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Probabilistic Modeling

2,252 papers

Papers per year

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Papers

Transition Constrained Bayesian Optimization via Markov Decision Processes NIPS 2024
Model-Based Transfer Learning for Contextual Reinforcement Learning NIPS 2024
Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation NIPS 2024
Decomposable Transformer Point Processes NIPS 2024
Intervention and Conditioning in Causal Bayesian Networks NIPS 2024
Spectral Learning of Shared Dynamics Between Generalized-Linear Processes NIPS 2024
Near-Optimality of Contrastive Divergence Algorithms NIPS 2024
Batched Energy-Entropy acquisition for Bayesian Optimization NIPS 2024
Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention NIPS 2024
Piecewise deterministic generative models NIPS 2024
Dynamic Conditional Optimal Transport through Simulation-Free Flows NIPS 2024
Nonstationary Sparse Spectral Permanental Process NIPS 2024
An Approximate Skolem Function Counter AAAI 2024
Explainable Origin-Destination Crowd Flow Interpolation via Variational Multi-Modal Recurrent Graph Auto-Encoder AAAI 2024
LINGO-Space: Language-Conditioned Incremental Grounding for Space AAAI 2024
Data-Driven Knowledge-Aware Inference of Private Information in Continuous Double Auctions AAAI 2024
Sample-Constrained Black Box Optimization for Audio Personalization AAAI 2024
Active Learning Guided by Efficient Surrogate Learners AAAI 2024
Neural Network Approximators for Marginal MAP in Probabilistic Circuits AAAI 2024
Scores for Learning Discrete Causal Graphs with Unobserved Confounders AAAI 2024
A Theory of Non-acyclic Generative Flow Networks AAAI 2024
Kernelized Normalizing Constant Estimation: Bridging Bayesian Quadrature and Bayesian Optimization AAAI 2024
Big Learning Expectation Maximization AAAI 2024
Conformal Autoregressive Generation: Beam Search with Coverage Guarantees AAAI 2024
Double-Descent Curves in Neural Networks: A New Perspective Using Gaussian Processes AAAI 2024