OpenRaiser/Intern-Atlas huggingface dataset:
A Visual Tour of Bias Mitigation Techniques for Word Representations
Authors: Archit Rathore, Sunipa Dev, Jeff M. Phillips, Vivek Srikumar, Bei Wang
Venue: KDD
Year: 2021

Word vector embeddings have been shown to contain and amplify biases in data they are extracted from. Consequently, many techniques have been proposed to identify, mitigate, and attenuate these biases in word representations. In this tutorial, we will review a collection of state-of-the-art debiasing techniques. To aid this, we provide an open source web-based visualization tool and offer hands-on experience in exploring the effects of these debiasing techniques on the geometry of high-dimensional word vectors. To help understand how various debiasing techniques change the underlying geometry, we decompose each technique into interpretable sequences of primitive operations, and study their effect on the word vectors using dimensionality reduction and interactive visual exploration.
RAPT: Pre-training of Time-Aware Transformer for Learning Robust Healthcare Representation
Authors: Houxing Ren, Jingyuan Wang, Wayne Xin Zhao, Ning Wu
Venue: KDD
Year: 2021

With the development of electronic health records (EHRs), prenatal care examination records have become available for developing automatic prediction or diagnosis approaches with machine learning methods. In this paper, we study how to effectively learn representations applied to various downstream tasks for EHR data. Although several methods have been proposed in this direction, they usually adapt classic sequential models to solve one specific diagnosis task or address unique EHR data issues. This makes it difficult to reuse these existing methods for the early diagnosis of pregnancy complications or provide a general solution to address the series of health problems caused by pregnancy complications. In this paper, we propose a novel model RAPT, which stands for RepresentAtion by Pre-training time-aware Transformer. To associate pre-training and EHR data, we design an architecture that is suitable for both modeling EHR data and pre-training, namely time-aware Transformer. To handle various characteristics in EHR data, such as insufficiency, we carefully devise three pre-training tasks to handle data insufficiency, data incompleteness and short sequence problems, namely similarity prediction, masked prediction and reasonability check. In this way, our representations can capture various EHR data characteristics. Extensive experimental results for four downstream tasks have shown the effectiveness of the proposed approach. We also introduce sensitivity analysis to interpret the model and design an interface to show results and interpretation for doctors. Finally, we implement a diagnosis system for pregnancy complications based on our pre-training model. Doctors and pregnant women can benefit from the diagnosis system in early diagnosis of pregnancy complications.
Spectral Clustering of Attributed Multi-relational Graphs
Authors: Ylli Sadikaj, Yllka Velaj, Sahar Behzadi, Claudia Plant
Venue: KDD
Year: 2021

Graph clustering aims at discovering a natural grouping of the nodes such that similar nodes are assigned to a common cluster. Many different algorithms have been proposed in the literature: for simple graphs, for graphs with attributes associated to nodes, and for graphs where edges represent different types of relations among nodes. However, complex data in many domains can be represented as both attributed and multi-relational networks. In this paper, we propose SpectralMix, a joint dimensionality reduction technique for multi-relational graphs with categorical node attributes. SpectralMix integrates all information available from the attributes, the different types of relations, and the graph structure to enable a sound interpretation of the clustering results. Moreover, it generalizes existing techniques: it reduces to spectral embedding and clustering when only applied to a single graph and to homogeneity analysis when applied to categorical data. Experiments conducted on several real-world datasets enable us to detect dependencies between graph structure and categorical attributes, moreover, they exhibit the superiority of SpectralMix over existing methods.
ACM KDD AI4Cyber: The 1st Workshop on Artificial Intelligence-enabled Cybersecurity Analytics
Authors: Sagar Samtani, Shanchieh Yang, Hsinchun Chen
Venue: KDD
Year: 2021

Despite significant contributions to various aspects of cybersecurity, cyber-attacks remain on the unfortunate rise. Increasingly, internationally recognized entities such as the National Science Foundation and National Science & Technology Council have noted Artificial Intelligence can help analyze billions of log files, Dark Web data, malware, and other data sources to help execute fundamental cybersecurity tasks. Our objective for the 1st Workshop on Artificial Intelligence-enabled Cybersecurity Analytics (half-day; co-located with ACM KDD) was to gather academic and practitioners to contribute recent work pertaining to AI-enabled cybersecurity analytics. We composed an outstanding, inter-disciplinary Program Committee with significant expertise in various aspects of AI-enabled Cybersecurity Analytics to evaluate the submitted work. Significant contributions to the half-day workshop were made in the areas of CTI, vulnerability assessment, and malware analysis.
Risk Identification & Quantification in Complex Human-Natural Systems via Convergent Data Intensive Research
Authors: Toryn L. J. Schafer, Ryan M. McGranaghan, Mila Getmansky Sherman, Mei-Ling E. Feng, Olukunle O. Owolabi, Sean E. Ryan, Marie-Christine Düker, Michael Jauch, David S. Matteson
Venue: KDD
Year: 2021

Human-natural systems involve complex interdependent processes, but domain specific processes are traditionally studied in non-overlapping research silos. The Predictive Risk Investigation SysteM (PRISM) for multi-layer dynamic interconnection analysis is a group of collaborators across multiple domains who work to discover data driven connections specifically among domain risks. We bring our inter-disciplinary approach to risk assessment to our KDD'21 workshop. Our workshop is a step toward a holistic approach to systemic risk analysis by welcoming speakers in applied and technical research at the forefront of risk and complex systems.
KDD 2021 Tutorial on Systemic Challenges and Solutions on Bias and Unfairness in Peer Review
Authors: Nihar B. Shah
Venue: KDD
Year: 2021

Introduction. Peer review is a cornerstone of academic practice [1]. The peer review process is highly regarded by the vast majority of researchers and considered by most to be essential to the communication of scholarly research [2–4]. However, there is also an overwhelming desire for improvement [2, 4, 5]. Problems in peer review have consequences much beyond the outcome for a specific paper or grant, particularly due to the widespread prevalence of the Matthew effect (“rich get richer”) in academia [6]. As noted by [7] “an incompetent review may lead to the rejection of the submitted paper, or of the grant application, and the ultimate failure of the career of the author.” (See also [8, 9].) The importance of peer review and the urgent need for improvements, behooves research on principled approaches towards addressing problems in peer review, particularly at scale. In this tutorial, we discuss a number of key challenges in peer review, outline several directions of research on this topic, and also highlight important open problems that we envisage to be exciting to the community. This document summarizes the contents of the tutorial and provides relevant references.
The 4th Workshop on Heterogeneous Information Network Analysis and Applications (HENA 2021)
Authors: Chuan Shi, Yuan Fang, Yanfang Ye, Jiawei Zhang
Venue: KDD
Year: 2021

The 4th Workshop on Heterogeneous Information Network Analysis and Applications (HENA 2021) is co-located with the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. The goal of this workshop is to bring together researchers and practitioners in the field and provide a forum for sharing new techniques and applications in heterogeneous information network analysis. This workshop has an exciting program that spans a number of subtopics, such as heterogeneous network embedding and graph neural networks, data mining techniques on heterogeneous information networks, and applications of heterogeneous information network analysis. The workshop program includes several invited speakers, lively discussion on emerging topics, and presentations of accepted research papers.
Language Scaling: Applications, Challenges and Approaches
Authors: Linjun Shou, Ming Gong, Jian Pei, Xiubo Geng, Xingjie Zhou, Daxin Jiang
Venue: KDD
Year: 2021

Language scaling aims to deploy Natural Language Processing (NLP) applications economically across many countries/regions with different languages. Language scaling has been heavily invested by industry since many parties want to deploy their applications/services to global markets. At the same time, scaling out NLP applications to various languages, essentially a data science problem, remains a grand challenge due to the huge differences in the morphology, syntaxes, and pragmatics among different languages. We present a comprehensive survey and tutorial on language scaling. We start with a clear problem description for language scaling and an intuitive discussion on the overall challenges. Then, we outline two major categories of approaches to language scaling, namely, model transfer and data transfer. We present a taxonomy to summarize various methods in literature. A large part of the tutorial is organized to address various types of NLP applications. Finally, we discuss several important challenges in this area and future directions.
Simple and Efficient Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes
Authors: Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter
Venue: KDD
Year: 2021

We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples for deep learning models solely based on information limited to output label (hard-label) to a queried data input. We propose a simple and efficient Bayesian Optimization (BO) based approach for developing black-box adversarial attacks. Issues with BO's performance in high dimensions are avoided by searching for adversarial examples in a structured low-dimensional subspace. We demonstrate the efficacy of our proposed attack method by evaluating both ℓ∞ and ℓ2 norm constrained untargeted and targeted hard label black-box attacks on three standard datasets - MNIST, CIFAR-10, and ImageNet. Our proposed approach consistently achieves 2x to 10x higher attack success rate while requiring 10x to 20x fewer queries compared to the current state-of-the-art black-box adversarial attacks.
Fruit-fly Inspired Neighborhood Encoding for Classification
Authors: Kaushik Sinha, Parikshit Ram
Venue: KDD
Year: 2021

Inspired by the fruit-fly olfactory circuit, the Fly Bloom Filter is able to efficiently summarize the data with a single pass and has been used for novelty detection. We propose a new classifier that effectively encodes the different local neighborhoods for each class with a per-class Fly Bloom Filter. The inference on test data requires an efficient Flyhash[6] operation followed by a high-dimensional, but very sparse, dot product with the per-class Bloom Filters. On the theoretical side, we establish conditions under which the predictions of our proposed classifier agrees with the predictions of the nearest neighbor classifier. We extensively evaluate our proposed scheme with 71 data sets of varied data dimensionality to demonstrate that the predictive performance of our proposed neuroscience inspired classifier is competitive to the nearest-neighbor classifiers and other single-pass classifiers.
Interpretable Drug Response Prediction using a Knowledge-based Neural Network
Authors: Oliver Snow, Hossein Sharifi-Noghabi, Jialin Lu, Olga I. Zolotareva, Mark Lee, Martin Ester
Venue: KDD
Year: 2021

Predicting drug response based on the genomic profile of a cancer patient is one of the hallmarks of precision oncology. Despite current methods for drug response prediction becoming more accurate, there is still a need to switch from 'black box' predictions to methods that offer high accuracy as well as interpretable predictions. This is of particular importance in real-world applications such as drug response prediction in cancer patients. In this paper, we propose BDKANN, a novel knowledge-based method that employs the hierarchical information on how proteins form complexes and act together in pathways to form the architecture of a deep neural network. We employ BDKANN to predict cancer drug response from cell line gene expression data and our experimental results demonstrate that not only does BDKANN have a low prediction error compared to baseline models but it also allows meaningful interpretation of the network. These interpretations can both explain predictions made and discover novel connections in the biological knowledge that may lead to new hypotheses about mechanisms of drug action.
Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries
Authors: Sukhdeep S. Sodhi, Ellie Ka In Chio, Ambarish Jash, Santiago Ontañón, Ajit Apte, Ankit Kumar, Ayooluwakunmi Jeje, Dima Kuzmin, Harry Fung, Heng-Tze Cheng, Jon Effrat, Tarush Bali, Nitin Jindal, Pei Cao, Sarvjeet Singh, Senqiang Zhou, Tameen Khan, Amol Wankhede, Moustafa Alzantot, Allen Wu, Tushar Chandra
Venue: KDD
Year: 2021

As more and more online search queries come from voice, automatic speech recognition becomes a key component to deliver relevant search results. Errors introduced by automatic speech recognition (ASR) lead to irrelevant search results returned to the user, thus causing user dissatisfaction. In this paper, we introduce an approach, "Mondegreen", to correct voice queries in text space without depending on audio signals, which may not always be available due to system constraints or privacy or bandwidth (for example, some ASR systems run on-device) considerations. We focus on voice queries transcribed via several proprietary commercial ASR systems. These queries come from users making internet, or online service search queries. We first present an analysis showing how different the language distribution coming from user voice queries is from that in traditional text corpora used to train off-the-shelf ASR systems. We then demonstrate that Mondegreen can achieve significant improvements in increased user interaction by correcting user voice queries in one of the largest search systems in Google. Finally, we see Mondegreen as complementing existing highly-optimized production ASR systems, which may not be frequently retrained and thus lag behind due to vocabulary drifts.
Robust Learning by Self-Transition for Handling Noisy Labels
Authors: Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, Jae-Gil Lee
Venue: KDD
Year: 2021

Real-world data inevitably contains noisy labels, which induce the poor generalization of deep neural networks. It is known that the network typically begins to rapidly memorize false-labeled samples after a certain point of training. Thus, to counter the label noise challenge, we propose a novel self-transitional learning method called MORPH, which automatically switches its learning phase at the transition point from seeding to evolution. In the seeding phase, the network is updated using all the samples to collect a seed of clean samples. Then, in the evolution phase, the network is updated using only the set of arguably clean samples, which precisely keeps expanding by the updated network. Thus, MORPH effectively avoids the overfitting to false-labeled samples throughout the entire training period. Extensive experiments using five real-world or synthetic benchmark datasets demonstrate substantial improvements over state-of-the-art methods in terms of robustness and efficiency.
Dynamic Social Media Monitoring for Fast-Evolving Online Discussions
Authors: Maya Srikanth, Anqi Liu, Nicholas Adams-Cohen, Jian Cao, R. Michael Alvarez, Anima Anandkumar
Venue: KDD
Year: 2021

Tracking and collecting fast-evolving online discussions provides vast data for studying social media usage and its role in people's public lives. However, collecting social media data using a static set of keywords fails to satisfy the growing need to monitor dynamic conversations and to study fast-changing topics. We propose a dynamic keyword search method to maximize the coverage of relevant information in fast-evolving online discussions. The method uses word embedding models to represent the semantic relations between keywords and predictive models to forecast the future trajectory of keywords. We also implement a visual user interface to aid in the decision making process in each round of keyword updates. This allows for both human-assisted tracking and fully-automated data collection. In simulations using historical #MeToo data in 2017, our human-assisted tracking method outperforms the traditional static baseline method significantly, achieving 37.1% improvement in F-1 score in the task of tracking the top trending keywords. We conduct a contemporary case study to cover dynamic conversations about the recent Presidential Inauguration and to test the dynamic data collection system. Our case studies reflect the effectiveness of our process and also points to the potential challenges in future deployment.
Redescription Model Mining
Authors: Felix I. Stamm, Martin Becker, Markus Strohmaier, Florian Lemmerich
Venue: KDD
Year: 2021

This paper introduces Redescription Model Mining, a novel approach to identify interpretable patterns across two datasets that share only a subset of attributes and have no common instances. In particular, Redescription Model Mining aims to find pairs of describable data subsets -- one for each dataset -- that induce similar exceptional models with respect to a prespecified model class. To achieve this, we combine two previously separate research areas: Exceptional Model Mining and Redescription Mining. For this new problem setting, we develop interestingness measures to select promising patterns, propose efficient algorithms, and demonstrate their potential on synthetic and real-world data. Uncovered patterns can hint at common underlying phenomena that manifest themselves across datasets, enabling the discovery of possible associations between (combinations of) attributes that do not appear in the same dataset.
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
Authors: Susheel Suresh, Vinith Budde, Jennifer Neville, Pan Li, Jianzhu Ma
Venue: KDD
Year: 2021

Graph neural networks (GNNs) have achieved tremendous success on multiple graph-based learning tasks by fusing network structure and node features. Modern GNN models are built upon iterative aggregation of neighbor's/proximity features by message passing. Its prediction performance has been shown to be strongly bounded by assortative mixing in the graph, a key property wherein nodes with similar attributes mix/connect with each other. We observe that real world networks exhibit heterogeneous or diverse mixing patterns and the conventional global measurement of assortativity, such as global assortativity coefficient, may not be a representative statistic in quantifying this mixing. We adopt a generalized concept, node-level assortativity, one that is based at the node level to better represent the diverse patterns and accurately quantify the learnability of GNNs. We find that the prediction performance of a wide range of GNN models is highly correlated with the node level assortativity. To break this limit, in this work, we focus on transforming the input graph into a computation graph which contains both proximity and structural information as distinct type of edges. The resulted multi-relational graph has an enhanced level of assortativity and, more importantly, preserves rich information from the original graph. We then propose to run GNNs on this computation graph and show that adaptively choosing between structure and proximity leads to improved performance under diverse mixing. Empirically, we show the benefits of adopting our transformation framework for semi-supervised node classification task on a variety of real world graph learning benchmarks.
A PLAN for Tackling the Locust Crisis in East Africa: Harnessing Spatiotemporal Deep Models for Locust Movement Forecasting
Authors: Maryam Tabar, Jared Gluck, Anchit Goyal, Fei Jiang, Derek Morr, Annalyse Kehs, Dongwon Lee, David P. Hughes, Amulya Yadav
Venue: KDD
Year: 2021

East Africa is experiencing the worst locust infestation in over 25 years, which has severely threatened the food security of millions of people across the region. The primary strategy adopted by human experts at the United Nations Food and Agricultural Organization (UN-FAO) to tackle locust outbreaks involves manually surveying at-risk geographical areas, followed by allocating and spraying pesticides in affected regions. In order to augment and assist human experts at the UN-FAO in this task, we utilize crowdsourced reports of locust observations collected by PlantVillage (the world's leading knowledge delivery system for East African farmers) and develop PLAN, a Machine Learning (ML) algorithm for forecasting future migration patterns of locusts at high spatial and temporal resolution across East Africa. PLAN's novel spatio-temporal deep learning architecture enables representing PlantVillage's crowdsourced locust observation data using novel image-based feature representations, and its design is informed by several unique insights about this problem domain. Experimental results show that PLAN achieves superior predictive performance against several baseline models - it achieves an AUC score of 0.9 when used with a data augmentation method. PLAN represents a first step in using deep learning to assist and augment human expertise at PlantVillage (and UN-FAO) in locust prediction, and its real-world usability is currently being evaluated by domain experts (including a potential idea to use the heatmaps created by PLAN in a Kenyan TV show). The source code is available at https://github.com/maryam-tabar/PLAN.
Artificial Intelligence for Drug Discovery
Authors: Jian Tang, Fei Wang, Feixiong Cheng
Venue: KDD
Year: 2021

Drug discovery is a long and costly process, taking on average 10 years and 2.5 billion dollars to develop a new drug. Artificial intelligence has the potential to significantly accelerate the process of drug discovery by analyzing a large amount of data generated in the biomedical domain such as bioassays, chemical experiments, and biomedical literature. Recently, there is a growing interesting in developing AI techniques for drug discovery in many different communities including machine learning, data mining, and biomedical community. In this tutorial, we will provide a detailed introduction to key problems in drug discovery such as molecular property prediction, de novo molecular design and molecular optimization, retrosynthesis reaction and prediction, and drug repurposing and combination, and also key technique advancements with artificial intelligence for these problems. This tutorial can be served as introduction materials for both computer scientist interested in drug discovery as well as drug discovery practitioners for learning the latest AI techniques along this direction.