Cover Image

On the Unknowable Limits to Prediction

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yan, Jiani, Rahal, Charles
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence Computers and Society Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Ethnography and Machine Learning: Synergies and New Directions
by: Li, Zhuofan, et al.
Published: (2024)

A Causal Analysis of CO2 Reduction Strategies in Electricity Markets Through Machine Learning-Driven Metalearners
by: Naeini, Iman Emtiazi, et al.
Published: (2024)

What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning
by: Deng, Zhihong, et al.
Published: (2024)

Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks
by: Zhang, Lujing, et al.
Published: (2024)

Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework
by: Schröder, Maresa, et al.
Published: (2023)

Partial Identification Approach to Counterfactual Fairness Assessment
by: Rho, Saeyoung, et al.
Published: (2025)

Auditing Fairness by Betting
by: Chugg, Ben, et al.
Published: (2023)

Distinguishing the Knowable from the Unknowable with Language Models
by: Ahdritz, Gustaf, et al.
Published: (2024)

Beyond Interaction Effects: Two Logics for Studying Population Inequalities
by: Daoud, Adel
Published: (2025)

Black Box Causal Inference: Effect Estimation via Meta Prediction
by: Bynum, Lucius E. J., et al.
Published: (2025)

Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
by: Kıcıman, Emre, et al.
Published: (2023)

Falsifying Predictive Algorithm
by: Coston, Amanda
Published: (2026)

Uplift Modeling Under Limited Supervision
by: Panagopoulos, George, et al.
Published: (2024)

Predictive Performance Comparison of Decision Policies Under Confounding
by: Guerdan, Luke, et al.
Published: (2024)

Embedding Trust: Semantic Isotropy Predicts Nonfactuality in Long-Form Text Generation
by: Bhardwaj, Dhrupad, et al.
Published: (2025)

Improvement of Applicability in Student Performance Prediction Based on Transfer Learning
by: Zhao, Yan
Published: (2024)

Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning
by: Auddy, Arnab, et al.
Published: (2026)

Is Machine Learning Unsafe and Irresponsible in Social Sciences? Paradoxes and Reconsidering from Recidivism Prediction Tasks
by: Liu, Jianhong, et al.
Published: (2023)

Augmenting Limited and Biased RCTs through Pseudo-Sample Matching-Based Observational Data Fusion Method
by: Han, Kairong, et al.
Published: (2025)

Length Optimization in Conformal Prediction
by: Kiyani, Shayan, et al.
Published: (2024)

Bayesian Hierarchical Invariant Prediction
by: Madaleno, Francisco, et al.
Published: (2025)

A Computational Approach to Improving Fairness in K-means Clustering
by: Zhou, Guancheng, et al.
Published: (2025)

Prediction-Intervention Games and Invariant Sets
by: Kühne, Linus, et al.
Published: (2026)

Limits of trust in medical AI
by: Hatherley, Joshua
Published: (2025)

Conformal Prediction for Causal Effects of Continuous Treatments
by: Schröder, Maresa, et al.
Published: (2024)

Modeling and Discovering Direct Causes for Predictive Models
by: Chen, Yizuo, et al.
Published: (2024)

On Predictive planning and counterfactual learning in active inference
by: Paul, Aswin, et al.
Published: (2024)

Generalizing Orthogonalization for Models with Non-Linearities
by: Rügamer, David, et al.
Published: (2024)

An Introduction to Sliced Optimal Transport
by: Nguyen, Khai
Published: (2025)

Uncertainty Quantification for Prior-Data Fitted Networks using Martingale Posteriors
by: Nagler, Thomas, et al.
Published: (2025)

Lightspeed Geometric Dataset Distance via Sliced Optimal Transport
by: Nguyen, Khai, et al.
Published: (2025)

Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization
by: Zhang, Dinghuai, et al.
Published: (2023)

An AI-powered Bayesian Generative Modeling Approach for Arbitrary Conditional Inference
by: Liu, Qiao, et al.
Published: (2026)

Discriminative classification with generative features: bridging Naive Bayes and logistic regression
by: Terner, Zachary, et al.
Published: (2025)

Adjustment for Confounding using Pre-Trained Representations
by: Schulte, Rickmer, et al.
Published: (2025)

Outlier-robust neural network training: variation regularization meets trimmed loss to prevent functional breakdown
by: Okuno, Akifumi, et al.
Published: (2023)

Prediction-Powered Inference with Imputed Covariates and Nonuniform Sampling
by: Kluger, Dan M., et al.
Published: (2025)

Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding
by: Rambachan, Ashesh, et al.
Published: (2022)

Adaptive Prediction-Powered AutoEval with Reliability and Efficiency Guarantees
by: Park, Sangwoo, et al.
Published: (2025)

Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores
by: Kaur, Jivat Neet, et al.
Published: (2025)