bluuebunny/crossref_metadata_2025 huggingface dataset:
Multiclass Classification of Systolic and Diastolic Peaks on Open Benchmark PPG Signals: Performance Evaluation
Authors: Simon Noor Liza , Adam Asrul , Low Chen Yik , Ibrahim Zuwairie , Shapiai Mohd Ibrahim
Published: September 2022

Photoplethysmogram (PPG) signals contain valuable health information that is in the relation between the volumetric variations of blood circulation and the cardiovascular and respiratory systems. This study introduces the performance evaluation on open clinical benchmark PPG signals with a multiclass neural network with random weights (NNRW) classification method for systolic peak and diastolic point detection. The best performance of the peak and point detection is crucial to be achieved at the early stage for extracting further valuable information in addition to future predictions of cardiovascular-related illness. Various open clinical datasets of PPG signals have been introduced, however, there is a lack of information on peak annotations. Due to the lack of peak annotation information, it is time-consuming to be prepared. One suitable clinical benchmark dataset with peak annotation information for peak detection has been previously evaluated, however, it cannot be generalized and rely upon only one dataset. Therefore, for generalization, there is a new open clinical benchmark dataset that is found in the year 2018 and our own collected data from normal participants is utilized in this study. The findings exhibit more convincing overall accuracy and Gmean of testing results with 94.86 and 94.74 percent, respectively. The findings of the comparison with previous work indicate that the proposed methodology to predict PPG-based multi-class systolic and diastolic points is more generalizable.
Thermodynamic parameters in protein-ligand model systems: determination of binding volume and enthalpy
Authors:   , Skvarnavičius Gediminas
Published: September 2022

Termodinaminiai parametrai baltymų-ligandų modelinėse sistemose: jungimosi tūrio ir sąveikos energijos tyrimas
Evaluation of the traditional social institutions’ impact on the population's adaptation to dynamic socio-economic changes: Sverdlovsk region case study
Authors: Kulkova Inna
Published: September 2022

The article reveals the results of the author's research on the impact of traditional
social institutions on the population's adaptation to dynamic socio-economic transformations. The study was conducted by the sociological survey method of more than three hundred respondents in the Sverdlovsk region, the article contains the rationale for the author's sample and the results of the survey. The object of the study were people of working age living in urban areas. The survey revealed the population’ adaptational level to such events of the last three years as: the digital technologies
introduction into the workplace, restrictions and changes caused by the Covid-19 coronavirus pandemic, raising the retirement age in Russia and changing the food products composition through the development of Foodtech technologies, including meat products from vegetable raw materials, 3D printing of food, molecular food, etc. The author identified the influence of such social institutions as: family, mass media, state, relatives and friends, religion, health care, money and property on the population’ adaptational process to these most acute social and economic transformations. The survey showed that the institutions of family, relatives and friends have the greatest positive impact on the adaptational process in the Sverdlovsk region. The institutions of religion, health care, money and property have a significant influence. Such social institutions as the state and the mass media practically do not have a positive impact on the population’ adaptational process to dynamic changes in the socio-economic environment. It is concluded that it is necessary to change the social policy of the state, local authorities in the direction of a favorable impact on accelerating the population’ adaptational process to dynamic social and economic changes.
Intervenções no local de trabalho com aconselhamento para a promoção de saúde e estilo de vida saudável sobre a dor musculoesquelética em trabalhadores de escritório: protocolo de revisão sistemática
Authors: Jerônimo Jeferson Santos , Lopes Samuel Volz , Siqueira Fernando Carlos Vinholes , Silva Marcelo Cozzensa da
Published: September 2022

Objetivo: Revisar a literatura científica sobre Estudos Clínicos Randomizados (ECR/Estudos Experimentais) e Estudos Clínicos não randomizados Controlados (ECC/Estudos Quase experimentais) que testaram a eficácia de intervenções no local de trabalho, com aconselhamentos para promoção de saúde e estilo de vida saudável sobre a dor musculoesquelética em trabalhadores de escritório. Métodos: Este protocolo segue as recomendações PRISMA-P (Preferred reporting items for systematic review and meta-analysis protocols statement) para protocolos de revisões sistemáticas. Foram realizadas buscas em cinco bases de dados eletrônicas indexadas: PubMed, Cochrane Central Register of Controlled Trails (CENTRAL), PEDro, BIREME e SciELO; além de buscas em uma base de dados da literatura cinzenta: Google Scholar. Foram utilizados descritores indexados no Medical Subject Headings (MeSH) e no Descritores em Ciências da Saúde (DeCS). Não foram aplicados limites em nenhuma das bases. O processo de seleção dos estudos será realizado de forma duplicada por dois pesquisadores independentes e se necessário, com auxílio de um terceiro para estabelecer consenso. Serão excluídos os artigos duplicados, lidos títulos e resumos e realizada a leitura de texto completo dos estudos elegíveis, além de buscas manuais nas listas de referências dos artigos selecionados; de acordo com os Principais itens para relatar Revisões sistemáticas e Meta-análises: a recomendação PRISMA. Resultados: Serão incluídos ECR e ECC publicados em inglês ou português. A qualidade metodológica será avaliada através da escala PEDro. Discussão: Esta revisão sistemática apresentará uma síntese atual sobre os efeitos de estratégias de aconselhamentos para promoção de saúde e estilo de vida saudável direcionadas ao local de trabalho sobre a dor musculoesquelética em trabalhadores de escritório. Essas informações podem contribuir com a literatura científica relativa à área da Saúde do trabalhador e auxiliar na implementação de intervenções de saúde relacionadas à dor musculoesquelética em trabalhadores de escritório.  Registro PROSPERO número: CRD42021227775.
Complex Coordinate-Based Meta-Analysis with Probabilistic Programming
Authors: Iovene Valentin , Zanitti Gaston E , Wassermann Demian
Published: September 2022

With the growing number of published functional magnetic resonance imaging (fMRI) studies, meta-analysis databases and models have become an integral part of brain mapping research. Coordinate-based meta-analysis (CBMA) databases are built by extracting both coordinates of reported peak activations and term associations using natural language processing techniques from neuroimaging studies. Solving term-based queries on these databases makes it possible to obtain statistical maps of the brain related to specific cognitive processes. However, existing tools for analysing CBMA data are limited in their expressivity to propositional logic, restricting the variety of their queries. Moreover, with tools like Neurosynth, term-based queries on multiple terms often lead to power failure, because too few studies from the database contribute to the statistical estimations. We design a probabilistic domain-specific language (DSL) standing on Datalog and one of its probabilistic extensions, CP-Logic, for expressing and solving complex logic-based queries. We show how CBMA databases can be encoded as probabilistic programs. Using the joint distribution of their Bayesian network translation, we show that solutions of queries on these programs compute the right probability distributions of voxel activations. We explain how recent lifted query processing algorithms make it possible to scale to the size of large neuroimaging data, where knowledge compilation techniques fail to solve queries fast enough for practical applications. Finally, we introduce a method for relating studies to terms probabilistically, leading to better solutions for two-term conjunctive queries (CQs) on smaller databases. We demonstrate results for two-term CQs, both on simulated meta-analysis databases and on the widely used Neurosynth database.
CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG
Authors: Sarkar Pritam , Etemad Ali
Published: September 2022

Electrocardiogram (ECG) is the electrical measurement of cardiac activity, whereas Photoplethysmogram (PPG) is the optical measurement of volumetric changes in blood circulation. While both signals are used for heart rate monitoring, from a medical perspective, ECG is more useful as it carries additional cardiac information. Despite many attempts toward incorporating ECG sensing in smartwatches or similar wearable devices for continuous and reliable cardiac monitoring, PPG sensors are the main feasible sensing solution available. In order to tackle this problem, we propose CardioGAN, an adversarial model which takes PPG as input and generates ECG as output. The proposed network utilizes an attention-based generator to learn local salient features, as well as dual discriminators to preserve the integrity of generated data in both time and frequency domains. Our experiments show that the ECG generated by CardioGAN provides more reliable heart rate measurements compared to the original input PPG, reducing the error from 9.74 beats per minute (measured from the PPG) to 2.89 (measured from the generated ECG).
Automated Symbolic Law Discovery: A Computer Vision Approach
Authors: Xing Hengrui , Salleb-Aouissi Ansaf , Verma Nakul
Published: September 2022

One of the most exciting applications of modern artificial intelligence is to automatically discover scientific laws from experimental data. This is not a trivial problem as it involves searching for a complex mathematical relationship over a large set of explanatory variables and operators that can be combined in an infinite number of ways.

Inspired by the incredible success of deep learning in computer vision, we tackle this problem by adapting various successful network architectures into the symbolic law discovery pipeline. The novelty of our approach is in (1) encoding the input data as an image with super-resolution, (2) developing an appropriate deep network pipeline, and (3) predicting the importance of each mathematical operator from the relationship image. This allows us to prior the exponentially large search with the predicted importance of the symbolic operators, which can significantly accelerate the discovery process.

We apply our model to a variety of plausible relationships---both simulated and from physics and mathematics domains---involving different dimensions and constituents. We show that our model is able to identify the underlying operators from data, achieving a high accuracy and AUC (91% and 0.96 on average resp.) for systems with as many as ten independent variables. Our method significantly outperforms the current state of the art in terms of data fitting (R^2), discovery rate (recovering the true relationship), and succinctness (output formula complexity). The discovered equations can be seen as first drafts of scientific laws that can be helpful to the scientists for (1) hypothesis building, and (2) understanding the complex underlying structure of the studied phenomena. Our approach holds a real promise to help speed up the rate of scientific discovery.
Bigram and Unigram Based Text Attack via Adaptive Monotonic Heuristic Search
Authors: Yang Xinghao , Liu Weifeng , Bailey James , Tao Dacheng , Liu Wei
Published: September 2022

Deep neural networks (DNNs) are known to be vulnerable to adversarial images, while their robustness in text classification are rarely studied. Several lines of text attack methods have been proposed in the literature, such as character-level, word-level, and sentence-level attacks. However, it is still a challenge to minimize the number of word distortions necessary to induce misclassification, while simultaneously ensuring the lexical correctness, syntactic correctness, and semantic similarity. In this paper, we propose the Bigram and Unigram based Monotonic Heuristic Search (BU-MHS) method to examine the vulnerability of deep models. Our method has three major merits. Firstly, we propose to attack text documents not only at the unigram word level but also at the bigram level to avoid producing meaningless outputs. Secondly, we propose a hybrid method to replace the input words with both their synonyms and sememe candidates, which greatly enriches potential substitutions compared to only using synonyms. Lastly, we design a search algorithm, i.e., Monotonic Heuristic Search (MHS), to determine the priority of word replacements, aiming to reduce the modification cost in an adversarial attack. We evaluate the effectiveness of BU-MHS on IMDB, AG's News, and Yahoo! Answers text datasets by attacking four state-of-the-art DNNs models. Experimental results show that our BU-MHS achieves the highest attack success rate by changing the smallest number of words compared with other existing models.
FLAME: Differentially Private Federated Learning in the Shuffle Model
Authors: Liu Ruixuan , Cao Yang , Chen Hong , Guo Ruoyang , Yoshikawa Masatoshi
Published: September 2022

Federated Learning (FL) is a promising machine learning paradigm that enables the analyzer to train a model without collecting users' raw data. To ensure users' privacy, differentially private federated learning has been intensively studied. The existing works are mainly based on the curator model or local model of differential privacy. However, both of them have pros and cons. The curator model allows greater accuracy but requires a trusted analyzer. In the local model where users randomize local data before sending them to the analyzer, a trusted analyzer is not required but the accuracy is limited. In this work, by leveraging the \textit{privacy amplification} effect in the recently proposed shuffle model of differential privacy, we achieve the best of two worlds, i.e., accuracy in the curator model and strong privacy without relying on any trusted party. We first propose an FL framework in the shuffle model and a simple protocol (SS-Simple) extended from existing work. We find that SS-Simple only provides an insufficient privacy amplification effect in FL since the dimension of the model parameter is quite large. To solve this challenge, we propose an enhanced protocol (SS-Double) to increase the privacy amplification effect by subsampling. Furthermore, for boosting the utility when the model size is greater than the user population, we propose an advanced protocol (SS-Topk) with gradient sparsification techniques. We also provide theoretical analysis and numerical evaluations of the privacy amplification of the proposed protocols. Experiments on real-world dataset validate that SS-Topk improves the testing accuracy by 60.7% than the local model based FL. We highlight an observation that SS-Topk improves the accuracy by 33.94\% than the curator model based FL without any trusted party. Compared with non-private FL, our protocol SS-Topk only lose 1.48% accuracy under (2.348, 5e-6)-DP per epoch.
Exacerbating Algorithmic Bias through Fairness Attacks
Authors: Mehrabi Ninareh , Naveed Muhammad , Morstatter Fred , Galstyan Aram
Published: September 2022

Algorithmic fairness has attracted significant attention in recent years, with many quantitative measures suggested for characterizing the fairness of different machine learning algorithms. Despite this interest, the robustness of those fairness measures with respect to an intentional adversarial attack has not been properly addressed. Indeed, most adversarial machine learning has focused on the impact of malicious attacks on the accuracy of the system, without any regard to the system's fairness. We propose new types of data poisoning attacks where an adversary intentionally targets the fairness of a system. Specifically, we propose two families of attacks that target fairness measures. In the anchoring attack, we skew the decision boundary by placing poisoned points near specific target points to bias the outcome. In the influence attack on fairness, we aim to maximize the covariance between the sensitive attributes and the decision outcome and affect the fairness of the model. We conduct extensive experiments that indicate the effectiveness of our proposed attacks.
Inverse Reinforcement Learning with Explicit Policy Estimates
Authors: Sanghvi Navyata , Usami Shinnosuke , Sharma Mohit , Groeger Joachim , Kitani Kris
Published: September 2022

Various methods for solving the inverse reinforcement learning (IRL) problem have been developed independently in machine learning and economics. In particular, the method of Maximum Causal Entropy IRL is based on the perspective of entropy maximization, while related advances in the field of economics instead assume the existence of unobserved action shocks to explain expert behavior (Nested Fixed Point Algorithm, Conditional Choice Probability method, Nested Pseudo-Likelihood Algorithm). In this work, we make previously unknown connections between these related methods from both fields. We achieve this by showing that they all belong to a class of optimization problems, characterized by a common form of the objective, the associated policy and the objective gradient. We demonstrate key computational and algorithmic differences which arise between the methods due to an approximation of the optimal soft value function, and describe how this leads to more efficient algorithms. Using insights which emerge from our study of this class of optimization problems, we identify various problem scenarios and investigate each method's suitability for these problems.
Active Feature Selection for the Mutual Information Criterion
Authors: Schnapp Shachar , Sabato Sivan
Published: September 2022

We study active feature selection, a novel feature selection setting in which unlabeled data is available, but the budget for labels is limited, and the examples to label can be actively selected by the algorithm. We focus on feature selection using the classical mutual information criterion, which selects the k features with the largest mutual information with the label. In the active feature selection setting, the goal is to use significantly fewer labels than the data set size and still find k features whose mutual information with the label based on the entire data set is large. We explain and experimentally study the choices that we make in the algorithm, and show that they lead to a successful algorithm, compared to other more naive approaches. Our design draws on insights which relate the problem of active feature selection to the study of pure-exploration multi-armed bandits settings. While we focus here on mutual information, our general methodology can be adapted to other feature-quality measures as well. The extended version of this paper, reporting all experiment results, is available at Schnapp and Sabato (2020). The code is available at the following url:
https://github.com/ShacharSchnapp/ActiveFeatureSelection
Raven's Progressive Matrices Completion with Latent Gaussian Process Priors
Authors: Shi Fan , Li Bin , Xue Xiangyang
Published: September 2022

Abstract reasoning ability is fundamental to human intelligence. It enables humans to uncover relations among abstract concepts and further deduce implicit rules from the relations. As a well-known abstract visual reasoning task, Raven's Progressive Matrices (RPM) are widely used in human IQ tests. Although extensive research has been conducted on RPM solvers with machine intelligence, few studies have considered further advancing the standard answer-selection (classification) problem to a more challenging answer-painting (generating) problem, which can verify whether the model has indeed understood the implicit rules. In this paper we aim to solve the latter one by proposing a deep latent variable model, in which multiple Gaussian processes are employed as priors of latent variables to separately learn underlying abstract concepts from RPMs; thus the proposed model is interpretable in terms of concept-specific latent variables. The latent Gaussian process also provides an effective way of extrapolation for answer painting based on the learned concept-changing rules. We evaluate the proposed model on RPM-like datasets with multiple continuously-changing visual concepts. Experimental results demonstrate that our model requires only few training samples to paint high-quality answers, generate novel RPM panels, and achieve interpretability through concept-specific latent variables.
UNIPoint: Universally Approximating Point Processes Intensities
Authors: Soen Alexander , Mathews Alexander , Grixti-Cheng Daniel , Xie Lexing
Published: September 2022

Point processes are a useful mathematical tool for describing events over time, and so there are many recent approaches for representing and learning them. One notable open question is how to precisely describe the flexibility of point process models and whether there exists a general model that can represent all point processes. Our work bridges this gap. Focusing on the widely used event intensity function representation of point processes, we provide a proof that a class of learnable functions can universally approximate any valid intensity function. The proof connects the well known Stone-Weierstrass Theorem for function approximation, the uniform density of non-negative continuous functions using a transfer functions, the formulation of the parameters of a piece-wise continuous functions as a dynamic system, and a recurrent neural network implementation for capturing the dynamics. Using these insights, we design and implement UNIPoint, a novel neural point process model, using recurrent neural networks to parameterise sums of basis function upon each event. Evaluations on synthetic and real world datasets show that this simpler representation performs better than Hawkes process variants and more complex neural network-based approaches. We expect this result will provide a practical basis for selecting and tuning models, as well as furthering theoretical work on representational complexity and learnability.
Gradient Descent Averaging and Primal-dual Averaging for Strongly Convex Optimization
Authors: Tao Wei , Li Wei , Pan Zhisong , Tao Qing
Published: September 2022

Averaging scheme has attracted extensive attention in deep learning as well as traditional machine learning. It achieves theoretically optimal convergence and also improves the empirical model performance. However, there is still a lack of sufficient convergence analysis for strongly convex optimization. Typically, the convergence about the last iterate of gradient descent methods, which is referred to as individual convergence, fails to attain its optimality due to the existence of logarithmic factor. In order to remove this factor, we first develop gradient descent averaging (GDA), which is a general projection-based dual averaging algorithm in the strongly convex setting. We further present primal-dual averaging for strongly convex cases (SC-PDA), where primal and dual averaging schemes are simultaneously utilized. We prove that GDA yields the optimal convergence rate in terms of output averaging, while SC-PDA derives the optimal individual convergence. Several experiments on SVMs and deep learning models validate the correctness of theoretical analysis and effectiveness of algorithms.
Meta Learning for Causal Direction
Authors: Ton Jean-François , Sejdinovic Dino , Fukumizu Kenji
Published: September 2022

The inaccessibility of controlled randomized trials due to inherent constraints in many fields of science has been a fundamental issue in causal inference. In this paper, we focus on distinguishing the cause from effect in the bivariate setting under limited observational data. Based on recent developments in meta learning as well as in causal inference, we introduce a novel generative model that allows distinguishing cause and effect in the small data setting. Using a learnt task variable that contains distributional information of each dataset, we propose an end-to-end algorithm that makes use of similar training datasets at test time. We demonstrate our method on various synthetic as well as real-world data and show that it is able to maintain high accuracy in detecting directions across varying dataset sizes.
Multi-View Information-Bottleneck Representation Learning
Authors: Wan Zhibin , Zhang Changqing , Zhu Pengfei , Hu Qinghua
Published: September 2022

In real-world applications, clustering or classification can usually be improved by fusing information from different views. Therefore, unsupervised representation learning on multi-view data becomes a compelling topic in machine learning. In this paper, we propose a novel and flexible unsupervised multi-view representation learning model termed Collaborative Multi-View Information Bottleneck Networks (CMIB-Nets), which comprehensively explores the common latent structure and the view-specific intrinsic information, and discards the superfluous information in the data significantly improving the generalization capability of the model. Specifically, our proposed model relies on the information bottleneck principle to integrate the shared representation among different views and the view-specific representation of each view, prompting the multi-view complete representation and flexibly balancing the complementarity and consistency among multiple views. We conduct extensive experiments (including clustering analysis, robustness experiment, and ablation study) on real-world datasets, which empirically show promising generalization ability and robustness compared to state-of-the-arts.
Learning from Noisy Labels with Complementary Loss Functions
Authors: Wang Deng-Bao , Wen Yong , Pan Lujia , Zhang Min-Ling
Published: September 2022

Recent researches reveal that deep neural networks are sensitive to label noises hence leading to poor generalization performance in some tasks. Although different robust loss functions have been proposed to remedy this issue, they suffer from an underfitting problem, thus are not sufficient to learn accurate models. On the other hand, the commonly used Cross Entropy (CE) loss, which shows high performance in standard supervised learning (with clean supervision), is non-robust to label noise. In this paper, we propose a general framework to learn robust deep neural networks with complementary loss functions. In our framework, CE and robust loss play complementary roles in a joint learning objective as per their learning sufficiency and robustness properties respectively. Specifically, we find that by exploiting the memorization effect of neural networks, we can easily filter out a proportion of hard samples and generate reliable pseudo labels for easy samples, and thus reduce the label noise to a quite low level. Then, we simply learn with CE on pseudo supervision and robust loss on original noisy supervision. In this procedure, CE can guarantee the sufficiency of optimization while the robust loss can be regarded as the supplement. Experimental results on benchmark classification datasets indicate that the proposed method helps achieve robust and sufficient deep neural network training simultaneously.
Tied Block Convolution: Leaner and Better CNNs with Shared Thinner Filters
Authors: Wang Xudong , Yu Stella X.
Published: September 2022

Convolution is the main building block of a convolutional neural network (CNN). We observe that an optimized CNN often has highly correlated filters as the number of channels increases with depth, reducing the expressive power of feature representations.

We propose Tied Block Convolution (TBC) that shares the same thinner filter over equal blocks of channels and produces multiple responses with a single filter. The concept of TBC can also be extended to group convolution and fully connected layers, and can be applied to various backbone networks and attention modules.

Our extensive experimentation on classification, detection, instance segmentation, and attention demonstrates that TBC is consistently leaner and significantly better than standard convolution and group convolution. On attention, with 64 times fewer parameters, our TiedSE performs on par with the standard SE.

On detection and segmentation, TBC can effectively handle highly overlapping instances, whereas standard CNNs often fail to accurately aggregate information in the presence of occlusion and result in multiple redundant partial object proposals. By sharing filters across channels, TBC reduces correlation and delivers a sizable gain of 6% in the average precision for object detection on MS-COCO when the occlusion ratio is 80%.
Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases
Authors: Wendler Chris , Amrollahi Andisheh , Seifert Bastian , Krause Andreas , Püschel Markus
Published: September 2022

Many applications of machine learning on discrete domains, such as learning preference functions in recommender systems
or auctions, can be reduced to estimating a set function that is sparse in the Fourier domain. In this work, we present a new
family of algorithms for learning Fourier-sparse set functions. They require at most nk − k log k + k queries (set function
evaluations), under mild conditions on the Fourier coefficients, where n is the size of the ground set and k the number of
non-zero Fourier coefficients. In contrast to other work that focused on the orthogonal Walsh-Hadamard transform (WHT),
our novel algorithms operate with recently introduced non-orthogonal Fourier transforms that offer different notions of
Fourier-sparsity. These naturally arise when modeling, e.g., sets of items forming substitutes and complements. We
demonstrate effectiveness on several real-world applications.
Interpreting Multivariate Shapley Interactions in DNNs
Authors: Zhang Hao , Xie Yichen , Zheng Longjie , Zhang Die , Zhang Quanshi
Published: September 2022

This paper aims to explain deep neural networks (DNNs) from the perspective of multivariate interactions. In this paper, we define and quantify the significance of interactions among multiple input variables of the DNN. Input variables with strong interactions usually form a coalition and reflect prototype features, which are memorized and used by the DNN for inference. We define the significance of interactions based on the Shapley value, which is designed to assign the attribution value of each input variable to the inference. We have conducted experiments with various DNNs. Experimental results have demonstrated the effectiveness of the proposed method.
Treatment Effect Estimation with Disentangled Latent Factors
Authors: Zhang Weijia , Liu Lin , Li Jiuyong
Published: September 2022

Much research has been devoted to the problem of estimating treatment effects from observational data; however, most methods assume that the observed variables only contain confounders, i.e., variables that affect both the treatment and the outcome. Unfortunately, this assumption is frequently violated in real-world applications, since some variables only affect the treatment but not the outcome, and vice versa. Moreover, in many cases only the proxy variables of the underlying confounding factors can be observed. In this work, we first show the importance of differentiating confounding factors from instrumental and risk factors for both average and conditional average treatment effect estimation, and then we propose a variational inference approach to simultaneously infer latent factors from the observed variables, disentangle the factors into three disjoint sets corresponding to the instrumental, confounding, and risk factors, and use the disentangled factors for treatment effect estimation. Experimental results demonstrate the effectiveness of the proposed method on a wide range of synthetic, benchmark, and real-world datasets.
How Does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?
Authors: Zhong Li , Fang Zhen , Liu Feng , Lu Jie , Yuan Bo , Zhang Guangquan
Published: September 2022

Unsupervised domain adaptation (UDA) aims to train a target classifier with labeled samples from the source domain and unlabeled samples from the target domain. Classical UDA learning bounds show that target risk is upper bounded by three terms: source risk, distribution discrepancy, and combined risk. Based on the assumption that the combined risk is a small fixed value, methods based on this bound train a target classifier by only minimizing estimators of the source risk and the distribution discrepancy. However, the combined risk may increase when minimizing both estimators, which makes the target risk uncontrollable. Hence the target classifier cannot achieve ideal performance if we fail to control the combined risk. To control the combined risk, the key challenge takes root in the unavailability of the labeled samples in the target domain. To address this key challenge, we propose a method named E-MixNet. E-MixNet employs enhanced mixup, a generic vicinal distribution, on the labeled source samples and pseudo-labeled target samples to calculate a proxy of the combined risk. Experiments show that the proxy can effectively curb the increase of the combined risk when minimizing the source risk and distribution discrepancy. Furthermore, we show that if the proxy of the combined risk is added into loss functions of four representative UDA methods, their performance is also improved.
Learning Task-Distribution Reward Shaping with Meta-Learning
Authors: Zou Haosheng , Ren Tongzheng , Yan Dong , Su Hang , Zhu Jun
Published: September 2022

Reward shaping is one of the most effective methods to tackle the crucial yet challenging problem of credit assignment and accelerate Reinforcement Learning. However, designing shaping functions usually requires rich expert knowledge and hand-engineering, and the difficulties are further exacerbated given multiple tasks to solve. In this paper, we consider reward shaping on a distribution of tasks that share state spaces but not necessarily action spaces. We provide insights into optimal reward shaping, and propose a novel meta-learning framework to automatically learn such reward shaping to apply on newly sampled tasks. Theoretical analysis and extensive experiments establish us as the state-of-the-art in learning task-distribution reward shaping, outperforming previous such works (Konidaris and Barto 2006; Snel and Whiteson 2014). We further show that our method outperforms learning intrinsic rewards (Yang et al. 2019; Zheng et al. 2020), outperforms Rainbow (Hessel et al. 2018) in complex pixel-based CoinRun games, and is also better than hand-designed reward shaping on grids. While the goal of this paper is to learn reward shaping rather than to propose new general meta-learning algorithms as PEARL (Rakelly et al. 2019) or MQL (Fakoor et al. 2020), our framework based on MAML (Finn, Abbeel, and Levine 2017) also outperforms PEARL / MQL, and could combine with them for further improvement.
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels
Authors: Chen Pengfei , Ye Junjie , Chen Guangyong , Zhao Jingwei , Heng Pheng-Ann
Published: September 2022

For multi-class classification under class-conditional label noise, we prove that the accuracy metric itself can be robust. We concretize this finding's inspiration in two essential aspects: training and validation, with which we address critical issues in learning with noisy labels. For training, we show that maximizing training accuracy on sufficiently many noisy samples yields an approximately optimal classifier. For validation, we prove that a noisy validation set is reliable, addressing the critical demand of model selection in scenarios like hyperparameter-tuning and early stopping. Previously, model selection using noisy validation samples has not been theoretically justified. We verify our theoretical results and additional claims with extensive experiments. We show characterizations of models trained with noisy labels, motivated by our theoretical results, and verify the utility of a noisy validation set by showing the impressive performance of a framework termed noisy best teacher and student (NTS). Our code is released.
Outlier Impact Characterization for Time Series Data
Authors: Li Jianbo , Zheng Lecheng , Zhu Yada , He Jingrui
Published: September 2022

For time series data, certain types of outliers are intrinsically more harmful for parameter estimation and future predictions than others, irrespective of their frequency. In this paper, for the first time, we study the characteristics of such outliers through the lens of the influence functional from robust statistics. In particular, we consider the input time series as a contaminated process, with the recurring outliers generated from an unknown contaminating process. Then we leverage the influence functional to understand the impact of the contaminating process on parameter estimation. The influence functional results in a multi-dimensional vector that measures the sensitivity of the predictive model to the contaminating process, which can be challenging to interpret especially for models with a large number of parameters. To this end, we further propose a comprehensive single-valued metric (the SIF) to measure outlier impacts on future predictions. It provides a quantitative measure regarding the outlier impacts, which can be used in a variety of scenarios, such as the evaluation of outlier detection methods, the creation of more harmful outliers, etc. The empirical results on multiple real data sets demonstrate the effectivenss of the proposed SIF metric.
Constrained Risk-Averse Markov Decision Processes
Authors: Ahmadi Mohamadreza , Rosolia Ugo , Ingham Michel D. , Murray Richard M. , Ames Aaron D.
Published: September 2022

We consider the problem of designing policies for Markov decision processes (MDPs) with dynamic coherent risk objectives and constraints. We begin by formulating the problem in a Lagrangian framework. Under the assumption that the risk objectives and constraints can be represented by a Markov risk transition mapping, we propose an optimization-based method to synthesize Markovian policies that lower-bound the constrained risk-averse problem. We demonstrate that the formulated optimization problems are in the form of difference convex programs (DCPs) and can be solved by the disciplined convex-concave programming (DCCP) framework. We show that these results generalize linear programs for constrained MDPs with total discounted expected costs and constraints. Finally, we illustrate the effectiveness of the proposed method with numerical experiments on a rover navigation problem involving conditional-value-at-risk (CVaR) and entropic-value-at-risk (EVaR) coherent risk measures.
Bike-Repositioning Using Volunteers: Crowd Sourcing with Choice Restriction
Authors: Huang Jinjia , Chou Mabel C. , Teo Chung-Piaw
Published: September 2022

Motivated by the Bike Angels Program in New York's Citi Bike and Boston's Blue Bikes, we study the use of (registered) volunteers to re-position empty bikes for riders in a bike sharing system. We propose a method that can be used to deploy the volunteers in the system, based on the real time distribution of the bikes in different stations. To account for (random) route demand in the network, we solve a related transshipment network design model and construct a sparse structure to restrict the re-balancing activities of the volunteers (concentrating re-balancing activities on essential routes). We also develop a comprehensive simulation model using a threshold-based policy to deploy the volunteers in real time, to test the effect of choice restriction on volunteers (suitably deployed) to re-position bikes. We use the Hubway system in Boston (with 60 stations) to demonstrate that using a sparse structure to concentrate the re-balancing activities of the volunteers, instead of allowing all admissible flows in the system (as in current practice), can reduce the number of re-balancing moves by a huge amount, losing only a small proportion of demand satisfied.
Synthesis of Search Heuristics for Temporal Planning via Reinforcement Learning
Authors: Micheli Andrea , Valentini Alessandro
Published: September 2022

Automated temporal planning is the problem of synthesizing, starting from a model of a system, a course of actions to achieve a desired goal when temporal constraints, such as deadlines, are present in the problem. Despite considerable successes in the literature, scalability is still a severe limitation for existing planners, especially when confronted with real-world, industrial scenarios.

In this paper, we aim at exploiting recent advances in reinforcement learning, for the synthesis of heuristics for temporal planning. Starting from a set of problems of interest for a specific domain, we use a customized reinforcement learning algorithm to construct a value function that is able to estimate the expected reward for as many problems as possible. We use a reward schema that captures the semantics of the temporal planning problem and we show how the value function can be transformed in a planning heuristic for a semi-symbolic heuristic search exploration of the planning model. We show on two case-studies how this method can widen the reach of current temporal planners with encouraging results.
Asking the Right Questions: Learning Interpretable Action Models Through Query Answering
Authors: Verma Pulkit , Marpally Shashank Rao , Srivastava Siddharth
Published: September 2022

This paper develops a new approach for estimating an interpretable, relational model of a black-box autonomous agent that can plan and act. Our main contributions are a new paradigm for estimating such models using a rudimentary query interface with the agent and a hierarchical querying algorithm that generates an interrogation policy for estimating the agent's internal model in a user-interpretable vocabulary. Empirical evaluation of our approach shows that despite the intractable search space of possible agent models, our approach allows correct and scalable estimation of interpretable agent models for a wide class of black-box autonomous agents. Our results also show that this approach can use predicate classifiers to learn interpretable models of planning agents that represent states as images.
Estimating Identifiable Causal Effects through Double Machine Learning
Authors: Jung Yonghan , Tian Jin , Bareinboim Elias
Published: September 2022

Identifying causal effects from observational data is a pervasive challenge found throughout the empirical sciences. Very general methods have been developed to decide the identifiability of a causal quantity from a combination of observational data and causal knowledge about the underlying system. In practice, however, there are still challenges to estimating identifiable causal functionals from finite samples. Recently, a method known as double/debiased machine learning (DML) (Chernozhukov et al. 2018) has been proposed to learn parameters leveraging modern machine learning techniques, which is both robust to model misspecification and bias-reducing. Still, DML has only been used for causal estimation in settings when the back-door condition (also known as conditional ignorability) holds. In this paper, we develop a new, general class of estimators for any identifiable causal functionals that exhibit DML properties, which we name DML-ID. In particular, we introduce a complete identification algorithm that returns an influence function (IF) for any identifiable causal functional. We then construct the DML estimator based on the derived IF. We show that DML-ID estimators hold the key properties of debiasedness and doubly robustness. Simulation results corroborate with the theory.
Bounding Causal Effects on Continuous Outcome
Authors: Zhang Junzhe , Bareinboim Elias
Published: September 2022

We investigate the problem of bounding causal effects from experimental studies in which treatment assignment is randomized but the subject compliance is imperfect. It is well known that under such conditions, the actual causal effects are not point-identifiable due to uncontrollable unobserved confounding. In their seminal work, Balke and Pearl (1994) derived the tightest bounds over the causal effects in this settings by employing an algebra program to derive analytic expressions. However, Pearl's approach assumes the primary outcome to be discrete and finite. Solving such a program could be intractable when high-dimensional context variables are present. In this paper, we present novel non-parametric methods to bound causal effects on the continuous outcome from studies with imperfect compliance. These bounds could be generalized to settings with a high-dimensional context.
Efficient Bayesian Network Structure Learning via Parameterized Local Search on Topological Orderings
Authors: Grüttemeier Niels , Komusiewicz Christian , Morawietz Nils
Published: September 2022

In Bayesian Network Structure Learning (BNSL), we are given a variable set and parent scores for each variable and aim to compute a DAG, called Bayesian network, that maximizes the sum of parent scores, possibly under some structural constraints. Even very restricted special cases of BNSL are computationally hard, and, thus, in practice heuristics such as local search are used. In a typical local search algorithm, we are given some BNSL solution and ask whether there is a better solution within some pre-defined neighborhood of the solution. We study ordering-based local search, where a solution is described via a topological ordering of the variables. We show that given such a topological ordering, we can compute an optimal DAG whose ordering is within inversion distance r in subexponential FPT time; the parameter r allows to balance between solution quality and running time of the local search algorithm. This running time bound can be achieved for BNSL without any structural constraints and for all structural constraints that can be expressed via a sum of weights that are associated with each parent set. We show that for other modification operations on the variable orderings, algorithms with an FPT time for r are unlikely. We also outline the limits of ordering-based local search by showing that it cannot be used for common structural constraints on the moralized graph of the network.
Bayes DistNet - A Robust Neural Network for Algorithm Runtime Distribution Predictions
Authors: Tuero Jake , Buro Michael
Published: September 2022

Randomized algorithms are used in many state-of-the-art solvers for constraint satisfaction problems (CSP) and Boolean satisfiability (SAT) problems. For many of these problems, there is no single solver which will dominate others. Having access to the underlying runtime distributions (RTD) of these solvers can allow for better use of algorithm selection, algorithm portfolios, and restart strategies. Previous state-of-the-art methods directly try to predict a fixed parametric distribution that the input instance follows. In this paper, we extend RTD prediction models into the Bayesian setting for the first time. This new model achieves robust predictive performance in the low observation setting, as well as handling censored observations. This technique also allows for richer representations which cannot be achieved by the classical models which restrict their output representations. Our model outperforms the previous state-of-the-art model in settings in which data is scarce, and can make use of censored data such as lower bound time estimates, where that type of data would otherwise be discarded. It can also quantify its uncertainty in its predictions, allowing for algorithm portfolio models to make better informed decisions about which algorithm to run on a particular instance.