OpenRaiser/Intern-Atlas huggingface dataset:
Causal inference and machine learning in practice with EconML and CausalML: Industrial use cases at microsoft, TripAdvisor, uber
Authors: Vasilis Syrgkanis, Greg Lewis, Miruna Oprescu, Maggie Hei, Keith Battocchi, Eleanor Dillon, Jing Pan, Yifeng Wu, Paul Lo, Huigang Chen, Totte Harinen, Jeong-Yoon Lee
Venue: Conference KDD 2021
Year: 2021

In recent years, both academic research and industry applications see an increased effort in using machine learning methods to measure granular causal effects and design optimal policies based on these causal estimates. Open source packages such as CausalML and EconML provide a unified interface for applied researchers and industry practitioners with a variety of machine learning methods for causal inference. The tutorial will cover the topics including conditional treatment effect estimators by meta-learners and tree-based algorithms, model validations and sensitivity analysis, optimization algorithms including policy leaner and cost optimization. In addition, the tutorial will demonstrate the production of these algorithms in industry use cases.
Cross-sentence n-ary relation extraction using entity link and discourse relation
Authors: Sanghak Lee, Seungmin Seo, Byungkook Oh, Kyong-Ho Lee, Dong-Hoon Shin, Yeonsoo Lee
Venue: Conference CIKM 2020
Year: 2020

This paper presents an efficient method of extracting n-ary relations from multiple sentences which is called Entity-path and Discourse relation-centric Relation Extractor (EDCRE). Unlike previous approaches, the proposed method focuses on an entity link, which consists of dependency edges between entities, and discourse relations between sentences. Specifically, the proposed model consists of two main sub-models. The first one encodes sentences with a higher weight on the entity link while considering the other edges with an attention mechanism. To consider various latent discourse relations between sentences, the second sub-model encodes discourse relations between adjacent sentences considering the contents of each sentence. Experiment results on the cross-sentence relation extraction dataset, PubMed, and the document-level relation extraction dataset, DocRED, show that the proposed model outperforms state-of-the-art methods of extracting relations across sentences. Furthermore, ablation study proves that both the two main sub-models have noticeable effect on the relation extraction task.
Communication-efficient decentralized online continuous dr-submodular maximization
Authors: Qixin Zhang, Zengde Deng, Xiangru Jian, Zaiyi Chen, Haoyuan Hu, Yu Yang
Venue: Conference CIKM 2023
Year: 2023

Maximizing a monotone submodular function is a fundamental task in data mining, machine learning, economics, and statistics. In this paper, we present two communication-efficient decentralized online algorithms for the monotone continuous DR-submodular maximization problem, both of which reduce the number of per-function gradient evaluations and per-round communication complexity from T3/2 to 1. The first one, One-shot Decentralized Meta-Frank-Wolfe~(Mono-DMFW), achieves a (1-1/e)-regret bound of O(T4/5). As far as we know, this is the first one-shot and projection-free decentralized online algorithm for monotone continuous DR-submodular maximization. Next, inspired by the non-oblivious boosting function[29], we propose the Decentralized Online Boosting Gradient Ascent (DOBGA) algorithm, which attains a (1-1/e)-regret of O(√T). To the best of our knowledge, this is the first result to obtain the optimal O(√T) against a (1-1/e)-approximation with only one gradient inquiry for each local objective function per step. Finally, various experimental results confirm the effectiveness of the proposed methods.
Pm-moe: Mixture of experts on private model parameters for personalized federated learning
Authors: Yu Feng, Yangli-ao Geng, Yifan Zhu, Zongfu Han, Xie Yu, Kaiwen Xue, Haoran Luo, Mengyang Sun, Guangwei Zhang, Meina Song
Venue: Conference WWW 2025
Year: 2025

Federated learning (FL) has gained widespread attention for its privacy-preserving and collaborative learning capabilities. Due to significant statistical heterogeneity, traditional FL struggles to generalize a shared model across diverse data domains. Personalized federated learning addresses this issue by dividing the model into a globally shared part and a locally private part, with the local model correcting representation biases introduced by the global model. Nevertheless, locally converged parameters more accurately capture domain-specific knowledge, and current methods overlook the potential benefits of these parameters. To address these limitations, we propose PM-MoE architecture. This architecture integrates a mixture of personalized modules and an energy-based personalized modules denoising, enabling each client to select beneficial personalized parameters from other clients. We applied the PM-MoE architecture to nine recent model-split-based personalized federated learning algorithms, achieving performance improvements with minimal additional training. Extensive experiments on six widely adopted datasets and two heterogeneity settings validate the effectiveness of our approach. The source code is available at \url{https://github.com/dannis97500/PM-MOE}.
Stealing training graphs from graph neural networks
Authors: Minhua Lin, Enyan Dai, Junjie Xu, Jinyuan Jia, Xiang Zhang, Suhang Wang
Venue: Conference KDD 2025
Year: 2025

Graph Neural Networks (GNNs) have shown promising results in modeling graphs in various tasks. The training of GNNs, especially on specialized tasks such as bioinformatics, demands extensive expert annotations, which are expensive and usually contain sensitive information of data providers. The trained GNN models are often shared for deployment in the real world. As neural networks can memorize the training samples, the model parameters of GNNs have a high risk of leaking private training data. Our theoretical analysis shows the strong connections between trained GNN parameters and the training graphs used, confirming the training graph leakage issue. However, explorations into training data leakage from trained GNNs are rather limited. Therefore, we investigate a novel problem of stealing graphs from trained GNNs. To obtain high-quality graphs that resemble the target training set, a graph diffusion model with diffusion noise optimization is deployed as a graph generator. Furthermore, we propose a selection method that effectively leverages GNN model parameters to identify training graphs from samples generated by the graph diffusion model. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed framework in stealing training graphs from the trained GNN.
Adaptive neural ranking framework: Toward maximized business goal for cascade ranking systems
Authors: Yunli Wang, Zhiqiang Wang, Jian Yang, Shiyang Wen, Dongying Kong, Han Li, Kun Gai
Venue: Conference WWW 2024
Year: 2024

Cascade ranking is widely used for large-scale top-k selection problems in online advertising and recommendation systems, and learning-to-rank is an important way to optimize the models in cascade ranking. Previous works on learning-to-rank usually focus on letting the model learn the complete order or top-k order, and adopt the corresponding rank metrics (e.g. OPA and NDCG@k) as optimization targets. However, these targets can not adapt to various cascade ranking scenarios with varying data complexities and model capabilities; and the existing metric-driven methods such as the Lambda framework can only optimize a rough upper bound of limited metrics, potentially resulting in sub-optimal and performance misalignment. To address these issues, we propose a novel perspective on optimizing cascade ranking systems by highlighting the adaptability of optimization targets to data complexities and model capabilities. Concretely, we employ multi-task learning to adaptively combine the optimization of relaxed and full targets, which refers to metrics Recall@m@k and OPA respectively. We also introduce permutation matrix to represent the rank metrics and employ differentiable sorting techniques to relax hard permutation matrix with controllable approximate error bound. This enables us to optimize both the relaxed and full targets directly and more appropriately. We named this method as Adaptive Neural Ranking Framework (abbreviated as ARF). Furthermore, we give a specific practice under ARF. We use the NeuralSort to obtain the relaxed permutation matrix and draw on the variant of the uncertainty weight method in multi-task learning to optimize the proposed losses jointly. Experiments on a total of 4 public and industrial benchmarks show the effectiveness and generalization of our method, and online experiment shows that our method has significant application value.
An empirical analysis of algorithmic pricing on amazon marketplace
Authors: Le Chen, Alan Mislove, Christo Wilson
Venue: Conference WWW 2016
Year: 2016

The rise of e-commerce has unlocked practical applications for algorithmic pricing (also called dynamic pricing algorithms), where sellers set prices using computer algorithms. Travel websites and large, well known e-retailers have already adopted algorithmic pricing strategies, but the tools and techniques are now available to small-scale sellers as well.
Color-based Tracking of Heads and Other Mobile Objects at Video Frame Rates
Authors: Paul W. Fieguth, Demetri Terzopoulos
Venue: Conference CVPR 1997
Year: 1997

We develop a simple and very fast method for object tracking based exclusively on color information in digitized video images. Running on a Silicon Graphics R4600 Indy system with an IndyCam, our algorithm is capable of simultaneously tracking objects at full frame size (640/spl times/480 pixels) and video frame rate (30 fps). Robustness with respect to occlusion is achieved via can explicit hypothesis-tree model of the occlusion process. We demonstrate the efficacy of our technique in the challenging task of tracking people, especially tracking human heads and hands.
Precise Sub-pixel Estimation on Area-Based Matching
Authors: Masao Shimizu, Masatoshi Okutomi
Venue: Conference ICCV 2001
Year: 2001

Area-based matching is a common procedure in various fields such as image-based measurements, stereo image processing, and fluidics. Sub-pixel estimation using parabola fitting over three points with their similarity measures is also a common method to increase the resolution of matching. However, few investigations or studies concerning the characteristics of this estimation have been reported. In this paper we have analyzed the sub-pixel estimation error by using an approximate image function and three kinds of similarity measures for matching. The results illustrate some inherently problematic phenomena such as so called "pixel-locking". In addition to this, we propose a new algorithm to greatly reduce sub-pixel estimation error. This method is independent from the similarity measure and quite simple to implement. The advantage of our novel method is confirmed through experiments using three different types of images.
Fast Vehicle Detection with Probabilistic Feature Grouping and its Application to Vehicle Tracking
Authors: Zu Whan Kim, Jitendra Malik
Venue: Conference ICCV 2003
Year: 2003

Generating vehicle trajectories from video data is an important application of ITS (intelligent transportation systems). We introduce a new tracking approach which uses model-based 3-D vehicle detection and description algorithm. Our vehicle detection and description algorithm is based on a probabilistic line feature grouping, and it is faster (by up to an order of magnitude) and more flexible than previous image-based algorithms. We present the system implementation and the vehicle detection and tracking results.
A sparse probabilistic learning algorithm for real-time tracking
Authors: Oliver Williams, Andrew Blake 0001, Roberto Cipolla
Venue: Conference ICCV 2003
Year: 2003

We address the problem of applying powerful pattern recognition algorithms based on kernels to efficient visual tracking. Recently S. Avidan, (2001) has shown that object recognizers using kernel-SVMs can be elegantly adapted to localization by means of spatial perturbation of the SVM, using optic flow. Whereas Avidan's SVM applies to each frame of a video independently of other frames, the benefits of temporal fusion of data are well known. Using a fully probabilistic 'relevance vector machine' (RVM) to generate observations with Gaussian distributions that can be fused over time is addressed. To improve performance further, rather than adapting a recognizer, we build a localizer directly using the regression form of the RVM. A classification SVM is used in tandem, for object verification, and this provides the capability of automatic initialization and recovery. The approach is demonstrated in real-time face and vehicle tracking systems. The 'sparsity' of the RVMs means that only a fraction of CPU time is required to track at frame rate. Tracker output is demonstrated in a camera management task in which zoom and pan are controlled in response to speaker/vehicle position and orientation, over an extended period. The advantages of temporal fusion in this system are demonstrated.
Recognition and reproduction of gestures using a probabilistic framework combining pca, ica and hmm
Authors: Sylvain Calinon, Aude Billard
Venue: Conference ICML 2005
Year: 2005

This paper explores the issue of recognizing, generalizing and reproducing arbitrary gestures. We aim at extracting a representation that encapsulates only the key aspects of the gesture and discards the variability intrinsic to each person's motion. We compare a decomposition into principal components (PCA) and independent components (ICA) as a first step of preprocessing in order to decorrelate and denoise the data, as well as to reduce the dimensionality of the dataset to make this one tractable. In a second stage of processing, we explore the use of a probabilistic encoding through continuous Hidden Markov Models (HMMs), as a way to encapsulate the sequential nature and intrinsic variability of the motions in stochastic finite state automata. Finally, the method is validated in a humanoid robot to reproduce a variety of gestures performed by a human demonstrator.
Self-Calibrating Depth from Refraction
Authors: Zhihu Chen, Kwan-Yee Kenneth Wong, Yasuyuki Matsushita, Xiaolong Zhu, Miaomiao Liu 0001
Venue: Conference ICCV 2011
Year: 2011

In this paper, we introduce a novel method for depth acquisition based on refraction of light. A scene is captured twice by a fixed perspective camera, with the first image captured directly by the camera and the second by placing a transparent medium between the scene and the camera. A depth map of the scene is then recovered from the displacements of scene points in the images. Unlike other existing depth from refraction methods, our method does not require the knowledge of the pose and refractive index of the transparent medium, but can recover them directly from the input images. We hence call our method self-calibrating depth from refraction. Experimental results on both synthetic and real-world data are presented, which demonstrate the effectiveness of the proposed method.
Using model-driven bundle-adjustment to model heads from raw video sequences
Authors: Pascal Fua
Venue: Conference ICCV 1999
Year: 1999

We show that we can effectively and automatically fit a complex facial animation model to uncalibrated image sequences. Our approach is based on model-driven bundle adjustment followed by least squares fitting. It takes advantage of three complementary sources of information: stereo data, silhouette edges and 2D feature points. In this way, complete head models can be acquired with a cheap and entirely passive sensor such as an ordinary video camera. They can then be fed to existing animation software to produce synthetic sequences.
Intelliclean: A knowledge-based intelligent data cleaner
Authors: Mong-Li Lee, Tok Wang Ling, Wai Lup Low
Venue: Conference KDD 2000
Year: 2000

Existing data cleaning methods work on the basis of computing the degree of similarity be t ween nearby records in a sorted database. High recall is achieved by accepting records with low degrees of similarity as duplicates, at the cost of lower precision. High precision is achieved analogously at the cost of lower recall. This is the r e c all-pr ecision dilemma. In this paper, we propose a generic knowledge-based framework for e ective data cleaning that implements existing cleaning strategies and more. We develop a new method to compute transitive closure under uncertaint ywhich handles the merging of groups of inexact duplicate records. Experimental results show that this framework can identify duplicates and anomalies with high recall and precision.
Towards automatic extraction of event and place semantics from flickr tags
Authors: Tye Rattenbury, Nathaniel Good, Mor Naaman
Venue: Conference SIGIR 2007
Year: 2007

We describe an approach for extracting semantics of tags, unstructured text-labels assigned to resources on the Web, based on each tag's usage patterns. In particular, we focus on the problem of extracting place and event semantics for tags that are assigned to photos on Flickr, a popular photo sharing website that supports time and location (latitude/longitude) metadata. We analyze two methods inspired by well-known burst-analysis techniques and one novel method: Scale-structure Identification. We evaluate the methods on a subset of Flickr data, and show that our Scale-structure Identification method outperforms the existing techniques. The approach and methods described in this work can be used in other domains such as geo-annotated web pages, where text terms can be extracted and associated with usage patterns.
Determining the semantic orientation of terms through gloss classification
Authors: Andrea Esuli, Fabrizio Sebastiani
Venue: Conference CIKM 2005
Year: 2005

Sentiment classification is a recent subdiscipline of text classification which is concerned not with the topic a document is about, but with the opinion it expresses. It has a rich set of applications, ranging from tracking users' opinions about products or about political candidates as expressed in online forums, to customer relationship management. Functional to the extraction of opinions from text is the determination of the orientation of ``subjective'' terms contained in text, i.e. the determination of whether a term that carries opinionated content has a positive or a negative connotation. In this paper we present a new method for determining the orientation of subjective terms. The method is based on the quantitative analysis of the glosses of such terms, i.e. the definitions that these terms are given in on-line dictionaries, and on the use of the resulting term representations for semi-supervised term classification. The method we present outperforms all known methods when tested on the recognized standard benchmarks for this task.
Classical music for rock fans?: novel recommendations for expanding user interests
Authors: Makoto Nakatsuji, Yasuhiro Fujiwara, Akimichi Tanaka, Toshio Uchiyama, Ko Fujimura, Toru Ishida
Venue: Conference CIKM 2010
Year: 2010

Most recommender algorithms produce types similar to those the active user has accessed before. This is because they measure user similarity only from the co-rating behaviors against items and compute recommendations by analyzing the items possessed by the users most similar to the active user. In this paper, we define item novelty as the smallest distance from the class the user accessed before to the class that includes target items over the taxonomy. Then, we try to accurately recommend highly novel items to the user. First, our method measures user similarity by employing items rated by users and a taxonomy of items. It can accurately identify many items that may suit the user. Second, it creates a graph whose nodes are users; weighted edges are set between users according to their similarity. It analyzes the user graph and extracts users that are related on the graph though the similarity between the active user and each of those users is not high. The users so extracted are likely to have highly novel items for the active user. An evaluation conducted on several datasets finds that our method accurately identifies items with higher novelty than previous methods.
Predicting product adoption in large-scale social networks
Authors: Rushi Bhatt, Vineet Chaoji, Rajesh Parekh
Venue: Conference CIKM 2010
Year: 2010

Online social networks offer opportunities to analyze user behavior and social connectivity and leverage resulting in-sights for effective online advertising. We study the adoption of a paid product by members of a large and well-connected Instant Messenger (IM) network. This product is important to the business and poses unique challenges to advertising due to its low baseline adoption rate. We find that adop-tion by highly connected individuals is correlated with their social connections (friends) adopting after them. However, there is little evidence of social influence by these high degree individuals. Further, the spread of adoption remains mostly local to first-adopters and their immediate friends. We ob-serve strong evidence of peer pressure wherein future adop-tion by an individual is more likely if the product has been
Understanding election candidate approval ratings using social media data
Authors: Danish Contractor, Tanveer A. Faruquie
Venue: Conference WWW 2013
Year: 2013

The last few years has seen an exponential increase in the amount of social media data generated daily. Thus, researchers have started exploring the use of social media data in building recommendation systems, prediction models, improving disaster management, discovery trending topics etc. An interesting application of social media is for the prediction of election results. The recently conducted 2012 US Presidential election was the "most tweeted" election in history and provides a rich source of social media posts. Previous work on predicting election outcomes from social media has been largely been based on sentiment about candidates, total volumes of tweets expressing electoral polarity and the like. In this paper we use a collection of tweets to predict the daily approval ratings of the two US presidential candidates and also identify topics that were causal to the approval ratings.
A maximum entropy web recommendation system: combining collaborative and content features
Authors: Xin Jin, Yanzan Zhou, Bamshad Mobasher
Venue: Conference KDD 2005
Year: 2005

Web users display their preferences implicitly by navigating through a sequence of pages or by providing numeric ratings to some items. Web usage mining techniques are used to extract useful knowledge about user interests from such data. The discovered user models are then used for a variety of applications such as personalized recommendations. Web site content or semantic features of objects provide another source of knowledge for deciphering users' needs or interests. We propose a novel Web recommendation system in which collaborative features such as navigation or rating data as well as the content features accessed by the users are seamlessly integrated under the maximum entropy principle. Both the discovered user patterns and the semantic relationships among Web objects are represented as sets of constraints that are integrated to fit the model. In the case of content features, we use a new approach based on Latent Dirichlet Allocation (LDA) to discover the hidden semantic relationships among items and derive constraints used in the model. Experiments on real Web site usage data sets show that this approach can achieve better recommendation accuracy, when compared to systems using only usage information. The integration of semantic information also allows for better interpretation of the generated recommendations.