Assisted and autonomous driving are rapidly gaining momentum, and will soon become a reality. Among their key enablers, artificial intelligence and machine learning are expected to play a prominent role, also thanks to the massive amount of data that smart vehicles will collect from their onboard sensors. In this domain, federated learning is one of the most effective and promising techniques for training global machine learning models, while preserving data privacy at the vehicles and optimizing communications resource usage. In this work, we propose VREM-FL, a computation-scheduling co-design for vehicular federated learning that leverages mobility of vehicles in conjunction with estimated 5G radio environment maps. VREM-FL jointly optimizes the global model learned at the server while wisely allocating communication resources. This is achieved by orchestrating local computations at the vehicles in conjunction with the transmission of their local model updates in an adaptive and predictive fashion, by exploiting radio channel maps. The proposed algorithm can be tuned to trade model training time for radio resource usage. Experimental results demonstrate the efficacy of utilizing radio maps. VREM-FL outperforms literature benchmarks for both a linear regression model (learning time reduced by 28%) and a deep neural network for a semantic image segmentation task (doubling the number of model updates within the same time window).

Deep neural networks have become a foundational tool for addressing imaging inverse problems. They are typically trained for a specific task, with a supervised loss to learn a mapping from the observations to the image to recover. However, real-world imaging challenges often lack ground truth data, rendering traditional supervised approaches ineffective. Moreover, for each new imaging task, a new model needs to be trained from scratch, wasting time and resources. To overcome these limitations, we introduce a novel approach based on meta-learning. Our method trains a meta-model on a diverse set of imaging tasks that allows the model to be efficiently fine-tuned for specific tasks with few fine-tuning steps. We show that the proposed method extends to the unsupervised setting, where no ground truth data is available. In its bilevel formulation, the outer level uses a supervised loss, that evaluates how well the fine-tuned model performs, while the inner loss can be either supervised or unsupervised, relying only on the measurement operator. This allows the meta-model to leverage a few ground truth samples for each task while being able to generalize to new imaging tasks. We show that in simple settings, this approach recovers the Bayes optimal estimator, illustrating the soundness of our approach. We also demonstrate our method's effectiveness on various tasks, including image processing and magnetic resonance imaging.

Dataset distillation (DD) offers a compelling approach in computer vision, with the goal of condensing extensive datasets into smaller synthetic versions without sacrificing much of the model performance. In this paper, we continue to study the methods for DD, by addressing its conceptually core objective: how to capture the essential representation of extensive datasets in smaller, synthetic forms. We propose a novel approach utilizing the Wasserstein distance, a metric rooted in optimal transport theory, to enhance distribution matching in DD. Our method leverages the Wasserstein barycenter, offering a geometrically meaningful way to quantify distribution differences and effectively capture the centroid of a set of distributions. Our approach retains the computational benefits of distribution matching-based methods while achieving new state-of-the-art performance on several benchmarks. To provide useful prior for learning the images, we embed the synthetic data into the feature space of pretrained classification models to conduct distribution matching. Extensive testing on various high-resolution datasets confirms the effectiveness and adaptability of our method, indicating the promising yet unexplored capabilities of Wasserstein metrics in dataset distillation.

With the proliferation of edge computing, efficient AI inference on edge devices has become essential for intelligent applications such as autonomous vehicles and VR/AR. In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers. We propose an online optimization framework to address the challenge of dynamic channel conditions and device mobility in an end-to-end communication system. Our approach builds upon existing methods by leveraging a semantic knowledge base to drive multi-level feature transmission, accounting for temporal factors and dynamic elements throughout the transmission process. To solve the online optimization problem, we design a novel soft actor-critic-based deep reinforcement learning system with a carefully designed reward function for real-time decision-making, overcoming the optimization difficulty of the NP-hard problem and achieving the minimization of semantic loss while respecting latency constraints. Numerical results showcase the superiority of our approach compared to traditional greedy methods under various system setups.

Counterfactual explanations (CFE) are methods that explain a machine learning model by giving an alternate class prediction of a data point with some minimal changes in its features. It helps the users to identify their data attributes that caused an undesirable prediction like a loan or credit card rejection. We describe an efficient and an actionable counterfactual (CF) generation method based on particle swarm optimization (PSO). We propose a simple objective function for the optimization of the instance-centric CF generation problem. The PSO brings in a lot of flexibility in terms of carrying out multi-objective optimization in large dimensions, capability for multiple CF generation, and setting box constraints or immutability of data attributes. An algorithm is proposed that incorporates these features and it enables greater control over the proximity and sparsity properties over the generated CFs. The proposed algorithm is evaluated with a set of action-ability metrics in real-world datasets, and the results were superior compared to that of the state-of-the-arts.

Many machine learning tasks can be solved by minimizing a convex function of an occupancy measure over the policies that generate them. These include reinforcement learning, imitation learning, among others. This more general paradigm is called the Concave Utility Reinforcement Learning problem (CURL). Since CURL invalidates classical Bellman equations, it requires new algorithms. We introduce MD-CURL, a new algorithm for CURL in a finite horizon Markov decision process. MD-CURL is inspired by mirror descent and uses a non-standard regularization to achieve convergence guarantees and a simple closed-form solution, eliminating the need for computationally expensive projection steps typically found in mirror descent approaches. We then extend CURL to an online learning scenario and present Greedy MD-CURL, a new method adapting MD-CURL to an online, episode-based setting with partially unknown dynamics. Like MD-CURL, the online version Greedy MD-CURL benefits from low computational complexity, while guaranteeing sub-linear or even logarithmic regret, depending on the level of information available on the underlying dynamics.

Science and Engineering applications are typically associated with expensive optimization problem to identify optimal design solutions and states of the system of interest. Bayesian optimization and active learning compute surrogate models through efficient adaptive sampling schemes to assist and accelerate this search task toward a given optimization goal. Both those methodologies are driven by specific infill/learning criteria which quantify the utility with respect to the set goal of evaluating the objective function for unknown combinations of optimization variables. While the two fields have seen an exponential growth in popularity in the past decades, their dualism and synergy have received relatively little attention to date. This paper discusses and formalizes the synergy between Bayesian optimization and active learning as symbiotic adaptive sampling methodologies driven by common principles. In particular, we demonstrate this unified perspective through the formalization of the analogy between the Bayesian infill criteria and active learning criteria as driving principles of both the goal-driven procedures. To support our original perspective, we propose a general classification of adaptive sampling techniques to highlight similarities and differences between the vast families of adaptive sampling, active learning, and Bayesian optimization. Accordingly, the synergy is demonstrated mapping the Bayesian infill criteria with the active learning criteria, and is formalized for searches informed by both a single information source and multiple levels of fidelity. In addition, we provide guidelines to apply those learning criteria investigating the performance of different Bayesian schemes for a variety of benchmark problems to highlight benefits and limitations over mathematical properties that characterize real-world applications.

B-spline-based trajectory optimization is widely used for robot navigation due to its computational efficiency and convex-hull property (ensures dynamic feasibility), especially as quadrotors, which have circular body shapes (enable efficient movement) and freedom to move each axis (enables convex-hull property utilization). However, using the B-spline curve for trajectory optimization is challenging for autonomous vehicles (AVs) because of their vehicle kinodynamics (rectangular body shapes and constraints to move each axis). In this study, we propose a novel trajectory optimization approach for AVs to circumvent this difficulty using an incremental path flattening (IPF), a disc type swept volume (SV) estimation method, and kinodynamic feasibility constraints. IPF is a new method that can find a collision-free path for AVs by flattening path and reducing SV using iteratively increasing curvature penalty around vehicle collision points. Additionally, we develop a disc type SV estimation method to reduce SV over-approximation and enable AVs to pass through a narrow corridor efficiently. Furthermore, a clamped B-spline curvature constraint, which simplifies a B-spline curvature constraint, is added to dynamical feasibility constraints (e.g., velocity and acceleration) for obtaining the kinodynamic feasibility constraints. Our experimental results demonstrate that our method outperforms state-of-the-art baselines in various simulated environments. We also conducted a real-world experiment using an AV, and our results validate the simulated tracking performance of the proposed approach.

We present ReLU-QP, a GPU-accelerated solver for quadratic programs (QPs) that is capable of solving high-dimensional control problems at real-time rates. ReLU-QP is derived by exactly reformulating the Alternating Direction Method of Multipliers (ADMM) algorithm for solving QPs as a deep, weight-tied neural network with rectified linear unit (ReLU) activations. This reformulation enables the deployment of ReLU-QP on GPUs using standard machine-learning toolboxes. We evaluate the performance of ReLU-QP across three model-predictive control (MPC) benchmarks: stabilizing random linear dynamical systems with control limits, balancing an Atlas humanoid robot on a single foot, and tracking whole-body reference trajectories on a quadruped equipped with a six-degree-of-freedom arm. These benchmarks indicate that ReLU-QP is competitive with state-of-the-art CPU-based solvers for small-to-medium-scale problems and offers order-of-magnitude speed improvements for larger-scale problems.

The booming use of text-to-image generative models has raised concerns about their high risk of producing copyright-infringing content. While probabilistic copyright protection methods provide a probabilistic guarantee against such infringement, in this paper, we introduce Virtually Assured Amplification Attack (VA3), a novel online attack framework that exposes the vulnerabilities of these protection mechanisms. The proposed framework significantly amplifies the probability of generating infringing content on the sustained interactions with generative models and a lower-bounded success probability of each engagement. Our theoretical and experimental results demonstrate the effectiveness of our approach and highlight the potential risk of implementing probabilistic copyright protection in practical applications of text-to-image generative models. Code is available at https://github.com/South7X/VA3.