Optimal Control under Uncertainty: From Regret Minimization to Distributional Robustness

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Atomistic machine learning has revolutionized atomistic modeling. Traditionally, molecular dynamics simulations relied on classical force fields or electronic structure calculations like Density Functional Theory. Classical force fields are fast but less p ...

We study a class of stochastic nonconvex optimization in the form of min(x is an element of X) F(x) := E-xi[f (phi(x, xi))], that is, F is a composition of a convex function f and a random function phi. Leveraging an (implicit) convex reformulation via a v ...

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

Avian perching maneuvers are one of the most frequent and agile flight sce narios, where highly optimized flight trajectories, produced by rapid wing and tail morphing that generate high angular rates and accelerations, reduce kinetic energy at impact. Whi ...

Tomographic Volumetric Additive Manufacturing (TVAM) allows printing of mesoscopic objects within seconds or minutes. In TVAM, tomographic patterns are illuminated onto a rotating glass vial which contains a photosensitive resin. Current pattern optimizati ...

The implantation of materials into in vivo peripheral nerves triggers the production of scar tissue. A scar capsule progressively incorporates foreign bodies, which become insulated from the surrounding environment. This phenomenon is particularly detrimen ...

The increasing availability of multimodal data from electronic health records (EHR) has paved the way for deep learning methods to improve diagnosis accuracy. However, deep learning models are data-driven, requiring large-scale datasets to achieve high gen ...

This paper considers the problem of second-degree price discrimination when the type distribution is unknown or imperfectly specified by means of an ambiguity set. As robustness measure we use a performance index, equivalent to relative regret, which quant ...

With the growing popularity of ride-hailing services and the desire to operate those services efficiently, ridehailing companies need to ensure a sufficiently large fleet size and an appropriate rebalancing of empty vehicles. Due to the complexity of city ...

Endogenous and exogenous uncertainties exert significant influences on energy planning. In this study, we propose a systematic methodology to excavate the uncertainty space, by combining mix-integer linear programming (MILP), Monte Carlo simulation, and ma ...

Wasserstein balls, which contain all probability measures within a pre-specified Wasserstein distance to a reference measure, have recently enjoyed wide popularity in the distributionally robust optimization and machine learning communities to formulate an ...

We address the problem of designing stabilizing control policies for nonlinear systems in discrete-time, while minimizing an arbitrary cost function. When the system is linear and the cost is convex, the System Level Synthesis (SLS) approach offers an effe ...

Discrete sizing and topology optimization of truss structures subject to stress and displacement constraints has been formulated as a Mixed-Integer Linear Programming (MILP) problem. The computation time to solve a MILP problem to global optimality via a b ...

Factorization of matrices where the rank of the two factors diverges linearly with their sizes has many applications in diverse areas such as unsupervised representation learning, dictionary learning or sparse coding. We consider a setting where the two fa ...

It was recently shown that almost all solutions in the symmetric binary perceptron are isolated, even at low constraint densities, suggesting that finding typical solutions is hard. In contrast, some algorithms have been shown empirically to succeed in fin ...

Byzantine resilience emerged as a prominent topic within the distributed machine learning community. Essentially, the goal is to enhance distributed optimization algorithms, such as distributed SGD, in a way that guarantees convergence despite the presence ...

Optimal Control under Uncertainty: From Regret Minimization to Distributional Robustness

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Advancing understanding and practical performance of machine learning interatomic potentials

Efficient Algorithms for a Class of Stochastic Hidden Convex Optimization and Its Applications in Network Revenue Management

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Agile perching maneuvers in birds and morphing-wing drones

Wave optical model for tomographic volumetric additive manufacturing

Novel In Silico Strategies to Model the In Vivo Nerve Scarring Around Implanted Parylene C Devices

CARER - ClinicAl Reasoning-Enhanced Representation for Temporal Health Risk Prediction

Price Discrimination with Robust Beliefs

On the Equilibrium and Stability Properties of a Macroscopic Model for Ride-Hailing Services with Limited Fleet Size

Deep excavation of the impact from endogenous and exogenous uncertainties on long-term energy planning

On Linear Optimization over Wasserstein Balls

Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systems

MILP-based discrete sizing and topology optimization of truss structures: new formulation and benchmarking

Perturbative construction of mean-field equations in extensive-rank matrix factorization and denoising

Binary Perceptron: Efficient Algorithms Can Find Solutions in a RareWell-Connected Cluster

Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums