Risk Analytics and Optimization Chair

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We develop a principled approach to end-to-end learning in stochastic optimization. First, we show that the standard end-to-end learning algorithm admits a Bayesian interpretation and trains a posterior Bayes action map. Building on the insights of this an ...

This paper presents a novel distributed approach for solving AC power flow (PF) problems. The optimization problem is reformulated into a distributed form using a communication structure corresponding to a hypergraph, by which complex relationships between ...

Omnichannel retail has emerged as the new standard in today's commerce landscape, with retailers integrating their physical and online channels to enhance the customer shopping experience. However, such integration presents significant challenges for retai ...

We address multi-robot safe mission planning in uncertain dynamic environments. This problem arises in several applications including safety-critical exploration, surveillance, and emergency rescue missions. Computation of a multi-robot optimal control pol ...

We address black-box convex optimization problems, where the objective and constraint functions are not explicitly known but can be sampled within the feasible set. The challenge is thus to generate a sequence of feasible points converging towards an optim ...

We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables. Existing ordering-based methods in the literature rec ...

This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box functions affected by exogenous time-varying contextual disturbances. A primal- ...

Many robotics problems are formulated as optimization problems. However, most optimization solvers in robotics are locally optimal and the performance depends a lot on the initial guess. For challenging problems, the solver will often get stuck at poor loc ...

Distributionally robust chance constrained programs minimize a deterministic cost function subject to the satisfaction of one or more safety conditions with high probability, given that the probability distribution of the uncertain problem parameters affec ...

This work aims to study the effects of wind uncertainties in civil engineering structural design. Optimising the design of a structure for safety or operability without factoring in these uncertainties can result in a design that is not robust to these per ...

Many transportation markets are characterized by oligopolistic competition. In these markets customers, suppliers and regulators make decisions that are influenced by the preferences and the decisions of all other agents. In particular, capturing and under ...

Risk Analytics and Optimization Chair

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End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Hypergraph-Based Fast Distributed AC Power Flow Optimization

Quantitative Methods for Omnichannel Decision-Making

Multi-robot task allocation for safe planning against stochastic hazard dynamics

Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations

Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables

Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints

Memory of Motion for Initializing Optimization in Robotics

On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls

Multi-Level Monte Carlo Methods for Uncertainty Quantification and Risk-Averse Optimisation

Optimization and equilibrium problems with discrete choice models