pwc-archive/papers-with-abstracts huggingface dataset:
Robust Stability of Neural Network-controlled Nonlinear Systems with Parametric Variability
Authors: Soumyabrata Talukder, Ratnesh Kumar
Date: 2021-09-13 00:00:00

Stability certification and identifying a safe and stabilizing initial set are two important concerns in ensuring operational safety, stability, and robustness of dynamical systems. With the advent of machine-learning tools, these issues need to be addressed for the systems with machine-learned components in the feedback loop. To develop a general theory for stability and stabilizability of a neural network (NN)-controlled nonlinear system subject to bounded parametric variation, a Lyapunov-based stability certificate is proposed and is further used to devise a maximal Lipschitz bound for the NN controller, and also a corresponding maximal region-of-attraction (RoA) inside a given safe operating domain. To compute such a robustly stabilizing NN controller that also maximizes the system's long-run utility, a stability-guaranteed training (SGT) algorithm is proposed. The effectiveness of the proposed framework is validated through an illustrative example.
Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection
Authors: Joseph M. Ackerson, Dave Rushit, Seliya Jim
Date: 2021-09-13 00:00:00
Tasks: Anomaly Detection, speech-recognition, Speech Recognition

Recurrent Neural Networks are powerful machine learning frameworks that allow for data to be saved and referenced in a temporal sequence. This opens many new possibilities in fields such as handwriting analysis and speech recognition. This paper seeks to explore current research being conducted on RNNs in four very important areas, being biometric authentication, expression recognition, anomaly detection, and applications to aircraft. This paper reviews the methodologies, purpose, results, and the benefits and drawbacks of each proposed method below. These various methodologies all focus on how they can leverage distinct RNN architectures such as the popular Long Short-Term Memory (LSTM) RNN or a Deep-Residual RNN. This paper also examines which frameworks work best in certain situations, and the advantages and disadvantages of each pro-posed model.
Exploiting Heterogeneity in Robust Federated Best-Arm Identification
Authors: Aritra Mitra, Hamed Hassani, George Pappas
Date: 2021-09-13 00:00:00
Tasks: Multi-Armed Bandits

We study a federated variant of the best-arm identification problem in stochastic multi-armed bandits: a set of clients, each of whom can sample only a subset of the arms, collaborate via a server to identify the best arm (i.e., the arm with the highest mean reward) with prescribed confidence. For this problem, we propose Fed-SEL, a simple communication-efficient algorithm that builds on successive elimination techniques and involves local sampling steps at the clients. To study the performance of Fed-SEL, we introduce a notion of arm-heterogeneity that captures the level of dissimilarity between distributions of arms corresponding to different clients. Interestingly, our analysis reveals the benefits of arm-heterogeneity in reducing both the sample- and communication-complexity of Fed-SEL. As a special case of our analysis, we show that for certain heterogeneous problem instances, Fed-SEL outputs the best-arm after just one round of communication. Our findings have the following key implication: unlike federated supervised learning where recent work has shown that statistical heterogeneity can lead to poor performance, one can provably reap the benefits of both local computation and heterogeneity for federated best-arm identification. As our final contribution, we develop variants of Fed-SEL, both for federated and peer-to-peer settings, that are robust to the presence of Byzantine clients, and hence suitable for deployment in harsh, adversarial environments.
Detecting Textual Adversarial Examples through Randomized Substitution and Vote
Authors: Xiaosen Wang, Yifeng Xiong, Kun He
Date: 2021-09-13 00:00:00

A line of work has shown that natural text processing models are vulnerable to adversarial examples. Correspondingly, various defense methods are proposed to mitigate the threat of textual adversarial examples, eg, adversarial training, input transformations, detection, etc. In this work, we treat the optimization process for synonym substitution based textual adversarial attacks as a specific sequence of word replacement, in which each word mutually influences other words. We identify that we could destroy such mutual interaction and eliminate the adversarial perturbation by randomly substituting a word with its synonyms. Based on this observation, we propose a novel textual adversarial example detection method, termed Randomized Substitution and Vote (RS&V), which votes the prediction label by accumulating the logits of k samples generated by randomly substituting the words in the input text with synonyms. The proposed RS&V is generally applicable to any existing neural networks without modification on the architecture or extra training, and it is orthogonal to prior work on making the classification network itself more robust. Empirical evaluations on three benchmark datasets demonstrate that our RS&V could detect the textual adversarial examples more successfully than the existing detection methods while maintaining the high classification accuracy on benign samples.
Finding Representative Group Fairness Metrics Using Correlation Estimations
Authors: Hadis Anahideh, Nazanin Nezami, Abolfazl Asudeh
Date: 2021-09-13 00:00:00
Tasks: Fairness

It is of critical importance to be aware of the historical discrimination embedded in the data and to consider a fairness measure to reduce bias throughout the predictive modeling pipeline. Given various notions of fairness defined in the literature, investigating the correlation and interaction among metrics is vital for addressing unfairness. Practitioners and data scientists should be able to comprehend each metric and examine their impact on one another given the context, use case, and regulations. Exploring the combinatorial space of different metrics for such examination is burdensome. To alleviate the burden of selecting fairness notions for consideration, we propose a framework that estimates the correlation among fairness notions. Our framework consequently identifies a set of diverse and semantically distinct metrics as representative for a given context. We propose a Monte-Carlo sampling technique for computing the correlations between fairness metrics by indirect and efficient perturbation in the model space. Using the estimated correlations, we then find a subset of representative metrics. The paper proposes a generic method that can be generalized to any arbitrary set of fairness metrics. We showcase the validity of the proposal using comprehensive experiments on real-world benchmark datasets.
SCORE-IT: A Machine Learning-based Tool for Automatic Standardization of EEG Reports
Authors: Samarth Rawal, Yogatheesan Varatharajah
Date: 2021-09-13 00:00:00
Tasks: BIG-bench Machine Learning, EEG, Electroencephalogram (EEG)

Machine learning (ML)-based analysis of electroencephalograms (EEGs) is playing an important role in advancing neurological care. However, the difficulties in automatically extracting useful metadata from clinical records hinder the development of large-scale EEG-based ML models. EEG reports, which are the primary sources of metadata for EEG studies, suffer from lack of standardization. Here we propose a machine learning-based system that automatically extracts components from the SCORE specification from unstructured, natural-language EEG reports. Specifically, our system identifies (1) the type of seizure that was observed in the recording, per physician impression; (2) whether the session recording was normal or abnormal according to physician impression; (3) whether the patient was diagnosed with epilepsy or not. We performed an evaluation of our system using the publicly available TUH EEG corpus and report F1 scores of 0.92, 0.82, and 0.97 for the respective tasks.
RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Search
Authors: Zheyu Yan, Weiwen Jiang, Xiaobo Sharon Hu, Yiyu Shi
Date: 2021-09-13 00:00:00
Tasks: GPU, Neural Architecture Search, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Differentiable neural architecture search (DNAS) is known for its capacity in the automatic generation of superior neural networks. However, DNAS based methods suffer from memory usage explosion when the search space expands, which may prevent them from running successfully on even advanced GPU platforms. On the other hand, reinforcement learning (RL) based methods, while being memory efficient, are extremely time-consuming. Combining the advantages of both types of methods, this paper presents RADARS, a scalable RL-aided DNAS framework that can explore large search spaces in a fast and memory-efficient manner. RADARS iteratively applies RL to prune undesired architecture candidates and identifies a promising subspace to carry out DNAS. Experiments using a workstation with 12 GB GPU memory show that on CIFAR-10 and ImageNet datasets, RADARS can achieve up to 3.41% higher accuracy with 2.5X search time reduction compared with a state-of-the-art RL-based method, while the two DNAS baselines cannot complete due to excessive memory usage or search time. To the best of the authors' knowledge, this is the first DNAS framework that can handle large search spaces with bounded memory usage.
Barzilai and Borwein conjugate gradient method equipped with a non-monotone line search technique and its application on non-negative matrix factorization
Authors: Sajad Fathi Hafshejani, Daya Gaur, Shahadat Hossain, Robert Benkoczi
Date: 2021-09-13 00:00:00

In this paper, we propose a new non-monotone conjugate gradient method for solving unconstrained nonlinear optimization problems. We first modify the non-monotone line search method by introducing a new trigonometric function to calculate the non-monotone parameter, which plays an essential role in the algorithm's efficiency. Then, we apply a convex combination of the Barzilai-Borwein method for calculating the value of step size in each iteration. Under some suitable assumptions, we prove that the new algorithm has the global convergence property. The efficiency and effectiveness of the proposed method are determined in practice by applying the algorithm to some standard test problems and non-negative matrix factorization problems.
An Adaptive Boosting Technique to Mitigate Popularity Bias in Recommender System
Authors: Ajay Gangwar, Shweta Jain
Date: 2021-09-13 00:00:00
Tasks: Recommendation Systems

The observed ratings in most recommender systems are subjected to popularity bias and are thus not randomly missing. Due to this, only a few popular items are recommended, and a vast number of non-popular items are hardly recommended. Not suggesting the non-popular items lead to fewer products dominating the market and thus offering fewer opportunities for creativity and innovation. In the literature, several fair algorithms have been proposed which mainly focused on improving the accuracy of the recommendation system. However, a typical accuracy measure is biased towards popular items, i.e., it promotes better accuracy for popular items compared to non-popular items. This paper considers a metric that measures the popularity bias as the difference in error on popular items and non-popular items. Motivated by the fair boosting algorithm on classification, we propose an algorithm that reduces the popularity bias present in the data while maintaining accuracy within acceptable limits. The main idea of our algorithm is that it lifts the weights of the non-popular items, which are generally underrepresented in the data. With the help of comprehensive experiments on real-world datasets, we show that our proposed algorithm outperforms the existing algorithms on the proposed popularity bias metric.
AMI-FML: A Privacy-Preserving Federated Machine Learning Framework for AMI
Authors: Milan Biswal, Abu Saleh Md Tayeen, Satyajayant Misra
Date: 2021-09-13 00:00:00
Tasks: BIG-bench Machine Learning, energy management, Federated Learning, Load Forecasting, Management, Privacy Preserving

Machine learning (ML) based smart meter data analytics is very promising for energy management and demand-response applications in the advanced metering infrastructure(AMI). A key challenge in developing distributed ML applications for AMI is to preserve user privacy while allowing active end-users participation. This paper addresses this challenge and proposes a privacy-preserving federated learning framework for ML applications in the AMI. We consider each smart meter as a federated edge device hosting an ML application that exchanges information with a central aggregator or a data concentrator, periodically. Instead of transferring the raw data sensed by the smart meters, the ML model weights are transferred to the aggregator to preserve privacy. The aggregator processes these parameters to devise a robust ML model that can be substituted at each edge device. We also discuss strategies to enhance privacy and improve communication efficiency while sharing the ML model parameters, suited for relatively slow network connections in the AMI. We demonstrate the proposed framework on a use case federated ML (FML) application that improves short-term load forecasting (STLF). We use a long short-term memory(LSTM) recurrent neural network (RNN) model for STLF. In our architecture, we assume that there is an aggregator connected to a group of smart meters. The aggregator uses the learned model gradients received from the federated smart meters to generate an aggregate, robust RNN model which improves the forecasting accuracy for individual and aggregated STLF. Our results indicate that with FML, forecasting accuracy is increased while preserving the data privacy of the end-users.
Training Fair Models in Federated Learning without Data Privacy Infringement
Authors: Xin Che, Jingdi Hu, Zirui Zhou, Yong Zhang, Lingyang Chu
Date: 2021-09-13 00:00:00
Tasks: Fairness, Federated Learning

Training fair machine learning models becomes more and more important. As many powerful models are trained by collaboration among multiple parties, each holding some sensitive data, it is natural to explore the feasibility of training fair models in federated learning so that the fairness of trained models, the data privacy of clients, and the collaboration between clients can be fully respected simultaneously. However, the task of training fair models in federated learning is challenging, since it is far from trivial to estimate the fairness of a model without knowing the private data of the participating parties, which is often constrained by privacy requirements in federated learning. In this paper, we first propose a federated estimation method to accurately estimate the fairness of a model without infringing the data privacy of any party. Then, we use the fairness estimation to formulate a novel problem of training fair models in federated learning. We develop FedFair, a well-designed federated learning framework, which can successfully train a fair model with high performance without data privacy infringement. Our extensive experiments on three real-world data sets demonstrate the excellent fair model training performance of our method.
Source Inference Attacks in Federated Learning
Authors: Hongsheng Hu, Zoran Salcic, Lichao Sun, Gillian Dobbie, Xuyun Zhang
Date: 2021-09-13 00:00:00
Tasks: Federated Learning, Inference Attack

Federated learning (FL) has emerged as a promising privacy-aware paradigm that allows multiple clients to jointly train a model without sharing their private data. Recently, many studies have shown that FL is vulnerable to membership inference attacks (MIAs) that can distinguish the training members of the given model from the non-members. However, existing MIAs ignore the source of a training member, i.e., the information of which client owns the training member, while it is essential to explore source privacy in FL beyond membership privacy of examples from all clients. The leakage of source information can lead to severe privacy issues. For example, identification of the hospital contributing to the training of an FL model for COVID-19 pandemic can render the owner of a data record from this hospital more prone to discrimination if the hospital is in a high risk region. In this paper, we propose a new inference attack called source inference attack (SIA), which can derive an optimal estimation of the source of a training member. Specifically, we innovatively adopt the Bayesian perspective to demonstrate that an honest-but-curious server can launch an SIA to steal non-trivial source information of the training members without violating the FL protocol. The server leverages the prediction loss of local models on the training members to achieve the attack effectively and non-intrusively. We conduct extensive experiments on one synthetic and five real datasets to evaluate the key factors in an SIA, and the results show the efficacy of the proposed source inference attack.
Inferential Wasserstein Generative Adversarial Networks
Authors: Yao Chen, Qingyi Gao, Xiao Wang
Date: 2021-09-13 00:00:00

Generative Adversarial Networks (GANs) have been impactful on many problems and applications but suffer from unstable training. The Wasserstein GAN (WGAN) leverages the Wasserstein distance to avoid the caveats in the minmax two-player training of GANs but has other defects such as mode collapse and lack of metric to detect the convergence. We introduce a novel inferential Wasserstein GAN (iWGAN) model, which is a principled framework to fuse auto-encoders and WGANs. The iWGAN model jointly learns an encoder network and a generator network motivated by the iterative primal dual optimization process. The encoder network maps the observed samples to the latent space and the generator network maps the samples from the latent space to the data space. We establish the generalization error bound of the iWGAN to theoretically justify its performance. We further provide a rigorous probabilistic interpretation of our model under the framework of maximum likelihood estimation. The iWGAN, with a clear stopping criteria, has many advantages over other autoencoder GANs. The empirical experiments show that the iWGAN greatly mitigates the symptom of mode collapse, speeds up the convergence, and is able to provide a measurement of quality check for each individual sample. We illustrate the ability of the iWGAN by obtaining competitive and stable performances for benchmark datasets.
Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?
Authors: Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, Doina Precup
Date: 2021-09-12 00:00:00
Tasks: Inductive Bias, Node Classification

Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using the graph structures based on the relational inductive bias (homophily assumption). Though GNNs are believed to outperform NNs in real-world tasks, performance advantages of GNNs over graph-agnostic NNs seem not generally satisfactory. Heterophily has been considered as a main cause and numerous works have been put forward to address it. In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation. Then, we propose new metrics based on a similarity matrix which considers the influence of both graph structure and input features on GNNs. The metrics demonstrate advantages over the commonly used homophily metrics by tests on synthetic graphs. From the metrics and the observations, we find some cases of harmful heterophily can be addressed by diversification operation. With this fact and knowledge of filterbanks, we propose the Adaptive Channel Mixing (ACM) framework to adaptively exploit aggregation, diversification and identity channels in each GNN layer to address harmful heterophily. We validate the ACM-augmented baselines with 10 real-world node classification tasks. They consistently achieve significant performance gain and exceed the state-of-the-art GNNs on most of the tasks without incurring significant computational burden.
Mixing between the Cross Entropy and the Expectation Loss Terms
Authors: Barak Battash, Lior Wolf, Tamir Hazan
Date: 2021-09-12 00:00:00

The cross entropy loss is widely used due to its effectiveness and solid theoretical grounding. However, as training progresses, the loss tends to focus on hard to classify samples, which may prevent the network from obtaining gains in performance. While most work in the field suggest ways to classify hard negatives, we suggest to strategically leave hard negatives behind, in order to focus on misclassified samples with higher probabilities. We show that adding to the optimization goal the expectation loss, which is a better approximation of the zero-one loss, helps the network to achieve better accuracy. We, therefore, propose to shift between the two losses during training, focusing more on the expectation loss gradually during the later stages of training. Our experiments show that the new training protocol improves performance across a diverse set of classification domains, including computer vision, natural language processing, tabular data, and sequences. Our code and scripts are available at supplementary.
Critical Learning Periods in Federated Learning
Authors: Gang Yan, Hao Wang, Jian Li
Date: 2021-09-12 00:00:00
Tasks: Federated Learning

Federated learning (FL) is a popular technique to train machine learning (ML) models with decentralized data. Extensive works have studied the performance of the global model; however, it is still unclear how the training process affects the final test accuracy. Exacerbating this problem is the fact that FL executions differ significantly from traditional ML with heterogeneous data characteristics across clients, involving more hyperparameters. In this work, we show that the final test accuracy of FL is dramatically affected by the early phase of the training process, i.e., FL exhibits critical learning periods, in which small gradient errors can have irrecoverable impact on the final test accuracy. To further explain this phenomenon, we generalize the trace of the Fisher Information Matrix (FIM) to FL and define a new notion called FedFIM, a quantity reflecting the local curvature of each clients from the beginning of the training in FL. Our findings suggest that the {\em initial learning phase} plays a critical role in understanding the FL performance. This is in contrast to many existing works which generally do not connect the final accuracy of FL to the early phase training. Finally, seizing critical learning periods in FL is of independent interest and could be useful for other problems such as the choices of hyperparameters such as the number of client selected per round, batch size, and more, so as to improve the performance of FL training and testing.