OpenRaiser/Intern-Atlas huggingface dataset:
Fedskill: Privacy preserved interpretable skill learning via imitation
Authors: Yushan Jiang, Wenchao Yu, Dongjin Song, Lu Wang 0029, Wei Cheng 0002, Haifeng Chen
Venue: Conference KDD 2023
Year: 2023

Imitation learning that replicates experts' skills via their demonstrations has shown significant success in various decision-making tasks. However, two critical challenges still hinder the deployment of imitation learning techniques in real-world application scenarios. First, existing methods lack the intrinsic interpretability to explicitly explain the underlying rationale of the learned skill and thus making learned policy untrustworthy. Second, due to the scarcity of expert demonstrations from each end user (client), learning a policy based on different data silos is necessary but challenging in privacy-sensitive applications such as finance and healthcare. To this end, we present a privacy-preserved interpretable skill learning framework (FedSkill) that enables global policy learning to incorporate data from different sources and provides explainable interpretations to each local user without violating privacy and data sovereignty. Specifically, our proposed interpretable skill learning model can capture the varying patterns in the trajectories of expert demonstrations, and extract prototypical information as skills that provide implicit guidance for policy learning and explicit explanations in the reasoning process. Moreover, we design a novel aggregation mechanism coupled with the based skill learning model to preserve global information utilization and maintain local interpretability under the federated framework. Thoroughly experiments on three datasets and empirical studies demonstrate that our proposed FedSkill framework not only outperforms state-of-the-art imitation learning methods but also exhibits good interpretability under a federated setting. Our proposed FedSkill framework is the first attempt to bridge the gaps among federated learning, interpretable machine learning, and imitation learning.
Multi-modal Time Series Analysis: A Tutorial and Survey
Authors: Yushan Jiang, Kanghui Ning, Zijie Pan, Xuyang Shen, Jingchao Ni, Wenchao Yu, Anderson Schneider, Haifeng Chen, Yuriy Nevmyvaka, Dongjin Song
Venue: Conference KDD 2025
Year: 2025

Multi-modal time series analysis has recently emerged as a prominent research area in data mining, driven by the increasing availability of diverse data modalities, such as text, images, and structured tabular data from real-world sources. However, effective analysis of multi-modal time series is hindered by data heterogeneity, modality gap, misalignment, and inherent noise. Recent advancements in multi-modal time series methods have exploited the multi-modal context via cross-modal interactions based on deep learning methods, significantly enhancing various downstream tasks. In this tutorial and survey, we present a systematic and up-to-date overview of multi-modal time series datasets and methods. We first state the existing challenges of multi-modal time series analysis and our motivations, with a brief introduction of preliminaries. Then, we summarize the general pipeline and categorize existing methods through a unified cross-modal interaction framework encompassing fusion, alignment, and transference at different levels (\textit{i.e.}, input, intermediate, output), where key concepts and ideas are highlighted. We also discuss the real-world applications of multi-modal analysis for both standard and spatial time series, tailored to general and specific domains. Finally, we discuss future research directions to help practitioners explore and exploit multi-modal time series. The up-to-date resources are provided in the GitHub repository: https://github.com/UConn-DSIS/Multi-modal-Time-Series-Analysis
Instructime: Advancing time series classification with multimodal language modeling
Authors: Mingyue Cheng, Yiheng Chen, Qi Liu, Zhiding Liu, Yucong Luo, Enhong Chen
Venue: Conference WSDM 2025
Year: 2025

For the advancement of time series classification, we can summarize that most existing methods adopt a common learning-to-classify paradigm - a classifier model tries to learn the relation between sequence inputs and target label encoded by one-hot distribution. Although effective, this paradigm conceals two inherent limitations: (1) one-hot distribution fails to reflect the comparability and similarity between labels, and (2) it is difficult to learn transferable representation across domains. In this work, we propose InstructTime, a novel attempt to reshape time series classification as a learning-to-generate paradigm. Relying on the generative capacity of the pre-trained language model, the core idea is to formulate the classification of time series as a multimodal understanding task. Specifically, firstly, a time series discretization module is designed to convert continuous inputs into a sequence of discrete tokens to solve the inconsistency issue across modality data. Secondly, we introduce an alignment projected layer before feeding the transformed token of time series into language models. Thirdly, prior to fine-tuning the language model for the target domain, it is essential to emphasize the necessity of auto-regressive pre-training across various modality inputs. Finally, extensive experimentation are conducted on several prevalent public benchmark datasets, indicating the superior performance of the InstructTime. Our code is at https://github.com/Mingyue-Cheng/InstructTime.
Hierarchical reinforcement learning for scarce medical resource allocation with imperfect information
Authors: Qianyue Hao, Fengli Xu, Lin Chen, Pan Hui, Yong Li
Venue: Conference KDD 2021
Year: 2021

Facing the outbreak of COVID-19, shortage in medical resources becomes increasingly outstanding. Therefore, efficient strategies for medical resource allocation are urgently called for. Reinforcement learning (RL) is powerful for decision making, but three key challenges exist in solving this problem via RL: (1) complex situation and countless choices for decision making in the real world; (2) only imperfect information are available due to the latency of pandemic spreading; (3) limitations on conducting experiments in real world since we cannot set pandemic outbreaks arbitrarily. In this paper, we propose a hierarchical reinforcement learning method with a corresponding training algorithm. We design a decomposed action space to deal with the countless choices to ensure efficient and real time strategies. We also design a recurrent neural network based framework to utilize the imperfect information obtained from the environment. We build a pandemic spreading simulator based on real world data, serving as the experimental platform. We conduct extensive experiments and the results show that our method outperforms all the baselines, which reduces infections and deaths by 14.25% on average.
Online Bidding under RoS Constraints without Knowing the Value
Authors: Sushant Vijayan, Zhe Feng, Swati Padmanabhan, Karthikeyan Shanmugam, Arun Sai Suggala, Di Wang
Venue: Conference WWW 2025
Year: 2025

We consider the problem of bidding in online advertising, where an advertiser aims to maximize value while adhering to budget and Return-on-Spend (RoS) constraints. Unlike prior work that assumes knowledge of the value generated by winning each impression ({e.g.,} conversions), we address the more realistic setting where the advertiser must simultaneously learn the optimal bidding strategy and the value of each impression opportunity. This introduces a challenging exploration-exploitation dilemma: the advertiser must balance exploring different bids to estimate impression values with exploiting current knowledge to bid effectively. To address this, we propose a novel Upper Confidence Bound (UCB)-style algorithm that carefully manages this trade-off. Via a rigorous theoretical analysis, we prove that our algorithm achieves $\widetilde{O}(\sqrt{T\log(|\mathcal{B}|T)})$ regret and constraint violation, where $T$ is the number of bidding rounds and $\mathcal{B}$ is the domain of possible bids. This establishes the first optimal regret and constraint violation bounds for bidding in the online setting with unknown impression values. Moreover, our algorithm is computationally efficient and simple to implement. We validate our theoretical findings through experiments on synthetic data, demonstrating that our algorithm exhibits strong empirical performance compared to existing approaches.
Graph machine learning meets multi-table relational data
Authors: Quan Gan, Minjie Wang, David Wipf, Christos Faloutsos
Venue: Conference KDD 2024
Year: 2024

While graph machine learning, and notably graph neural networks (GNNs), have gained immense traction in recent years, application is predicated on access to a known input graph upon which predictive models can be trained. And indeed, within the most widely-studied public evaluation benchmarks such graphs are provided, with performance comparisons conditioned on curated data explicitly adhering to this graph. However, in real-world industrial applications, the situation is often quite different. Instead of a known graph, data are originally collected and stored across multiple tables in a repository, at times with ambiguous or incomplete relational structure. As such, to leverage the latest GNN architectures it is then up to a skilled data scientist to first manually construct a graph using intuition and domain knowledge, a laborious process that may discourage adoption in the first place. To narrow this gap and broaden the applicability of graph ML, we survey existing tools and strategies that can be combined to address the more fundamental problem of predictive tabular modeling over data native to multiple tables, with no explicit relational structure assumed a priori. This involves tracing a comprehensive path through related table join discovery and fuzzy table joining, column alignment, automated relational database (RDB) construction, extracting graphs from RDBs, graph sampling, and finally, graph-centric trainable predictive architectures. Although efforts to build deployable systems that integrate all of these components while minimizing manual effort remain in their infancy, this survey will nonetheless reduce barriers to entry and help steer the graph ML community towards promising research directions and wider real-world impact.
Unifying offline causal inference and online bandit learning for data driven decision
Authors: Ye Li, Hong Xie, Yishi Lin, John C. S. Lui
Venue: Conference WWW 2021
Year: 2021

A fundamental question for companies with large amount of logged data is: How to use such logged data together with incoming streaming data to make good decisions? Many companies currently make decisions via online A/B tests, but wrong decisions during testing hurt users’ experiences and cause irreversible damage. A typical alternative is offline causal inference, which analyzes logged data alone to make decisions. However, these decisions are not adaptive to the new incoming data, and so a wrong decision will continuously hurt users’ experiences. To overcome the aforementioned limitations, we propose a framework to unify offline causal inference algorithms (e.g., weighting, matching) and online learning algorithms (e.g., UCB, LinUCB). We propose novel algorithms and derive bounds on the decision accuracy via the notion of “regret”. We derive the first upper regret bound for forest-based online bandit algorithms. Experiments on two real datasets show that our algorithms outperform other algorithms that use only logged data or online feedbacks, or algorithms that do not use the data properly.
Efficient mini-batch training for stochastic optimization
Authors: Mu Li, Tong Zhang, Yuqiang Chen, Alexander J. Smola
Venue: Conference KDD 2014
Year: 2014

Stochastic gradient descent (SGD) is a popular technique for large-scale optimization problems in machine learning. In order to parallelize SGD, minibatch training needs to be employed to reduce the communication cost. However, an increase in minibatch size typically decreases the rate of convergence. This paper introduces a technique based on approximate optimization of a conservatively regularized objective function within each minibatch. We prove that the convergence rate does not decrease with increasing minibatch size. Experiments demonstrate that with suitable implementations of approximate optimization, the resulting algorithm can outperform standard SGD in many scenarios.
An axiomatic approach for result diversification
Authors: Sreenivas Gollapudi, Aneesh Sharma
Venue: Conference WWW 2009
Year: 2009

Understanding user intent is key to designing an effective ranking system in a search engine. In the absence of any explicit knowledge of user intent, search engines want to diversify results to improve user satisfaction. In such a setting, the probability ranking principle-based approach of presenting the most relevant results on top can be sub-optimal, and hence the search engine would like to trade-off relevance for diversity in the results.
Fighting opinion control in social networks via link recommendation
Authors: Victor Amelkin, Ambuj K. Singh
Venue: Conference KDD 2019
Year: 2019

The process of opinion formation is inherently a network process, with user opinions in a social network being driven to a certain average opinion. One simple and intuitive incarnation of this opinion attractor is the average of user opinions weighted by the users' eigenvector centralities. This value is a lucrative target for control, as altering it essentially changes the mass opinion in the network. Since any potentially malicious influence upon the opinion distribution in a society is undesirable, it is important to design methods to prevent external attacks upon it. In this work, we assume that the adversary aims to maliciously change the network's average opinion by altering the opinions of some unknown users. We, then, state an NP-hard problem of disabling such opinion control attempts via strategically altering the network's users' eigencentralities by recommending a limited number of links to the users. Relying on Markov chain theory, we provide perturbation analysis that shows how eigencentrality and, hence, our problem's objective change in response to a link's addition to the network. The latter leads to the design of a pseudo-linear-time heuristic, relying on efficient estimation of mean first passage times in Markov chains. We have confirmed our theoretical and algorithmic findings, and studied effectiveness and efficiency of our heuristic in experiments with synthetic and real networks.
Debiasing social wisdom
Authors: Abhimanyu Das, Sreenivas Gollapudi, Rina Panigrahy, Mahyar Salek
Venue: Conference KDD 2013
Year: 2013

With the explosive growth of social networks, many applications are increasingly harnessing the pulse of online crowds for a variety of tasks such as marketing, advertising, and opinion mining. An important example is the wisdom of crowd effect that has been well studied for such tasks when the crowd is non-interacting. However, these studies don't explicitly address the network effects in social networks. A key difference in this setting is the presence of social influences that arise from these interactions and can undermine the wisdom of the crowd [17].
Im balanced: influence maximization under balance constraints
Authors: Shay Gershtein, Tova Milo, Brit Youngmann, Gal Zeevi
Venue: Conference CIKM 2018
Year: 2018

Influence Maximization (IM) is the problem of finding a set of influential users in a social network, so that their aggregated influence is maximized. IM has natural applications in viral marketing and has been the focus of extensive recent research. One critical problem, however, is that while existing IM algorithms serve the goal of reaching a large audience, they may obliviously focus on certain well-connected populations, at the expense of key demographics, creating an undesirable imbalance, an illustration of a broad phenomenon referred to as algorithmic discrimination. Indeed, we demonstrate an inherent trade-off between two objectives: (1) maximizing the overall influence and (2) maximizing influence over a predefined "protected" demographic, with the optimal balance between the two being open to different interpretations. To this end, we present IM-Balanced, a system enabling end users to declaratively specify the desired trade-off between these objectives w.r.t. an emphasized population. IM-Balanced provides theoretical guarantees for the proximity to the optimal solution in terms of both objectives and ensures an efficient, scalable computation via careful adaptation of existing state-of-the-art IM algorithms. Our demonstration illustrates the effectiveness of our approach through real-life viral marketing scenarios in an academic social network.
Fast Computation for the Forest Matrix of an Evolving Graph
Authors: Haoxin Sun, Xiaotian Zhou, Zhongzhi Zhang
Venue: Conference KDD 2024
Year: 2024

The forest matrix plays a crucial role in network science, opinion dynamics, and machine learning, offering deep insights into the structure of and dynamics on networks. In this paper, we study the problem of querying entries of the forest matrix in evolving graphs, which more accurately represent the dynamic nature of real-world networks compared to static graphs. To address the unique challenges posed by evolving graphs, we first introduce two approximation algorithms, \textsc{SFQ} and \textsc{SFQPlus}, for static graphs. \textsc{SFQ} employs a probabilistic interpretation of the forest matrix, while \textsc{SFQPlus} incorporates a novel variance reduction technique and is theoretically proven to offer enhanced accuracy. Based on these two algorithms, we further devise two dynamic algorithms centered around efficiently maintaining a list of spanning converging forests. This approach ensures $O(1)$ runtime complexity for updates, including edge additions and deletions, as well as for querying matrix elements, and provides an unbiased estimation of forest matrix entries. Finally, through extensive experiments on various real-world networks, we demonstrate the efficiency and effectiveness of our algorithms. Particularly, our algorithms are scalable to massive graphs with more than forty million nodes.
FORA: Simple and Effective Approximate Single-Source Personalized PageRank
Authors: Sibo Wang, Renchi Yang, Xiaokui Xiao, Zhewei Wei, Yin Yang
Venue: Conference KDD 2017
Year: 2017

Given a graph G, a source node s and a target node t, the personalized PageRank (PPR) of t with respect to s is the probability that a random walk starting from s terminates at t. A single-source PPR (SSPPR) query enumerates all nodes in G, and returns the top-k nodes with the highest PPR values with respect to a given source node s. SSPPR has important applications in web search and social networks, e.g., in Twitter's Who-To-Follow recommendation service. However, SSPPR computation is immensely expensive, and at the same time resistant to indexing and materialization. So far, existing solutions either use heuristics, which do not guarantee result quality, or rely on the strong computing power of modern data centers, which is costly.
Power-law graphs have minimal scaling of kemeny constant for random walks
Authors: Wanyue Xu, Yibin Sheng, Zuobai Zhang, Haibin Kan, Zhongzhi Zhang
Venue: Conference WWW 2020
Year: 2020

The mean hitting time from a node i to a node j selected randomly according to the stationary distribution of random walks is called the Kemeny constant, which has found various applications. It was proved that over all graphs with N vertices, complete graphs have the exact minimum Kemeny constant, growing linearly with N. Here we study numerically or analytically the Kemeny constant on many sparse real-world and model networks with scale-free small-world topology, and show that their Kemeny constant also behaves linearly with N. Thus, sparse networks with scale-free and small-world topology are favorable architectures with optimal scaling of Kemeny constant. We then present a theoretically guaranteed estimation algorithm, which approximates the Kemeny constant for a graph in nearly linear time with respect to the number of edges. Extensive numerical experiments on model and real networks show that our approximation algorithm is both efficient and accurate.
Towards safe synthetic image generation on the web: A multimodal robust nsfw defense and million scale dataset
Authors: Muhammad Shahid Muneer, Simon S. Woo
Venue: Conference WWW 2025
Year: 2025

In the past years, we have witnessed the remarkable success of Text-to-Image (T2I) models and their widespread use on the web. Extensive research in making T2I models produce hyper-realistic images has led to new concerns, such as generating Not-Safe-For-Work (NSFW) web content and polluting the web society. To help prevent misuse of T2I models and create a safer web environment for users features like NSFW filters and post-hoc security checks are used in these models. However, recent work unveiled how these methods can easily fail to prevent misuse. In particular, adversarial attacks on text and image modalities can easily outplay defensive measures. %Exploiting such leads to the growing concern of preventing adversarial attacks on text and image modalities. Moreover, there is currently no robust multimodal NSFW dataset that includes both prompt and image pairs and adversarial examples. This work proposes a million-scale prompt and image dataset generated using open-source diffusion models. Second, we develop a multimodal defense to distinguish safe and NSFW text and images, which is robust against adversarial attacks and directly alleviates current challenges. Our extensive experiments show that our model performs well against existing SOTA NSFW detection methods in terms of accuracy and recall, drastically reducing the Attack Success Rate (ASR) in multimodal adversarial attack scenarios. Code: https://github.com/shahidmuneer/multimodal-nsfw-defense.
Multi-label sparse coding for automatic image annotation
Authors: Changhu Wang, Shuicheng Yan, Lei Zhang 0001, Hong-Jiang Zhang
Venue: Conference CVPR 2009
Year: 2009

In this paper, we present a multi-label sparse coding framework for feature extraction and classification within the context of automatic image annotation. First, each image is encoded into a so-called supervector, derived from the universal Gaussian Mixture Models on orderless image patches. Then, a label sparse coding based subspace learning algorithm is derived to effectively harness multi-label information for dimensionality reduction. Finally, the sparse coding method for multi-label data is proposed to propagate the multi-labels of the training images to the query image with the sparse ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> reconstruction coefficients. Extensive image annotation experiments on the Corel5k and Corel30k databases both show the superior performance of the proposed multi-label sparse coding framework over the state-of-the-art algorithms.
Graphcite: Citation intent classification in scientific publications via graph embeddings
Authors: Dan Berrebbi, Nicolas Huynh, Oana Balalau
Venue: Conference WWW 2022
Year: 2022

Citations are crucial in scientific works as they help position a new publication. Each citation carries a particular intent, for example, to highlight the importance of a problem or to compare against results provided by another method. The authors’ intent when making a new citation has been studied to understand the evolution of a field over time or to make recommendations for further citations. In this work, we address the task of citation intent prediction from a new perspective. In addition to textual clues present in the citation phrase, we also consider the citation graph, leveraging high-level information of citation patterns. In this novel setting, we perform a thorough experimental evaluation of graph-based models for intent prediction. We show that our model, GraphCite, improves significantly upon models that take into consideration only the citation phrase. Our code is available online1.