OpenRaiser/Intern-Atlas huggingface dataset:
DDCDR: A Disentangle-based Distillation Framework for Cross-Domain Recommendation
Authors: Zhicheng An, Zhexu Gu, Li Yu, Ke Tu, Zhengwei Wu, Binbin Hu, Zhiqiang Zhang 0012, Lihong Gu, Jinjie Gu
Venue: KDD
Year: 2024

Modern recommendation platforms frequently encompass multiple domains to cater to the varied preferences of users. Recently, cross-domain learning has gained traction as a significant paradigm within the context of recommendation systems, enabling the leveraging of rich information from a well-endowed source domain to enhance a target domain, often limited by inadequate data resources. A primary concern in cross-domain recommendation is the mitigation of negative transfer-ensuring the selective transference of pertinent knowledge from the source (domain-shared knowledge) while maintaining the integrity of domain-unique insights within the target domain (domain-specific knowledge).
Dense Subgraph Discovery Meets Strong Triadic Closure
Authors: Chamalee Wickrama Arachchi, Iiro Kumpulainen, Nikolaj Tatti
Venue: KDD
Year: 2024

Finding dense subgraphs is a core problem with numerous graph mining applications such as community detection in social networks and anomaly detection. However, in many real-world networks connections are not equal. One way to label edges as either strong or weak is to use strong triadic closure~(STC). Here, if one node connects strongly with two other nodes, then those two nodes should be connected at least with a weak edge. STC-labelings are not unique and finding the maximum number of strong edges is NP-hard. In this paper, we apply STC to dense subgraph discovery. More formally, our score for a given subgraph is the ratio between the sum of the number of strong edges and weak edges, weighted by a user parameter λ, and the number of nodes of the subgraph. Our goal is to find a subgraph and an STC-labeling maximizing the score. We show that for λ = 1, our problem is equivalent to finding the densest subgraph, while for λ = 0, our problem is equivalent to finding the largest clique, making our problem NP-hard. We propose an exact algorithm based on integer linear programming and four practical polynomial-time heuristics. We present an extensive experimental study that shows that our algorithms can find the ground truth in synthetic datasets and run efficiently in real-world datasets.
From Word-prediction to Complex Skills: Compositional Thinking and Metacognition in LLMs
Authors: Sanjeev Arora
Venue: KDD
Year: 2024

The talk will present evidence that today's large language models (LLMs) display somewhat deeper "understanding'' than one would naively expect.1. When asked to solve a task by combining a set of k simpler skills ("test of compositional capability"), they are able to do so despite not having seen the same combination of skills during their training.2. They demonstrate ability to reason about of their own learning processes, which is analogous to "metacognitive knowledge"[Flavel'76] in humans. For instance, given examples of an evaluation task, they can produce a catalog of suitably named skills that are relevant for solving each example of that task. Furthermore, this catalog of skills is meaningful, in the sense that incorporating it into training pipelines improves performance (including of other unrelated LLMs) on that task.We discuss mechanisms by which such complex understanding could arise (including a theory by [Arora,Goyal'23] that tries to explain (a)) and also give examples of how to leverage LLM meta knowledge to improve LLM training pipelines as well as evaluations.
Semi-Supervised Learning for Time Series Collected at a Low Sampling Rate
Authors: Minyoung Bae, Yooju Shin, Youngeun Nam, Youngseop Lee, Jae-Gil Lee 0001
Venue: KDD
Year: 2024

Although time-series classification has many applications in healthcare and manufacturing, the high cost of data collection and labeling hinders its widespread use. To reduce data collection and labeling costs while maintaining high classification accuracy, we propose a novel problem setting, called semi-supervised learning with low-sampling-rate time series, in which the majority of time series are collected at a low sampling rate and are unlabeled whereas the minority of time series are collected at a high sampling rate and are labeled. For this novel problem scenario, we develop the SemiTSR framework equipped with the super-resolution module and the semi-supervised learning module. Here, low-sampling-rate time series are upsampled precisely, taking periodicity and trend at each timestamp into account, and both labeled and unlabeled high-sampling-rate time series are utilized for training. In particular, consistency regularization between artificially downsampled time series derived from an original high-sampling-rate time series is effective at overcoming limited sampling rates. We demonstrate that SemiTSR significantly outperforms conventional semi-supervised learning techniques by assuring high classification accuracy with low-sampling-rate time series.
Responsible AI Day
Authors: Ricardo Baeza-Yates, Nataly Buslón
Venue: KDD
Year: 2024

We summarize the goals of the Responsible AI day, giving a glimpse on the program as well as a short biography of the organizers.
AdKDD 2024
Authors: Abraham Bagherjeiran, Nemanja Djuric, Kuang-Chih Lee, Linsey Pang, Vladan Radosavljevic, Suju Rajan
Venue: KDD
Year: 2024

The digital advertising field has always had challenging ML problems, learning from petabytes of data that is highly imbalanced, reactivity times in the milliseconds, and more recently compounded with the complex user's path to purchase across devices, across platforms, and even online/real-world behavior. The AdKDD workshop continues to be a forum for researchers in advertising, during and after KDD. Our website which hosts slides and abstracts receives approximately 2,000 monthly visits and 1,800 active users during the KDD 2021. In surveys during AdKDD 2019 and 2020, over 60% agreed that AdKDD is the reason they attended KDD, and over 90% indicated they would attend next year. The 2024 edition is particularly timely because of the increasing application of Graph-based NN and Generative AI models in advertising. Coupled with privacy-preserving initiatives enforced by GDPR, CCPA the future of computational advertising is at an interesting crossroads. For this edition, we plan to solicit papers that span the spectrum of deep user understanding while remaining privacy-preserving. In addition, we will seek papers that discuss fairness in the context of advertising, to what extent does hyper-personalization work, and whether the ad industry as a whole needs to think through more effective business models such as incrementality. We have hosted several academic and industry luminaries as keynote speakers and have found our invited speaker series hosting expert practitioners to be an audience favorite. We will continue fielding a diverse set of keynote speakers and invited talks for this edition as well. As with past editions, we hope to motivate researchers in this space to think not only about the ML aspects but also to spark conversations about the societal impact of online advertising.
A Hands-on Introduction to Time Series Classification and Regression
Authors: Anthony J. Bagnall, Matthew Middlehurst, Germain Forestier, Ali Ismail-Fawaz, Antoine Guillaume, David Guijo-Rubio, Chang Wei Tan, Angus Dempster, Geoffrey I. Webb
Venue: KDD
Year: 2024

Time series classification and regression are rapidly evolving fields that find areas of application in all domains of machine learning and data science. This hands on tutorial will provide an accessible overview of the recent research in these fields, using code examples to introduce the process of implementing and evaluating an estimator. We will show how to easily reproduce published results and how to compare a new algorithm to state-of-the-art. Finally, we will work through real world examples from the field of Electroencephalogram (EEG) classification and regression. EEG machine learning tasks arise in medicine, brain-computer interface research and psychology. We use these problems to how to compare algorithms on problems from a single domain and how to deal with data with different characteristics, such as missing values, unequal length and high dimensionality. The latest advances in the fields of time series classification and regression are all available through the aeon toolkit, an open source, scikit-learn compatible framework for time series machine learning which we use to provide our code examples.
GradCraft: Elevating Multi-task Recommendations through Holistic Gradient Crafting
Authors: Yimeng Bai, Yang Zhang 0072, Fuli Feng, Jing Lu, Xiaoxue Zang, Chenyi Lei, Yang Song 0008
Venue: KDD
Year: 2024

Recommender systems require the simultaneous optimization of multiple objectives to accurately model user interests, necessitating the application of multi-task learning methods. However, existing multi-task learning methods in recommendations overlook the specific characteristics of recommendation scenarios, falling short in achieving proper gradient balance. To address this challenge, we set the target of multi-task learning as attaining the appropriate magnitude balance and the global direction balance, and propose an innovative methodology named GradCraft in response. GradCraft dynamically adjusts gradient magnitudes to align with the maximum gradient norm, mitigating interference from gradient magnitudes for subsequent manipulation. It then employs projections to eliminate gradient conflicts in directions while considering all conflicting tasks simultaneously, theoretically guaranteeing the global resolution of direction conflicts. GradCraft ensures the concurrent achievement of appropriate magnitude balance and global direction balance, aligning with the inherent characteristics of recommendation scenarios. Both offline and online experiments attest to the efficacy of GradCraft in enhancing multi-task performance in recommendations. The source code for GradCraft can be accessed at https://github.com/baiyimeng/GradCraft.
Towards Robust Information Extraction via Binomial Distribution Guided Counterpart Sequence
Authors: Yinhao Bai, Yuhua Zhao 0001, Zhixin Han, Hang Gao 0003, Chao Xue 0003, Mengting Hu
Venue: KDD
Year: 2024

Information extraction (IE) aims to extract meaningful structured tuples from unstructured text. Existing studies usually utilize a pre-trained generative language model that rephrases the original sentence into a target sequence, which can be easily decoded as tuples. However, traditional evaluation metrics treat a slight error within the tuple as an entire prediction failure, which is unable to perceive the correctness extent of a tuple. For this reason, we first propose a novel IE evaluation metric called Matching Score to evaluate the correctness of the predicted tuples in more detail. Moreover, previous works have ignored the effects of semantic uncertainty when focusing on the generation of the target sequence. We argue that leveraging the built-in semantic uncertainty of language models is beneficial for improving its robustness. In this work, we propose <u>B</u>inomial distribution guided <u>c</u>ounterpart <u>s</u>equence (BCS) method, which is a model-agnostic approach. Specifically, we propose to quantify the built-in semantic uncertainty of the language model by bridging all local uncertainties with the whole sequence. Subsequently, with the semantic uncertainty and Matching Score, we formulate a unique binomial distribution for each local decoding step. By sampling from this distribution, a counterpart sequence is obtained, which can be regarded as a semantic complement to the target sequence. Finally, we employ the Kullback-Leibler divergence to align the semantics of the target sequence and its counterpart. Extensive experiments on 14 public datasets over 5 information extraction tasks demonstrate the effectiveness of our approach on various methods. Our code and dataset are available at https://github.com/byinhao/BCS.
Improved Active Covering via Density-Based Space Transformation
Authors: MohammadHossein Bateni, Hossein Esfandiari, Samira Hossein Ghorban, Alipasha Montaseri
Venue: KDD
Year: 2024

In this work, we study active covering, a variant of the active-learning problem that involves labeling (or identifying) all of the examples with a positive label. We propose a couple of algorithms, namely Density-Adjusted Non-Adaptive (DANA) learner and Density-Adjusted Adaptive (DAA) learner, that query the labels according to a distance function that is adjusted by the density function. Under mild assumptions, we prove that our algorithms discover all of the positive labels while querying only a sublinear number of examples from the support of negative labels for constant-dimensional spaces (see Theorems 5 and 6). Our experiments show that our champion algorithm DAA consistently improves over the prior work on some standard benchmark datasets, including those used by the previous work, as well as a couple of data sets on credit card fraud. For instance, when measuring performance using AUC, our algorithm is the best in 25 out of 27 experiments over 7 different datasets.
Large Scale Generative AI Text Applied to Sports and Music
Authors: Aaron K. Baughman, Eduardo Morales, Rahul Agarwal, Gozde Akay, Rogério Feris, Tony Johnson, Stephen Hammer, Leonid Karlinsky
Venue: KDD
Year: 2024

We address the problem of scaling up the production of media content, including commentary and personalized news stories, for large-scale sports and music events worldwide. Our approach relies on generative AI models to transform a large volume of multimodal data (e.g., videos, articles, real-time scoring feeds, statistics, and fact sheets) into coherent and fluent text. Based on this approach, we introduce, for the first time, an AI commentary system, which was deployed to produce automated narrations for highlight packages at the 2023 US Open, Wimbledon, and Masters tournaments. In the same vein, our solution was extended to create personalized content for ESPN Fantasy Football and stories about music artists for the GRAMMY awards. These applications were built using a common software architecture achieved a 15x speed improvement with an average Rouge-L of 82.00 and perplexity of 6.6. Our work was successfully deployed at the aforementioned events, supporting 90 million fans around the world with 8 billion page views, continuously pushing the bounds on what is possible at the intersection of sports, entertainment, and AI.
AI for Nature: From Science to Impact
Authors: Tanya Y. Berger-Wolf
Venue: KDD
Year: 2024

Computation has fundamentally changed the way we study nature. New data collection technologies, such as GPS, high-definition cameras, autonomous vehicles under water, on the ground, and in the air, genotyping, acoustic sensors, and crowdsourcing, are generating data about life on the planet that are orders of magnitude richer than any previously collected. Yet, our ability to extract insight from this data lags substantially behind our ability to collect it.The need for understanding is more urgent than ever and the challenges are great. We are in the middle of the 6th extinction, losing the planet's biodiversity at an unprecedented rate and scale. In many cases, we do not even have the basic numbers of what species we are losing, which impacts our ability to understand biodiversity loss drivers, predict the impact on ecosystems, and implement policy. From the basic science perspective, the new data opens the possibility of understanding function of traits of organisms and ecosystems, which is critical for biologists to predict effects of environmental change or genetic manipulation and to understand the significance of patterns in the four-billion-year evolutionary history of life.The key to unlocking the potential of this data are machine learning (ML) and artificial intelligence (AI) methods, which are already beginning to have significant impacts on research across ecology and conservation. AI can turn data into high resolution information source about living organisms, enabling scientific inquiry, conservation, and policy decisions.The talk introduces a new field of science, imageomics, and presents a vision and examples of AI as a trustworthy partner both in science and biodiversity conservation, discussing opportunities and challenges.
Evading Community Detection via Counterfactual Neighborhood Search
Authors: Andrea Bernini, Fabrizio Silvestri, Gabriele Tolomei
Venue: KDD
Year: 2024

Community detection techniques are useful for social media platforms to discover tightly connected groups of users who share common interests. However, this functionality often comes at the expense of potentially exposing individuals to privacy breaches by inadvertently revealing their tastes or preferences. Therefore, some users may wish to preserve their anonymity and opt out of community detection for various reasons, such as affiliation with political or religious organizations, without leaving the platform. In this study, we address the challenge of community membership hiding, which involves strategically altering the structural properties of a network graph to prevent one or more nodes from being identified by a given community detection algorithm. We tackle this problem by formulating it as a constrained counterfactual graph objective, and we solve it via deep reinforcement learning. Extensive experiments demonstrate that our method outperforms existing baselines, striking the best balance between accuracy and cost.
Lessons Learned while Running ML Models in Harsh Environments
Authors: Pedro Bizarro
Venue: KDD
Year: 2024

Once a very large payment processor client told us: 'if we are down for 5 minutes, we open the evening news - so don't screw up'. Processing billions of dollars per day, many financial institutions, need to continuously fight organized crime in the form of transaction fraud, stolen cards, anti-money laundering, account opening fraud, impersonations scams, phishing, and many other exotic and ever changing attacks from organized crime groups worldwide. In fact, it is estimated that in 2023 the global losses in fraud scams and bank fraud reached 485.6 billion. However, in addition to having very good detection rates and very low false positive rates, financial institutions also need to maintain very high availability rates, very low latencies, very high throughputs, automatic fault tolerance, auto scale up and down, and more. In this talk we cover some lessons related to running ML models in harsh, mission critical environments. We describe data issues, scale issues, ethical issues, system issues, security issues, compliance issues, business and regulation issues, and some architectural tradeoffs and architectural evolutions.
Making Temporal Betweenness Computation Faster and Restless
Authors: Filippo Brunelli, Pierluigi Crescenzi, Laurent Viennot
Venue: KDD
Year: 2024

Bu{\ss} et al [KDD 2020] recently proved that the problem of computing the betweenness of all nodes of a temporal graph is computationally hard in the case of foremost and fastest paths, while it is solvable in time O(n 3 T 2 ) in the case of shortest and shortest foremost paths, where n is the number of nodes and T is the number of distinct time steps. A new algorithm for temporal betweenness computation is introduced in this paper. In the case of shortest and shortest foremost paths, it requires O(n + M ) space and runs in time where M is the number of temporal edges, thus significantly improving the algorithm of Bu{\ss} et al in terms of time complexity (note that T is usually large). Experimental evidence is provided that our algorithm performs between twice and almost 250 times better than the algorithm of Bu{\ss} et al. Moreover, we were able to compute the exact temporal betweenness values of several large temporal graphs with over a million of temporal edges. For such size, only approximate computation was possible by using the algorithm of Santoro and Sarpe [WWW 2022]. Maybe more importantly, our algorithm extends to the case of restless walks (that is, walks with waiting constraints in each node), thus providing a polynomial-time algorithm (with complexity O(nM )) for computing the temporal betweenness in the case of several different optimality criteria. Such restless computation was known only for the shortest criterion (Rymar et al [JGAA 2023]), with complexity O(n 2 M T 2 ). We performed an extensive experimental validation by comparing different waiting constraints and different optimisation criteria. Moreover, as a case study, we investigate six public transit networks including Berlin, Rome, and Paris. Overall we find a general consistency between the different variants of betweenness centrality. However, we do measure a sensible influence of waiting constraints, and note some cases of low correlation for certain pairs of criteria in some networks.
FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering
Authors: Tianchi Cai, Zhiwen Tan, Xierui Song, Tao Sun 0018, Jiyan Jiang, Yunqi Xu, Yinger Zhang, Jinjie Gu
Venue: KDD
Year: 2024

Retrieval Augmented Generation (RAG) has become prevalent in question-answering (QA) tasks due to its ability of utilizing search engine to enhance the quality of long-form question-answering (LFQA). Despite the emergence of various open source methods and web-enhanced commercial systems such as Bing Chat, two critical problems remain unsolved, i.e., the lack of factuality and clear logic in the generated long-form answers. In this paper, we remedy these issues via a systematic study on answer generation in web-enhanced LFQA. Specifically, we first propose a novel outline-enhanced generator to achieve clear logic in the generation of multifaceted answers and construct two datasets accordingly. Then we propose a factuality optimization method based on a carefully designed doubly fine-grained RLHF framework, which contains automatic evaluation and reward modeling in different levels of granularity. Our generic framework comprises conventional fine-grained RLHF methods as special cases. Extensive experiments verify the superiority of our proposed Factuality-optimized RAG (FoRAG) method on both English and Chinese benchmarks. In particular, when applying our method to Llama2-7B-chat, the derived model FoRAG-L-7B outperforms WebGPT-175B in terms of three commonly used metrics (i.e., coherence, helpfulness, and factuality), while the number of parameters is much smaller (only 1/24 of that of WebGPT-175B). Our datasets and models are made publicly available for better reproducibility.https://huggingface.co/forag łabelfootnote_dataset_url