pwc-archive/papers-with-abstracts huggingface dataset:
On the Detection of Aircraft Single Engine Taxi using Deep Learning Models
Authors: Gabriel Jarry, Philippe Very, Ramon Dalmau, Daniel Delahaye, Arthur Houdant
Date: 2024-10-10 00:00:00

The aviation industry is vital for global transportation but faces increasing pressure to reduce its environmental footprint, particularly CO2 emissions from ground operations such as taxiing. Single Engine Taxiing (SET) has emerged as a promising technique to enhance fuel efficiency and sustainability. However, evaluating SET's benefits is hindered by the limited availability of SET-specific data, typically accessible only to aircraft operators. In this paper, we present a novel deep learning approach to detect SET operations using ground trajectory data. Our method involves using proprietary Quick Access Recorder (QAR) data of A320 flights to label ground movements as SET or conventional taxiing during taxi-in operations, while using only trajectory features equivalent to those available in open-source surveillance systems such as Automatic Dependent Surveillance-Broadcast (ADS-B) or ground radar. This demonstrates that SET can be inferred from ground movement patterns, paving the way for future work with non-proprietary data sources. Our results highlight the potential of deep learning to improve SET detection and support more comprehensive environmental impact assessments.
Towards Trustworthy Web Attack Detection: An Uncertainty-Aware Ensemble Deep Kernel Learning Model
Authors: Yonghang Zhou, Hongyi Zhu, Yidong Chai, Yuanchun Jiang, Yezheng Liu
Date: 2024-10-10 00:00:00
Tasks: Ensemble Learning, Feature Engineering

Web attacks are one of the major and most persistent forms of cyber threats, which bring huge costs and losses to web application-based businesses. Various detection methods, such as signature-based, machine learning-based, and deep learning-based, have been proposed to identify web attacks. However, these methods either (1) heavily rely on accurate and complete rule design and feature engineering, which may not adapt to fast-evolving attacks, or (2) fail to estimate model uncertainty, which is essential to the trustworthiness of the prediction made by the model. In this study, we proposed an Uncertainty-aware Ensemble Deep Kernel Learning (UEDKL) model to detect web attacks from HTTP request payload data with the model uncertainty captured from the perspective of both data distribution and model parameters. The proposed UEDKL utilizes a deep kernel learning model to distinguish normal HTTP requests from different types of web attacks with model uncertainty estimated from data distribution perspective. Multiple deep kernel learning models were trained as base learners to capture the model uncertainty from model parameters perspective. An attention-based ensemble learning approach was designed to effectively integrate base learners' predictions and model uncertainty. We also proposed a new metric named High Uncertainty Ratio-F Score Curve to evaluate model uncertainty estimation. Experiments on BDCI and SRBH datasets demonstrated that the proposed UEDKL framework yields significant improvement in both web attack detection performance and uncertainty estimation quality compared to benchmark models.
Understanding Adversarially Robust Generalization via Weight-Curvature Index
Authors: Yuelin Xu, Xiao Zhang
Date: 2024-10-10 00:00:00
Tasks: Adversarial Robustness, Generalization Bounds

Despite extensive research on adversarial examples, the underlying mechanisms of adversarially robust generalization, a critical yet challenging task for deep learning, remain largely unknown. In this work, we propose a novel perspective to decipher adversarially robust generalization through the lens of the Weight-Curvature Index (WCI). The proposed WCI quantifies the vulnerability of models to adversarial perturbations using the Frobenius norm of weight matrices and the trace of Hessian matrices. We prove generalization bounds based on PAC-Bayesian theory and second-order loss function approximations to elucidate the interplay between robust generalization gap, model parameters, and loss landscape curvature. Our theory and experiments show that WCI effectively captures the robust generalization performance of adversarially trained models. By offering a nuanced understanding of adversarial robustness based on the scale of model parameters and the curvature of the loss landscape, our work provides crucial insights for designing more resilient deep learning models, enhancing their reliability and security.
Rethinking the Principle of Gradient Smooth Methods in Model Explanation
Authors: Linjiang Zhou, Chao Ma, Zepeng Wang, Xiaochuan Shi
Date: 2024-10-10 00:00:00

Gradient Smoothing is an efficient approach to reducing noise in gradient-based model explanation method. SmoothGrad adds Gaussian noise to mitigate much of these noise. However, the crucial hyper-parameter in this method, the variance $\sigma$ of Gaussian noise, is set manually or with heuristic approach. However, it results in the smoothed gradients still containing a certain amount of noise. In this paper, we aim to interpret SmoothGrad as a corollary of convolution, thereby re-understanding the gradient noise and the role of $\sigma$ from the perspective of confidence level. Furthermore, we propose an adaptive gradient smoothing method, AdaptGrad, based on these insights. Through comprehensive experiments, both qualitative and quantitative results demonstrate that AdaptGrad could effectively reduce almost all the noise in vanilla gradients compared with baselines methods. AdaptGrad is simple and universal, making it applicable for enhancing gradient-based interpretability methods for better visualization.
A Generalization Result for Convergence in Learning-to-Optimize
Authors: Michael Sucker, Peter Ochs
Date: 2024-10-10 00:00:00

Learning-to-optimize leverages machine learning to accelerate optimization algorithms. While empirical results show tremendous improvements compared to classical optimization algorithms, theoretical guarantees are mostly lacking, such that the outcome cannot be reliably assured. Especially, convergence is hardly studied in learning-to-optimize, because conventional convergence guarantees in optimization are based on geometric arguments, which cannot be applied easily to learned algorithms. Thus, we develop a probabilistic framework that resembles classical optimization and allows for transferring geometric arguments into learning-to-optimize. Based on our new proof-strategy, our main theorem is a generalization result for parametric classes of potentially non-smooth, non-convex loss functions and establishes the convergence of learned optimization algorithms to critical points with high probability. This effectively generalizes the results of a worst-case analysis into a probabilistic framework, and frees the design of the learned algorithm from using safeguards.
Enhancing Zeroth-order Fine-tuning for Language Models with Low-rank Structures
Authors: Yiming Chen, Yuan Zhang, Liyuan Cao, Kun Yuan, Zaiwen Wen
Date: 2024-10-10 00:00:00
Tasks: parameter-efficient fine-tuning

Parameter-efficient fine-tuning (PEFT) significantly reduces memory costs when adapting large language models (LLMs) for downstream applications. However, traditional first-order (FO) fine-tuning algorithms incur substantial memory overhead due to the need to store activation values for back-propagation during gradient computation, particularly in long-context fine-tuning tasks. Zeroth-order (ZO) algorithms offer a promising alternative by approximating gradients using finite differences of function values, thus eliminating the need for activation storage. Nevertheless, existing ZO methods struggle to capture the low-rank gradient structure common in LLM fine-tuning, leading to suboptimal performance. This paper proposes a low-rank ZO gradient estimator and introduces a novel low-rank ZO algorithm (LOZO) that effectively captures this structure in LLMs. We provide convergence guarantees for LOZO by framing it as a subspace optimization method. Additionally, its low-rank nature enables LOZO to integrate with momentum techniques while incurring negligible extra memory costs. Extensive experiments across various model sizes and downstream tasks demonstrate that LOZO and its momentum-based variant outperform existing ZO methods and closely approach the performance of FO algorithms.
Meta-Learning from Learning Curves for Budget-Limited Algorithm Selection
Authors: Manh Hung Nguyen, Lisheng Sun-Hosoya, Isabelle Guyon
Date: 2024-10-10 00:00:00
Tasks: AutoML, Meta-Learning

Training a large set of machine learning algorithms to convergence in order to select the best-performing algorithm for a dataset is computationally wasteful. Moreover, in a budget-limited scenario, it is crucial to carefully select an algorithm candidate and allocate a budget for training it, ensuring that the limited budget is optimally distributed to favor the most promising candidates. Casting this problem as a Markov Decision Process, we propose a novel framework in which an agent must select in the process of learning the most promising algorithm without waiting until it is fully trained. At each time step, given an observation of partial learning curves of algorithms, the agent must decide whether to allocate resources to further train the most promising algorithm (exploitation), to wake up another algorithm previously put to sleep, or to start training a new algorithm (exploration). In addition, our framework allows the agent to meta-learn from learning curves on past datasets along with dataset meta-features and algorithm hyperparameters. By incorporating meta-learning, we aim to avoid myopic decisions based solely on premature learning curves on the dataset at hand. We introduce two benchmarks of learning curves that served in international competitions at WCCI'22 and AutoML-conf'22, of which we analyze the results. Our findings show that both meta-learning and the progression of learning curves enhance the algorithm selection process, as evidenced by methods of winning teams and our DDQN baseline, compared to heuristic baselines or a random search. Interestingly, our cost-effective baseline, which selects the best-performing algorithm w.r.t. a small budget, can perform decently when learning curves do not intersect frequently.
Learning to Compress: Local Rank and Information Compression in Deep Neural Networks
Authors: Niket Patel, Ravid Shwartz-Ziv
Date: 2024-10-10 00:00:00
Tasks: Representation Learning

Deep neural networks tend to exhibit a bias toward low-rank solutions during training, implicitly learning low-dimensional feature representations. This paper investigates how deep multilayer perceptrons (MLPs) encode these feature manifolds and connects this behavior to the Information Bottleneck (IB) theory. We introduce the concept of local rank as a measure of feature manifold dimensionality and demonstrate, both theoretically and empirically, that this rank decreases during the final phase of training. We argue that networks that reduce the rank of their learned representations also compress mutual information between inputs and intermediate layers. This work bridges the gap between feature manifold rank and information compression, offering new insights into the interplay between information bottlenecks and representation learning.
FedEP: Tailoring Attention to Heterogeneous Data Distribution with Entropy Pooling for Decentralized Federated Learning
Authors: Chao Feng, Hongjie Guan, Alberto Huertas Celdrán, Jan von der Assen, Gérôme Bovet, Burkhard Stiller
Date: 2024-10-10 00:00:00
Tasks: Federated Learning

Non-Independent and Identically Distributed (non-IID) data in Federated Learning (FL) causes client drift issues, leading to slower convergence and reduced model performance. While existing approaches mitigate this issue in Centralized FL (CFL) using a central server, Decentralized FL (DFL) remains underexplored. In DFL, the absence of a central entity results in nodes accessing a global view of the federation, further intensifying the challenges of non-IID data. Drawing on the entropy pooling algorithm employed in financial contexts to synthesize diverse investment opinions, this work proposes the Federated Entropy Pooling (FedEP) algorithm to mitigate the non-IID challenge in DFL. FedEP leverages Gaussian Mixture Models (GMM) to fit local data distributions, sharing statistical parameters among neighboring nodes to estimate the global distribution. Aggregation weights are determined using the entropy pooling approach between local and global distributions. By sharing only synthetic distribution information, FedEP preserves data privacy while minimizing communication overhead. Experimental results demonstrate that FedEP achieves faster convergence and outperforms state-of-the-art methods in various non-IID settings.
Scalable and Resource-Efficient Second-Order Federated Learning via Over-the-Air Aggregation
Authors: Abdulmomen Ghalkha, Chaouki Ben Issaid, Mehdi Bennis
Date: 2024-10-10 00:00:00
Tasks: Federated Learning

Second-order federated learning (FL) algorithms offer faster convergence than their first-order counterparts by leveraging curvature information. However, they are hindered by high computational and storage costs, particularly for large-scale models. Furthermore, the communication overhead associated with large models and digital transmission exacerbates these challenges, causing communication bottlenecks. In this work, we propose a scalable second-order FL algorithm using a sparse Hessian estimate and leveraging over-the-air aggregation, making it feasible for larger models. Our simulation results demonstrate more than $67\%$ of communication resources and energy savings compared to other first and second-order baselines.
Mechanistic Permutability: Match Features Across Layers
Authors: Nikita Balagansky, Ian Maksimov, Daniil Gavrilov
Date: 2024-10-10 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling

Understanding how features evolve across layers in deep neural networks is a fundamental challenge in mechanistic interpretability, particularly due to polysemanticity and feature superposition. While Sparse Autoencoders (SAEs) have been used to extract interpretable features from individual layers, aligning these features across layers has remained an open problem. In this paper, we introduce SAE Match, a novel, data-free method for aligning SAE features across different layers of a neural network. Our approach involves matching features by minimizing the mean squared error between the folded parameters of SAEs, a technique that incorporates activation thresholds into the encoder and decoder weights to account for differences in feature scales. Through extensive experiments on the Gemma 2 language model, we demonstrate that our method effectively captures feature evolution across layers, improving feature matching quality. We also show that features persist over several layers and that our approach can approximate hidden states across layers. Our work advances the understanding of feature dynamics in neural networks and provides a new tool for mechanistic interpretability studies.
Theoretical limits of descending $\ell_0$ sparse-regression ML algorithms
Authors: Mihailo Stojnic
Date: 2024-10-10 00:00:00
Tasks: compressed sensing, regression

We study the theoretical limits of the $\ell_0$ (quasi) norm based optimization algorithms when employed for solving classical compressed sensing or sparse regression problems. Considering standard contexts with deterministic signals and statistical systems, we utilize \emph{Fully lifted random duality theory} (Fl RDT) and develop a generic analytical program for studying performance of the \emph{maximum-likelihood} (ML) decoding. The key ML performance parameter, the residual \emph{root mean square error} ($\textbf{RMSE}$), is uncovered to exhibit the so-called \emph{phase-transition} (PT) phenomenon. The associated aPT curve, which separates the regions of systems dimensions where \emph{an} $\ell_0$ based algorithm succeeds or fails in achieving small (comparable to the noise) ML optimal $\textbf{RMSE}$ is precisely determined as well. In parallel, we uncover the existence of another dPT curve which does the same separation but for practically feasible \emph{descending} $\ell_0$ ($d\ell_0$) algorithms. Concrete implementation and practical relevance of the Fl RDT typically rely on the ability to conduct a sizeable set of the underlying numerical evaluations which reveal that for the ML decoding the Fl RDT converges astonishingly fast with corrections in the estimated quantities not exceeding $\sim 0.1\%$ already on the third level of lifting. Analytical results are supplemented by a sizeable set of numerical experiments where we implement a simple variant of $d\ell_0$ and demonstrate that its practical performance very accurately matches the theoretical predictions. Completely surprisingly, a remarkably precise agreement between the simulations and the theory is observed for fairly small dimensions of the order of 100.
On Reward Transferability in Adversarial Inverse Reinforcement Learning: Insights from Random Matrix Theory
Authors: Yangchun Zhang, Wang Zhou, Yirui Zhou
Date: 2024-10-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

In the context of inverse reinforcement learning (IRL) with a single expert, adversarial inverse reinforcement learning (AIRL) serves as a foundational approach to providing comprehensive and transferable task descriptions. However, AIRL faces practical performance challenges, primarily stemming from the framework's overly idealized decomposability condition, the unclear proof regarding the potential equilibrium in reward recovery, or questionable robustness in high-dimensional environments. This paper revisits AIRL in \textbf{high-dimensional scenarios where the state space tends to infinity}. Specifically, we first establish a necessary and sufficient condition for reward transferability by examining the rank of the matrix derived from subtracting the identity matrix from the transition matrix. Furthermore, leveraging random matrix theory, we analyze the spectral distribution of this matrix, demonstrating that our rank criterion holds with high probability even when the transition matrices are unobservable. This suggests that the limitations on transfer are not inherent to the AIRL framework itself, but are instead related to the training variance of the reinforcement learning algorithms employed within it. Based on this insight, we propose a hybrid framework that integrates on-policy proximal policy optimization in the source environment with off-policy soft actor-critic in the target environment, leading to significant improvements in reward transfer effectiveness.
Provable Privacy Attacks on Trained Shallow Neural Networks
Authors: Guy Smorodinsky, Gal Vardi, Itay Safran
Date: 2024-10-10 00:00:00

We study what provable privacy attacks can be shown on trained, 2-layer ReLU neural networks. We explore two types of attacks; data reconstruction attacks, and membership inference attacks. We prove that theoretical results on the implicit bias of 2-layer neural networks can be used to provably reconstruct a set of which at least a constant fraction are training points in a univariate setting, and can also be used to identify with high probability whether a given point was used in the training set in a high dimensional setting. To the best of our knowledge, our work is the first to show provable vulnerabilities in this implicit-bias-driven setting.
The Plug-in Approach for Average-Reward and Discounted MDPs: Optimal Sample Complexity Analysis
Authors: Matthew Zurek, Yudong Chen
Date: 2024-10-10 00:00:00

We study the sample complexity of the plug-in approach for learning $\varepsilon$-optimal policies in average-reward Markov decision processes (MDPs) with a generative model. The plug-in approach constructs a model estimate then computes an average-reward optimal policy in the estimated model. Despite representing arguably the simplest algorithm for this problem, the plug-in approach has never been theoretically analyzed. Unlike the more well-studied discounted MDP reduction method, the plug-in approach requires no prior problem information or parameter tuning. Our results fill this gap and address the limitations of prior approaches, as we show that the plug-in approach is optimal in several well-studied settings without using prior knowledge. Specifically it achieves the optimal diameter- and mixing-based sample complexities of $\widetilde{O}\left(SA \frac{D}{\varepsilon^2}\right)$ and $\widetilde{O}\left(SA \frac{\tau_{\mathrm{unif}}}{\varepsilon^2}\right)$, respectively, without knowledge of the diameter $D$ or uniform mixing time $\tau_{\mathrm{unif}}$. We also obtain span-based bounds for the plug-in approach, and complement them with algorithm-specific lower bounds suggesting that they are unimprovable. Our results require novel techniques for analyzing long-horizon problems which may be broadly useful and which also improve results for the discounted plug-in approach, removing effective-horizon-related sample size restrictions and obtaining the first optimal complexity bounds for the full range of sample sizes without reward perturbation.
Large Vision Model-Enhanced Digital Twin with Deep Reinforcement Learning for User Association and Load Balancing in Dynamic Wireless Networks
Authors: Zhenyu Tao, Wei Xu, Xiaohu You
Date: 2024-10-10 00:00:00
Tasks: Deep Reinforcement Learning

Optimization of user association in a densely deployed cellular network is usually challenging and even more complicated due to the dynamic nature of user mobility and fluctuation in user counts. While deep reinforcement learning (DRL) emerges as a promising solution, its application in practice is hindered by high trial-and-error costs in real world and unsatisfactory physical network performance during training. Also, existing DRL-based user association methods are typically applicable to scenarios with a fixed number of users due to convergence and compatibility challenges. To address these limitations, we introduce a large vision model (LVM)-enhanced digital twin (DT) for wireless networks and propose a parallel DT-driven DRL method for user association and load balancing in networks with dynamic user counts, distribution, and mobility patterns. To construct this LVM-enhanced DT for DRL training, we develop a zero-shot generative user mobility model, named Map2Traj, based on the diffusion model. Map2Traj estimates user trajectory patterns and spatial distributions solely from street maps. DRL models undergo training in the DT environment, avoiding direct interactions with physical networks. To enhance the generalization ability of DRL models for dynamic scenarios, a parallel DT framework is further established to alleviate strong correlation and non-stationarity in single-environment training and improve training efficiency. Numerical results show that the developed LVM-enhanced DT achieves closely comparable training efficacy to the real environment, and the proposed parallel DT framework even outperforms the single real-world environment in DRL training with nearly 20\% gain in terms of cell-edge user performance.