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Everything about Constrained conditional model
Machine learning and inference framework
A constrained conditional model (CCM) is a machine learning and inference framework that augments the learning of conditional models with declarative constraints. The constraint can be used as a way to incorporate expressive prior knowledge into the model and bias the assignments made by the learned model to satisfy these constraints. The framework can be used to support decisions in an expressive output space while maintaining modularity and tractability of training and inference. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Constrained conditional modelA 5-minute orientation
- Skim a recent paper: Overtaking decision and trajectory planning in highway via hierarchical architecture of conditional state machine and chance constrained model predictive controlInfluential work since 2022
- Pick up a classic book: Optimization of Objective FunctionsThe most-reprinted book on the subject
- Read the landmark paper: Structured learning with constrained conditional modelsCited 50 times
- Go sideways: Discriminative modelA closely related topic
- Go sideways: Constrained least squaresA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Structured learning with constrained conditional models
Constrained Conditional Moment Restriction Models
Conditional chance‐constrained model for reservoir control
Correspondence Analysis and Association Models Constrained by a Conditional Independence Graph
Conditional average treatment effect estimation with marginally constrained models
A global-local interaction and conditional consistency constrained diffusion model for SAR-guided optical image cloud removal
A Data-Constrained and Physics-Guided Conditional Diffusion Model for Electrical Impedance Tomography Image Reconstruction
Exact and stochastic nonlinear constrained estimation in the conditional poisson log-linear model
🆕 Influential since 2022
Overtaking decision and trajectory planning in highway via hierarchical architecture of conditional state machine and chance constrained model predictive control
ECPNet: An Efficient Lightweight Conditional Parameterization Model for Medical Image Segmentation for Resource-Constrained Devices
A Multi-Source Constrained Conditional Latent Diffusion Model for Reconstruction of Incomplete Turbulent Flow Fields
AI-derived constrained conditional model for screening marker genes through integrated high-throughput transcriptome big data
Diffusion-RarePHM: A Physics-Constrained Conditional Latent World Model for Multi-Future Degradation Forecasting and Rare-Fault Prognostics
📚 Books worth reading
Optimization of Objective FunctionsNinul Anatoly Sergeevich · 2009
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Discriminative model Discriminative models, also referred to as conditional models, are a class of models frequently used for classification. In machine learning, it typically…
- Constrained least squares In constrained least squares one solves a linear least squares problem with an additional constraint on the solution. This means, the unconstrained equation…
- Augmented Lagrangian method Augmented Lagrangian methods are a certain class of algorithms for solving constrained optimization problems. They have similarities to penalty methods…
- Markov random field In the domain of physics and probability, a Markov random field (MRF), Markov network or undirected graphical model is a set of random variables having…
- Constrained optimization In mathematical optimization, constrained optimization (in some contexts called constraint optimization) is the process of optimizing an objective function…
- List of optimization software Given a transformation between input and output values, described by a mathematical function, optimization deals with generating and selecting the best…
- Gekko (optimization software) The GEKKO Python package solves large-scale mixed-integer and differential algebraic equations with nonlinear programming solvers (IPOPT, APOPT, BPOPT…
- Conditional random field In pattern recognition and machine learning, conditional random field (CRF) is a class of statistical modeling methods often used for structured prediction…