label information. However, their scalability to large datasets remains a challenge, rendering common UFS methods impractical for such applications. To address this issue, we introduce QMR-FS, a greedy forward filtering approach that selects linearly independent features up to a specified relative tolerance, ensuring that any excluded features can be reconstructed from the retained set within this tolerance. This is achieved through the QMR matrix decomposition, which builds upon the well-known QR decomposition. QMR-FS benefits from linear complexity relative to the number of instances and boasts exceptional performance due to its ability to leverage parallelized computation on both CPU and GPU. Despite its greedy nature, QMR-FS achieves comparable classification and clustering accuracies across multiple datasets when compared to other UFS methods, while achieving runtimes approximately 10 times faster than recently proposed scalable UFS methods for datasets ranging from 100 million to 1 billion elements.
End-to-End Aspect Based Sentiment Analysis Using Graph Attention Network
Authors: Abir Chakraborty
Venue: CIKM
Year: 2024

In this work we investigate the capability of Graph Attention Network for extracting aspect and opinion terms. Aspect and opinion term extraction is posed as a token-level classification task akin to named entity recognition. We use the dependency tree of the input query as additional feature in a Graph Attention Network along with the token and part-of-speech features. We show that the dependency structure is a powerful feature that in the presence of a CRF layer substantially improves the performance and generates the best result on the commonly used datasets from SemEval 2014, 2015 and 2016. We experiment with additional layers like BiLSTM and Transformer in addition to the CRF layer. We also show that our approach works well in the presence of multiple aspects or sentiments in the same query and it is not necessary to modify the dependency tree based on a single aspect as was the original application for sentiment classification.
DiHAN: A Novel Dynamic Hierarchical Graph Attention Network for Fake News Detection
Authors: Ya-Ting Chang, Zhibo Hu, Xiaoyu Li, Shuiqiao Yang, Jiaojiao Jiang, Nan Sun
Venue: CIKM
Year: 2024

The rapid spread of fake news on social media has caused great harm to society in recent years, which raises the detection of fake news as an urgent task. Recent methods utilize the interactions among different entities such as authors, subjects, and news articles to model news propagation as a static heterogeneous information network (HIN). However, this is suboptimal since fake news emerges dynamically, and the latent chronological interactions between news in HIN are essential signals for fake news detection. To this end, we model the dynamics of news and associated entities as a News-Driven Dynamic Heterogeneous Information Network (News-DyHIN), where the temporal relationships among news articles are well captured with meta-path based temporal neighbors. With the support of News-DyHIN, we propose a novel fake news detection framework, named D ynam i c H ierarchical A ttention N etwork (DiHAN), which learns news representations via a hierarchical attention mechanism to fuse temporal interactions among news articles. In particular, DiHAN first employs a temporal node level attention to learn the temporal information from meta-path based news neighbors through the modeled News-DyHIN. Then, a semantic attention layer is adopted to fuse different types of meta-path based temporal information for news representation learning. Extensive evaluations conducted on two public real-world datasets demonstrate that our proposed DiHAN achieves significant improvements over established baseline models.
Deep Noise-Aware Quality Loss for Speaker Verification
Authors: Pantid Chantangphol, Theerat Sakdejayont, Monchai Lertsutthiwong, Tawunrat Chalothorn
Venue: CIKM
Year: 2024

This paper addresses the common challenge of system performance degradation due to speech inconsistency and mismatched acoustic conditions across various domains in speaker verification tasks. We propose a Noise-Aware Quality Network designed to estimate a score based on speech quality and the presence of speech obscured by noise in real-world environments. The score, derived from the normalization of estimated speech quality evaluations, is incorporated into a proposed Noise-Aware Quality loss function, aiming to prioritize speech quality by weighting the embedding distances based on the quality score. Our methodology significantly improves speaker verification performance, particularly in noisy environments. Furthermore, our work highlights the importance of speech quality and the potential benefits of incorporating speech quality weight into the loss function for speaker verification tasks.
Traversing the Journey of Data and AI: From Convergence to Translation
Authors: Nitesh V. Chawla
Venue: CIKM
Year: 2024

In this talk, I will present our work on fundamental advances in AI, inspired by interdisciplinary problem statements and societal challenges. I will also highlight our innovation journey that encapsulates both the opportunities and challenges inherent in harnessing the full potential of AI for societal benefit, in particular highlighting the realization of societal impact through translational work and partnerships. Additionally, I will highlight our educational endeavors, emphasizing experiential learning and interdisciplinary approaches as fundamental elements of the student experience.
Hyperedge Importance Estimation via Identity-aware Hypergraph Attention Network
Authors: Yin Chen, Xiaoyang Wang, Chen Chen
Venue: CIKM
Year: 2024

Hypergraphs provide a more flexible representation for group interactions in complex systems compared to ordinary graphs, where each hyperedge can connect any number of nodes. In practice, data modeled as hypergraphs often contain hyperedge importance values, which indicate the influence or popularity of the group collaborations. For example, in a co-authorship hypergraph, a paper (hyperedge) is co-authored by multiple authors (nodes). The number of citations a paper receives can be regarded as the importance value of its corresponding hyperedge, reflecting its academic influence and significance. In this work, we introduce hyperedge importance estimation as a new problem in hypergraph learning. The flexibility of hyperedges enables hypergraph modeling to capture high-order relationships between entities, which has attracted widespread attention. The importance value of hyperedge has also been proven to be highly valuable in many applications. To address this problem, we propose the Identity-aware Hypergraph Attention Network (ID-HAN) for efficient hyperedge importance estimation. ID-HAN~employs a special attention mechanism to model the importance contribution of each node within the hyperedge, which injects identity information according to the hyperedge-dependent node labels. Additionally, a centrality-aware positional encoding module generates learnable positional embeddings of nodes and hyperedges based on the relative order of degree centrality and identity information, thereby enhancing the consistency between message passing and importance propagation. Extensive experiments on four real-world datasets demonstrate that ID-HAN~significantly outperforms the state-of-the-art hypergraph neural networks on the hyperedge importance estimation task.
Unifying Spectral and Spatial Graph Neural Networks
Authors: Zhiqian Chen, Lei Zhang, Liang Zhao
Venue: CIKM
Year: 2024

In recent years, Graph Neural Networks (GNNs) have attracted considerable attention. However, the rapid emergence of diverse GNN models, each grounded in different theoretical foundations, complicates the model selection process, as these models are not easily understood within a unified framework. Initial GNNs were constructed using spectral theory, while others were developed based on spatial theory. This theoretical divergence makes direct comparisons difficult. Furthermore, the variety of models within each theoretical domain further complicates their evaluation. In this tutorial, we explore state-of-the-art GNNs and present a comprehensive framework that bridges the spatial and spectral domains, clarifying their interrelationship. This framework deepens our understanding of GNN operations. The tutorial delves into key paradigms, such as spatial and spectral methods, through a synthesis of spectral graph theory and approximation theory. We conduct an in-depth analysis of recent research advancements, addressing emerging issues like over-smoothing, using well-established GNN models to illustrate the universality of our framework.
Honest-Majority Maliciously Secure Skyline Queries on Outsourced Data
Authors: Yu Chen, Lin Liu, Rongmao Chen, Shaojing Fu, Yuexiang Yang
Venue: CIKM
Year: 2024

The application of skyline queries on outsourced databases significantly aids online analysis, yet efficiently handling encrypted queries remains a formidable obstacle. Moreover, query outcomes are vulnerable to potential malicious cloud services. To circumvent these limitations, this work presents the Honest-Majority and Maliciously Skyline Query scheme (HMMSQ), which facilitates efficient skyline queries while safeguarding the privacy of datasets, queries, and skylines, as well as detecting malevolent activities. The core of HMMSQ is an optimized skyline diagram constructed by a novel skyline region-splitting algorithm for accurate skyline queries. Furthermore, it mitigates the frequency of dataset accesses by leveraging a multi-path R-tree for secure skyline retrieval. Notably, the majority of malicious behavior detection is focused on the servers, thereby minimizing user authentication overhead. The complexity and security are thoroughly analyzed, and experimental evaluations on various datasets demonstrate its efficiency and practicality in terms of computational cost and communication overhead. Remarkably, HMMSQ outperforms existing methods in query latency, achieving up to an order of magnitude improvement.
Towards Online and Safe Configuration Tuning with Semi-supervised Anomaly Detection
Authors: Haitian Chen, Xu Chen, Zibo Liang, Xiushi Feng, Jiandong Xie, Han Su, Kai Zheng
Venue: CIKM
Year: 2024

The performance of modern database management systems highly relies on hundreds of adjustable knobs. Traditionally, these knobs are manually adjusted by database administrators, a process that is both inefficient and ineffective for tuning large-scale databases in cloud environments. Recent research has explored the use of machine learning techniques to enable the automatic tuning of database configurations. Although most existing learning-based methods achieve satisfactory results on static workloads, they often experience performance degradation and low sampling efficiency in real-world environments. According to our study, this is primarily due to a lack of safety guarantees during the configuration sampling process. To address the aforementioned issues, we propose SafeTune, an online tuning system that adapts to dynamic workloads. Our core idea is to filter out a large number of configurations with potential risks during the configuration sampling process. We employ a two-stage filtering approach: The first stage utilizes a semi-supervised outlier ensemble with feature learning to achieve high-quality feature representation. The second stage employs a ranking-based classifier to refine the filtering process. In addition, to alleviate the cold-start problem, we leverage the historical tuning experience to provide high-quality initial samples during the initialization phase. We conducted comprehensive evaluations on static and dynamic workloads. In comparison to offline baseline methods, SafeTune reduces 95.6%-98.6% unsafe configuration suggestions. In contrast with state-of-the-art methods, SafeTune has improved cumulative performance by 10.5%-46.6% and tuning speed by 15.1%-35.4%.
PTSR: Prefix-Target Graph-based Sequential Recommendation
Authors: Jiayu Chen, Xiaoyu Du, Yonghua Pan, Jinhui Tang
Venue: CIKM
Year: 2024

Sequential recommendation approaches predict the next items (targets) by analyzing prefix subsequences. These methods primarily model the correlations between prefixes and targets but often neglect the inherent correlations among prefixes and items. In this paper, we propose a Prefix-Target Graph-based Sequential Recommendation Approach (PTSR), which constructs a prefix-target graph (PTG) to collect observed correlations among prefixes and targets. It utilizes a graph neural network to model these inherent correlations, thus improving the item representations used in the predictive model. Specifically, prefixes linked to the same target reflect similar intents, while targets linked to the same prefix indicate available choices. This allows the graph neural network to effectively capture high-level correlations among prefixes and items, enhancing recommendation accuracy. We conduct extensive experiments on four real-world datasets to demonstrate the superiority of PTSR compared to state-of-the-art (SOTA) sequential recommendation methods. The source code of the PTSR is available at https://github.com/TosakRin/PTSR.