arXiv AI July 17, 2026 By Osvaldo Simeone

Decision Making Needs Uncertainty Quantification [Lecture Notes]

arXiv:2607. 14407v1 Announce Type: cross Abstract: Many signal processing systems ultimately exist to {act}.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

agents

arXiv AI

23h ago

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

arXiv:2608. 17574v1 Announce Type: new Abstract: How cautious should an agent be while it is still learning its environment?

By Deep Kumar Ganguly, Jan Kretinsky

llms agents safety

arXiv AI

Aug 12

Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness

arXiv:2605. 23146v3 Announce Type: replace-cross Abstract: Classical reinforcement learning assumes the agent interacts with a fixed environment whose behavior does not depend on the agent's policy.

By Manish Aryal, Faiyaz Azam, Agnivo Banerjee, Syed Mahir Ahamed, Sai Sidhanth Manoharan Jayanthi, Allegra Laro, Cl\'ement Legentilhomme, Andrew Lin, Florian Lorkowski, Marina P\'erez del Valle, Radman Rakhshandehroo, Patric Rommel, Emanuel Ruzak, Nathan Theng, Paul Yushin Rapoport

agents reinforcement-learning safety

arXiv Machine Learning

Jul 3

Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Prediction

arXiv:2607. 02206v1 Announce Type: cross Abstract: Predictions are increasingly used to guide high-stakes decisions, from treatment selection to policy making.

By Yurui Zheng, Ying Jin

arXiv AI

Jul 17

Subjective Risk Decomposition: A New View for Uncertainty Quantification

arXiv:2607. 15196v1 Announce Type: cross Abstract: We present a novel viewpoint for uncertainty quantification.

By Raghad Alamri, Michele Caprio, Gavin Brown

arXiv AI

Jun 24

Ensemble Distributionally Robust Bayesian Optimisation with Continuous Context

arXiv:2605. 07565v2 Announce Type: replace-cross Abstract: We study Bayesian Optimisation (BO) in settings where the objective function is influenced by uncontrollable environmental contexts governed by an unknown probability distribution.

By Tigran Ramazyan, Denis Derkach

benchmarks

arXiv Machine Learning

Jun 19

On the QUEST for Uncertainty Quantification via Highest Density Regions

arXiv:2606. 19569v1 Announce Type: new Abstract: Uncertainty quantification (UQ) is essential for reliable decision-making in safety-critical applications in probabilistic machine learning.

By Sam Goring, Tom Kuipers, Nicola Paoletti, David S. Watson

benchmarks safety

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

Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness

Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Prediction

Subjective Risk Decomposition: A New View for Uncertainty Quantification

Ensemble Distributionally Robust Bayesian Optimisation with Continuous Context

On the QUEST for Uncertainty Quantification via Highest Density Regions