% Key papers for Learning under log-concave distributions (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2023_on_69474,
  title = {{On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70438},
}

@inproceedings{icml2020_robust_38711,
  title = {{Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6772},
}

@inproceedings{neurips2020_the_60945,
  title = {{The Generalized Lasso with Nonlinear Observations and Generative Priors}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16926},
}

@inproceedings{neurips2017_sample_56648,
  title = {{Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9256},
}

@inproceedings{neurips2019_flexible_58290,
  title = {{Flexible Modeling of Diversity with Strongly Log-Concave Distributions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14422},
}

@inproceedings{neurips2019_a_57829,
  title = {{A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13852},
}

@inproceedings{neurips2020_robust_60733,
  title = {{Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17494},
}

@inproceedings{iclr2022_sqrt_30861,
  title = {{Sqrt(d) Dimension Dependence of Langevin Monte Carlo}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6903},
}

@inproceedings{icml2023_improved_43165,
  title = {{Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/23775},
}

@inproceedings{neurips2019_list_58551,
  title = {{List-decodable Linear Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14554},
}

@inproceedings{neurips2021_list_63243,
  title = {{List-Decodable Mean Estimation in Nearly-PCA Time}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28766},
}

@inproceedings{neurips2023_private_68051,
  title = {{Private estimation algorithms for stochastic block models and mixture models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71729},
}

@inproceedings{icml2024_diffusion_45596,
  title = {{Diffusion Posterior Sampling is Computationally Intractable}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32856},
}

@inproceedings{neurips2022_differentially_65395,
  title = {{Differentially Private Generalized Linear Models Revisited}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52915},
}

@inproceedings{icml2020_spread_38782,
  title = {{Spread Divergence}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6040},
}

@inproceedings{icml2021_consistent_39167,
  title = {{Consistent regression when oblivious outliers overwhelm}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10579},
}

@inproceedings{icml2021_oblivious_39678,
  title = {{Oblivious Sketching for Logistic Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10263},
}

@inproceedings{icml2023_phase_41374,
  title = {{Phase Transitions in the Detection of Correlated Databases}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24578},
}

@inproceedings{icml2023_multicalibration_42283,
  title = {{Multicalibration as Boosting for Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24024},
}

@inproceedings{icml2023_polynomial_42561,
  title = {{Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24389},
}

@inproceedings{iclr2026_learning_85392,
  title = {{Learning the Inverse Temperature of Ising Models under Hard Constraints using One Sample}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=DyDTtBUBEd},
}

@inproceedings{aaai2025_a_7876,
  title = {{A (1+?)-Approximation for Ultrametric Embedding in Subquadratic Time}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33703},
}

@inproceedings{icml2024_robustly_43689,
  title = {{Robustly Learning Single-Index Models via Alignment Sharpness}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34763},
}

@inproceedings{icml2024_robust_43851,
  title = {{Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34601},
}

@inproceedings{icml2024_a_44583,
  title = {{A Subquadratic Time Algorithm for Robust Sparse Mean Estimation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33869},
}

@inproceedings{icml2024_optimal_45377,
  title = {{Optimal bounds for \$ell\_p\$ sensitivity sampling via \$ell\_2\$ augmentation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33074},
}

@inproceedings{icml2025_learning_45856,
  title = {{Learning multivariate Gaussians with imperfect advice}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46687},
}

@inproceedings{icml2025_improved_47520,
  title = {{Improved Learning via k-DTW: A Novel Dissimilarity Measure for Curves}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45076},
}

@inproceedings{icml2025_batch_47551,
  title = {{Batch List-Decodable Linear Regression via Higher Moments}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45045},
}

@inproceedings{icml2025_parallel_48706,
  title = {{Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43916},
}

@inproceedings{icml2026_new_50142,
  title = {{New Bounds for Kernel Sums via Fast Spherical Embeddings}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=9Mms28wD82},
}

@inproceedings{icml2026_active_50807,
  title = {{Active Regression for Single-Index Models with Unknown Link Functions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Fuaz3QFAdB},
}

@inproceedings{icml2026_high_50869,
  title = {{High-Accuracy Sampling for Diffusion Models and Log-Concave Distributions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=GW3umRqsZZ},
}

@inproceedings{neurips2024_reverse_71043,
  title = {{Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96167},
}

@inproceedings{neurips2024_data_71201,
  title = {{Data subsampling for Poisson regression with pth-root-link}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96031},
}

@inproceedings{neurips2024_sample_71761,
  title = {{Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95528},
}

@inproceedings{neurips2024_log_72600,
  title = {{Log-concave Sampling from a Convex Body with a Barrier: a Robust and Unified Dikin Walk}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94777},
}

@inproceedings{neurips2024_robust_72701,
  title = {{Robust Sparse Regression with Non-Isotropic Designs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94686},
}

@inproceedings{neurips2024_a_74667,
  title = {{A Separation in Heavy-Tailed Sampling: Gaussian vs. Stable Oracles for Proximal Samplers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/92929},
}