pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive debiased machine learning using data-driven model selection techniques
Authors: Lars van der Laan, Marco Carone, Alex Luedtke, Mark van der Laan
Date: 2023-07-24 00:00:00
Tasks: Model Selection, valid

Debiased machine learning estimators for nonparametric inference of smooth functionals of the data-generating distribution can suffer from excessive variability and instability. For this reason, practitioners may resort to simpler models based on parametric or semiparametric assumptions. However, such simplifying assumptions may fail to hold, and estimates may then be biased due to model misspecification. To address this problem, we propose Adaptive Debiased Machine Learning (ADML), a nonparametric framework that combines data-driven model selection and debiased machine learning techniques to construct asymptotically linear, adaptive, and superefficient estimators for pathwise differentiable functionals. By learning model structure directly from data, ADML avoids the bias introduced by model misspecification and remains free from the restrictions of parametric and semiparametric models. While they may exhibit irregular behavior for the target parameter in a nonparametric statistical model, we demonstrate that ADML estimators provides regular and locally uniformly valid inference for a projection-based oracle parameter. Importantly, this oracle parameter agrees with the original target parameter for distributions within an unknown but correctly specified oracle statistical submodel that is learned from the data. This finding implies that there is no penalty, in a local asymptotic sense, for conducting data-driven model selection compared to having prior knowledge of the oracle submodel and oracle parameter. To demonstrate the practical applicability of our theory, we provide a broad class of ADML estimators for estimating the average treatment effect in adaptive partially linear regression models.
Information Geometry of Wasserstein Statistics on Shapes and Affine Deformations
Authors: Shun-ichi Amari, Takeru Matsuda
Date: 2023-07-24 00:00:00

Information geometry and Wasserstein geometry are two main structures introduced in a manifold of probability distributions, and they capture its different characteristics. We study characteristics of Wasserstein geometry in the framework of Li and Zhao (2023) for the affine deformation statistical model, which is a multi-dimensional generalization of the location-scale model. We compare merits and demerits of estimators based on information geometry and Wasserstein geometry. The shape of a probability distribution and its affine deformation are separated in the Wasserstein geometry, showing its robustness against the waveform perturbation in exchange for the loss in Fisher efficiency. We show that the Wasserstein estimator is the moment estimator in the case of the elliptically symmetric affine deformation model. It coincides with the information-geometrical estimator (maximum-likelihood estimator) when the waveform is Gaussian. The role of the Wasserstein efficiency is elucidated in terms of robustness against waveform change.
Concentration inequalities for high-dimensional linear processes with dependent innovations
Authors: Eduardo Fonseca Mendes, Fellipe Lopes
Date: 2023-07-23 00:00:00
Tasks: Time Series

We develop concentration inequalities for the $l_\infty$ norm of vector linear processes with sub-Weibull, mixingale innovations. This inequality is used to obtain a concentration bound for the maximum entrywise norm of the lag-$h$ autocovariance matrix of linear processes. We apply these inequalities to sparse estimation of large-dimensional VAR(p) systems and heterocedasticity and autocorrelation consistent (HAC) high-dimensional covariance estimation.
Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty Computation
Authors: Du Nguyen Duy, Michael Affenzeller, Ramin-Nikzad Langerodi
Date: 2023-07-22 00:00:00
Tasks: Privacy Preserving, regression, Symbolic Regression

Symbolic Regression is a powerful data-driven technique that searches for mathematical expressions that explain the relationship between input variables and a target of interest. Due to its efficiency and flexibility, Genetic Programming can be seen as the standard search technique for Symbolic Regression. However, the conventional Genetic Programming algorithm requires storing all data in a central location, which is not always feasible due to growing concerns about data privacy and security. While privacy-preserving research has advanced recently and might offer a solution to this problem, their application to Symbolic Regression remains largely unexplored. Furthermore, the existing work only focuses on the horizontally partitioned setting, whereas the vertically partitioned setting, another popular scenario, has yet to be investigated. Herein, we propose an approach that employs a privacy-preserving technique called Secure Multiparty Computation to enable parties to jointly build Symbolic Regression models in the vertical scenario without revealing private data. Preliminary experimental results indicate that our proposed method delivers comparable performance to the centralized solution while safeguarding data privacy.
HeteFedRec: Federated Recommender Systems with Model Heterogeneity
Authors: Wei Yuan, Liang Qu, Lizhen Cui, Yongxin Tong, Xiaofang Zhou, Hongzhi Yin
Date: 2023-07-24 00:00:00
Tasks: Knowledge Distillation, model, Recommendation Systems

Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems. However, most existing FedRecs only allow participating clients to collaboratively train a recommendation model of the same public parameter size. Training a model of the same size for all clients can lead to suboptimal performance since clients possess varying resources. For example, clients with limited training data may prefer to train a smaller recommendation model to avoid excessive data consumption, while clients with sufficient data would benefit from a larger model to achieve higher recommendation accuracy. To address the above challenge, this paper introduces HeteFedRec, a novel FedRec framework that enables the assignment of personalized model sizes to participants. In HeteFedRec, we present a heterogeneous recommendation model aggregation strategy, including a unified dual-task learning mechanism and a dimensional decorrelation regularization, to allow knowledge aggregation among recommender models of different sizes. Additionally, a relation-based ensemble knowledge distillation method is proposed to effectively distil knowledge from heterogeneous item embeddings. Extensive experiments conducted on three real-world recommendation datasets demonstrate the effectiveness and efficiency of HeteFedRec in training federated recommender systems under heterogeneous settings.
Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction
Authors: Yifan Wang, Peijie Sun, Min Zhang, Qinglin Jia, Jingjie Li, Shaoping Ma
Date: 2023-07-24 00:00:00
Tasks: counterfactual

Conversion rate prediction is critical to many online applications such as digital display advertising. To capture dynamic data distribution, industrial systems often require retraining models on recent data daily or weekly. However, the delay of conversion behavior usually leads to incorrect labeling, which is called delayed feedback problem. Existing work may fail to introduce the correct information about false negative samples due to data sparsity and dynamic data distribution. To directly introduce the correct feedback label information, we propose an Unbiased delayed feedback Label Correction framework (ULC), which uses an auxiliary model to correct labels for observed negative feedback samples. Firstly, we theoretically prove that the label-corrected loss is an unbiased estimate of the oracle loss using true labels. Then, as there are no ready training data for label correction, counterfactual labeling is used to construct artificial training data. Furthermore, since counterfactual labeling utilizes only partial training data, we design an embedding-based alternative training method to enhance performance. Comparative experiments on both public and private datasets and detailed analyses show that our proposed approach effectively alleviates the delayed feedback problem and consistently outperforms the previous state-of-the-art methods.
Interface Design to Mitigate Inflation in Recommender Systems
Authors: Rana Shahout, Yehonatan Peisakhovsky, SASHA STOIKOV, Nikhil Garg
Date: 2023-07-23 00:00:00
Tasks: Recommendation Systems

Recommendation systems rely on user-provided data to learn about item quality and provide personalized recommendations. An implicit assumption when aggregating ratings into item quality is that ratings are strong indicators of item quality. In this work, we test this assumption using data collected from a music discovery application. Our study focuses on two factors that cause rating inflation: heterogeneous user rating behavior and the dynamics of personalized recommendations. We show that user rating behavior substantially varies by user, leading to item quality estimates that reflect the users who rated an item more than the item quality itself. Additionally, items that are more likely to be shown via personalized recommendations can experience a substantial increase in their exposure and potential bias toward them. To mitigate these effects, we analyze the results of a randomized controlled trial in which the rating interface was modified. The test resulted in a substantial improvement in user rating behavior and a reduction in item quality inflation. These findings highlight the importance of carefully considering the assumptions underlying recommendation systems and designing interfaces that encourage accurate rating behavior.
Conformal Group Recommender System
Authors: Venkateswara Rao Kagita, Anshuman Singh, Vikas Kumar, Pavan Kalyan Reddy Neerudu, Arun K Pujari, Rohit Kumar Bondugula
Date: 2023-07-22 00:00:00
Tasks: Conformal Prediction, Recommendation Systems

Group recommender systems (GRS) are critical in discovering relevant items from a near-infinite inventory based on group preferences rather than individual preferences, like recommending a movie, restaurant, or tourist destination to a group of individuals. The traditional models of group recommendation are designed to act like a black box with a strict focus on improving recommendation accuracy, and most often, they place the onus on the users to interpret recommendations. In recent years, the focus of Recommender Systems (RS) research has shifted away from merely improving recommendation accuracy towards value additions such as confidence and explanation. In this work, we propose a conformal prediction framework that provides a measure of confidence with prediction in conjunction with a group recommender system to augment the system-generated plain recommendations. In the context of group recommender systems, we propose various nonconformity measures that play a vital role in the efficiency of the conformal framework. We also show that defined nonconformity satisfies the exchangeability property. Experimental results demonstrate the effectiveness of the proposed approach over several benchmark datasets. Furthermore, our proposed approach also satisfies validity and efficiency properties.
XWalk: Random Walk Based Candidate Retrieval for Product Search
Authors: Jon Eskreis-Winkler, Yubin Kim, Andrew Stanton
Date: 2023-07-22 00:00:00
Tasks: Retrieval

In e-commerce, head queries account for the vast majority of gross merchandise sales and improvements to head queries are highly impactful to the business. While most supervised approaches to search perform better in head queries vs. tail queries, we propose a method that further improves head query performance dramatically. We propose XWalk, a random-walk based graph approach to candidate retrieval for product search that borrows from recommendation system techniques. XWalk is highly efficient to train and inference in a large-scale high traffic e-commerce setting, and shows substantial improvements in head query performance over state-of-the-art neural retreivers. Ensembling XWalk with a neural and/or lexical retriever combines the best of both worlds and the resulting retrieval system outperforms all other methods in both offline relevance-based evaluation and in online A/B tests.
HTP: Exploiting Holistic Temporal Patterns for Sequential Recommendation
Authors: Chen Rui, Liang Guotao, Ma Chenrui, Han Qilong, Li Li, Huang Xiao
Date: 2023-07-22 00:00:00
Tasks: Recommendation Systems, Sequential Recommendation

Sequential recommender systems have demonstrated a huge success for next-item recommendation by explicitly exploiting the temporal order of users' historical interactions. In practice, user interactions contain more useful temporal information beyond order, as shown by some pioneering studies. In this paper, we systematically investigate various temporal information for sequential recommendation and identify three types of advantageous temporal patterns beyond order, including absolute time information, relative item time intervals and relative recommendation time intervals. We are the first to explore item-oriented absolute time patterns. While existing models consider only one or two of these three patterns, we propose a novel holistic temporal pattern based neural network, named HTP, to fully leverage all these three patterns. In particular, we introduce novel components to address the subtle correlations between relative item time intervals and relative recommendation time intervals, which render a major technical challenge. Extensive experiments on three real-world benchmark datasets show that our HTP model consistently and substantially outperforms many state-of-the-art models. Our code is publically available at https://github.com/623851394/HTP/tree/main/HTP-main
DeepAuth: A DNN Authentication Framework by Model-Unique and Fragile Signature Embedding
Authors: Yingjie Lao, Weijie Zhao, Peng Yang, Ping Li
Date: 2022-06-28 00:00:00
Proceeding: Proceedings of the AAAI Conference on Artificial Intelligence 2022 6

Along with the evolution of deep neural networks (DNNs) in many real-world applications, the complexity of model building has also dramatically increased. It is thus vital to protect the intellectual property (IP) of the model builder and ensure the trustworthiness of the deployed models. Meanwhile, adversarial attacks on DNNs (e.g., backdoor and poisoning attacks) that seek to inject malicious behaviors have been investigated recently, demanding a means for verifying the integrity of the deployed model to protect the users. In this paper, we present a novel DNN authentication framework DeepAuth which embeds a unique and fragile signature to each protected DNN model. Our approach exploits sensitive key samples that are well crafted from the input space to latent space and then to logit space for producing signatures. After embedding, each model will respond distinctively to these key samples, which creates a model-unique signature as a strong tool for authentication and user identity. The signature embedding process is also designed to ensure the fragility of the signature, which can be used to detect malicious modifications such that an illegitimate user or an altered model should not have the intact signature. Extensive evaluations on various models over a wide range of datasets demonstrate the effectiveness and efficiency of the proposed DeepAuth.
A Hybrid Deep Learning-Based Model for Detection of Electricity Losses Using Big Data in Power Systems
Authors: Adnan Khattak 1, Rasool Bukhsh 2, Sheraz Aslam 3, *, Ayman Yafoz 4, Omar Alghushairy 5 and Raed Alsini
Date: 2022-10-21 00:00:00
Proceeding: Sustainability 2022 2022 10

Electricity theft harms smart grids and results in huge revenue losses for electric companies.
Deep learning (DL), machine learning (ML), and statistical methods have been used in recent research
studies to detect anomalies and illegal patterns in electricity consumption (EC) data collected by
smart meters. In this paper, we propose a hybrid DL model for detecting theft activity in EC data.
The model combines both a gated recurrent unit (GRU) and a convolutional neural network (CNN).
The model distinguishes between legitimate and malicious EC patterns. GRU layers are used to
extract temporal patterns, while the CNN is used to retrieve optimal abstract or latent patterns from
EC data. Moreover, imbalance of data classes negatively affects the consistency of ML and DL. In
this paper, an adaptive synthetic (ADASYN) method and TomekLinks are used to deal with the
imbalance of data classes. In addition, the performance of the hybrid model is evaluated using a
real-time EC dataset from the State Grid Corporation of China (SGCC). The proposed algorithm
is computationally expensive, but on the other hand, it provides higher accuracy than the other
algorithms used for comparison. With more and more computational resources available nowadays,
researchers are focusing on algorithms that provide better efficiency in the face of widespread data.
Various performance metrics such as F1-score, precision, recall, accuracy, and false positive rate are
used to investigate the effectiveness of the hybrid DL model. The proposed model outperforms its
counterparts with 0.985 Precision–Recall Area Under Curve (PR-AUC) and 0.987 Receiver Operating
Characteristic Area Under Curve (ROC-AUC) for the data of EC.
Practical Low-density Codes for PB-ToF Sensing
Authors: Alvaro Lopez Paredes, Miguel Heredia Conde
Date: 2023-07-21 00:00:00

An indirect Pulse-based Time-of-Flight camera can be modelled as a linear sensing system in which the target's depth is recovered from few measurements through a sensing matrix formed by a set of demodulation functions. Each demodulation function is the result of the convolution of a (0,1)-binary code and a cross-correlation function which models the entire modulation-demodulation process. In this paper, we present a practical scheme for the construction of the sensing matrix which relies on the optimization of the coherence, and is based on low-density codes. We demonstrate that our methodology eliminates the intrinsic variability of random and pseudo-random approaches, and allows for the recovery of the target's depth in a grid much finer than the number of distinct elements in the binary codes.
Train-bridge coupling vibration of long-span railway cable-stayed bridge under non-uniform seismic excitation
Authors: LEI Hujun，LI Shubin，HUANG Bingkun
Date: 2022-12-01 00:00:00
Proceeding: EARTHQUAKE ENGINEERING AND ENGINEERING DYNAMICS 2022 12

To study the influence of seismic spatial variability on the dynamic response of train-bridge system（TBS）
of long-span railway cable-stayed bridge（LRCB），a LRCB with steel truss girder with a main span of 432 m was
taking as the engineering background. The full bridge lattice model was constructed by simplifying orthotropic steel
bridge deck by grillage method，and the simulation calculation was carried out by artificial synthesis of space-related
ground motion input self-compiled program based on the spectral method. The effects of traveling wave，site and
incoherence on the dynamic response of TBS were studied systematically. The calculation shows that the three kinds
of spatial variability of ground motion have significant influence on the dynamic response of TBS. Among them，the
train safety index decreases with the increase of traveling wave speed，and the most disadvantageous traveling wave
speed is 250 m/s. The dynamic response of TBS increases sharply with the increase of the site type of the bridge tower.
The de-coherence effect will reduce the dynamic response of the car-bridge system，and has the greatest influence on
the centroid acceleration of the car body. After considering the three kinds of spatial variability，the derailment
coefficient，wheel unloading rate and wheel axle lateral force will increase by 13.4%，13.9% and 8.3%，respectively.
The research achievements of this paper can provide theoretical support for the design of LRCB with steel truss girder.
Agreement forests of caterpillar trees: complexity, kernelization and branching
Authors: Steven Kelk, Ruben Meuwese
Date: 2023-07-22 00:00:00

Given a set $X$ of species, a phylogenetic tree is an unrooted binary tree whose leaves are bijectively labelled by $X$. Such trees can be used to show the way species evolve over time. One way of understanding how topologically different two phylogenetic trees are, is to construct a minimum-size agreement forest: a partition of $X$ into the smallest number of blocks, such that the blocks induce homeomorphic, non-overlapping subtrees in both trees. This comparison yields insight into commonalities and differences in the evolution of $X$ across the two trees. Computing a smallest agreement forest is NP-hard (Hein, Jiang, Wang and Zhang, Discrete Applied Mathematics 71(1-3), 1996). In this work we study the problem on caterpillars, which are path-like phylogenetic trees. We will demonstrate that, even if we restrict the input to this highly restricted subclass, the problem remains NP-hard and is in fact APX-hard. Furthermore we show that for caterpillars two standard reductions rules well known in the literature yield a tight kernel of size at most $7k$, compared to $15k$ for general trees (Kelk and Simone, SIAM Journal on Discrete Mathematics 33(3), 2019). Finally we demonstrate that we can determine if two caterpillars have an agreement forest with at most $k$ blocks in $O^*(2.49^k)$ time, compared to $O^*(3^k)$ for general trees (Chen, Fan and Sze, Theoretical Computater Science 562, 2015), where $O^*(.)$ suppresses polynomial factors.
High-performance real-world optical computing trained by in situ gradient-based model-free optimization
Authors: Guangyuan Zhao, Xin Shu, Renjie Zhou
Date: 2023-07-22 00:00:00

Optical computing systems provide high-speed and low-energy data processing but face deficiencies in computationally demanding training and simulation-to-reality gaps. We propose a gradient-based model-free optimization (G-MFO) method based on a Monte Carlo gradient estimation algorithm for computationally efficient in situ training of optical computing systems. This approach treats an optical computing system as a black box and back-propagates the loss directly to the optical computing weights' probability distributions, circumventing the need for a computationally heavy and biased system simulation. Our experiments on diffractive optical computing systems show that G-MFO outperforms hybrid training on the MNIST and FMNIST datasets. Furthermore, we demonstrate image-free and high-speed classification of cells from their marker-free phase maps. Our method's model-free and high-performance nature, combined with its low demand for computational resources, paves the way for accelerating the transition of optical computing from laboratory demonstrations to practical, real-world applications.