Risk and Portfolio Optimization

Neueste Paper

• Distributionally Robust Schr\"odinger Bridge

  Jinhwan Sul, Panagiotis Theodoropoulos, Vincent Pacelli, Jaemoo Choi, Evangelos Theodorou · 2. Oktober 2026

  Schr\"odinger bridge (SB) learns stochastic transport between prescribed initial and target distributions. When the initial distribution shifts at test time, the learned dynamics can fail to recover the target distribution. We introduce the Distributionally Robust Schr\"odinger Bridge (DRSB), which …

• Risk-Averse Online POMDP Planning via CVaR of the Immediate Cost with Performance Guarantees

  Yaacov Pariente, Vadim Indelman · 30. September 2026

  Online POMDP planners optimize the expected cumulative cost, which can mask dangerous states when the belief places significant mass on high-cost states. Existing risk-averse methods apply static or dynamic Conditional Value at Risk (CVaR) to the value function, capturing trajectory-level risk, but …

• Brenier Meets Adversarial Training: Optimal Transport Geometry for Robust Learning

  Alireza Abdollahpoorrostam, Ehsan Sharifian, Buse \c{S}en, Marco Cuturi, Daniel Kuhn · 28. September 2026

  Distributionally robust optimization (DRO) provides a principled framework for learning under distribution shift, but its practical use is hindered by the difficulty of evaluating worst-case risks for nonconvex loss functions. We study a penalized DRO formulation in which the adversary may choose an…

• Learning Chance-Constrained MDPs with Bellman Distributional Certificates

  Chenbei Lu, Hongyu Yi · 28. September 2026

  Safe reinforcement learning (RL) commonly enforces expected-cost constraints, but such expectation safety may fail to control the probability of rare high-cost trajectories. Chance-constrained MDPs (CCMDPs) impose a stronger probability-level requirement, but are widely viewed as harder because the …

• Vector Bellman Theory for Multichain Robust Average-Reward Markov Decision Processes

  Yue Wang, George Atia · 25. September 2026

  Robust average-reward Markov decision processes provide a fundamental framework for long-term performance optimization under uncertainty, and can have optimal long-run rewards that depend on the initial state. This state dependence requires a vector Bellman theory that accounts for both recurrent-cl…

• Conformal Robustness in Prediction-Driven Decision-Making

  Lingjie Zhao, Hansheng Jiang, Wei Qi · 22. September 2026

  Modern prediction-driven decision systems often rely on black-box predictors, but a point forecast alone does not provide the uncertainty scale required for robust downstream decision-making. We build a score-calibrated robustness framework that converts any fixed point predictor into a decision-rel…

• Decision-Focused Learning for Mean-Variance Portfolio Optimization via KKT-Based Reformulation

  Kensei Nosaka, Shunnosuke Ikeda, Yuichi Takano · 21. September 2026

  Mean-variance portfolio optimization (MVO) is a central framework in data-driven asset management. A widely adopted approach is a two-stage framework that first predicts expected returns and then solves the optimization problem based on these predictions, with the predictive models trained by minimi…

• Linear Exponential Quadratic Gaussian Covariance Steering

  Chiran B. Cherian, Yasemin Isik, Abhishek Halder · 14. September 2026

  We formulate and analyze the linear exponential quadratic Gaussian (LEQG) covariance steering problem in continuous time over a given deadline (finite time horizon). The solution for this problem can be seen as a risk-sensitive Schr\"{o}dinger bridge between Gaussian endpoints in the linear quadrati…

• Sparsity Regularized and Robust Mean Variance Portfolio Selection Under Ellipsoidal Uncertainty

  Deniz Akkaya, Emre Can Yayla, Buse \c{S}en, Mustafa \c{C}. P{\i}nar · 11. September 2026

  We investigate mean-variance portfolio selection with an $\ell_0$-penalty to promote sparsity in asset allocations. Uncertainty in the mean return vector is incorporated through an ellipsoidal uncertainty set, yielding a robust sparse optimization framework. We characterize the structure of both loc…

• Risk-Averse Decision Making with Multi-Level Reliability Guarantees

  Amirmohammad Farzaneh, Osvaldo Simeone · 11. September 2026

  Many applications in engineering, including wireless broadcasting, require designs that provide performance certificates at different target outage levels. This paper studies the problem of maximizing the weighted average of such certificates in the presence of uncertainty about the true system stat…

• Certifying Lower Bounds for Risk-Sensitive Reinforcement Learning under Adversarial State Perturbations

  Tong Li, Saunak Kumar Panda, Yisha Xiang · 11. September 2026

  Reinforcement learning (RL) agents deployed in real-world environments are often vulnerable to adversarial perturbations in state observations, creating risks in safety-critical applications. Certification methods can improve robustness against adversarial perturbations by providing lower bounds on …

• Mini-Batch Risk-Averse Deep Q-Learning: A Robot Navigation Case Study

  Aayush Patel, Andrzej Ruszczy\'nski · 9. September 2026

  We study the control of Markov decision processes in which the quality of a policy is evaluated by a dynamic, time-consistent Markov risk measure rather than by an expected discounted cost. The main obstacle to combining such measures with reinforcement learning is that a transition risk mapping dep…

• Occupancy-based Quantile Risk Control

  Zihao Shi, Huajun Xi, Bingyi Jing, Hongxin Wei · 4. September 2026

  Conformal risk control is an emerging framework for the safe deployment of machine learning models with finite-sample guarantees. To accommodate a broader class of risk notions, quantile risk control extends this framework to quantile-based risk measures. However, existing methods either suffer from…

• Conformal Risk-Averse Decision Making with Optimized Certainty Equivalent Risk Control

  Amirmohammad Farzaneh, Osvaldo Simeone · 31. August 2026

  We study risk-averse decision making, in which an agent selects actions while being uncertain about the true system state. The risk is measured via optimized certainty equivalent (OCE) metrics, which generalize popular criteria such as mean-variance risk and conditional value-at-risk (CVaR). We char…

• Robust Risk Under Evolving Uncertainty: A Wasserstein Counterpart of the Entropic Value-at-Risk

  Deep Kumar Ganguly, Jan K\v{r}et\'insk\'y · 20. August 2026

  An agent still learning its environment should be cautious while ignorant and bold once confident. The entropic value-at-risk captures this through a robust-optimization identity---a confidence level fixes the radius of a relative-entropy ball of alternative models---but that ball cannot reach catas…

• Adaptive Policy Portfolios for Robust Markov Decision Processes

  Kasper Engelen, Sebastian Junges, Guillermo A. Pérez, Marnix Suilen · 19. August 2026

  Robust Markov decision processes optimize one policy against a set of plausible transition functions. This can be conservative when the unknown dynamics are fixed and become partially identifiable after deployment. We study adaptive policy portfolios: finite sets of memoryless randomized policies sy…

• Quantifying Risk Under Evolving Uncertainty: Belief-Dependent Robustness for Safe Sequential Decision Making

  Deep Kumar Ganguly, Jan Kretinsky · 19. August 2026

  How cautious should an agent be while it is still learning its environment? We propose RATTL (Risk-Adversarial Total-Reward Learning), which ties caution to epistemic uncertainty: the agent holds a Bayesian posterior over unknown dynamics and plans against a Wasserstein ambiguity set whose radius is…

• Chance-constrained selection of sequential intervention strategies from counterfactual estimates

  Minkyoung Kim, Beakcheol Jang · 14. August 2026

  Many operational decisions are sequences of interventions under a cumulative resource limit, such as a maintenance schedule within a crew-hour budget. Choosing among them calls for the outcome and the cumulative cost each would produce, counterfactual quantities identified from observational data. T…

• Statistical Properties of Robust Learning under Distributional Shifts

  Zhiyi Li, Xiaojie Mao, Yunbei Xu, Ruohan Zhan · 14. August 2026

  Distributional shifts arise when the target deployment environment differs from the source environment that generated the training data. Robust learning frameworks such as Distributionally Robust Optimization (DRO) and Robust Satisficing (RS) aim to address this challenge, yet their finite-sample gu…

• Fine-Tuning Generative Models for Extreme Events via CVaR-Penalized Wasserstein Gradient Flows

  Thejani Gamage, Hyemin Gu, Zhizhen Zhang, Ziyu Chen, Markos Katsoulakis, Luc Rey-Bellet · 13. August 2026

  We propose CVaR-penalized Generative Particle Algorithm (CVaR-GPA), a robust, tail-agnostic algorithm for fine-tuning generative models to learn heavy-tailed distributions and capture extreme events, requiring no prior knowledge or estimation of the target's tail characteristics. The method is the W…

• Decision-Focused Learning in Network Interdiction Games

  Luca M. Hartmann, Parinaz Naghizadeh · 11. August 2026

  We study decision-focused learning (DFL) in shortest-path network interdiction (SPNI) games, a Stackelberg game where an interdictor (leader) strengthens the networks' arcs against attacks, while an evader (follower) who is uncertain about costs of attacking network arcs relies on a machine-learned …

• Optimized Certainty Equivalent Risk Minimization Using Samples: Algorithms, Convergence Rates, and Applications

  Sumedh Gupte, Prashanth L. A., Sanjay P. Bhat · 10. August 2026

  We consider the optimization of the Optimized Certainty Equivalent (OCE) risk, with applications including portfolio optimization in finance, and uncertainty quantification, classification, and regression in machine learning. Our contributions cover popular special cases of OCE, such as entropic ris…

• Robust Average-Reward Markov Decision Processes: Minimax-Optimal Learning via Plug-in Reductions

  Yuepeng Yang, Yuxin Chen, Yuejie Chi · 10. August 2026

  Distributionally robust Markov decision processes provide a principled framework for sequential decision making under model uncertainty. We study how many samples are necessary and sufficient to learn an $\varepsilon$-optimal robust policy under the average-reward criterion. A generative model provi…

• Posture and Sustainment Optimization Under Adversarial Uncertainty

  Amelie Norris, Alyssa Lee, Natan Vidra, Spurthi Setty · 7. August 2026

  Pre-commitment posture, the assignment of military assets to theater locations before conflict scenarios resolve, is a critical and formally unsolved problem in joint operational planning. Current practice relies on greedy heuristics that maximize value and ignore geographic coverage and are structu…

• Generative Distributionally Robust Optimization

  Ziwei Zhang, Jonathan Yu-Meng Li, Zhihao Jin · 29. Juli 2026

  Generative models are increasingly adopted in distributionally robust optimization (DRO), but existing approaches trade off model compatibility and adversarial structure: methods that accept arbitrary samplers do not restrict worst-case laws to a generator family, while generator-parameterized adver…