identifiability
Identifiability in the context of AI and statistical modeling refers to the ability to uniquely determine model parameters or structure based on the observed data. A model is said to be identifiable if its parameters can be recovered solely from the data observations without ambiguity.
- Differentiable Structure Learning and Causal Discovery for General Binary Data
- Disentangling misreporting from genuine adaptation in strategic settings: a causal approach
- Diverse Influence Component Analysis: A Geometric Approach to Nonlinear Mixture Identifiability
- Equivariance by Contrast: Identifiable Equivariant Embeddings from Unlabeled Finite Group Actions
- Graph–Smoothed Bayesian Black-Box Shift Estimator and Its Information Geometry
- Identifiability of Deep Polynomial Neural Networks
- Identifiability of Deep Polynomial Neural Networks
- Model–Behavior Alignment under Flexible Evaluation: When the Best-Fitting Model Isn’t the Right One
- Online Time Series Forecasting with Theoretical Guarantees
- Reward-oriented Causal Representation Learning
- Transferring Causal Effects using Proxies