continuous-time markov chain

Efficient Sampling with Discrete Diffusion Models: Sharp and Adaptive Guarantees COLT 2026 Convergence of Score-Based Discrete Diffusion Models: A Discrete-Time Analysis ICLR 2025 Efficient Perplexity Bound and Ratio Matching in Discrete Diffusion Language Models ICLR 2025 LEAPS: A discrete neural sampler via locally equivariant networks ICML 2025 Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design ICML 2024 Fast Computation of Branching Process Transition Probabilities via ADMM AISTATS 2023 A Continuous Time Framework for Discrete Denoising Models NeurIPS 2022 Forward-Backward Latent State Inference for Hidden Continuous-Time semi-Markov Chains NeurIPS 2022 Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler JMLR 2021 Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification NeurIPS 2021 Fast and Accurate Ranking Regression AISTATS 2020 Infinity Learning: Learning Markov Chains from Aggregate Steady-State Observations AAAI 2020 Integrating Specialized Classifiers Based on Continuous Time Markov Chain IJCAI 2017 Efficient Continuous-Time Markov Chain Estimation ICML 2014 Fast MCMC Sampling for Markov Jump Processes and Extensions JMLR 2013 Maximum likelihood trajectories for continuous-time Markov chains NeurIPS 2009