likelihood maximization

Flow Matching for Few-Trial Neural Adaptation with Stable Latent Dynamics ICML 2025 From Your Block to Our Block: How to Find Shared Structure Between Stochastic Block Models over Multiple Graphs AAAI 2025 Learning Regularization for Graph Inverse Problems AAAI 2025 Distribution Learning via Neural Differential Equations: A Nonparametric Statistical Perspective JMLR 2024 Soft Learning Probabilistic Circuits PGM 2024 On the Estimation of Gaussian Mixture Copula Models ICML 2023 Exploring Image Regions Not Well Encoded by an INN AISTATS 2022 Truth-Tracking via Approval Voting: Size Matters AAAI 2022 Improving Gaussian mixture latent variable model convergence with Optimal Transport ACML 2021 Scalable Normalizing Flows for Permutation Invariant Densities ICML 2021 Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification NeurIPS 2021 Studying word order through iterative shuffling EMNLP 2021 Truly Batch Model-Free Inverse Reinforcement Learning about Multiple Intentions AISTATS 2020 Representation Degeneration Problem in Training Natural Language Generation Models ICLR 2019 Estimation of Markov Chain via Rank-Constrained Likelihood ICML 2018 Learning Hawkes Processes from Short Doubly-Censored Event Sequences ICML 2017 A Probabilistic Collaborative Representation Based Approach for Pattern Classification CVPR 2016 Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm JMLR 2016 The Most Generative Maximum Margin Bayesian Networks ICML 2013 Quadratic Weighted Automata:Spectral Algorithm and Likelihood Maximization ACML 2011 Sensitivity analysis in HMMs with application to likelihood maximization NeurIPS 2009