OpenRaiser/Intern-Atlas huggingface dataset:
Metric decomposition in a/b tests
Authors: Alex Deng, Luke Hagar, Nathaniel T. Stevens, Tatiana Xifara, Amit Gandhi
Venue: Conference KDD 2024
Year: 2024

More than a decade ago, CUPED (Controlled Experiments Utilizing Pre-Experiment Data) mainstreamed the idea of variance reduction leveraging pre-experiment covariates. Since its introduction, it has been implemented, extended, and modernized by major online experimentation platforms. Despite the wide adoption, it is known by practitioners that the variance reduction rate from CUPED utilizing pre-experimental data varies case by case and has a theoretical limit. In theory, CUPED can be extended to augment a treatment effect estimator utilizing in-experiment data, but practical guidance on how to construct such an augmentation is lacking. In this article, we fill this gap by proposing a new direction for sensitivity improvement via treatment effect augmentation whereby a target metric of interest is decomposed into components with high signal-to-noise disparity. Inference in the context of this decomposition is developed using both frequentist and Bayesian theory. We provide three real world applications demonstrating different flavors of metric decomposition; these applications illustrate the gain in agility metric decomposition yields relative to an un-decomposed analysis.
Adversarial Cooperative Imitation Learning for Dynamic Treatment Regimes
Authors: Lu Wang, Wenchao Yu, Xiaofeng He, Wei Cheng, Martin Renqiang Ren, Wei Wang, Bo Zong, Haifeng Chen, Hongyuan Zha
Venue: Conference WWW 2020
Year: 2020

Recent developments in discovering dynamic treatment regimes (DTRs) have heightened the importance of deep reinforcement learning (DRL) which are used to recover the doctor’s treatment policies. However, existing DRL-based methods expose the following limitations: 1) supervised methods based on behavior cloning suffer from compounding errors; 2) the self-defined reward signals in reinforcement learning models are either too sparse or need clinical guidance; 3) only positive trajectories (e.g. survived patients) are considered in current imitation learning models, with negative trajectories (e.g. deceased patients) been largely ignored, which are examples of what not to do and could help the learned policy avoid repeating mistakes. To address these limitations, in this paper, we propose the adversarial cooperative imitation learning model, ACIL, to deduce the optimal dynamic treatment regimes that mimics the positive trajectories while differs from the negative trajectories. Specifically, two discriminators are used to help achieve this goal: an adversarial discriminator is designed to minimize the discrepancies between the trajectories generated from the policy and the positive trajectories, and a cooperative discriminator is used to distinguish the negative trajectories from the positive and generated trajectories. The reward signals from the discriminators are utilized to refine the policy for dynamic treatment regimes. Experiments on the publicly real-world medical data demonstrate that ACIL improves the likelihood of patient survival and provides better dynamic treatment regimes with the exploitation of information from both positive and negative trajectories.
Multi-scale object detection by clustering lines
Authors: Björn Ommer, Jitendra Malik
Venue: Conference ICCV 2009
Year: 2009

Object detection in cluttered, natural scenes has a high complexity since many local observations compete for object hypotheses. Voting methods provide an efficient solution to this problem. When Hough voting is extended to location and scale, votes naturally become lines through scale space due to the local scale-location-ambiguity. In contrast to this, current voting methods stick to the location-only setting and cast point votes, which require local estimates of scale. Rather than searching for object hypotheses in the Hough accumulator, we propose a weighted, pairwise clustering of voting lines to obtain globally consistent hypotheses directly. In essence, we propose a hierarchical approach that is based on a sparse representation of object boundary shape. Clustering of voting lines (CVL) condenses the information from these edge points in few, globally consistent candidate hypotheses. A final verification stage concludes by refining the candidates. Experiments on the ETHZ shape dataset show that clustering voting lines significantly improves state-of-the-art Hough voting techniques.
Conservative uncertainty estimation by fitting prior networks
Authors: Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard E. Turner
Venue: Conference ICLR 2020
Year: 2020

Obtaining high-quality uncertainty estimates is essential for many applications of deep neural networks. In this paper, we theoretically justify a scheme for estimating uncertainties, based on sampling from a prior distribution. Crucially, the uncertainty estimates are shown to be conservative in the sense that they never underestimate a posterior uncertainty obtained by a hypothetical Bayesian algorithm. We also show concentration, implying that the uncertainty estimates converge to zero as we get more data. Uncertainty estimates obtained from random priors can be adapted to any deep network architecture and trained using standard supervised learning pipelines. We provide experimental evaluation of random priors on calibration and out-of-distribution detection on typical computer vision tasks, demonstrating that they outperform deep ensembles in practice.
An efficient content-based time series retrieval system
Authors: Chin-Chia Michael Yeh, Huiyuan Chen, Xin Dai, Yan Zheng, Junpeng Wang, Vivian Lai, Yujie Fan, Audrey Der, Zhongfang Zhuang, Liang Wang, Wei Zhang, Jeff M. Phillips
Venue: Conference CIKM 2023
Year: 2023

A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for users to interact with time series emerged from multiple domains, such as finance, healthcare, and manufacturing. For example, users seeking to learn more about the source of a time series can submit the time series as a query to the CTSR system and retrieve a list of relevant time series with associated metadata. By analyzing the retrieved metadata, users can gather more information about the source of the time series. Because the CTSR system is required to work with time series data from diverse domains, it needs a high-capacity model to effectively measure the similarity between different time series. On top of that, the model within the CTSR system has to compute the similarity scores in an efficient manner as the users interact with the system in real-time. In this paper, we propose an effective and efficient CTSR model that outperforms alternative models, while still providing reasonable inference runtimes. To demonstrate the capability of the proposed method in solving business problems, we compare it against alternative models using our in-house transaction data. Our findings reveal that the proposed model is the most suitable solution compared to others for our transaction data problem.
Autotransfer: AutoML with knowledge transfer - an application to graph neural networks
Authors: Kaidi Cao, Jiaxuan You, Jiaju Liu, Jure Leskovec
Venue: Conference ICLR 2023
Year: 2023

AutoML has demonstrated remarkable success in finding an effective neural architecture for a given machine learning task defined by a specific dataset and an evaluation metric. However, most present AutoML techniques consider each task independently from scratch, which requires exploring many architectures, leading to high computational cost. Here we propose AutoTransfer, an AutoML solution that improves search efficiency by transferring the prior architectural design knowledge to the novel task of interest. Our key innovation includes a task-model bank that captures the model performance over a diverse set of GNN architectures and tasks, and a computationally efficient task embedding that can accurately measure the similarity among different tasks. Based on the task-model bank and the task embeddings, we estimate the design priors of desirable models of the novel task, by aggregating a similarity-weighted sum of the top-K design distributions on tasks that are similar to the task of interest. The computed design priors can be used with any AutoML search algorithm. We evaluate AutoTransfer on six datasets in the graph machine learning domain. Experiments demonstrate that (i) our proposed task embedding can be computed efficiently, and that tasks with similar embeddings have similar best-performing architectures; (ii) AutoTransfer significantly improves search efficiency with the transferred design priors, reducing the number of explored architectures by an order of magnitude. Finally, we release GNN-Bank-101, a large-scale dataset of detailed GNN training information of 120,000 task-model combinations to facilitate and inspire future research.
Improving Micro-video Recommendation via Contrastive Multiple Interests
Authors: Beibei Li, Beihong Jin, Jiageng Song, Yisong Yu, Yiyuan Zheng, Wei Zhou
Venue: Conference SIGIR 2022
Year: 2022

With the rapid increase of micro-video creators and viewers, how to make personalized recommendations from a large number of candidates to viewers begins to attract more and more attention. However, existing micro-video recommendation models rely on expensive multi-modal information and learn an overall interest embedding that cannot reflect the user's multiple interests in micro-videos. Recently, contrastive learning provides a new opportunity for refining the existing recommendation techniques. Therefore, in this paper, we propose to extract contrastive multi-interests and devise a micro-video recommendation model CMI. Specifically, CMI learns multiple interest embeddings for each user from his/her historical interaction sequence, in which the implicit orthogonal micro-video categories are used to decouple multiple user interests. Moreover, it establishes the contrastive multi-interest loss to improve the robustness of interest embeddings and the performance of recommendations. The results of experiments on two micro-video datasets demonstrate that CMI achieves state-of-the-art performance over existing baselines.
Addressing Correlated Latent Exogenous Variables in Debiased Recommender Systems
Authors: Shuqiang Zhang, Yuchao Zhang, Jinkun Chen, Haochen Sui
Venue: Conference KDD 2025
Year: 2025

Recommendation systems (RS) aim to provide personalized content, but they face a challenge in unbiased learning due to selection bias, where users only interact with items they prefer. This bias leads to a distorted representation of user preferences, which hinders the accuracy and fairness of recommendations. To address the issue, various methods such as error imputation based, inverse propensity scoring, and doubly robust techniques have been developed. Despite the progress, from the structural causal model perspective, previous debiasing methods in RS assume the independence of the exogenous variables. In this paper, we release this assumption and propose a learning algorithm based on likelihood maximization to learn a prediction model. We first discuss the correlation and difference between unmeasured confounding and our scenario, then we propose a unified method that effectively handles latent exogenous variables. Specifically, our method models the data generation process with latent exogenous variables under mild normality assumptions. We then develop a Monte Carlo algorithm to numerically estimate the likelihood function. Extensive experiments on synthetic datasets and three real-world datasets demonstrate the effectiveness of our proposed method. The code is at https://github.com/WallaceSUI/kdd25-background-variable.
A two-stage pretraining-finetuning framework for treatment effect estimation with unmeasured confounding
Authors: Chuan Zhou, Yaxuan Li, Chunyuan Zheng, Haiteng Zhang, Min Zhang, Haoxuan Li, Mingming Gong
Venue: Conference KDD 2025
Year: 2025

Estimating the conditional average treatment effect (CATE) from observational data plays a crucial role in areas such as e-commerce, healthcare, and economics. Existing studies mainly rely on the strong ignorability assumption that there are no unmeasured confounders, whose presence cannot be tested from observational data and can invalidate any causal conclusion. In contrast, data collected from randomized controlled trials (RCT) do not suffer from confounding, but are usually limited by a small sample size. In this paper, we propose a two-stage pretraining-finetuning (TSPF) framework using both large-scale observational data and small-scale RCT data to estimate the CATE in the presence of unmeasured confounding. In the first stage, a foundational representation of covariates is trained to estimate counterfactual outcomes through large-scale observational data. In the second stage, we propose to train an augmented representation of the covariates, which is concatenated to the foundational representation obtained in the first stage to adjust for the unmeasured confounding. To avoid overfitting caused by the small-scale RCT data in the second stage, we further propose a partial parameter initialization approach, rather than training a separate network. The superiority of our approach is validated on two public datasets with extensive experiments. The code is available at https://github.com/zhouchuanCN/KDD25-TSPF.
Who should be given incentives? counterfactual optimal treatment regimes learning for recommendation
Authors: Haoxuan Li 0001, Chunyuan Zheng 0001, Peng Wu 0012, Kun Kuang 0001, Yue Liu, Peng Cui 0001
Venue: Conference KDD 2023
Year: 2023

Effective personalized incentives can improve user experience and increase platform revenue, resulting in a win-win situation between users and e-commerce companies. Previous studies have used uplift modeling methods to estimate the conditional average treatment effects of users' incentives, and then placed the incentives by maximizing the sum of estimated treatment effects under a limited budget. However, some users will always buy whether incentives are given or not, and they will actively collect and use incentives if provided, named "Always Buyers". Identifying and predicting these "Always Buyers" and reducing incentive delivery to them can lead to a more rational incentive allocation. In this paper, we first divide users into five strata from an individual counterfactual perspective, and reveal the failure of previous uplift modeling methods to identify and predict the "Always Buyers". Then, we propose principled counterfactual identification and estimation methods and prove their unbiasedness. We further propose a counterfactual entire-space multi-task learning approach to accurately perform personalized incentive policy learning with a limited budget. We also theoretically derive a lower bound on the reward of the learned policy. Extensive experiments are conducted on three real-world datasets with two common incentive scenarios, and the results demonstrate the effectiveness of the proposed approaches.
Learning bellman complete representations for offline policy evaluation
Authors: Jonathan D. Chang, Kaiwen Wang, Nathan Kallus, Wen Sun 0002
Venue: Conference ICML 2022
Year: 2022

We study representation learning for Offline Reinforcement Learning (RL), focusing on the important task of Offline Policy Evaluation (OPE). Recent work shows that, in contrast to supervised learning, realizability of the Q-function is not enough for learning it. Two sufficient conditions for sample-efficient OPE are Bellman completeness and coverage. Prior work often assumes that representations satisfying these conditions are given, with results being mostly theoretical in nature. In this work, we propose BCRL, which directly learns from data an approximately linear Bellman complete representation with good coverage. With this learned representation, we perform OPE using Least Square Policy Evaluation (LSPE) with linear functions in our learned representation. We present an end-to-end theoretical analysis, showing that our two-stage algorithm enjoys polynomial sample complexity provided some representation in the rich class considered is linear Bellman complete. Empirically, we extensively evaluate our algorithm on challenging, image-based continuous control tasks from the Deepmind Control Suite. We show our representation enables better OPE compared to previous representation learning methods developed for off-policy RL (e.g., CURL, SPR). BCRL achieve competitive OPE error with the state-of-the-art method Fitted Q-Evaluation (FQE), and beats FQE when evaluating beyond the initial state distribution. Our ablations show that both linear Bellman complete and coverage components of our method are crucial.
Efficient coreset selection with cluster-based methods
Authors: Chengliang Chai, Jiayi Wang 0002, Nan Tang 0001, Ye Yuan 0001, Jiabin Liu, Yuhao Deng, Guoren Wang
Venue: Conference KDD 2023
Year: 2023

Coreset selection is a technique for efficient machine learning, which selects a subset of the training data to achieve similar model performance as using the full dataset. It can be performed with or without training machine learning models. Coreset selection with training, which iteratively trains the machine model and updates data items in the coreset, is time consuming. Coreset selection without training can select the coreset before training. Gradient approximation is the typical method, but it can also be slow when dealing with large training datasets as it requires multiple iterations and pairwise distance computations for each iteration. The state-of-the-art (SOTA) results w.r.t. effectiveness are achieved by the latter approach, i.e. gradient approximation.
Preference amplification in recommender systems
Authors: Dimitris Kalimeris, Smriti Bhagat, Shankar Kalyanaraman, Udi Weinsberg
Venue: Conference KDD 2021
Year: 2021

Recommender systems have become increasingly accurate in suggesting content to users, resulting in users primarily consuming content through recommendations. This can cause the user's interest to narrow toward the recommended content, something we refer to as preference amplification. While this can contribute to increased engagement, it can also lead to negative experiences such as lack of diversity and echo chambers. We propose a theoretical framework for studying such amplification in a matrix factorization based recommender system. We model the dynamics of the system, where users interact with the recommender systems and gradually "drift'' toward the recommended content, with the recommender system adapting, based on user feedback, to the updated preferences. We study the conditions under which preference amplification manifests, and validate our results with simulations. Finally, we evaluate mitigation strategies that prevent the adverse effects of preference amplification and present experimental results using a real-world large-scale video recommender system showing that by reducing exposure to potentially objectionable content we can increase user engagement by up to 2%.
Combining Document Representations for Known-item Search
Authors: Paul Ogilvie, James P. Callan
Venue: Conference SIGIR 2003
Year: 2003

This paper investigates the pre-conditions for successful combination of document representations formed from structural markup for the task of known-item search. As this task is very similar to work in meta-search and data fusion, we adapt several hypotheses from those research areas and investigate them in this context. To investigate these hypotheses, we present a mixture-based language model and also examine many of the current meta-search algorithms. We find that compatible output from systems is important for successful combination of document representations. We also demonstrate that combining low performing document representations can improve performance, but not consistently. We find that the techniques best suited for this task are robust to the inclusion of poorly performing document representations. We also explore the role of variance of results across systems and its impact on the performance of fusion, with the surprising result that the correct documents have higher variance across document representations than highly ranking incorrect documents.
Efficient interactive fuzzy keyword search
Authors: Shengyue Ji, Guoliang Li, Chen Li, Jianhua Feng
Venue: Conference WWW 2009
Year: 2009

Traditional information systems return answers after a user submits a complete query. Users often feel "left in the dark" when they have limited knowledge about the underlying data, and have to use a try-and-see approach for finding information. A recent trend of supporting autocomplete in these systems is a first step towards solving this problem. In this paper, we study a new information-access paradigm, called "interactive, fuzzy search," in which the system searches the underlying data "on the fly" as the user types in query keywords. It extends autocomplete interfaces by (1) allowing keywords to appear in multiple attributes (in an arbitrary order) of the underlying data; and (2) finding relevant records that have keywords matching query keywords approximately. This framework allows users to explore data as they type, even in the presence of minor errors. We study research challenges in this framework for large amounts of data. Since each keystroke of the user could invoke a query on the backend, we need efficient algorithms to process each query within milliseconds. We develop various incremental-search algorithms using previously computed and cached results in order to achieve an interactive speed. We have deployed several real prototypes using these techniques. One of them has been deployed to support interactive search on the UC Irvine people directory, which has been used regularly and well received by users due to its friendly interface and high efficiency.
Do language models understand time?
Authors: Xi Ding, Lei Wang
Venue: Conference WWW 2025
Year: 2025

Large language models (LLMs) have revolutionized video-based computer vision applications, including action recognition, anomaly detection, and video summarization. Videos inherently pose unique challenges, combining spatial complexity with temporal dynamics that are absent in static images or textual data. Current approaches to video understanding with LLMs often rely on pretrained video encoders to extract spatiotemporal features and text encoders to capture semantic meaning. These representations are integrated within LLM frameworks, enabling multimodal reasoning across diverse video tasks. However, the critical question persists: Can LLMs truly understand the concept of time, and how effectively can they reason about temporal relationships in videos? This work critically examines the role of LLMs in video processing, with a specific focus on their temporal reasoning capabilities. We identify key limitations in the interaction between LLMs and pretrained encoders, revealing gaps in their ability to model long-term dependencies and abstract temporal concepts such as causality and event progression. Furthermore, we analyze challenges posed by existing video datasets, including biases, lack of temporal annotations, and domain-specific limitations that constrain the temporal understanding of LLMs. To address these gaps, we explore promising future directions, including the co-evolution of LLMs and encoders, the development of enriched datasets with explicit temporal labels, and innovative architectures for integrating spatial, temporal, and semantic reasoning. By addressing these challenges, we aim to advance the temporal comprehension of LLMs, unlocking their full potential in video analysis and beyond. Our paper's GitHub repository can be found at https://github.com/Darcyddx/Video-LLM.