pwc-archive/papers-with-abstracts huggingface dataset:
Byzantine-resilient Federated Learning Employing Normalized Gradients on Non-IID Datasets
Authors: Shiyuan Zuo, Xingrun Yan, Rongfei Fan, Li Shen, Puning Zhao, Jie Xu, Han Hu
Date: 2024-08-18 00:00:00
Tasks: Federated Learning

In practical federated learning (FL) systems, the presence of malicious Byzantine attacks and data heterogeneity often introduces biases into the learning process. However, existing Byzantine-robust methods typically only achieve a compromise between adaptability to different loss function types (including both strongly convex and non-convex) and robustness to heterogeneous datasets, but with non-zero optimality gap. Moreover, this compromise often comes at the cost of high computational complexity for aggregation, which significantly slows down the training speed. To address this challenge, we propose a federated learning approach called Federated Normalized Gradients Algorithm (Fed-NGA). Fed-NGA simply normalizes the uploaded local gradients to be unit vectors before aggregation, achieving a time complexity of $\mathcal{O}(pM)$, where $p$ represents the dimension of model parameters and $M$ is the number of participating clients. This complexity scale achieves the best level among all the existing Byzantine-robust methods. Furthermore, through rigorous proof, we demonstrate that Fed-NGA transcends the trade-off between adaptability to loss function type and data heterogeneity and the limitation of non-zero optimality gap in existing literature. Specifically, Fed-NGA can adapt to both non-convex loss functions and non-IID datasets simultaneously, with zero optimality gap at a rate of $\mathcal{O} (1/T^{\frac{1}{2} - \delta})$, where T is the iteration number and $\delta \in (0,\frac{1}{2})$. In cases where the loss function is strongly convex, the zero optimality gap achieving rate can be improved to be linear. Experimental results provide evidence of the superiority of our proposed Fed-NGA on time complexity and convergence performance over baseline methods.
Efficient Budget Allocation for Large-Scale LLM-Enabled Virtual Screening
Authors: Zaile Li, Weiwei Fan, L. Jeff Hong
Date: 2024-08-18 00:00:00
Tasks: Computational Efficiency

Screening tasks that aim to identify a small subset of top alternatives from a large pool are common in business decision-making processes. These tasks often require substantial human effort to evaluate each alternative's performance, making them time-consuming and costly. Motivated by recent advances in large language models (LLMs), particularly their ability to generate outputs that align well with human evaluations, we consider an LLM-as-human-evaluator approach for conducting screening virtually, thereby reducing the cost burden. To achieve scalability and cost-effectiveness in virtual screening, we identify that the stochastic nature of LLM outputs and their cost structure necessitate efficient budget allocation across all alternatives. To address this, we propose using a top-$m$ greedy evaluation mechanism, a simple yet effective approach that keeps evaluating the current top-$m$ alternatives, and design the explore-first top-$m$ greedy (EFG-$m$) algorithm. We prove that EFG-$m$ is both sample-optimal and consistent in large-scale virtual screening. Surprisingly, we also uncover a bonus ranking effect, where the algorithm naturally induces an indifference-based ranking within the selected subset. To further enhance practicality, we design a suite of algorithm variants to improve screening performance and computational efficiency. Numerical experiments validate our results and demonstrate the effectiveness of our algorithms. Lastly, we conduct a case study on LLM-based virtual screening. The study shows that while LLMs alone may not provide meaningful screening and ranking results when directly queried, integrating them with our sample-optimal algorithms unlocks their potential for cost-effective, large-scale virtual screening.
Deep Limit Model-free Prediction in Regression
Authors: Kejin Wu, Dimitris N. Politis
Date: 2024-08-18 00:00:00
Tasks: model, Prediction, regression

In this paper, we provide a novel Model-free approach based on Deep Neural Network (DNN) to accomplish point prediction and prediction interval under a general regression setting. Usually, people rely on parametric or non-parametric models to bridge dependent and independent variables (Y and X). However, this classical method relies heavily on the correct model specification. Even for the non-parametric approach, some additive form is often assumed. A newly proposed Model-free prediction principle sheds light on a prediction procedure without any model assumption. Previous work regarding this principle has shown better performance than other standard alternatives. Recently, DNN, one of the machine learning methods, has received increasing attention due to its great performance in practice. Guided by the Model-free prediction idea, we attempt to apply a fully connected forward DNN to map X and some appropriate reference random variable Z to Y. The targeted DNN is trained by minimizing a specially designed loss function so that the randomness of Y conditional on X is outsourced to Z through the trained DNN. Our method is more stable and accurate compared to other DNN-based counterparts, especially for optimal point predictions. With a specific prediction procedure, our prediction interval can capture the estimation variability so that it can render a better coverage rate for finite sample cases. The superior performance of our method is verified by simulation and empirical studies.
Fine-gained air quality inference based on low-quality sensing data using self-supervised learning
Authors: Meng Xu, Ke Han, Weijian Hu, Wen Ji
Date: 2024-08-18 00:00:00
Tasks: Air Quality Inference, Self-Supervised Learning

Fine-grained air quality (AQ) mapping is made possible by the proliferation of cheap AQ micro-stations (MSs). However, their measurements are often inaccurate and sensitive to local disturbances, in contrast to standardized stations (SSs) that provide accurate readings but fall short in number. To simultaneously address the issues of low data quality (MSs) and high label sparsity (SSs), a multi-task spatio-temporal network (MTSTN) is proposed, which employs self-supervised learning to utilize massive unlabeled data, aided by seasonal and trend decomposition of MS data offering reliable information as features. The MTSTN is applied to infer NO$_2$, O$_3$ and PM$_{2.5}$ concentrations in a 250 km$^2$ area in Chengdu, China, at a resolution of 500m$\times$500m$\times$1hr. Data from 55 SSs and 323 MSs were used, along with meteorological, traffic, geographic and timestamp data as features. The MTSTN excels in accuracy compared to several benchmarks, and its performance is greatly enhanced by utilizing low-quality MS data. A series of ablation and pressure tests demonstrate the results' robustness and interpretability, showcasing the MTSTN's practical value for accurate and affordable AQ inference.
Enhancing Quantum Memory Lifetime with Measurement-Free Local Error Correction and Reinforcement Learning
Authors: Mincheol Park, Nishad Maskara, Marcin Kalinowski, Mikhail D. Lukin
Date: 2024-08-18 00:00:00

Reliable quantum computation requires systematic identification and correction of errors that occur and accumulate in quantum hardware. To diagnose and correct such errors, standard quantum error-correcting protocols utilize $\textit{global}$ error information across the system obtained by mid-circuit readout of ancillary qubits. We investigate circuit-level error-correcting protocols that are measurement-free and based on $\textit{local}$ error information. Such a local error correction (LEC) circuit consists of faulty multi-qubit gates to perform both syndrome extraction and ancilla-controlled error removal. We develop and implement a reinforcement learning framework that takes a fixed set of faulty gates as inputs and outputs an optimized LEC circuit. To evaluate this approach, we quantitatively characterize an extension of logical qubit lifetime by a noisy LEC circuit. For the 2D classical Ising model and 4D toric code, our optimized LEC circuit performs better at extending a memory lifetime compared to a conventional LEC circuit based on Toom's rule in a sub-threshold gate error regime. We further show that such circuits can be used to reduce the rate of mid-circuit readouts to preserve a 2D toric code memory. Finally, we discuss the application of the LEC protocol on dissipative preparation of quantum states with topological phases.
Directed Exploration in Reinforcement Learning from Linear Temporal Logic
Authors: Marco Bagatella, Andreas Krause, Georg Martius
Date: 2024-08-18 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Linear temporal logic (LTL) is a powerful language for task specification in reinforcement learning, as it allows describing objectives beyond the expressivity of conventional discounted return formulations. Nonetheless, recent works have shown that LTL formulas can be translated into a variable rewarding and discounting scheme, whose optimization produces a policy maximizing a lower bound on the probability of formula satisfaction. However, the synthesized reward signal remains fundamentally sparse, making exploration challenging. We aim to overcome this limitation, which can prevent current algorithms from scaling beyond low-dimensional, short-horizon problems. We show how better exploration can be achieved by further leveraging the LTL specification and casting its corresponding Limit Deterministic B\"uchi Automaton (LDBA) as a Markov reward process, thus enabling a form of high-level value estimation. By taking a Bayesian perspective over LDBA dynamics and proposing a suitable prior distribution, we show that the values estimated through this procedure can be treated as a shaping potential and mapped to informative intrinsic rewards. Empirically, we demonstrate applications of our method from tabular settings to high-dimensional continuous systems, which have so far represented a significant challenge for LTL-based reinforcement learning algorithms.
Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training
Authors: Huitong Jin, Yipeng Zhou, Laizhong Cui, Quan Z. Sheng
Date: 2024-08-18 00:00:00
Tasks: Federated Learning

Pre-training exploits public datasets to pre-train an advanced machine learning model, so that the model can be easily tuned to adapt to various downstream tasks. Pre-training has been extensively explored to mitigate computation and communication resource consumption. Inspired by these advantages, we are the first to explore how model pre-training can mitigate noise detriment in differentially private federated learning (DPFL). DPFL is upgraded from federated learning (FL), the de-facto standard for privacy preservation when training the model across multiple clients owning private data. DPFL introduces differentially private (DP) noises to obfuscate model gradients exposed in FL, which however can considerably impair model accuracy. In our work, we compare head fine-tuning (HT) and full fine-tuning (FT), which are based on pre-training, with scratch training (ST) in DPFL through a comprehensive empirical study. Our experiments tune pre-trained models (obtained by pre-training on ImageNet-1K) with CIFAR-10, CHMNIST and Fashion-MNIST (FMNIST) datasets, respectively. The results demonstrate that HT and FT can significantly mitigate noise influence by diminishing gradient exposure times. In particular, HT outperforms FT when the privacy budget is tight or the model size is large. Visualization and explanation study further substantiates our findings. Our pioneering study introduces a new perspective on enhancing DPFL and expanding its practical applications.
Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches
Authors: Tanya Liyaqat, Tanvir Ahmad, Chandni Saxena
Date: 2024-08-18 00:00:00
Tasks: Drug Discovery, Molecular Property Prediction, Property Prediction, Representation Learning

Molecular Property Prediction (MPP) plays a pivotal role across diverse domains, spanning drug discovery, material science, and environmental chemistry. Fueled by the exponential growth of chemical data and the evolution of artificial intelligence, recent years have witnessed remarkable strides in MPP. However, the multifaceted nature of molecular data, such as molecular structures, SMILES notation, and molecular images, continues to pose a fundamental challenge in its effective representation. To address this, representation learning techniques are instrumental as they acquire informative and interpretable representations of molecular data. This article explores recent AI/-based approaches in MPP, focusing on both single and multiple modality representation techniques. It provides an overview of various molecule representations and encoding schemes, categorizes MPP methods by their use of modalities, and outlines datasets and tools available for feature generation. The article also analyzes the performance of recent methods and suggests future research directions to advance the field of MPP.
Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling
Authors: Harry Jake Cunningham, Giorgio Giannone, Mingtian Zhang, Marc Peter Deisenroth
Date: 2024-08-18 00:00:00

Global convolutions have shown increasing promise as powerful general-purpose sequence models. However, training long convolutions is challenging, and kernel parameterizations must be able to learn long-range dependencies without overfitting. This work introduces reparameterized multi-resolution convolutions ($\texttt{MRConv}$), a novel approach to parameterizing global convolutional kernels for long-sequence modelling. By leveraging multi-resolution convolutions, incorporating structural reparameterization and introducing learnable kernel decay, $\texttt{MRConv}$ learns expressive long-range kernels that perform well across various data modalities. Our experiments demonstrate state-of-the-art performance on the Long Range Arena, Sequential CIFAR, and Speech Commands tasks among convolution models and linear-time transformers. Moreover, we report improved performance on ImageNet classification by replacing 2D convolutions with 1D $\texttt{MRConv}$ layers.
Parameterized Physics-informed Neural Networks for Parameterized PDEs
Authors: Woojin Cho, Minju Jo, Haksoo Lim, Kookjin Lee, Dongeun Lee, Sanghyun Hong, Noseong Park
Date: 2024-08-18 00:00:00
Tasks: Uncertainty Quantification

Complex physical systems are often described by partial differential equations (PDEs) that depend on parameters such as the Reynolds number in fluid mechanics. In applications such as design optimization or uncertainty quantification, solutions of those PDEs need to be evaluated at numerous points in the parameter space. While physics-informed neural networks (PINNs) have emerged as a new strong competitor as a surrogate, their usage in this scenario remains underexplored due to the inherent need for repetitive and time-consuming training. In this paper, we address this problem by proposing a novel extension, parameterized physics-informed neural networks (P$^2$INNs). P$^2$INNs enable modeling the solutions of parameterized PDEs via explicitly encoding a latent representation of PDE parameters. With the extensive empirical evaluation, we demonstrate that P$^2$INNs outperform the baselines both in accuracy and parameter efficiency on benchmark 1D and 2D parameterized PDEs and are also effective in overcoming the known "failure modes".
Reefknot: A Comprehensive Benchmark for Relation Hallucination Evaluation, Analysis and Mitigation in Multimodal Large Language Models
Authors: Kening Zheng, Junkai Chen, Yibo Yan, Xin Zou, Xuming Hu
Date: 2024-08-18 00:00:00
Tasks: Attribute, Hallucination, Hallucination Evaluation, Relation

Hallucination issues continue to affect multimodal large language models (MLLMs), with existing research mainly addressing object-level or attribute-level hallucinations, neglecting the more complex relation hallucinations that require advanced reasoning. Current benchmarks for relation hallucinations lack detailed evaluation and effective mitigation, and their datasets often suffer from biases due to systematic annotation processes. To address these challenges, we introduce Reefknot, a comprehensive benchmark targeting relation hallucinations, comprising over 20,000 real-world samples. We provide a systematic definition of relation hallucinations, integrating perceptive and cognitive perspectives, and construct a relation-based corpus using the Visual Genome scene graph dataset. Our comparative evaluation reveals significant limitations in current MLLMs' ability to handle relation hallucinations. Additionally, we propose a novel confidence-based mitigation strategy, which reduces the hallucination rate by an average of 9.75% across three datasets, including Reefknot. Our work offers valuable insights for achieving trustworthy multimodal intelligence.
Enhancing Startup Success Predictions in Venture Capital: A GraphRAG Augmented Multivariate Time Series Method
Authors: Zitian Gao, Yihao Xiao
Date: 2024-08-18 00:00:00
Tasks: Time Series, Time Series Analysis

In the Venture Capital (VC) industry, predicting the success of startups is challenging due to limited financial data and the need for subjective revenue forecasts. Previous methods based on time series analysis often fall short as they fail to incorporate crucial inter-company relationships such as competition and collaboration. To fill the gap, this paper aims to introduce a novel approach using GraphRAG augmented time series model. With GraphRAG, time series predictive methods are enhanced by integrating these vital relationships into the analysis framework, allowing for a more dynamic understanding of the startup ecosystem in venture capital. Our experimental results demonstrate that our model significantly outperforms previous models in startup success predictions.
Clustering and Alignment: Understanding the Training Dynamics in Modular Addition
Authors: Tiberiu Musat
Date: 2024-08-18 00:00:00
Tasks: Clustering

Recent studies have revealed that neural networks learn interpretable algorithms for many simple problems. However, little is known about how these algorithms emerge during training. In this article, I study the training dynamics of a small neural network with 2-dimensional embeddings on the problem of modular addition. I observe that embedding vectors tend to organize into two types of structures: grids and circles. I study these structures and explain their emergence as a result of two simple tendencies exhibited by pairs of embeddings: clustering and alignment. I propose explicit formulae for these tendencies as interaction forces between different pairs of embeddings. To show that my formulae can fully account for the emergence of these structures, I construct an equivalent particle simulation where I show that identical structures emerge. I discuss the role of weight decay in my setup and reveal a new mechanism that links regularization and training dynamics. To support my findings, I also release an interactive demo available at https://modular-addition.vercel.app/.
GRLinQ: An Intelligent Spectrum Sharing Mechanism for Device-to-Device Communications with Graph Reinforcement Learning
Authors: Zhiwei Shan, Xinping Yi, Le Liang, Chung-Shou Liao, Shi Jin
Date: 2024-08-18 00:00:00
Tasks: Combinatorial Optimization, Scheduling

Device-to-device (D2D) spectrum sharing in wireless communications is a challenging non-convex combinatorial optimization problem, involving entangled link scheduling and power control in a large-scale network. The state-of-the-art methods, either from a model-based or a data-driven perspective, exhibit certain limitations such as the critical need for channel state information (CSI) and/or a large number of (solved) instances (e.g., network layouts) as training samples. To advance this line of research, we propose a novel hybrid model/datadriven spectrum sharing mechanism with graph reinforcement learning for link scheduling (GRLinQ), injecting information theoretical insights into machine learning models, in such a way that link scheduling and power control can be solved in an intelligent yet explainable manner. Through an extensive set of experiments, GRLinQ demonstrates superior performance to the existing model-based and data-driven link scheduling and/or power control methods, with a relaxed requirement for CSI, a substantially reduced number of unsolved instances as training samples, a possible distributed deployment, reduced online/offline computational complexity, and more remarkably excellent scalability and generalizability over different network scenarios and system configurations.
Mutual Information Multinomial Estimation
Authors: Yanzhi Chen, Zijing Ou, Adrian Weller, Yingzhen Li
Date: 2024-08-18 00:00:00

Estimating mutual information (MI) is a fundamental yet challenging task in data science and machine learning. This work proposes a new estimator for mutual information. Our main discovery is that a preliminary estimate of the data distribution can dramatically help estimate. This preliminary estimate serves as a bridge between the joint and the marginal distribution, and by comparing with this bridge distribution we can easily obtain the true difference between the joint distributions and the marginal distributions. Experiments on diverse tasks including non-Gaussian synthetic problems with known ground-truth and real-world applications demonstrate the advantages of our method.
Behavioral Learning of Dish Rinsing and Scrubbing based on Interruptive Direct Teaching Considering Assistance Rate
Authors: Shumpei Wakabayashi, Kento Kawaharazuka, Kei Okada, Masayuki Inaba
Date: 2024-08-18 00:00:00

Robots are expected to manipulate objects in a safe and dexterous way. For example, washing dishes is a dexterous operation that involves scrubbing the dishes with a sponge and rinsing them with water. It is necessary to learn it safely without splashing water and without dropping the dishes. In this study, we propose a safe and dexterous manipulation system. The robot learns a dynamics model of the object by estimating the state of the object and the robot itself, the control input, and the amount of human assistance required (assistance rate) after the human corrects the initial trajectory of the robot's hands by interruptive direct teaching. By backpropagating the error between the estimated and the reference value using the acquired dynamics model, the robot can generate a control input that approaches the reference value, for example, so that human assistance is not required and the dish does not move excessively. This allows for adaptive rinsing and scrubbing of dishes with unknown shapes and properties. As a result, it is possible to generate safe actions that require less human assistance.