Chance-Constrained Optimization

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Beyond Average Safety: Chance-Constrained LLM Fine-tuning
Sep 24, 2026Taha Entesari, Mahyar FazlyabStochastic Optimization Chance-Constrained Optimization
Department of Electrical and Computer Engineering Johns Hopkins University
Risk-Averse Decision Making with Multi-Level Reliability Guarantees
Sep 10, 2026Amirmohammad Farzaneh, Osvaldo SimeoneStochastic Optimization Chance-Constrained Optimization
Institute for Intelligent Networked Systems (INSI), Northeastern University London, London, UK
Planning and Scheduling Business Processes under Control-Flow Uncertainty: Extended Version
Sep 4, 2026Michel Kunkler, Stefanie Rinderle-MaStochastic Optimization Chance-Constrained Optimization
Technical University of Munich, Germany TUM School of Computation, Information, and Technology
Chance-constrained selection of sequential intervention strategies from counterfactual estimates
Aug 13, 2026Minkyoung Kim, Beakcheol JangStochastic Optimization Chance-Constrained Optimization
Graduate School of Information, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
ReliableNet: A Chance-Constrained Approach to Trustworthy Classification in Deep Learning
Aug 10, 2026Ange-Clément Akazan, Ineza Remy Mugenga, Abebe Geletu +2Chance-Constrained Optimization Classification
School of Agriculture and Science, University of KwaZulu-Natal, Pietermaritzburg, KwaZulu-Natal, South Africa · 2AIMS Research and Innovation Centre, African Institute for Mathematical Sciences, Kigali, Rwanda · 3African Institute for Mathematical Sciences, Kigali, Rwanda
Learning-Based Stochastic Optimal Control with Infinite-Horizon Probabilistic Constraints
Aug 2, 2026Francesco Cordiano, Kanghui He, Bart De SchutterChance-Constrained Optimization Markov Decision Processes
Delft Center for Systems and Control, Delft University of Technology, 2628 CD, Delft, The Netherlands · Department of Engineering Science, University of Oxford, OX1 3PJ Oxford, U.K.
Robust Chance-Constrained Optimization using a Continuous Parameter Space Wasserstein-2 Ambiguity Set of Gaussian Mixtures
Jul 19, 2026Shibshankar Dey, Sanjay MehrotraChance-Constrained Optimization Gaussian Mixture Models
Department of Industrial Engineering and Management Sciences, Northwestern University
Relative Entropy-Bounded Ambiguous Chance Constraints for Robust Planning in Nonlinear Systems
Jul 18, 2026Trevor N. Wolf, Jay W. McMahonChance-Constrained Optimization Stochastic Optimal Control
Smead Department of Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, CO 80303, USA
N(CO) $^2$ : Neural Combinatorial Optimization with Chance Constraints to Solve Stochastic Orienteering
Jun 16, 2026Anas Saeed, Marcos Abel Zuzuárregui, Stefano CarpinStochastic Optimization Chance-Constrained Optimization
Department of Computer Science and Engineering, University of California, Merced, CA, USA
On the use of evolutionary optimization for the dynamic chance constrained open-pit mine scheduling problem
Apr 15, 2026Ishara Hewa Pathiranage, Aneta NeumannStochastic Optimization Chance-Constrained Optimization
Machine Learning and Optimisation, School of Computer Science and Information Technology, Adelaide University, Adelaide, Australia
Conformal Predictive Programming for Chance Constrained Optimization
Feb 12, 2024Yiqi Zhao, Xinyi Yu, Matteo Sesia +2Stochastic Optimization Chance-Constrained Optimization
Thomas Lord Department of Computer Science, University of Southern California · Department of Data Sciences and Operations, University of Southern California · Automatic Control Laboratory, ETH Zürich

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Beyond Average Safety: Chance-Constrained LLM Fine-tuning

Taha Entesari, Mahyar Fazlyab

Department of Electrical and Computer Engineering Johns Hopkins University

Stochastic Optimization Chance-Constrained Optimization LLM Fine-Tuning LLM Reliability LLM Safety

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Fine-tuning large language models on new objectives can improve helpfulness, instruction following, or domain-specific performance, but it can also induce regressions on safety-critical prompts. Existing safety-preserving fine-tuning methods typically control average safety loss or use weighted auxiliary penalties, which can obscure rare but severe failures. We propose a chance-constrained formulation for safety-preserving fine-tuning that limits the fraction of safety examples whose degradation relative to a reference model exceeds a prescribed threshold. Because the resulting empirical chance constraint contains a discontinuous indicator, we introduce a differentiable majorization of the violation rate, yielding a tractable conservative constraint. We then develop a constraint-aware gradient descent method that treats the majorized constraint as a safe set in parameter space and minimally modifies the fine-tuning direction to preserve feasibility. The resulting update admits a closed form and produces a tail-aware safety correction that emphasizes examples near or above the degradation threshold. We conduct an extensive set of experiments on harmful fine-tuning across three different tasks and three models and show that our approach consistently outperforms the baselines that exist in the literature. These results suggest that safety preservation in LLM fine-tuning is better viewed as a reliability-constrained optimization problem than as average-risk regularization.

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