Fast and Robust Distributed Learning in High Dimension

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Artificial Intelligence has revolutionized numerous fields, driving advancements in healthcare, finance, and autonomous systems. At the core of this revolution lies Deep Learning---algorithms that learn representations from data through computational layer ...

The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and electronic properties and therefore guide computational materials design. Given ...

Toward bridging classical optimal control and online learning, regret minimization has recently been proposed as a control design criterion. This competitive paradigm penalizes the loss relative to the optimal control actions chosen by a clairvoyant policy ...

Deep reinforcement learning (DRL) is a powerful method for local motion planning in automated driving. However, training of DRL agents is difficult and subject to instability. We propose Regularized Adversarial Fine-Tuning (RAFT), an adversarial DRL traini ...

Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm. Traditional FL requires all data owners (a.k.a. FL clients) to train the same local model. This design is not well-suited for scenarios involving data and/or system ...

Automatic speech recognition (ASR) and spoken language understanding (SLU) is the core component of current voice-powered AI assistants such as Siri and Alexa. It involves speech transcription with ASR and its comprehension with natural language understand ...

Meta-Learning aims to speed up the learning process on new tasks by acquiring useful inductive biases from datasets of related learning tasks. While, in practice, the number of related tasks available is often small, most of the existing approaches assume ...

Perovskite solar cells are among the most dominant emerging PV technologies which have shown unparallel improvement in power conversion efficiency in just over a decade. Long charge carrier diffusion lengths and lifetimes, high absorption coefficients, ban ...

A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...

In several machine learning settings, the data of interest are well described by graphs. Examples include data pertaining to transportation networks or social networks. Further, biological data, such as proteins or molecules, lend themselves well to graph- ...

Many pathologies cause impairments in the speech production mechanism resulting in reduced speech intelligibility and communicative ability. To assist the clinical diagnosis, treatment and management of speech disorders, automatic pathological speech asses ...

YY We consider the symmetric binary perceptron model, a simple model of neural networks that has gathered significant attention in the statistical physics, information theory and probability theory communities, with recent connections made to the performan ...

This paper presents an experimental validation of a new design method for adaptive structures that counteract the effect of loading through shape morphing. The structure is designed to morph into target shapes that are optimal to take external loads throug ...

We examine a flexible algorithmic framework for solving monotone variational inequalities in the presence of randomness and uncertainty. The proposed template encompasses a wide range of popular first-order methods, including dual averaging, dual extrapola ...

Soft Magnetic Composites (SMCs) possess promising electromagnetic characteristics and attract intense research and application interest in the engineering community. Fabrication of composites with customized architecture is feasible due to the recent advan ...

Fast and Robust Distributed Learning in High Dimension

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How data structures affect generalization in Kernel Methods and Deep Learning

Adaptive energy reference for machine-learning models of the electronic density of states

On the Guarantees of Minimizing Regret in Receding Horizon

RAFT: Regularized Adversarial Fine-Tuning to Enhance Deep Reinforcement Learning for Self-Parking

FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning

Low-Resource Speech Recognition and Understanding for Challenging Applications

Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice

Critical analysis of decision variables for high-throughput experimentation (HTE) with perovskite solar cells

Multi-agent Learning with Privacy Guarantees

Graph Representation Learning with Optimal Transport: Analysis and Applications

Automatic pathological speech assessment

Proof of the Contiguity Conjecture and Lognormal Limit for the Symmetric Perceptron

Design and testing of a low-energy and -carbon prototype structure that adapts to loading through shape morphing

Sifting through the Noise: Universal First-Order Methods for Stochastic Variational Inequalities

Shape Optimization of Soft Magnetic Composites using Level-set Method