Poster "kullback-leibler divergence" Papers
11 papers found
Conference
Better Estimation of the Kullback--Leibler Divergence Between Language Models
Afra Amini, Tim Vieira, Ryan Cotterell
NEURIPS 2025arXiv:2504.10637
4
citations
Bounds on $L_p$ Errors in Density Ratio Estimation via $f$-Divergence Loss Functions
Yoshiaki Kitazawa
ICLR 2025
2
citations
Connecting Jensen–Shannon and Kullback–Leibler Divergences: A New Bound for Representation Learning
Reuben Dorent, Polina Golland, William (Sandy) Wells
NEURIPS 2025arXiv:2510.20644
DKDR: Dynamic Knowledge Distillation for Reliability in Federated Learning
Yueyang Yuan, Wenke Huang, Guancheng Wan et al.
NEURIPS 2025
Provable Benefit of Annealed Langevin Monte Carlo for Non-log-concave Sampling
Wei Guo, Molei Tao, Yongxin Chen
ICLR 2025arXiv:2407.16936
19
citations
Provable Robust Overfitting Mitigation in Wasserstein Distributionally Robust Optimization
Shuang Liu, Yihan Wang, Yifan Zhu et al.
ICLR 2025arXiv:2503.04315
Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold
Hoang Phuc Hau Luu, Hanlin Yu, Bernardo Williams et al.
ICLR 2025arXiv:2410.02490
1
citations
Variational Inference with Mixtures of Isotropic Gaussians
Marguerite Petit-Talamon, Marc Lambert, Anna Korba
NEURIPS 2025arXiv:2506.13613
A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization
Sebastian Sanokowski, Sepp Hochreiter, Sebastian Lehner
ICML 2024arXiv:2406.01661
53
citations
DistiLLM: Towards Streamlined Distillation for Large Language Models
Jongwoo Ko, Sungnyun Kim, Tianyi Chen et al.
ICML 2024arXiv:2402.03898
73
citations
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians
Tom Huix, Anna Korba, Alain Oliviero Durmus et al.
ICML 2024arXiv:2406.04012
10
citations