Decision Making Under Uncertainty

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

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Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 57 papers with code (263 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Neur2SP: Neural Two-Stage Stochastic Programming
  20 May 2022 5 repositories listed Syntology ran 5 of 10 samples · 5 unverified · 10 pointer-only (licence)
  Stochastic Programming is a powerful modeling framework for decision-making under uncertainty.
• Emulation of physical processes with Emukit
  25 Oct 2021 2 repositories listed
  Decision making in uncertain scenarios is an ubiquitous challenge in real world systems.
• Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification
  4 Dec 2020 2 repositories listed Syntology ran 1 of 3 samples · 2 unverified · 3 pointer-only (licence)
  Bayesian neural networks (BNNs) have been long considered an ideal, yet unscalable solution for improving the robustness and the predictive uncertainty of deep neural networks.
• Bayesian Optimization of Risk Measures
  10 Jul 2020 2 repositories listed Syntology ran 2 of 4 samples · 2 unverified · 4 pointer-only (licence)
  We consider Bayesian optimization of objective functions of the form ρ[ F(x, W) ], where F is a black-box expensive-to-evaluate function and ρ denotes either the VaR or CVaR risk measure, computed with respect to the…
• Synthetic Time Series Forecasting with Transformer Architectures: Extensive Simulation Benchmarks
  26 May 2025 1 repository listed
  Time series forecasting plays a critical role in domains such as energy, finance, and healthcare, where accurate predictions inform decision-making under uncertainty.
• Comparing Exploration-Exploitation Strategies of LLMs and Humans: Insights from Standard Multi-armed Bandit Tasks
  15 May 2025 1 repository listed
  In simple stationary tasks, reasoning-enabled LLMs exhibit similar levels of random and directed exploration compared to humans.
• ICNN-enhanced 2SP: Leveraging input convex neural networks for solving two-stage stochastic programming
  8 May 2025 1 repository listed
  Existing learning-based methods like Neural Two-Stage Stochastic Programming (Neur2SP) employ neural networks (NNs) as recourse function surrogates but rely on computationally intensive mixed-integer programming (MIP)…
• A friendly introduction to triangular transport
  27 Mar 2025 1 repository listed
  Decision making under uncertainty is a cross-cutting challenge in science and engineering.
• NeuroSep-CP-LCB: A Deep Learning-based Contextual Multi-armed Bandit Algorithm with Uncertainty Quantification for Early Sepsis Prediction
  20 Mar 2025 1 repository listed
  In critical care settings, timely and accurate predictions can significantly impact patient outcomes, especially for conditions like sepsis, where early intervention is crucial.
• Crafting desirable climate trajectories with RL explored socio-environmental simulations
  9 Oct 2024 1 repository listed
  For example, the UN uses outputs of IAMs for their recent Intergovernmental Panel on Climate Change (IPCC) reports.
• End-to-End Conformal Calibration for Optimization Under Uncertainty
  30 Sep 2024 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  However, ensuring robustness guarantees requires well-calibrated uncertainty estimates, which can be difficult to achieve in high-capacity prediction models such as deep neural networks.
• Adaptive Two-Stage Cloud Resource Scaling via Hierarchical Multi-Indicator Forecasting and Bayesian Decision-Making
  2 Aug 2024 1 repository listed
  The surging demand for cloud computing resources, driven by the rapid growth of sophisticated large-scale models and data centers, underscores the critical importance of efficient and adaptive resource allocation.
• Differentiable Distributionally Robust Optimization Layers
  24 Jun 2024 1 repository listed
  As an application of the proposed differentiable DRO layers, we develop a novel decision-focused learning pipeline for contextual distributionally robust decision-making tasks and compare it with the prediction-focused…
• Conditional Bayesian Quadrature
  24 Jun 2024 1 repository listed
  We propose a novel approach for estimating conditional or parametric expectations in the setting where obtaining samples or evaluating integrands is costly.
• Decision-Focused Forecasting: A Differentiable Multistage Optimisation Architecture
  23 May 2024 1 repository listed
  Most decision-focused learning work has focused on single stage problems whereas many real-world decision problems are more appropriately modelled using multistage optimisation.
• High-dimensional forecasting with known knowns and known unknowns
  26 Jan 2024 1 repository listed
  Forecasts play a central role in decision making under uncertainty.
• Decision Making in Non-Stationary Environments with Policy-Augmented Search
  6 Jan 2024 1 repository listed
  In this paper, we introduce \textit{Policy-Augmented Monte Carlo tree search} (PA-MCTS), which combines action-value estimates from an out-of-date policy with an online search using an up-to-date model of the…
• Solving Long-run Average Reward Robust MDPs via Stochastic Games
  21 Dec 2023 1 repository listed Syntology ran 1 of 4 samples · 3 unverified · 4 pointer-only (licence)
  We present a novel perspective on this problem and show that it can be reduced to solving long-run average reward turn-based stochastic games with finite state and action spaces.
• Explaining Predictive Uncertainty with Information Theoretic Shapley Values
  21 Sep 2023 1 repository listed
  Researchers in explainable artificial intelligence have developed numerous methods for helping users understand the predictions of complex supervised learning models.
• Measurement Simplification in ρ-POMDP with Performance Guarantees
  19 Sep 2023 1 repository listed
  In both cases we show a significant speed-up in planning with performance guarantees.
• Simple Modification of the Upper Confidence Bound Algorithm by Generalized Weighted Averages
  28 Aug 2023 1 repository listed
  In preliminary experiments, we investigated the optimal parameters of a simple generalized UCB1 (G-UCB1), prepared for comparison and GWA-UCB1, in a stochastic MAB problem with two arms.
• Uncertainty Quantification for Image-based Traffic Prediction across Cities
  11 Aug 2023 1 repository listed
  We compare two epistemic and two aleatoric UQ methods on both temporal and spatio-temporal transfer tasks, and find that meaningful uncertainty estimates can be recovered.
• Collective Intelligence in Human-AI Teams A Bayesian Theory of Mind Approach
  26 Jun 2023 1 repository listed
  Observing teams' first 25% of messages explains about 8% of the variation in final team performance, a 170% improvement compared to the current state of the art.
• Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers
  23 May 2023 1 repository listed
  Recent works show that the data distribution in a network's latent space is useful for estimating classification uncertainty and detecting Out-of-distribution (OOD) samples.
• Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs
  29 Mar 2023 1 repository listed
  Inverse optimal control can be used to characterize behavior in sequential decision-making tasks.
• Minimax-Bayes Reinforcement Learning
  21 Feb 2023 1 repository listed
  While the Bayesian decision-theoretic framework offers an elegant solution to the problem of decision making under uncertainty, one question is how to appropriately select the prior distribution.
• Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO Algorithm
  10 Nov 2022 1 repository listed
  We consider the problem of decision-making under uncertainty in an environment with safety constraints.
• Bayesian Optimization with Conformal Prediction Sets
  22 Oct 2022 1 repository listed Syntology ran 3 of 3 samples · 0 unverified
  Bayesian optimization is a coherent, ubiquitous approach to decision-making under uncertainty, with applications including multi-arm bandits, active learning, and black-box optimization.
• Calibration tests beyond classification
  21 Oct 2022 1 repository listed
  In the machine learning literature, different measures and statistical tests have been proposed and studied for evaluating the calibration of classification models.
• Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem
  24 Sep 2022 1 repository listed Syntology ran 0 of 2 samples · 2 unverified
  Bandits with knapsacks (BwK) is an influential model of sequential decision-making under uncertainty that incorporates resource consumption constraints.

Syntology lines on 7 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.