OpenRaiser/Intern-Atlas huggingface dataset:
On 3-d surface reconstruction using shape from shadows
Authors: Michael Daum 0002, Gregory Dudek
Venue: Conference CVPR 1998
Year: 1998

In this paper we discuss new results on the Shape From Darkness problem: using the motion of cast shadows to recover scene structure. Our approach is based on collecting a set of images from a fixed viewpoint as a known light source mover; "across the sky". Previously published solutions to this problem have performed the reconstruction only for cross sections of the scene. In this paper, we present a reconstruction algorithm and discuss the reconstruction of an entire 3-D scene under various light source trajectories. We also consider the constraints on reconstruction. We conclude with experimental results that illustrate the convergence properties of the solution process and its robustness properties.
Variational autoencoders for top-k recommendation with implicit feedback
Authors: Bahare Askari, Jaroslaw Szlichta, Amirali Salehi-Abari
Venue: Conference SIGIR 2021
Year: 2021

Variational Autoencoders (VAEs) have shown to be effective for recommender systems with implicit feedback (e.g., browsing history, purchasing patterns, etc.). However, a little attention is given to ensembles of VAEs, that can learn user and item representations jointly. We introduce Joint Variational Autoencoder (JoVA), an ensemble of two VAEs, which jointly learns both user and item representations to predict user preferences. This design allows JoVA to capture user-user and item-item correlations simultaneously. We also introduce JoVA-Hinge, a JoVA's extension with a hinge-based pairwise loss function, to further specialize it in recommendation with implicit feedback. Our extensive experiments on four real-world datasets demonstrate that JoVA-Hinge outperforms a broad set of state-of-the-art methods under a variety of commonly-used metrics. Our empirical results also illustrate the effectiveness of JoVA-Hinge for handling users with limited training data.
FedPS: A Privacy Protection Enhanced Personalized Search Framework
Authors: Jing Yao, Zhicheng Dou, Ji-Rong Wen
Venue: Conference WWW 2021
Year: 2021

Personalized search returns each user more accurate results by collecting the user’s historical search behaviors to infer her interests and query intents. However, it brings the risk of user privacy leakage, and this may greatly limit the practical application of personalized search. In this paper, we focus on the problem of privacy protection in personalized search, and propose a privacy protection enhanced personalized search framework, denoted with FedPS. Under this framework, we keep each user’s private data on her individual client, and train a shared personalized ranking model with all users’ decentralized data by means of federated learning. We implement two models within the framework: the first one applies a personalization model with a personal module that fits the user’s data distribution to alleviate the challenge of data heterogeneity in federated learning; the second model introduces trustworthy proxies and group servers to solve the problems of limited communication, performance bottleneck and privacy attack for FedPS. Experimental results verify that our proposed framework can enhance privacy protection without losing too much accuracy.
Cache-based gnn system for dynamic graphs
Authors: Haoyang Li, Lei Chen
Venue: Conference CIKM 2021
Year: 2021

Graph Neural Networks (GNNs) have achieved great success in downstream applications due to their ability to learn node representations. However, in many applications, graphs are not static. They often evolve with changes, such as the adjustment of node attributes or graph structures. These changes require node representations to be updated accordingly. It is non-trivial to apply current GNNs to update node representations in a scalable manner. Recent research proposes two types of solutions. The first solution, sampling neighbors for the influenced nodes, requires expensive processing for each node. The second solution, reducing the repeated computations by merging the shared neighbors, cannot speed up the updating process if the influenced nodes do not share neighbors. Most importantly, the above solutions ignore the hidden representations obtained in the previous times that can be reused to accelerate the representation updating. In this paper, we propose a general cache-based GNN system to accelerate the representation updating. Specifically, we cache a set of hidden representations obtained in the previous times, and then reuse them in the next time. To identify valuable hidden representations, we first estimate the number of hidden representations and their combinations that can be reused. Secondly, we formulate the k-assembler problem that selects k representations to maximize the saved time for the next updating process. Experiments on three real-world graphs show that the cache-based GNN system can significantly speed up the representation updating for various GNNs.
When Fair Ranking Meets Uncertain Inference
Authors: Avijit Ghosh, Ritam Dutt, Christo Wilson
Venue: Conference SIGIR 2021
Year: 2021

Existing fair ranking systems, especially those designed to be demographically fair, assume that accurate demographic information about individuals is available to the ranking algorithm. In practice, however, this assumption may not hold --- in real-world contexts like ranking job applicants or credit seekers, social and legal barriers may prevent algorithm operators from collecting peoples' demographic information. In these cases, algorithm operators may attempt to infer peoples' demographics and then supply these inferences as inputs to the ranking algorithm.
Specify robust causal representation from mixed observations
Authors: Mengyue Yang, Xinyu Cai, Furui Liu, Weinan Zhang 0001, Jun Wang 0012
Venue: Conference KDD 2023
Year: 2023

Learning representations purely from observations concerns the problem of learning a low-dimensional, compact representation which is beneficial to prediction models. Under the hypothesis that the intrinsic latent factors follow some casual generative models, we argue that by learning a causal representation, which is the minimal sufficient causes of the whole system, we can improve the robustness and generalization performance of machine learning models. In this paper, we develop a learning method to learn such representation from observational data by regularizing the learning procedure with mutual information measures, according to the hypothetical factored causal graph. We theoretically and empirically show that the models trained with the learned causal representations are more robust under adversarial attacks and distribution shifts compared with baselines. The supplementary materials are available at https://github.com/ymy $4323460 / \mathrm{CaRI} /$.
High- Dimensional Similarity Query Processing for Data Science
Authors: Jianbin Qin, Wei Wang, Chuan Xiao, Ying Zhang, Yaoshu Wang
Venue: Conference KDD 2021
Year: 2021

Similarity query (a.k.a. nearest neighbor query) processing has been an active research topic for several decades. It is an essential procedure in a wide range of applications (e.g., classification & regression, deduplication, image retrieval, and recommender systems). Recently, representation learning and auto-encoding methods as well as pre-trained models have gained popularity. They basically deal with dense high-dimensional data, and this trend brings new opportunities and challenges to similarity query processing. Meanwhile, new techniques have emerged to tackle this long-standing problem theoretically and empirically. This tutorial aims to provide a comprehensive review of high-dimensional similarity query processing for data science. It introduces solutions from a variety of research communities, including data mining (DM), database (DB), machine learning (ML), computer vision (CV), natural language processing (NLP), and theoretical computer science (TCS), thereby highlighting the interplay between modern computer science and artificial intelligence technologies. We first discuss the importance of high-dimensional similarity query processing in data science applications, and then review query processing algorithms such as cover tree, locality sensitive hashing, product quantization, proximity graphs, as well as recent advancements such as learned indexes. We analyze their strengths and weaknesses and discuss the selection of algorithms in various application scenarios. Moreover, we consider the selectivity estimation of high-dimensional similarity queries, and show how researchers are bringing in state-of-the-art ML techniques to address this problem. We expect that this tutorial will provide an impetus towards new technologies for data science.
Meta Multi-Agent Exercise Recommendation: A Game Application Perspective
Authors: Fei Liu 0038, Xuegang Hu, Shuochen Liu, Chenyang Bu, Le Wu
Venue: Conference KDD 2023
Year: 2023

Exercise recommendation is a fundamental and important task in the E-learning system, facilitating students' personalized learning. Most existing exercise recommendation algorithms design a scoring criterion (e.g., weakest mastery, lowest historical correctness) in conjunction with experience, and then recommend the recommended knowledge concepts (KCs). These algorithms rely entirely on the scoring criteria by treating exercise recommendations as a centralized system. However, it is a complex problem for the centralized system to choose a limited number of exercises in a period of time to consolidate and learn the KCs efficiently. Moreover, different groups of students (e.g., different countries, schools, or classes) have different solutions for the same group of KCs according to their own situations, in the spirit of competency-based instructing. Therefore, we propose Meta Multi-Agent Exercise Recommendation (MMER). Specifically, we design the multi-agent exercise recommendation module, in which the KCs involved in exercises are considered agents with competition and cooperation among them. And the meta-training stage is designed to learn a robust recommendation module for new student groups. Extensive experiments on real-world datasets validate the satisfactory performance of the proposed model. Furthermore, the effectiveness of the multi-agent and meta-training part is demonstrated for the model in recommendation applications.
Task affinity with maximum bipartite matching in few-shot learning
Authors: Cat Phuoc Le, Juncheng Dong, Mohammadreza Soltani, Vahid Tarokh
Venue: Conference ICLR 2022
Year: 2022

We propose an asymmetric affinity score for representing the complexity of utilizing the knowledge of one task for learning another one. Our method is based on the maximum bipartite matching algorithm and utilizes the Fisher Information matrix. We provide theoretical analyses demonstrating that the proposed score is mathematically well-defined, and subsequently use the affinity score to propose a novel algorithm for the few-shot learning problem. In particular, using this score, we find relevant training data labels to the test data and leverage the discovered relevant data for episodically fine-tuning a few-shot model. Results on various few-shot benchmark datasets demonstrate the efficacy of the proposed approach by improving the classification accuracy over the state-of-the-art methods even when using smaller models.
Carl-g: Clustering-accelerated representation learning on graphs
Authors: William Shiao, Uday Singh Saini, Yozen Liu, Tong Zhao 0003, Neil Shah, Evangelos E. Papalexakis
Venue: Conference KDD 2023
Year: 2023

Self-supervised learning on graphs has made large strides in achieving great performance in various downstream tasks. However, many state-of-the-art methods suffer from a number of impediments, which prevent them from realizing their full potential. For instance, contrastive methods typically require negative sampling, which is often computationally costly. While non-contrastive methods avoid this expensive step, most existing methods either rely on overly complex architectures or dataset-specific augmentations. In this paper, we ask: Can we borrow from classical unsupervised machine learning literature in order to overcome those obstacles? Guided by our key insight that the goal of distance-based clustering closely resembles that of contrastive learning: both attempt to pull representations of similar items together and dissimilar items apart. As a result, we propose CARL-G - a novel clustering-based framework for graph representation learning that uses a loss inspired by Cluster Validation Indices (CVIs), i.e., internal measures of cluster quality (no ground truth required). CARL-G is adaptable to different clustering methods and CVIs, and we show that with the right choice of clustering method and CVI, CARL-G outperforms node classification baselines on 4/5 datasets with up to a 79× training speedup compared to the best-performing baseline. CARL-G also performs at par or better than baselines in node clustering and similarity search tasks, training up to 1,500× faster than the best-performing baseline. Finally, we also provide theoretical foundations for the use of CVI-inspired losses in graph representation learning.
Comet: Learning cardinality constrained mixture of experts with trees and local search
Authors: Shibal Ibrahim, Wenyu Chen 0003, Hussein Hazimeh 0001, Natalia Ponomareva 0001, Zhe Zhao 0001, Rahul Mazumder
Venue: Conference KDD 2023
Year: 2023

The sparse Mixture-of-Experts (Sparse-MoE) framework efficiently scales up model capacity in various domains, such as natural language processing and vision. Sparse-MoEs select a subset of the "experts" (thus, only a portion of the overall network) for each input sample using a sparse, trainable gate. Existing sparse gates are prone to convergence and performance issues when training with first-order optimization methods. In this paper, we introduce two improvements to current MoE approaches. First, we propose a new sparse gate: COMET, which relies on a novel tree-based mechanism. COMET is differentiable, can exploit sparsity to speed up computation, and outperforms state-of-the-art gates. Second, due to the challenging combinatorial nature of sparse expert selection, first-order methods are typically prone to low-quality solutions. To deal with this challenge, we propose a novel, permutation-based local search method that can complement first-order methods in training any sparse gate, e.g., Hash routing, Top-k, DSelect-k, and COMET. We show that local search can help networks escape bad initializations or solutions. We performed large-scale experiments on various domains, including recommender systems, vision, and natural language processing. On standard vision and recommender systems benchmarks, COMET+ (COMET with local search) achieves up to 13% improvement in ROC AUC over popular gates, e.g., Hash routing and Top-k, and up to 9% over prior differentiable gates e.g., DSelect-k. When Top-k and Hash gates are combined with local search, we see up to $100\times$ reduction in the budget needed for hyperparameter tuning. Moreover, for language modeling, our approach improves over the state-of-the-art MoEBERT model for distilling BERT on 5/7 GLUE benchmarks as well as SQuAD dataset.
Learning equivariant structured output svm regressors
Authors: Andrea Vedaldi, Matthew B. Blaschko, Andrew Zisserman
Venue: Conference ICCV 2011
Year: 2011

Equivariance and invariance are often desired properties of a computer vision system. However, currently available strategies generally rely on virtual sampling, leaving open the question of how many samples are necessary, on the use of invariant feature representations, which can mistakenly discard information relevant to the vision task, or on the use of latent variable models, which result in non-convex training and expensive inference at test time. We propose here a generalization of structured output SVM regressors that can incorporate equivariance and invariance into a convex training procedure, enabling the incorporation of large families of transformations, while maintaining optimality and tractability. Importantly, test time inference does not require the estimation of latent variables, resulting in highly efficient objective functions. This results in a natural formulation for treating equivariance and invariance that is easily implemented as an adaptation of off-the-shelf optimization software, obviating the need for ad hoc sampling strategies. Theoretical results relating to vicinal risk, and experiments on challenging aerial car and pedestrian detection tasks show the effectiveness of the proposed solution.
T-cell receptor-peptide interaction prediction with physical model augmented pseudo-labeling
Authors: Yiren Jian, Erik Kruus, Martin Renqiang Min
Venue: Conference KDD 2022
Year: 2022

Predicting the interactions between T-cell receptors (TCRs) and peptides is crucial for the development of personalized medicine and targeted vaccine in immunotherapy. Current datasets for training deep learning models of this purpose remain constrained without diverse TCRs and peptides. To combat the data scarcity issue presented in the current datasets, we propose to extend the training dataset by physical modeling of TCR-peptide pairs. Specifically, we compute the docking energies between auxiliary unknown TCR-peptide pairs as surrogate training labels. Then, we use these extended example-label pairs to train our model in a supervised fashion. Finally, we find that the AUC score for the prediction of the model can be further improved by pseudo-labeling of such unknown TCR-peptide pairs (by a trained teacher model), and re-training the model with those pseudo-labeled TCR-peptide pairs. Our proposed method that trains the deep neural network with physical modeling and data-augmented pseudo-labeling improves over baselines in the available two datasets. We also introduce a new dataset that contains over 80,000 unknown TCR-peptide pairs with docking energy scores.
Knowledge graph embedding preserving soft logical regularity
Authors: Shu Guo, Lin Li, Zhen Hui, Lingshuai Meng, Bingnan Ma, Wei Liu, Lihong Wang, Haibin Zhai, Hong Zhang
Venue: Conference CIKM 2020
Year: 2020

Embedding knowledge graphs (KGs) into continuous vector spaces is currently an active research area. Soft rules, despite their uncertainty, are highly beneficial to KG embedding. However, they have not been studied enough in recent methods. A major challenge here is how to devise a principled framework, which efficiently and effectively integrates such soft logical information into embedding models. This paper proposes a highly scalable and effective method for preserving soft logical regularities by imposing soft rule constraints on relation representations. Specifically, we first represent relations as bilinear forms and map entity representations into a non-negative and bounded space. Then we derive a rule-based regularization that merely enforces relation representations to satisfy constraints introduced by soft rules. The proposed method has the following advantages: 1) it regularizes relations directly with the complexity of rule learning independent of entity set size, improving scalability; 2) it imposes prior logical information upon the structure of the embedding space, and would be beneficial to knowledge reasoning. Evaluation in link prediction on Freebase and DBpedia shows the effectiveness of our approach over many competitive baselines. Code and datasets are available at https://github.com/StudyGroup-lab/SLRE.
Logarithmic dimension reduction for quantum neural networks
Authors: Hankyul Baek, Soohyun Park, Joongheon Kim
Venue: Conference CIKM 2023
Year: 2023

In recent years, quantum neural network (QNN) based on quantum computing has attracted attention due to its potential for computation-acceleration and parallelism. However, the intrinsic limitations of QNN, where the output (i.e., observables) can only be obtained through a measurement process, pose scalability challenges. Motivated by this, this paper aims to address the scalability challenges by incorporating Pauli-Z measurement and Basis measurement. In conventional frameworks, QNN typically relies on classical fully connected networks (FCNs) or increases the number of qubits to achieve large output dimensions. However, by leveraging our proposed framework, this paper successfully expands the output dimensions to an exponential scale, surpassing the limitations imposed by the limited number of qubits without relying on FCNs. Through extensive experiments, this paper demonstrates that the proposed framework outperforms existing QNN frameworks in multi-class classification tasks that require numerous output dimensions.
Hierarchical knowledge guided fault intensity diagnosis of complex industrial systems
Authors: Yu Sha, Shuiping Gou, Bo Liu 0009, Johannes Faber, Ningtao Liu, Stefan Schramm, Horst Stoecker, Thomas Steckenreiter, Domagoj Vnucec, Nadine Wetzstein, Andreas Widl, Kai Zhou 0017
Venue: Conference KDD 2024
Year: 2024

Fault intensity diagnosis (FID) plays a pivotal role in monitoring and maintaining mechanical devices within complex industrial systems. As current FID methods are based on chain of thought without considering dependencies among target classes. To capture and explore dependencies, we propose a hierarchical knowledge guided fault intensity diagnosis framework (HKG) inspired by the tree of thought, which is amenable to any representation learning methods. The HKG uses graph convolutional networks to map the hierarchical topological graph of class representations into a set of interdependent global hierarchical classifiers, where each node is denoted by word embeddings of a class. These global hierarchical classifiers are applied to learned deep features extracted by representation learning, allowing the entire model to be end-to-end learnable. In addition, we develop a re-weighted hierarchical knowledge correlation matrix (Re-HKCM) scheme by embedding inter-class hierarchical knowledge into a data-driven statistical correlation matrix (SCM) which effectively guides the information sharing of nodes in graphical convolutional neural networks and avoids over-smoothing issues. The Re-HKCM is derived from the SCM through a series of mathematical transformations. Extensive experiments are performed on four real-world datasets from different industrial domains (three cavitation datasets from SAMSON AG and one existing publicly) for FID, all showing superior results and outperform recent state-of-the-art FID methods.
Detecting concept drift in medical triage
Authors: Hamish Huggard, Yun Sing Koh, Gillian Dobbie, Edmond Zhang
Venue: Conference SIGIR 2020
Year: 2020

in their accompanying referral documents, which contain a mix of free text and structured data. By training a model to predict triage decisions from these referral documents, we can partially automate the triage process, resulting in more efficient and systematic triage decisions. One of the difficulties of this task is maintaining robustness against changes in triage priorities due to changes in policy, funding, staff, or other factors. This is reflected as changes in relationship between document features and triage labels, also known as concept drift. These changes must be detected so that the model can be retrained to reflect the new environment. We introduce a new concept drift detection algorithm for this domain called calibrated drift detection method (CDDM). We evaluated CDDM on benchmark and synthetic medical triage datasets, and find it competitive with state-of-the-art detectors, while also being less prone to false positives from feature drift.