pwc-archive/papers-with-abstracts huggingface dataset:
Distributional Black-Box Model Inversion Attack with Multi-Agent Reinforcement Learning
Authors: Huan Bao, Kaimin Wei, Yongdong Wu, Jin Qian, Robert H. Deng
Date: 2024-04-22 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning

A Model Inversion (MI) attack based on Generative Adversarial Networks (GAN) aims to recover the private training data from complex deep learning models by searching codes in the latent space. However, they merely search a deterministic latent space such that the found latent code is usually suboptimal. In addition, the existing distributional MI schemes assume that an attacker can access the structures and parameters of the target model, which is not always viable in practice. To overcome the above shortcomings, this paper proposes a novel Distributional Black-Box Model Inversion (DBB-MI) attack by constructing the probabilistic latent space for searching the target privacy data. Specifically, DBB-MI does not need the target model parameters or specialized GAN training. Instead, it finds the latent probability distribution by combining the output of the target model with multi-agent reinforcement learning techniques. Then, it randomly chooses latent codes from the latent probability distribution for recovering the private data. As the latent probability distribution closely aligns with the target privacy data in latent space, the recovered data will leak the privacy of training samples of the target model significantly. Abundant experiments conducted on diverse datasets and networks show that the present DBB-MI has better performance than state-of-the-art in attack accuracy, K-nearest neighbor feature distance, and Peak Signal-to-Noise Ratio.
ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction
Authors: Yi Rong, Yingchi Mao, Yinqiu Liu, Ling Chen, Xiaoming He, Dusit Niyato
Date: 2024-04-22 00:00:00
Tasks: Graph Generation

Traffic speed prediction is significant for intelligent navigation and congestion alleviation. However, making accurate predictions is challenging due to three factors: 1) traffic diffusion, i.e., the spatial and temporal causality existing between the traffic conditions of multiple neighboring roads, 2) the poor interpretability of traffic data with complicated spatio-temporal correlations, and 3) the latent pattern of traffic speed fluctuations over time, such as morning and evening rush. Jointly considering these factors, in this paper, we present a novel architecture for traffic speed prediction, called Interpretable Causal Spatio-Temporal Diffusion Network (ICST-DNET). Specifically, ICST-DENT consists of three parts, namely the Spatio-Temporal Causality Learning (STCL), Causal Graph Generation (CGG), and Speed Fluctuation Pattern Recognition (SFPR) modules. First, to model the traffic diffusion within road networks, an STCL module is proposed to capture both the temporal causality on each individual road and the spatial causality in each road pair. The CGG module is then developed based on STCL to enhance the interpretability of the traffic diffusion procedure from the temporal and spatial perspectives. Specifically, a time causality matrix is generated to explain the temporal causality between each road's historical and future traffic conditions. For spatial causality, we utilize causal graphs to visualize the diffusion process in road pairs. Finally, to adapt to traffic speed fluctuations in different scenarios, we design a personalized SFPR module to select the historical timesteps with strong influences for learning the pattern of traffic speed fluctuations. Extensive experimental results prove that ICST-DNET can outperform all existing baselines, as evidenced by the higher prediction accuracy, ability to explain causality, and adaptability to different scenarios.
Data-Driven Performance Guarantees for Classical and Learned Optimizers
Authors: Rajiv Sambharya, Bartolomeo Stellato
Date: 2024-04-22 00:00:00
Tasks: Learning Theory, Meta-Learning

We introduce a data-driven approach to analyze the performance of continuous optimization algorithms using generalization guarantees from statistical learning theory. We study classical and learned optimizers to solve families of parametric optimization problems. We build generalization guarantees for classical optimizers, using a sample convergence bound, and for learned optimizers, using the Probably Approximately Correct (PAC)-Bayes framework. To train learned optimizers, we use a gradient-based algorithm to directly minimize the PAC-Bayes upper bound. Numerical experiments in signal processing, control, and meta-learning showcase the ability of our framework to provide strong generalization guarantees for both classical and learned optimizers given a fixed budget of iterations. For classical optimizers, our bounds are much tighter than those that worst-case guarantees provide. For learned optimizers, our bounds outperform the empirical outcomes observed in their non-learned counterparts.
Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference
Authors: Yujin Han, Difan Zou
Date: 2024-04-22 00:00:00
Tasks: Attribute

Standard empirical risk minimization (ERM) models may prioritize learning spurious correlations between spurious features and true labels, leading to poor accuracy on groups where these correlations do not hold. Mitigating this issue often requires expensive spurious attribute (group) labels or relies on trained ERM models to infer group labels when group information is unavailable. However, the significant performance gap in worst-group accuracy between using pseudo group labels and using oracle group labels inspires us to consider further improving group robustness through preciser group inference. Therefore, we propose GIC, a novel method that accurately infers group labels, resulting in improved worst-group performance. GIC trains a spurious attribute classifier based on two key properties of spurious correlations: (1) high correlation between spurious attributes and true labels, and (2) variability in this correlation between datasets with different group distributions. Empirical studies on multiple datasets demonstrate the effectiveness of GIC in inferring group labels, and combining GIC with various downstream invariant learning methods improves worst-group accuracy, showcasing its powerful flexibility. Additionally, through analyzing the misclassifications in GIC, we identify an interesting phenomenon called semantic consistency, which may contribute to better decoupling the association between spurious attributes and labels, thereby mitigating spurious correlation. The code for GIC is available at https://github.com/yujinhanml/GIC.
General Item Representation Learning for Cold-start Content Recommendations
Authors: Jooeun Kim, Jinri Kim, Kwangeun Yeo, Eungi Kim, Kyoung-Woon On, Jonghwan Mun, Joonseok Lee
Date: 2024-04-22 00:00:00
Tasks: News Recommendation, Recommendation Systems, Representation Learning

Cold-start item recommendation is a long-standing challenge in recommendation systems. A common remedy is to use a content-based approach, but rich information from raw contents in various forms has not been fully utilized. In this paper, we propose a domain/data-agnostic item representation learning framework for cold-start recommendations, naturally equipped with multimodal alignment among various features by adopting a Transformer-based architecture. Our proposed model is end-to-end trainable completely free from classification labels, not just costly to collect but suboptimal for recommendation-purpose representation learning. From extensive experiments on real-world movie and news recommendation benchmarks, we verify that our approach better preserves fine-grained user taste than state-of-the-art baselines, universally applicable to multiple domains at large scale.
Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks
Authors: Bing Luo, Wenli Xiao, Shiqiang Wang, Jianwei Huang, Leandros Tassiulas
Date: 2024-04-22 00:00:00
Tasks: Federated Learning

Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communication bandwidth is limited. Recent works on the convergence analysis of FL have focused on unbiased client sampling, e.g., sampling uniformly at random, which suffers from slow wall-clock time for convergence due to high degrees of system heterogeneity and statistical heterogeneity. This paper aims to design an adaptive client sampling algorithm for FL over wireless networks that tackles both system and statistical heterogeneity to minimize the wall-clock convergence time. We obtain a new tractable convergence bound for FL algorithms with arbitrary client sampling probability. Based on the bound, we analytically establish the relationship between the total learning time and sampling probability with an adaptive bandwidth allocation scheme, which results in a non-convex optimization problem. We design an efficient algorithm for learning the unknown parameters in the convergence bound and develop a low-complexity algorithm to approximately solve the non-convex problem. Our solution reveals the impact of system and statistical heterogeneity parameters on the optimal client sampling design. Moreover, our solution shows that as the number of sampled clients increases, the total convergence time first decreases and then increases because a larger sampling number reduces the number of rounds for convergence but results in a longer expected time per-round due to limited wireless bandwidth. Experimental results from both hardware prototype and simulation demonstrate that our proposed sampling scheme significantly reduces the convergence time compared to several baseline sampling schemes.
Soar: Design and Deployment of A Smart Roadside Infrastructure System for Autonomous Driving
Authors: Shuyao Shi, Neiwen Ling, Zhehao Jiang, Xuan Huang, Yuze He, Xiaoguang Zhao, Bufang Yang, Chen Bian, Jingfei Xia, Zhenyu Yan, Raymond Yeung, Guoliang Xing
Date: 2024-04-21 00:00:00
Tasks: Autonomous Driving

Recently,smart roadside infrastructure (SRI) has demonstrated the potential of achieving fully autonomous driving systems. To explore the potential of infrastructure-assisted autonomous driving, this paper presents the design and deployment of Soar, the first end-to-end SRI system specifically designed to support autonomous driving systems. Soar consists of both software and hardware components carefully designed to overcome various system and physical challenges. Soar can leverage the existing operational infrastructure like street lampposts for a lower barrier of adoption. Soar adopts a new communication architecture that comprises a bi-directional multi-hop I2I network and a downlink I2V broadcast service, which are designed based on off-the-shelf 802.11ac interfaces in an integrated manner. Soar also features a hierarchical DL task management framework to achieve desirable load balancing among nodes and enable them to collaborate efficiently to run multiple data-intensive autonomous driving applications. We deployed a total of 18 Soar nodes on existing lampposts on campus, which have been operational for over two years. Our real-world evaluation shows that Soar can support a diverse set of autonomous driving applications and achieve desirable real-time performance and high communication reliability. Our findings and experiences in this work offer key insights into the development and deployment of next-generation smart roadside infrastructure and autonomous driving systems.
How to Inverting the Leverage Score Distribution?
Authors: Zhihang Li, Zhao Song, Weixin Wang, Junze Yin, Zheng Yu
Date: 2024-04-21 00:00:00

Leverage score is a fundamental problem in machine learning and theoretical computer science. It has extensive applications in regression analysis, randomized algorithms, and neural network inversion. Despite leverage scores are widely used as a tool, in this paper, we study a novel problem, namely the inverting leverage score problem. We analyze to invert the leverage score distributions back to recover model parameters. Specifically, given a leverage score $\sigma \in \mathbb{R}^n$, the matrix $A \in \mathbb{R}^{n \times d}$, and the vector $b \in \mathbb{R}^n$, we analyze the non-convex optimization problem of finding $x \in \mathbb{R}^d$ to minimize $\| \mathrm{diag}( \sigma ) - I_n \circ (A(x) (A(x)^\top A(x) )^{-1} A(x)^\top ) \|_F$, where $A(x):= S(x)^{-1} A \in \mathbb{R}^{n \times d} $, $S(x) := \mathrm{diag}(s(x)) \in \mathbb{R}^{n \times n}$ and $s(x) : = Ax - b \in \mathbb{R}^n$. Our theoretical studies include computing the gradient and Hessian, demonstrating that the Hessian matrix is positive definite and Lipschitz, and constructing first-order and second-order algorithms to solve this regression problem. Our work combines iterative shrinking and the induction hypothesis to ensure global convergence rates for the Newton method, as well as the properties of Lipschitz and strong convexity to guarantee the performance of gradient descent. This important study on inverting statistical leverage opens up numerous new applications in interpretation, data recovery, and security.
Automated Text Mining of Experimental Methodologies from Biomedical Literature
Authors: Ziqing Guo
Date: 2024-04-21 00:00:00
Tasks: Articles, Classification, Language Modeling, Language Modelling

Biomedical literature is a rapidly expanding field of science and technology. Classification of biomedical texts is an essential part of biomedicine research, especially in the field of biology. This work proposes the fine-tuned DistilBERT, a methodology-specific, pre-trained generative classification language model for mining biomedicine texts. The model has proven its effectiveness in linguistic understanding capabilities and has reduced the size of BERT models by 40\% but by 60\% faster. The main objective of this project is to improve the model and assess the performance of the model compared to the non-fine-tuned model. We used DistilBert as a support model and pre-trained on a corpus of 32,000 abstracts and complete text articles; our results were impressive and surpassed those of traditional literature classification methods by using RNN or LSTM. Our aim is to integrate this highly specialised and specific model into different research industries.
Training-Conditional Coverage Bounds for Uniformly Stable Learning Algorithms
Authors: Mehrdad Pournaderi, Yu Xiang
Date: 2024-04-21 00:00:00
Tasks: Conformal Prediction, Prediction

The training-conditional coverage performance of the conformal prediction is known to be empirically sound. Recently, there have been efforts to support this observation with theoretical guarantees. The training-conditional coverage bounds for jackknife+ and full-conformal prediction regions have been established via the notion of $(m,n)$-stability by Liang and Barber~[2023]. Although this notion is weaker than uniform stability, it is not clear how to evaluate it for practical models. In this paper, we study the training-conditional coverage bounds of full-conformal, jackknife+, and CV+ prediction regions from a uniform stability perspective which is known to hold for empirical risk minimization over reproducing kernel Hilbert spaces with convex regularization. We derive coverage bounds for finite-dimensional models by a concentration argument for the (estimated) predictor function, and compare the bounds with existing ones under ridge regression.
TF2AIF: Facilitating development and deployment of accelerated AI models on the cloud-edge continuum
Authors: Aimilios Leftheriotis, Achilleas Tzenetopoulos, George Lentaris, Dimitrios Soudris, Georgios Theodoridis
Date: 2024-04-21 00:00:00
Tasks: Management

The B5G/6G evolution relies on connect-compute technologies and highly heterogeneous clusters with HW accelerators, which require specialized coding to be efficiently utilized. The current paper proposes a custom tool for generating multiple SW versions of a certain AI function input in high-level language, e.g., Python TensorFlow, while targeting multiple diverse HW+SW platforms. TF2AIF builds upon disparate tool-flows to create a plethora of relative containers and enable the system orchestrator to deploy the requested function on any peculiar node in the cloud-edge continuum, i.e., to leverage the performance/energy benefits of the underlying HW upon any circumstances. TF2AIF fills an identified gap in today's ecosystem and facilitates research on resource management or automated operations, by demanding minimal time or expertise from users.
Learning Galaxy Intrinsic Alignment Correlations
Authors: Sneh Pandya, Yuanyuan Yang, Nicholas Van Alfen, Jonathan Blazek, Robin Walters
Date: 2024-04-21 00:00:00
Tasks: Position

The intrinsic alignments (IA) of galaxies, regarded as a contaminant in weak lensing analyses, represents the correlation of galaxy shapes due to gravitational tidal interactions and galaxy formation processes. As such, understanding IA is paramount for accurate cosmological inferences from weak lensing surveys; however, one limitation to our understanding and mitigation of IA is expensive simulation-based modeling. In this work, we present a deep learning approach to emulate galaxy position-position ($\xi$), position-orientation ($\omega$), and orientation-orientation ($\eta$) correlation function measurements and uncertainties from halo occupation distribution-based mock galaxy catalogs. We find strong Pearson correlation values with the model across all three correlation functions and further predict aleatoric uncertainties through a mean-variance estimation training procedure. $\xi(r)$ predictions are generally accurate to $\leq10\%$. Our model also successfully captures the underlying signal of the noisier correlations $\omega(r)$ and $\eta(r)$, although with a lower average accuracy. We find that the model performance is inhibited by the stochasticity of the data, and will benefit from correlations averaged over multiple data realizations. Our code will be made open source upon journal publication.
Resampling-free Particle Filters in High-dimensions
Authors: Akhilan Boopathy, Aneesh Muppidi, Peggy Yang, Abhiram Iyer, William Yue, Ila Fiete
Date: 2024-04-21 00:00:00
Tasks: 6D Pose Estimation, Pose Estimation, State Estimation

State estimation is crucial for the performance and safety of numerous robotic applications. Among the suite of estimation techniques, particle filters have been identified as a powerful solution due to their non-parametric nature. Yet, in high-dimensional state spaces, these filters face challenges such as 'particle deprivation' which hinders accurate representation of the true posterior distribution. This paper introduces a novel resampling-free particle filter designed to mitigate particle deprivation by forgoing the traditional resampling step. This ensures a broader and more diverse particle set, especially vital in high-dimensional scenarios. Theoretically, our proposed filter is shown to offer a near-accurate representation of the desired posterior distribution in high-dimensional contexts. Empirically, the effectiveness of our approach is underscored through a high-dimensional synthetic state estimation task and a 6D pose estimation derived from videos. We posit that as robotic systems evolve with greater degrees of freedom, particle filters tailored for high-dimensional state spaces will be indispensable.
Detecting Compromised IoT Devices Using Autoencoders with Sequential Hypothesis Testing
Authors: Md Mainuddin, Zhenhai Duan, Yingfei Dong
Date: 2024-04-21 00:00:00
Tasks: Anomaly Detection

IoT devices fundamentally lack built-in security mechanisms to protect themselves from security attacks. Existing works on improving IoT security mostly focus on detecting anomalous behaviors of IoT devices. However, these existing anomaly detection schemes may trigger an overwhelmingly large number of false alerts, rendering them unusable in detecting compromised IoT devices. In this paper we develop an effective and efficient framework, named CUMAD, to detect compromised IoT devices. Instead of directly relying on individual anomalous events, CUMAD aims to accumulate sufficient evidence in detecting compromised IoT devices, by integrating an autoencoder-based anomaly detection subsystem with a sequential probability ratio test (SPRT)-based sequential hypothesis testing subsystem. CUMAD can effectively reduce the number of false alerts in detecting compromised IoT devices, and moreover, it can detect compromised IoT devices quickly. Our evaluation studies based on the public-domain N-BaIoT dataset show that CUMAD can on average reduce the false positive rate from about 3.57% using only the autoencoder-based anomaly detection scheme to about 0.5%; in addition, CUMAD can detect compromised IoT devices quickly, with less than 5 observations on average.
Reproducible data science over data lakes: replayable data pipelines with Bauplan and Nessie
Authors: Jacopo Tagliabue, Ciro Greco
Date: 2024-04-21 00:00:00
Tasks: Management

As the Lakehouse architecture becomes more widespread, ensuring the reproducibility of data workloads over data lakes emerges as a crucial concern for data engineers. However, achieving reproducibility remains challenging. The size of data pipelines contributes to slow testing and iterations, while the intertwining of business logic and data management complicates debugging and increases error susceptibility. In this paper, we highlight recent advancements made at Bauplan in addressing this challenge. We introduce a system designed to decouple compute from data management, by leveraging a cloud runtime alongside Nessie, an open-source catalog with Git semantics. Demonstrating the system's capabilities, we showcase its ability to offer time-travel and branching semantics on top of object storage, and offer full pipeline reproducibility with a few CLI commands.
Rate Analysis of Coupled Distributed Stochastic Approximation for Misspecified Optimization
Authors: Yaqun Yang, Jinlong Lei
Date: 2024-04-21 00:00:00
Tasks: Distributed Optimization

We consider an $n$ agents distributed optimization problem with imperfect information characterized in a parametric sense, where the unknown parameter can be solved by a distinct distributed parameter learning problem. Though each agent only has access to its local parameter learning and computational problem, they mean to collaboratively minimize the average of their local cost functions. To address the special optimization problem, we propose a coupled distributed stochastic approximation algorithm, in which every agent updates the current beliefs of its unknown parameter and decision variable by stochastic approximation method; and then averages the beliefs and decision variables of its neighbors over network in consensus protocol. Our interest lies in the convergence analysis of this algorithm. We quantitatively characterize the factors that affect the algorithm performance, and prove that the mean-squared error of the decision variable is bounded by $\mathcal{O}(\frac{1}{nk})+\mathcal{O}\left(\frac{1}{\sqrt{n}(1-\rho_w)}\right)\frac{1}{k^{1.5}}+\mathcal{O}\big(\frac{1}{(1-\rho_w)^2} \big)\frac{1}{k^2}$, where $k$ is the iteration count and $(1-\rho_w)$ is the spectral gap of the network weighted adjacency matrix. It reveals that the network connectivity characterized by $(1-\rho_w)$ only influences the high order of convergence rate, while the domain rate still acts the same as the centralized algorithm. In addition, we analyze that the transient iteration needed for reaching its dominant rate $\mathcal{O}(\frac{1}{nk})$ is $\mathcal{O}(\frac{n}{(1-\rho_w)^2})$. Numerical experiments are carried out to demonstrate the theoretical results by taking different CPUs as agents, which is more applicable to real-world distributed scenarios.