Copertina

Seeing Through Risk: A Symbolic Approximation of Prospect Theory

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Yousaf, Ali Arslan, Rehman, Umair, Danish, Muhammad Umair
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Artificial Intelligence Machine Learning Optimization and Control Methodology
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Provable Offline Reinforcement Learning for Structured Cyclic MDPs
di: Lee, Kyungbok, et al.
Pubblicazione: (2026)

Leveraging Hypernetworks and Learnable Kernels for Consumer Energy Forecasting Across Diverse Consumer Types
di: Danish, Muhammad Umair, et al.
Pubblicazione: (2025)

Data-Driven Sequential Sampling for Tail Risk Mitigation
di: Ahn, Dohyun, et al.
Pubblicazione: (2025)

Decision-Focused Bias Correction for Fluid Approximation
di: Er, Can, et al.
Pubblicazione: (2025)

Kolmogorov-Arnold Recurrent Network for Short Term Load Forecasting Across Diverse Consumers
di: Danish, Muhammad Umair, et al.
Pubblicazione: (2025)

Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning
di: Dickens, Charles, et al.
Pubblicazione: (2024)

Optimal Pattern Detection Tree for Symbolic Rule-Based Classification
di: Hong, Young-Chae, et al.
Pubblicazione: (2026)

An Approximate Ascent Approach To Prove Convergence of PPO
di: Doering, Leif, et al.
Pubblicazione: (2026)

Logistics Hub Location Optimization: A K-Means and P-Median Model Hybrid Approach Using Road Network Distances
di: Rahman, Muhammad Abdul, et al.
Pubblicazione: (2023)

Physics-Guided Memory Network for Building Energy Modeling
di: Danish, Muhammad Umair, et al.
Pubblicazione: (2025)

DiscoverDCP: A Data-Driven Approach for Construction of Disciplined Convex Programs via Symbolic Regression
di: Myhre, Sveinung
Pubblicazione: (2025)

LEGAL-UQA: A Low-Resource Urdu-English Dataset for Legal Question Answering
di: Faisal, Faizan, et al.
Pubblicazione: (2024)

Universal Approximation Theorem for Deep Q-Learning via FBSDE System
di: Qi, Qian
Pubblicazione: (2025)

Weighted Low-rank Approximation via Stochastic Gradient Descent on Manifolds
di: Xu, Conglong, et al.
Pubblicazione: (2025)

Ginger: An Efficient Curvature Approximation with Linear Complexity for General Neural Networks
di: Hao, Yongchang, et al.
Pubblicazione: (2024)

Asymptotic and Finite Sample Analysis of Nonexpansive Stochastic Approximations with Markovian Noise
di: Blaser, Ethan, et al.
Pubblicazione: (2024)

DCILP: A Distributed Approach for Large-Scale Causal Structure Learning
di: Dong, Shuyu, et al.
Pubblicazione: (2024)

A distribution-free mixed-integer optimization approach to hierarchical modelling of clustered and longitudinal data
di: Sankaranarayanan, Madhav, et al.
Pubblicazione: (2023)

Bridging Constraints and Stochasticity: A Fully First-Order Method for Stochastic Bilevel Optimization with Linear Constraints
di: Phan, Cac, et al.
Pubblicazione: (2025)

Fitted Q-Iteration via Max-Plus-Linear Approximation
di: Liu, Y., et al.
Pubblicazione: (2024)

Fuzzy hyperparameters update in a second order optimization
di: Bensadok, Abdelaziz, et al.
Pubblicazione: (2024)

AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization
di: Vaswani, Namrata
Pubblicazione: (2025)

Global Group Fairness in Federated Learning via Function Tracking
di: Rychener, Yves, et al.
Pubblicazione: (2025)

Optimizing Input Data Collection for Ranking and Selection
di: Song, Eunhye, et al.
Pubblicazione: (2025)

Calibrated Multi-Level Quantile Forecasting
di: Ding, Tiffany, et al.
Pubblicazione: (2025)

Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design
di: Saves, Paul, et al.
Pubblicazione: (2025)

"Over-optimizing" for Normality: Budget-constrained Uncertainty Quantification for Contextual Decision-making
di: Wang, Yanyuan, et al.
Pubblicazione: (2025)

Automatic Outlier Rectification via Optimal Transport
di: Blanchet, Jose, et al.
Pubblicazione: (2024)

Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decomposition
di: Dong, Shuyu, et al.
Pubblicazione: (2022)

Robust Data-driven Prescriptiveness Optimization
di: Poursoltani, Mehran, et al.
Pubblicazione: (2023)

Fast Robust Kernel Regression through Sign Gradient Descent with Early Stopping
di: Allerbo, Oskar
Pubblicazione: (2023)

SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems
di: Islam, H M Mohaimanul, et al.
Pubblicazione: (2024)

Random Pareto front surfaces
di: Tu, Ben, et al.
Pubblicazione: (2024)

Optimal Cross-Validation for Sparse Linear Regression
di: Cory-Wright, Ryan, et al.
Pubblicazione: (2023)

Changing the Kernel During Training Leads to Double Descent in Kernel Regression
di: Allerbo, Oskar
Pubblicazione: (2023)

Learning Sequential Decisions from Multiple Sources via Group-Robust Markov Decision Processes
di: Xu, Mingyuan, et al.
Pubblicazione: (2026)

From Contextual Data to Newsvendor Decisions: On the Actual Performance of Data-Driven Algorithms
di: Besbes, Omar, et al.
Pubblicazione: (2023)

Arc travel time and path choice model estimation subsumed
di: Mohammadpour, Sobhan, et al.
Pubblicazione: (2022)

Data-Driven Influence Functions for Optimization-Based Causal Inference
di: Jordan, Michael I., et al.
Pubblicazione: (2022)

Locally Adaptive Multi-Objective Learning
di: Kaur, Jivat Neet, et al.
Pubblicazione: (2026)