pwc-archive/papers-with-abstracts huggingface dataset:
Reparameterized Policy Learning for Multimodal Trajectory Optimization
Authors: Zhiao Huang, Litian Liang, Zhan Ling, Xuanlin Li, Chuang Gan, Hao Su
Date: 2023-07-20 00:00:00
Tasks: Reinforcement Learning (RL)

We investigate the challenge of parametrizing policies for reinforcement learning (RL) in high-dimensional continuous action spaces. Our objective is to develop a multimodal policy that overcomes limitations inherent in the commonly-used Gaussian parameterization. To achieve this, we propose a principled framework that models the continuous RL policy as a generative model of optimal trajectories. By conditioning the policy on a latent variable, we derive a novel variational bound as the optimization objective, which promotes exploration of the environment. We then present a practical model-based RL method, called Reparameterized Policy Gradient (RPG), which leverages the multimodal policy parameterization and learned world model to achieve strong exploration capabilities and high data efficiency. Empirical results demonstrate that our method can help agents evade local optima in tasks with dense rewards and solve challenging sparse-reward environments by incorporating an object-centric intrinsic reward. Our method consistently outperforms previous approaches across a range of tasks. Code and supplementary materials are available on the project page https://haosulab.github.io/RPG/
Decentralized Smart Charging of Large-Scale EVs using Adaptive Multi-Agent Multi-Armed Bandits
Authors: Sharyal Zafar, Raphaël Feraud, Anne Blavette, Guy Camilleri, Hamid Ben
Date: 2023-07-20 00:00:00
Tasks: Fairness, Multi-Armed Bandits, Philosophy

The drastic growth of electric vehicles and photovoltaics can introduce new challenges, such as electrical current congestion and voltage limit violations due to peak load demands. These issues can be mitigated by controlling the operation of electric vehicles i.e., smart charging. Centralized smart charging solutions have already been proposed in the literature. But such solutions may lack scalability and suffer from inherent drawbacks of centralization, such as a single point of failure, and data privacy concerns. Decentralization can help tackle these challenges. In this paper, a fully decentralized smart charging system is proposed using the philosophy of adaptive multi-agent systems. The proposed system utilizes multi-armed bandit learning to handle uncertainties in the system. The presented system is decentralized, scalable, real-time, model-free, and takes fairness among different players into account. A detailed case study is also presented for performance evaluation.
Graphs in State-Space Models for Granger Causality in Climate Science
Authors: Víctor Elvira, Émilie Chouzenoux, Jordi Cerdà, Gustau Camps-Valls
Date: 2023-07-20 00:00:00
Tasks: Econometrics, State Space Models, Time Series

Granger causality (GC) is often considered not an actual form of causality. Still, it is arguably the most widely used method to assess the predictability of a time series from another one. Granger causality has been widely used in many applied disciplines, from neuroscience and econometrics to Earth sciences. We revisit GC under a graphical perspective of state-space models. For that, we use GraphEM, a recently presented expectation-maximisation algorithm for estimating the linear matrix operator in the state equation of a linear-Gaussian state-space model. Lasso regularisation is included in the M-step, which is solved using a proximal splitting Douglas-Rachford algorithm. Experiments in toy examples and challenging climate problems illustrate the benefits of the proposed model and inference technique over standard Granger causality methods.
Fractional Denoising for 3D Molecular Pre-training
Authors: Shikun Feng, Yuyan Ni, Yanyan Lan, Zhi-Ming Ma, Wei-Ying Ma
Date: 2023-07-20 00:00:00
Tasks: Denoising, Drug Discovery, molecular representation

Coordinate denoising is a promising 3D molecular pre-training method, which has achieved remarkable performance in various downstream drug discovery tasks. Theoretically, the objective is equivalent to learning the force field, which is revealed helpful for downstream tasks. Nevertheless, there are two challenges for coordinate denoising to learn an effective force field, i.e. low coverage samples and isotropic force field. The underlying reason is that molecular distributions assumed by existing denoising methods fail to capture the anisotropic characteristic of molecules. To tackle these challenges, we propose a novel hybrid noise strategy, including noises on both dihedral angel and coordinate. However, denoising such hybrid noise in a traditional way is no more equivalent to learning the force field. Through theoretical deductions, we find that the problem is caused by the dependency of the input conformation for covariance. To this end, we propose to decouple the two types of noise and design a novel fractional denoising method (Frad), which only denoises the latter coordinate part. In this way, Frad enjoys both the merits of sampling more low-energy structures and the force field equivalence. Extensive experiments show the effectiveness of Frad in molecular representation, with a new state-of-the-art on 9 out of 12 tasks of QM9 and on 7 out of 8 targets of MD17.
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
Authors: Jiawei Shao, Zijian Li, Wenqiang Sun, Tailin Zhou, Yuchang Sun, Lumin Liu, Zehong Lin, Yuyi Mao, Jun Zhang
Date: 2023-07-20 00:00:00
Tasks: Federated Learning, Privacy Preserving, Survey

Federated learning (FL) has emerged as a secure paradigm for collaborative training among clients. Without data centralization, FL allows clients to share local information in a privacy-preserving manner. This approach has gained considerable attention, promoting numerous surveys to summarize the related works. However, the majority of these surveys concentrate on FL methods that share model parameters during the training process, while overlooking the possibility of sharing local information in other forms. In this paper, we present a systematic survey from a new perspective of what to share in FL, with an emphasis on the model utility, privacy leakage, and communication efficiency. First, we present a new taxonomy of FL methods in terms of three sharing methods, which respectively share model, synthetic data, and knowledge. Second, we analyze the vulnerability of different sharing methods to privacy attacks and review the defense mechanisms. Third, we conduct extensive experiments to compare the learning performance and communication overhead of various sharing methods in FL. Besides, we assess the potential privacy leakage through model inversion and membership inference attacks, while comparing the effectiveness of various defense approaches. Finally, we identify future research directions and conclude the survey.
Conditional expectation network for SHAP
Authors: Ronald Richman, Mario V. Wüthrich
Date: 2023-07-20 00:00:00
Tasks: regression

A very popular model-agnostic technique for explaining predictive models is the SHapley Additive exPlanation (SHAP). The two most popular versions of SHAP are a conditional expectation version and an unconditional expectation version (the latter is also known as interventional SHAP). Except for tree-based methods, usually the unconditional version is used (for computational reasons). We provide a (surrogate) neural network approach which allows us to efficiently calculate the conditional version for both neural networks and other regression models, and which properly considers the dependence structure in the feature components. This proposal is also useful to provide drop1 and anova analyses in complex regression models which are similar to their generalized linear model (GLM) counterparts, and we provide a partial dependence plot (PDP) counterpart that considers the right dependence structure in the feature components.
Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Services
Authors: Manqing Dong, Zhanxiang Zhao, Yitong Geng, Wentao Li, Wei Wang, Huai Jiang
Date: 2023-07-20 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Anomaly Detection

Time series anomaly detection is crucial for industrial monitoring services that handle a large volume of data, aiming to ensure reliability and optimize system performance. Existing methods often require extensive labeled resources and manual parameter selection, highlighting the need for automation. This paper proposes a comprehensive framework for automatic parameter optimization in time series anomaly detection models. The framework introduces three optimization targets: prediction score, shape score, and sensitivity score, which can be easily adapted to different model backbones without prior knowledge or manual labeling efforts. The proposed framework has been successfully applied online for over six months, serving more than 50,000 time series every minute. It simplifies the user's experience by requiring only an expected sensitive value, offering a user-friendly interface, and achieving desired detection results. Extensive evaluations conducted on public datasets and comparison with other methods further confirm the effectiveness of the proposed framework.
Data-Driven Latency Probability Prediction for Wireless Networks: Focusing on Tail Probabilities
Authors: Samie Mostafavi, Gourav Prateek Sharma, James Gross
Date: 2023-07-20 00:00:00

With the emergence of new application areas, such as cyber-physical systems and human-in-the-loop applications, there is a need to guarantee a certain level of end-to-end network latency with extremely high reliability, e.g., 99.999%. While mechanisms specified under IEEE 802.1as time-sensitive networking (TSN) can be used to achieve these requirements for switched Ethernet networks, implementing TSN mechanisms in wireless networks is challenging due to their stochastic nature. To conform the wireless link to a reliability level of 99.999%, the behavior of extremely rare outliers in the latency probability distribution, or the tail of the distribution, must be analyzed and controlled. This work proposes predicting the tail of the latency distribution using state-of-the-art data-driven approaches, such as mixture density networks (MDN) and extreme value mixture models, to estimate the likelihood of rare latencies conditioned on the network parameters, which can be used to make more informed decisions in wireless transmission. Actual latency measurements of IEEE 802.11g (WiFi), commercial private and a software-defined 5G network are used to benchmark the proposed approaches and evaluate their sensitivities concerning the tail probabilities.
Fisher-Rao distance and pullback SPD cone distances between multivariate normal distributions
Authors: Frank Nielsen
Date: 2023-07-20 00:00:00
Tasks: Clustering

Data sets of multivariate normal distributions abound in many scientific areas like diffusion tensor imaging, structure tensor computer vision, radar signal processing, machine learning, just to name a few. In order to process those normal data sets for downstream tasks like filtering, classification or clustering, one needs to define proper notions of dissimilarities between normals and paths joining them. The Fisher-Rao distance defined as the Riemannian geodesic distance induced by the Fisher information metric is such a principled metric distance which however is not known in closed-form excepts for a few particular cases. In this work, we first report a fast and robust method to approximate arbitrarily finely the Fisher-Rao distance between multivariate normal distributions. Second, we introduce a class of distances based on diffeomorphic embeddings of the normal manifold into a submanifold of the higher-dimensional symmetric positive-definite cone corresponding to the manifold of centered normal distributions. We show that the projective Hilbert distance on the cone yields a metric on the embedded normal submanifold and we pullback that cone distance with its associated straight line Hilbert cone geodesics to obtain a distance and smooth paths between normal distributions. Compared to the Fisher-Rao distance approximation, the pullback Hilbert cone distance is computationally light since it requires to compute only the extreme minimal and maximal eigenvalues of matrices. Finally, we show how to use those distances in clustering tasks.
Generative Language Models on Nucleotide Sequences of Human Genes
Authors: Musa Nuri Ihtiyar, Arzucan Ozgur
Date: 2023-07-20 00:00:00
Tasks: Language Modelling

Language models, primarily transformer-based ones, obtained colossal success in NLP. To be more precise, studies like BERT in NLU and works such as GPT-3 for NLG are very crucial. DNA sequences are very close to natural language in terms of structure, so if the DNA-related bioinformatics domain is concerned, discriminative models, like DNABert, exist. Yet, the generative side of the coin is mainly unexplored to the best of our knowledge. Consequently, we focused on developing an autoregressive generative language model like GPT-3 for DNA sequences. Because working with whole DNA sequences is challenging without substantial computational resources, we decided to carry out our study on a smaller scale, focusing on nucleotide sequences of human genes, unique parts in DNA with specific functionalities, instead of the whole DNA. This decision did not change the problem structure a lot due to the fact that both DNA and genes can be seen as 1D sequences consisting of four different nucleotides without losing much information and making too much simplification. First of all, we systematically examined an almost entirely unexplored problem and observed that RNNs performed the best while simple techniques like N-grams were also promising. Another beneficial point was learning how to work with generative models on languages we do not understand, unlike natural language. How essential using real-life tasks beyond the classical metrics such as perplexity is observed. Furthermore, checking whether the data-hungry nature of these models can be changed through selecting a language with minimal vocabulary size, four owing to four different types of nucleotides, is examined. The reason for reviewing this was that choosing such a language might make the problem easier. However, what we observed in this study was it did not provide that much of a change in the amount of data needed.
Multi-Method Self-Training: Improving Code Generation With Text, And Vice Versa
Authors: Shriyash K. Upadhyay, Etan J. Ginsberg
Date: 2023-07-20 00:00:00
Tasks: Code Generation, Prompt Engineering

Large Language Models have many methods for solving the same problem. This introduces novel strengths (different methods may work well for different problems) and weaknesses (it may be difficult for users to know which method to use). In this paper, we introduce Multi-Method Self-Training (MMST), where one method is trained on the filtered outputs of another, allowing us to augment the strengths and ameliorate the weaknesses of each method. Using a 176B parameter model trained on both language and code, we show that MMST can 1) improve the less performant method (up to 30%) making the model easier to use, 2) improve the more performant method (up to 32.2%) making the model more performant, and 3) improve the performance of related but distinct tasks (up to 10.3%) by improving the ability of the model to generate rationales. We then conduct ablation analyses to explore why MMST works. We show that MMST generates more data than traditional self-training, but the improvement in performance is driven by the use of multiple methods. We also analyze prompt-engineering and anti-correlated performance between methods as means of making MMST more effective. We hope the evidence from our paper motivates machine learning researchers to explore ways in which advances in language models allow for new forms of training.
Ensemble Learning based Anomaly Detection for IoT Cybersecurity via Bayesian Hyperparameters Sensitivity Analysis
Authors: Tin Lai, Farnaz Farid, Abubakar Bello, Fariza Sabrina
Date: 2023-07-20 00:00:00
Tasks: Anomaly Detection, Ensemble Learning, Sensitivity

The Internet of Things (IoT) integrates more than billions of intelligent devices over the globe with the capability of communicating with other connected devices with little to no human intervention. IoT enables data aggregation and analysis on a large scale to improve life quality in many domains. In particular, data collected by IoT contain a tremendous amount of information for anomaly detection. The heterogeneous nature of IoT is both a challenge and an opportunity for cybersecurity. Traditional approaches in cybersecurity monitoring often require different kinds of data pre-processing and handling for various data types, which might be problematic for datasets that contain heterogeneous features. However, heterogeneous types of network devices can often capture a more diverse set of signals than a single type of device readings, which is particularly useful for anomaly detection. In this paper, we present a comprehensive study on using ensemble machine learning methods for enhancing IoT cybersecurity via anomaly detection. Rather than using one single machine learning model, ensemble learning combines the predictive power from multiple models, enhancing their predictive accuracy in heterogeneous datasets rather than using one single machine learning model. We propose a unified framework with ensemble learning that utilises Bayesian hyperparameter optimisation to adapt to a network environment that contains multiple IoT sensor readings. Experimentally, we illustrate their high predictive power when compared to traditional methods.
A Holistic Assessment of the Reliability of Machine Learning Systems
Authors: Anthony Corso, David Karamadian, Romeo Valentin, Mary Cooper, Mykel J. Kochenderfer
Date: 2023-07-20 00:00:00
Tasks: Adversarial Robustness, Out-of-Distribution Detection

As machine learning (ML) systems increasingly permeate high-stakes settings such as healthcare, transportation, military, and national security, concerns regarding their reliability have emerged. Despite notable progress, the performance of these systems can significantly diminish due to adversarial attacks or environmental changes, leading to overconfident predictions, failures to detect input faults, and an inability to generalize in unexpected scenarios. This paper proposes a holistic assessment methodology for the reliability of ML systems. Our framework evaluates five key properties: in-distribution accuracy, distribution-shift robustness, adversarial robustness, calibration, and out-of-distribution detection. A reliability score is also introduced and used to assess the overall system reliability. To provide insights into the performance of different algorithmic approaches, we identify and categorize state-of-the-art techniques, then evaluate a selection on real-world tasks using our proposed reliability metrics and reliability score. Our analysis of over 500 models reveals that designing for one metric does not necessarily constrain others but certain algorithmic techniques can improve reliability across multiple metrics simultaneously. This study contributes to a more comprehensive understanding of ML reliability and provides a roadmap for future research and development.
Intelligent model for offshore China sea fog forecasting
Authors: Yanfei Xiang, Qinghong Zhang, Mingqing Wang, Ruixue Xia, Yang Kong, Xiaomeng Huang
Date: 2023-07-20 00:00:00
Tasks: Ensemble Learning, model

Accurate and timely prediction of sea fog is very important for effectively managing maritime and coastal economic activities. Given the intricate nature and inherent variability of sea fog, traditional numerical and statistical forecasting methods are often proven inadequate. This study aims to develop an advanced sea fog forecasting method embedded in a numerical weather prediction model using the Yangtze River Estuary (YRE) coastal area as a case study. Prior to training our machine learning model, we employ a time-lagged correlation analysis technique to identify key predictors and decipher the underlying mechanisms driving sea fog occurrence. In addition, we implement ensemble learning and a focal loss function to address the issue of imbalanced data, thereby enhancing the predictive ability of our model. To verify the accuracy of our method, we evaluate its performance using a comprehensive dataset spanning one year, which encompasses both weather station observations and historical forecasts. Remarkably, our machine learning-based approach surpasses the predictive performance of two conventional methods, the weather research and forecasting nonhydrostatic mesoscale model (WRF-NMM) and the algorithm developed by the National Oceanic and Atmospheric Administration (NOAA) Forecast Systems Laboratory (FSL). Specifically, in regard to predicting sea fog with a visibility of less than or equal to 1 km with a lead time of 60 hours, our methodology achieves superior results by increasing the probability of detection (POD) while simultaneously reducing the false alarm ratio (FAR).
SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning
Authors: Ziyao Ren, Yan Kang, Lixin Fan, Linghua Yang, Yongxin Tong, Qiang Yang
Date: 2023-07-20 00:00:00
Tasks: Federated Learning, Privacy Preserving, Vertical Federated Learning

SecureBoost is a tree-boosting algorithm leveraging homomorphic encryption to protect data privacy in vertical federated learning setting. It is widely used in fields such as finance and healthcare due to its interpretability, effectiveness, and privacy-preserving capability. However, SecureBoost suffers from high computational complexity and risk of label leakage. To harness the full potential of SecureBoost, hyperparameters of SecureBoost should be carefully chosen to strike an optimal balance between utility, efficiency, and privacy. Existing methods either set hyperparameters empirically or heuristically, which are far from optimal. To fill this gap, we propose a Constrained Multi-Objective SecureBoost (CMOSB) algorithm to find Pareto optimal solutions that each solution is a set of hyperparameters achieving optimal tradeoff between utility loss, training cost, and privacy leakage. We design measurements of the three objectives. In particular, the privacy leakage is measured using our proposed instance clustering attack. Experimental results demonstrate that the CMOSB yields not only hyperparameters superior to the baseline but also optimal sets of hyperparameters that can support the flexible requirements of FL participants.
Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples
Date: 2023-07-20 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: backdoor defense

Backdoor attacks are serious security threats to machine learning models where an adversary can inject poisoned samples into the training set, causing a backdoored model which predicts poisoned samples with particular triggers to particular target classes, while behaving normally on benign samples. In this paper, we explore the task of purifying a backdoored model using a small clean dataset. By establishing the connection between backdoor risk and adversarial risk, we derive a novel upper bound for backdoor risk, which mainly captures the risk on the shared adversarial examples (SAEs) between the backdoored model and the purified model. This upper bound further suggests a novel bi-level optimization problem for mitigating backdoor using adversarial training techniques. To solve it, we propose Shared Adversarial Unlearning (SAU). Specifically, SAU first generates SAEs, and then, unlearns the generated SAEs such that they are either correctly classified by the purified model and/or differently classified by the two models, such that the backdoor effect in the backdoored model will be mitigated in the purified model. Experiments on various benchmark datasets and network architectures show that our proposed method achieves state-of-the-art performance for backdoor defense.