joint distribution
Joint distribution refers to the probability distribution that captures the likelihood of two or more random variables simultaneously, providing insight into their interdependencies which is crucial for many AI tasks.
- ARIA: Training Language Agents with Intention-driven Reward Aggregation
- Conditional Distribution Compression via the Kernel Conditional Mean Embedding
- Constrained Best Arm Identification
- Large Language Bayes
- MGE-LDM: Joint Latent Diffusion for Simultaneous Music Generation and Source Extraction
- Maximizing the Value of Predictions in Control: Accuracy Is Not Enough
- Missing Data Imputation by Reducing Mutual Information with Rectified Flows
- Modeling Dynamic Neural Activity by combining Naturalistic Video Stimuli and Stimulus-independent Latent Factors
- Non-stationary Equivariant Graph Neural Networks for Physical Dynamics Simulation
- Topology-aware Graph Diffusion Model with Persistent Homology
- Towards Identifiability of Hierarchical Temporal Causal Representation Learning
- Unveiling Extraneous Sampling Bias with Data Missing-Not-At-Random