domain generalization， OOD以及causality相关问题的前沿文章阅读清单，链接为笔记，没有链接就是还没看，欢迎大家commit
- Arxiv: Towards a Theoretical Framework of Out-of-Distribution Generalization （新理论）
- Arxiv(Yoshua Bengio) Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization (当OOD遇到信息瓶颈理论)
- Arxiv Generalization of Reinforcement Learning with Policy-Aware Adversarial Data Augmentation
- Arxiv Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation(使用知识蒸馏作为正则化手段)
- Arxiv Delving Deep into the Generalization of Vision Transformers under Distribution Shifts (视觉transformer的泛化性讨论)
- Arxiv Training Data Subset Selection for Regression with Controlled Generalization Error (从大量训练实例中选择数据子集，并保持可比的泛化性)
- Arxiv(MIT) Measuring Generalization with Optimal Transport (网络复杂度与泛化性的理论研究，)
- Arxiv(SJTU) OoD-Bench: Benchmarking and Understanding Out-of-Distribution Generalization Datasets and Algorithms (揭示OOD的评测标准尚不完善并提出评测方案)
- Arxiv (Tsinghu) Domain-Irrelevant Representation Learning for Unsupervised Domain Generalization (新的task：无监督的DG，源域的数据标签不可以用)
- ICML Oral： Can Subnetwork Structure be the Key to Out-of-Distribution Generalization? （彩票模型寻找模型中泛化能力更强的小模型）
- ICML Oral：Domain Generalization using Causal Matching (contrastive loss特征对齐+特征不变性约束)
- ICML Oral: Just Train Twice: Improving Group Robustness without Training Group Information
- ICML Spotlight: Environment Inference for Invariant Learning (没有域标签如何学习域不变性特征？)
- ICLR Poster: Understanding the failure modes of out-of-distribution generalization （OOD失败的两种原因）
- ICLR Poster: An Empirical Study of Invariant Risk Minimization(对IRM的实验性探索，如可见域的diversity如何影响IRM性能等)
- ICLR Poster In Search of Lost Domain Generalization (没有model selection的方法不是好方法，如何根据验证集选择模型？)
- ICLR Poster Modeling the Second Player in Distributionally Robust Optimization(用对抗学习建模DRO的uncertainty set)
- ICLR Poster Learning perturbation sets for robust machine learning(使用生成模型学习扰动集合)
- ICLR Spotlight(Yoshua Bengio) Systematic generalisation with group invariant predictions (将每个类分成不同的domain(environment inference，然后约束每个域的特征尽可能一致从而避免虚假依赖))
- CVPR Oral: Reducing Domain Gap by Reducing Style Bias (channel-wise 均值作为图像风格，减少CNN对风格的依赖)
- AISTATS Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions
- AISTATS Oral Does Invariant Risk Minimization Capture Invariance(IRM只有在满足特定条件的情况下才能真正捕捉不变形特征)
- Arxiv I-SPEC: An End-to-End Framework for Learning Transportable, Shift-Stable Models(将Domain Adaptation看作是因果图推理问题)
- Arxiv (Stanford)Distributionally Robust Lossesfor Latent Covariate Mixtures.
- NeurIPS Energy-based Out-of-distribution Detection(使用能量模型检测OOD样本)
- NeurIPS Fairness without demographics through adversarially reweighted learning (利用对抗学习对难样本进行加权，希望加权后的样本使得分类器的损失更大)
- NeurIPS Self-training Avoids Using Spurious Features Under Domain Shift (使用target domain的无标签数据训练有助于避免使用虚假特征)
- NeurIPS What shapes feature representations? Exploring datasets, architectures, and training(Simplicity Bias，神经网络倾向于拟合“容易”的特征)
- Arxiv Invariant Risk Minimization (奠基之作，跳出经验风险最小化--不变风险最小化)
- ICLR Poster The Risks of Invariant Risk Minimization (不变风险最小化的缺陷:域数目过少IRM即失败)
- ICLR Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization(GroupDRO: 拥有强正则的DRO)
- ICML An investigation of why overparameterizationexacerbates spurious correlations(神经网络的过参数化是造成网络使用虚假相关性的重要原因)
- ICML UDA workshop Learning Robust Representations with Score Invariant Learning(非归一化统计模型：用能量学习的方式做OOD)
- ICML 2018 Oral (Stanford) Fairness Without Demographics in Repeated Loss Minimization.
- ICCV 2017 CCSA--Unified Deep Supervised Domain Adaptation and Generalization (对比损失对齐源域目标域样本空间)
- Domain Adaptation基础概念与相关文章解读
- ICLR Poster Learning perturbation sets for robust machine learning(使用生成模型学习扰动集合)
- ICCV Generalized Source-free Domain Adaptation(不使用源域数据，只有源域预训练的模型时如何adaptation并保证source domain的性能)
- Available at Optimization Online Kullback-Leibler Divergence Constrained Distributionally Robust Optimization(开篇之作，使用KL散度构造DRO中的uncertainty set)
- ICLR 2018 Oral Certifying Some Distributional Robustnesswith Principled Adversarial Training(基于 Wasserstein-ball构造uncertainty set，用于adversarial robustness)
- ICML 2018 Oral Does Distributionally Robust Supervised Learning Give Robust Classifiers?(DRO就一定比ERM好？不一定！必须引入额外信息)
- NeurIPS 2019 Distributionally Robust Optimization and Generalization in Kernel Methods(本文使用MMD(maximummean discrepancy)对uncertainty set进行建模，得到了MMD DRO)
- EMNLP 2019 Distributionally Robust Language Modeling(Coarse-grained mixture models在NLP中的经典案例)
- JSTOR (Peters)Causal inference by using invariant prediction: identification and confidence intervals.
- ICML 2015 [Towards a Learning Theory of Cause-Effect Inference](使用kernel mean embedding和分类器进行casual inference )
- IJCAI 2020 (CMU) Causal Discovery from Heterogeneous/Nonstationary Data
- Causality 基础概念汇总
- ICML An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
- NeurIPS Deep Structural Causal Models for Tractable Counterfactual Inference
- ICML 2018 Bilevel Programming for Hyperparameter Optimization and Meta-Learning(用bi-level programming建模超参数搜索与meta-learning)
- NeurIPS Energy-based Out-of-distribution Detection
- NeurIPS 2020: The Lottery Ticket Hypothesis for Pre-trained BERT Networks (彩票假设用于BERT fine-tune))
- ICML 2021 Oral： Can Subnetwork Structure be the Key to Out-of-Distribution Generalization? (彩票假设用于OOD泛化)
- CVPR 2021: The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models (彩票假设用于视觉模型预训练)
- Estimation of Non-Normalized Statistical Models by Score Matching(使用分步积分（Score Matching）的方法解决非归一化分布的估计问题)
- UAI 2019 Sliced Score Matching: A Scalable Approach to Density and Score Estimation(将高维的梯度场沿随即方向投影到一维的标量场再进行score-macthing)
- NeurIPS 2019 Oral Generative Modeling by Estimating Gradients of the Data Distribution(通过添加噪声的方法，增强Langevin MCMC对低概率密度区域的建模能力)
- NeurIPS 2020 improved techniques for training score-based generative models(对score-based generative model失败案例的分析和改进，生成能力开始媲美GAN)
- NeurIPS 2020 Denoising Diffusion Probabilistic Models(除VAE, GAN, FLOW外又一生成范式)
- ICLR 2021 Outstanding Paper Award Score-Based Generative Modeling through Stochastic Differential Equations
- Arxiv 2021 Diffusion Models Beat GANs on Image Synthesis(Diffusion Models在图像和合成上超越GAN)
- Arxiv 2021 Variational Diffusion Models
- ACL 2021 WARP: Word-level Adversarial ReProgramming(Continuous Prompt开篇之作)
- Arxiv 2021 StanfordPrefix-Tuning: Optimizing Continuous Prompts for Generation(Continuous Prompt用于NLG的各种任务)
- Arxiv 2021 GoogleThe Power of Scale for Parameter-Efficient Prompt Tuning
- Arxiv 2021 PrincetonFactual Probing Is [MASK]: Learning vs. Learning to Recall
- Arxiv 2021 DeepMindMultimodal Few-Shot Learning with Frozen Language Models