XVII th Conference on Stochastic Programming

Session

Plenary session

Plenary

28 juil. 2025, 11:15

Caquot

Présidents de session

Plenary session: Francis Bach

• Guanghui Lan (Georgia Tech)

Plenary session: Alois Pichler

• Andrzej Ruszczynski (Rutgers University)

Plenary session: Huifu Xu

• Daniel Kuhn (EPFL)

Plenary session: Jim Luedtke

• David Morton

Plenary session: Francesca Maggioni

• Guzin Bayraksan (The Ohio State University)

Plenary session: Erick Delage

• Claudia Sagastizábal

298. Denoising diffusion models without diffusions

Francis Bach

28/07/2025 11:15

Plenary talk

Denoising diffusion models have enabled remarkable advances in generative modeling across various domains. These methods rely on a two-step process: first, sampling a noisy version of the data—an easier computational task—and then denoising it, either in a single step or through a sequential procedure. Both stages hinge on the same key component: the score function, which is closely tied to...

188. Quantization with Maximum Mean Discrepancies

Dr Alois Pichler (TU Chemnitz)

29/07/2025 09:15

Stochastic Programming

Plenary talk

We consider the distance of probability measures from varying angles. We discuss balanced and unbalanced transport, we consider entropic regularization and the maximum mean discrepancy distance.
Quantization is the approximation of probability measures by simple and discrete measures. The quantization measures behave differently in these metrics – an aspect, which the talk addresses as well.

297. Preference robust optimization

Huifu Xu (The Chinese University of Hong Kong)

29/07/2025 16:30

Plenary talk

Preference robust optimization (PRO) is a relatively new area of robust optimization. In this talk, I give an overview of recent research on utility-based PRO models and computational methods primarily conducted by my collaborators and myself over the past few years. I begin with a description on one-stage maximin utility PRO model where the true utility function representing the decision...

253. Recent advances in the branch-and-cut approach for solving stochastic integer programs

Jim Luedtke (University of Wisconsin-Madison)

30/07/2025 09:15

Stochastic integer programming

Plenary talk

Stochastic integer programs model problems where discrete decisions must be made under uncertainty. This combination provides significant modeling power, leading to wide a wide variety of applications such as supply chain network design, power systems design and operations, and service systems design and operations. This combination also leads to computational challenges due to the need to...

243. Bounding Large Scale Optimization Problems under Uncertainty

Francesca Maggioni (Department of Management, Information and Production Engineering, University of Bergamo)

31/07/2025 09:15

Stochastic Programming

Invited talk

Many real world decision problems are dynamic and affected by uncertainty. Stochastic Programming provides a powerful approach to handle this uncertainty within a multi-period decision framework. However, as the number of stages increases, the computational complexity of these problems grows exponentially, posing significant challenges. To tackle this, approximation techniques are often used...

275. Reinforcement Learning Methods for Risk-Sensitive Sequential Decision Making

Erick Delage (HEC Montréal)

01/08/2025 09:15

Stochastic Programming

Plenary talk

This talk surveys recent developments in reinforcement learning (RL) methods for risk-aware model-free decision-making in Markov decision processes (MDPs). In the discounted setting, we adapt two popular risk neutral RL methods to account for risk aversion. The first approach minimizes a dynamic utility-based shortfall risk measure, while the other optimizes a specific quantile of the total...