pwc-archive/papers-with-abstracts huggingface dataset:
MCMC for Bayesian estimation of Differential Privacy from Membership Inference Attacks
Authors: Ceren Yildirim, Kamer Kaya, Sinan Yildirim, Erkay Savas
Date: 2025-04-23 00:00:00

We propose a new framework for Bayesian estimation of differential privacy, incorporating evidence from multiple membership inference attacks (MIA). Bayesian estimation is carried out via a Markov chain Monte Carlo (MCMC) algorithm, named MCMC-DP-Est, which provides an estimate of the full posterior distribution of the privacy parameter (e.g., instead of just credible intervals). Critically, the proposed method does not assume that privacy auditing is performed with the most powerful attack on the worst-case (dataset, challenge point) pair, which is typically unrealistic. Instead, MCMC-DP-Est jointly estimates the strengths of MIAs used and the privacy of the training algorithm, yielding a more cautious privacy analysis. We also present an economical way to generate measurements for the performance of an MIA that is to be used by the MCMC method to estimate privacy. We present the use of the methods with numerical examples with both artificial and real data.
Reduced order asymptotic observer of friction in motion control
Authors: Michael Ruderman
Date: 2025-04-30 00:00:00
Tasks: Friction

An asymptotic observer of the motion state variables with nonlinear friction [1] benefits from a robust to the noise state-space representation of the dynamic friction force, including pre-sliding transitions, and implements the reduced order Luenberger observation law with only measurable output displacement. The uniform asymptotic stability and convergence analysis of the proposed observer are elaborated by using the Lyapunov function-based stability criteria by Ignatyev and imposing the parametric constraints on the time dependent eigenvalues to be always negative real. A design procedure for assigning a dominant (thus slowest) real pole of the observer system matrix is proposed. A thorough experimental evaluation is given for the proposed observer-based friction compensation, which is performed for positioning and tracking tasks and compared with an optimally tuned PID feedback control.
Provable wavelet-based neural approximation
Authors: Youngmi Hur, Hyojae Lim, Mikyoung Lim
Date: 2025-04-23 00:00:00

In this paper, we develop a wavelet-based theoretical framework for analyzing the universal approximation capabilities of neural networks over a wide range of activation functions. Leveraging wavelet frame theory on the spaces of homogeneous type, we derive sufficient conditions on activation functions to ensure that the associated neural network approximates any functions in the given space, along with an error estimate. These sufficient conditions accommodate a variety of smooth activation functions, including those that exhibit oscillatory behavior. Furthermore, by considering the $L^2$-distance between smooth and non-smooth activation functions, we establish a generalized approximation result that is applicable to non-smooth activations, with the error explicitly controlled by this distance. This provides increased flexibility in the design of network architectures.
Unified Network Modeling for Six Cross-Layer Scenarios in Space-Air-Ground Integrated Networks
Authors: Yalin Liu, Yaru Fu, Qubeijian Wang, Hong-Ning Dai
Date: 2025-04-30 00:00:00

The space-air-ground integrated network (SAGIN) can enable global range and seamless coverage in the future network. SAGINs consist of three spatial layer network nodes: 1) satellites on the space layer, 2) aerial vehicles on the aerial layer, and 3) ground devices on the ground layer. Data transmissions in SAGINs include six unique cross-spatial-layer scenarios, i.e., three uplink and three downlink transmissions across three spatial layers. For simplicity, we call them \textit{six cross-layer scenarios}. Considering the diverse cross-layer scenarios, it is crucial to conduct a unified network modeling regarding node coverage and distributions in all scenarios. To achieve this goal, we develop a unified modeling approach of coverage regions for all six cross-layer scenarios. Given a receiver in each scenario, its coverage region on a transmitter-distributed surface is modeled as a spherical dome. Utilizing spherical geometry, the analytical models of the spherical-dome coverage regions are derived and unified for six cross-layer scenarios. We conduct extensive numerical results to examine the coverage models under varying carrier frequencies, receiver elevation angles, and transceivers' altitudes. Based on the coverage model, we develop an algorithm to generate node distributions under spherical coverage regions, which can assist in testing SAGINs before practical implementations.
Representation Learning via Non-Contrastive Mutual Information
Authors: Zhaohan Daniel Guo, Bernardo Avila Pires, Khimya Khetarpal, Dale Schuurmans, Bo Dai
Date: 2025-04-23 00:00:00
Tasks: Representation Learning

Labeling data is often very time consuming and expensive, leaving us with a majority of unlabeled data. Self-supervised representation learning methods such as SimCLR (Chen et al., 2020) or BYOL (Grill et al., 2020) have been very successful at learning meaningful latent representations from unlabeled image data, resulting in much more general and transferable representations for downstream tasks. Broadly, self-supervised methods fall into two types: 1) Contrastive methods, such as SimCLR; and 2) Non-Contrastive methods, such as BYOL. Contrastive methods are generally trying to maximize mutual information between related data points, so they need to compare every data point to every other data point, resulting in high variance, and thus requiring large batch sizes to work well. Non-contrastive methods like BYOL have much lower variance as they do not need to make pairwise comparisons, but are much trickier to implement as they have the possibility of collapsing to a constant vector. In this paper, we aim to develop a self-supervised objective that combines the strength of both types. We start with a particular contrastive method called the Spectral Contrastive Loss (HaoChen et al., 2021; Lu et al., 2024), and we convert it into a more general non-contrastive form; this removes the pairwise comparisons resulting in lower variance, but keeps the mutual information formulation of the contrastive method preventing collapse. We call our new objective the Mutual Information Non-Contrastive (MINC) loss. We test MINC by learning image representations on ImageNet (similar to SimCLR and BYOL) and show that it consistently improves upon the Spectral Contrastive loss baseline.
Power Flow Approximations for Multiphase Distribution Networks using Gaussian Processes
Authors: Daniel Glover, Parikshit Pareek, Deepjyoti Deka, Anamika Dubey
Date: 2025-04-30 00:00:00
Tasks: Gaussian Processes

Learning-based approaches are increasingly leveraged to manage and coordinate the operation of grid-edge resources in active power distribution networks. Among these, model-based techniques stand out for their superior data efficiency and robustness compared to model-free methods. However, effective model learning requires a learning-based approximator for the underlying power flow model. This study extends existing work by introducing a data-driven power flow method based on Gaussian Processes (GPs) to approximate the multiphase power flow model, by mapping net load injections to nodal voltages. Simulation results using the IEEE 123-bus and 8500-node distribution test feeders demonstrate that the trained GP model can reliably predict the nonlinear power flow solutions with minimal training data. We also conduct a comparative analysis of the training efficiency and testing performance of the proposed GP-based power flow approximator against a deep neural network-based approximator, highlighting the advantages of our data-efficient approach. Results over realistic operating conditions show that despite an 85% reduction in the training sample size (corresponding to a 92.8% improvement in training time), GP models produce a 99.9% relative reduction in mean absolute error compared to the baselines of deep neural networks.
Data-driven operator learning for energy-efficient building control
Authors: Yuexin Bian, Yuanyuan Shi
Date: 2025-04-30 00:00:00
Tasks: Computational Efficiency, Management, Operator learning

Energy-efficient ventilation control plays a vital role in reducing building energy consumption while ensuring occupant health and comfort. While Computational Fluid Dynamics (CFD) simulations offer high-fidelity modeling of airflow for building HVAC design, their high computational cost makes them impractical for practical adoption in real-time building management system. In this work, we present a data-driven framework that combines the physical accuracy of CFD with the computational efficiency of machine learning to enable energy-efficient building ventilation control. Our method jointly optimizes airflow supply rates and vent angles to reduce energy use and adhere to air quality constraints. We train a neural operator transformer to learn the mapping from building control actions to airflow field distributions using high-resolution CFD data. This learned operator enables a gradient-based control framework capable of optimal decision-making. Experimental results demonstrate that our approach achieves substantial energy savings compared to maximum airflow rate control, rule-based control, and data-driven control based on regional average CO2 predictions, while consistently maintaining safe indoor air quality. These results highlight the practicality and scalability of our method for enabling safe and energy-efficient building management.
Enhancing Variable Selection in Large-scale Logistic Regression: Leveraging Manual Labeling with Beneficial Noise
Authors: Xiaofei Wu, Rongmei Liang
Date: 2025-04-23 00:00:00
Tasks: Distributed Computing, Variable Selection

In large-scale supervised learning, penalized logistic regression (PLR) effectively addresses the overfitting problem by introducing regularization terms yet its performance still depends on efficient variable selection strategies. This paper theoretically demonstrates that label noise stemming from manual labeling, which is solely related to classification difficulty, represents a type of beneficial noise for variable selection in PLR. This benefit is reflected in a more accurate estimation of the selected non-zero coefficients when compared with the case where only truth labels are used. Under large-scale settings, the sample size for PLR can become very large, making it infeasible to store on a single machine. In such cases, distributed computing methods are required to handle PLR model with manual labeling. This paper presents a partition-insensitive parallel algorithm founded on the ADMM (alternating direction method of multipliers) algorithm to address PLR by incorporating manual labeling. The partition insensitivity of the proposed algorithm refers to the fact that the solutions obtained by the algorithm will not change with the distributed storage of data. In addition, the algorithm has global convergence and a sublinear convergence rate. Experimental results indicate that, as compared with traditional variable selection classification techniques, the PLR with manually-labeled noisy data achieves higher estimation and classification accuracy across multiple large-scale datasets.
Floating Car Observers in Intelligent Transportation Systems: Detection Modeling and Temporal Insights
Authors: Jeremias Gerner, Klaus Bogenberger, Stefanie Schmidtner
Date: 2025-04-29 00:00:00
Tasks: 3D Object Detection, object-detection, Object Detection, State Estimation

Floating Car Observers (FCOs) extend traditional Floating Car Data (FCD) by integrating onboard sensors to detect and localize other traffic participants, providing richer and more detailed traffic data. In this work, we explore various modeling approaches for FCO detections within microscopic traffic simulations to evaluate their potential for Intelligent Transportation System (ITS) applications. These approaches range from 2D raytracing to high-fidelity co-simulations that emulate real-world sensors and integrate 3D object detection algorithms to closely replicate FCO detections. Additionally, we introduce a neural network-based emulation technique that effectively approximates the results of high-fidelity co-simulations. This approach captures the unique characteristics of FCO detections while offering a fast and scalable solution for modeling. Using this emulation method, we investigate the impact of FCO data in a digital twin of a traffic network modeled in SUMO. Results demonstrate that even at a 20% penetration rate, FCOs using LiDAR-based detections can identify 65% of vehicles across various intersections and traffic demand scenarios. Further potential emerges when temporal insights are integrated, enabling the recovery of previously detected but currently unseen vehicles. By employing data-driven methods, we recover over 80% of these vehicles with minimal positional deviations. These findings underscore the potential of FCOs for ITS, particularly in enhancing traffic state estimation and monitoring under varying penetration rates and traffic conditions.
Confidence Sequences for Generalized Linear Models via Regret Analysis
Authors: Eugenio Clerico, Hamish Flynn, Wojciech Kotłowski, Gergely Neu
Date: 2025-04-23 00:00:00

We develop a methodology for constructing confidence sets for parameters of statistical models via a reduction to sequential prediction. Our key observation is that for any generalized linear model (GLM), one can construct an associated game of sequential probability assignment such that achieving low regret in the game implies a high-probability upper bound on the excess likelihood of the true parameter of the GLM. This allows us to develop a scheme that we call online-to-confidence-set conversions, which effectively reduces the problem of proving the desired statistical claim to an algorithmic question. We study two varieties of this conversion scheme: 1) analytical conversions that only require proving the existence of algorithms with low regret and provide confidence sets centered at the maximum-likelihood estimator 2) algorithmic conversions that actively leverage the output of the online algorithm to construct confidence sets (and may be centered at other, adaptively constructed point estimators). The resulting methodology recovers all state-of-the-art confidence set constructions within a single framework, and also provides several new types of confidence sets that were previously unknown in the literature.
No-Regret Model Predictive Control with Online Learning of Koopman Operators
Authors: HongYu Zhou, Vasileios Tzoumas
Date: 2025-04-22 00:00:00
Tasks: Model Predictive Control

We study a problem of simultaneous system identification and model predictive control of nonlinear systems. Particularly, we provide an algorithm for systems with unknown residual dynamics that can be expressed by Koopman operators. Such residual dynamics can model external disturbances and modeling errors, such as wind and wave disturbances to aerial and marine vehicles, or inaccurate model parameters. The algorithm has finite-time near-optimality guarantees and asymptotically converges to the optimal non-causal controller. Specifically, the algorithm enjoys sublinear \textit{dynamic regret}, defined herein as the suboptimality against an optimal clairvoyant controller that knows how the unknown dynamics will adapt to its states and actions. To this end, we assume the algorithm is given Koopman observable functions such that the unknown dynamics can be approximated by a linear dynamical system. Then, it employs model predictive control based on the current learned model of the unknown residual dynamics. This model is updated online using least squares in a self-supervised manner based on the data collected while controlling the system. We validate our algorithm in physics-based simulations of a cart-pole system aiming to maintain the pole upright despite inaccurate model parameters.
Local energy communities optimization considering cost and greenhouse gases minimization
Authors: S. Barja-Martinez, M. Aragues-Penalba, A. Sumper, R. Villafafila-Robles
Date: 2025-04-29 00:00:00

Local Energy Communities (LECs) facilitate consumer involvement in local electricity generation and distribution, offering a significant opportunity for society to participate in the energy transition. This paper presents the optimization of a renewable energy community in Spain, consisting of four office buildings, a collectively owned centralized photovoltaic system, and a Li-ion battery storage system. The case study assesses the performance and feasibility of the proposed solutions. The results indicate a 6% reduction in emissions and a 20% reduction in electricity costs, demonstrating the potential of LECs to enhance energy security by saving costs and emission while mitigating the vulnerability of local areas to power outages and disruptions
MAGIC: Near-Optimal Data Attribution for Deep Learning
Authors: Andrew Ilyas, Logan Engstrom
Date: 2025-04-23 00:00:00
Tasks: Deep Learning

The goal of predictive data attribution is to estimate how adding or removing a given set of training datapoints will affect model predictions. In convex settings, this goal is straightforward (i.e., via the infinitesimal jackknife). In large-scale (non-convex) settings, however, existing methods are far less successful -- current methods' estimates often only weakly correlate with ground truth. In this work, we present a new data attribution method (MAGIC) that combines classical methods and recent advances in metadifferentiation to (nearly) optimally estimate the effect of adding or removing training data on model predictions.
Natural Policy Gradient for Average Reward Non-Stationary RL
Authors: Neharika Jali, Eshika Pathak, Pranay Sharma, Guannan Qu, Gauri Joshi
Date: 2025-04-23 00:00:00
Tasks: Reinforcement Learning (RL)

We consider the problem of non-stationary reinforcement learning (RL) in the infinite-horizon average-reward setting. We model it by a Markov Decision Process with time-varying rewards and transition probabilities, with a variation budget of $\Delta_T$. Existing non-stationary RL algorithms focus on model-based and model-free value-based methods. Policy-based methods despite their flexibility in practice are not theoretically well understood in non-stationary RL. We propose and analyze the first model-free policy-based algorithm, Non-Stationary Natural Actor-Critic (NS-NAC), a policy gradient method with a restart based exploration for change and a novel interpretation of learning rates as adapting factors. Further, we present a bandit-over-RL based parameter-free algorithm BORL-NS-NAC that does not require prior knowledge of the variation budget $\Delta_T$. We present a dynamic regret of $\tilde{\mathscr O}(|S|^{1/2}|A|^{1/2}\Delta_T^{1/6}T^{5/6})$ for both algorithms, where $T$ is the time horizon, and $|S|$, $|A|$ are the sizes of the state and action spaces. The regret analysis leverages a novel adaptation of the Lyapunov function analysis of NAC to dynamic environments and characterizes the effects of simultaneous updates in policy, value function estimate and changes in the environment.
Non-Linear Modeling and Analysis of Amplifier-Less Potentiostat Architectures
Authors: Andrea Sannino, David-Peter Wiens, Maurits Ortmanns, José I. Artigas, Aránzazu Otín
Date: 2025-04-29 00:00:00

In this article, a previously published amplifier-less potentiostat architecture is further examined. Starting with a linearized model, the impact of the most important parameters is studied taking in account the electrodes-solution electrochemical interface. A detailed model is obtained and thoroughly verified, and recommended operating conditions are given for certain limit load conditions. Then, a more complete non-linear model is developed to take in account the measurement uncertainty introduced by the circuit non-linear components. This non-linear model is compared to a time domain description of the circuit and it is verified that it can predict the non-linear behavior with a precision better than 20%. This result enables the circuit designers to compensate for these effects and ultimately reduce the overall measurement uncertainty.
Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees
Authors: Jiahe Lin, Yikai Zhang, George Michailidis
Date: 2025-04-23 00:00:00

Graphical models are widely used in diverse application domains to model the conditional dependencies amongst a collection of random variables. In this paper, we consider settings where the graph structure is covariate-dependent, and investigate a deep neural network-based approach to estimate it. The method allows for flexible functional dependency on the covariate, and fits the data reasonably well in the absence of a Gaussianity assumption. Theoretical results with PAC guarantees are established for the method, under assumptions commonly used in an Empirical Risk Minimization framework. The performance of the proposed method is evaluated on several synthetic data settings and benchmarked against existing approaches. The method is further illustrated on real datasets involving data from neuroscience and finance, respectively, and produces interpretable results.
A Geometric Approach to Problems in Optimization and Data Science
Authors: Naren Sarayu Manoj
Date: 2025-04-22 00:00:00
Tasks: Data Poisoning, Graph Clustering

We give new results for problems in computational and statistical machine learning using tools from high-dimensional geometry and probability. We break up our treatment into two parts. In Part I, we focus on computational considerations in optimization. Specifically, we give new algorithms for approximating convex polytopes in a stream, sparsification and robust least squares regression, and dueling optimization. In Part II, we give new statistical guarantees for data science problems. In particular, we formulate a new model in which we analyze statistical properties of backdoor data poisoning attacks, and we study the robustness of graph clustering algorithms to ``helpful'' misspecification.
Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learning
Authors: Lucas Howard, Aneesh C. Subramanian, Gregory Thompson, Benjamin Johnson, Thomas Auligne
Date: 2025-04-22 00:00:00
Tasks: Computational Efficiency, Efficient Neural Network

The continuous improvement in weather forecast skill over the past several decades is largely due to the increasing quantity of available satellite observations and their assimilation into operational forecast systems. Assimilating these observations requires observation operators in the form of radiative transfer models. Significant efforts have been dedicated to enhancing the computational efficiency of these models. Computational cost remains a bottleneck, and a large fraction of available data goes unused for assimilation. To address this, we used machine learning to build an efficient neural network based probabilistic emulator of the Community Radiative Transfer Model (CRTM), applied to the GOES Advanced Baseline Imager. The trained NN emulator predicts brightness temperatures output by CRTM and the corresponding error with respect to CRTM. RMSE of the predicted brightness temperature is 0.3 K averaged across all channels. For clear sky conditions, the RMSE is less than 0.1 K for 9 out of 10 infrared channels. The error predictions are generally reliable across a wide range of conditions. Explainable AI methods demonstrate that the trained emulator reproduces the relevant physics, increasing confidence that the model will perform well when presented with new data.