[1] Constrained Causal Bayesian Optimization (ICML 2023)

[2] Additive Causal Bandits with Unknown Graph (ICML 2023)

[3] GFlowNets for Causal Discovery: an Overview (ICML SPIGM Workshop)

[4] Large-scale differentiable causal discovery of factor graphs (NeurIPS 2022)

[5] Bayesian structure learning with generative flow networks (UAI 2022)

[6] Bayesian learning of causal structure and mechanisms with GFlowNets and variational bayes (arXiv 2022)

[7] Dyngfn: Bayesian dynamic causal discovery using generative flow networks (arXiv 2023)

[8] Causal Discovery with Language Models as Imperfect Experts (ICML SPIGM Workshop)

[9] Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting (ICML 2023)

[10] Which Invariance Should We Transfer? A Causal Minimax Learning Approach (ICML 2023)

[11] Neural Algorithmic Reasoning with Causal Regulation (ICML 2023)

[12] Denoising Diffusion Probabilistic Models (NeurIPS 2020)

[13] E(n) Equivariant Graph Neural Networks (ICML 2021)

[14] DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking (ICLR 2023)

[15] Equivariant Diffusion for Molecule Generation in 3D (ICML 2022)

[16] GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation (ICLR 2022)

[17] SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks (NeurIPS 2020)

[18] Geometric Latent Diffusion Models for 3D Molecule Generation (ICML 2023)

[19] Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models (ICML 2023)

[20] GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration (ICML 2023)

[21] Mixed-curvature Variational Autoencoders (ICLR 2020)

[22] Hyperbolic Graph Convolutional Neural Networks (NeurIPS 2019)

[23] Learning Affinity with Hyperbolic Representation for Spatial Propagation (ICML 2023)

[24] Dynamic Spatial Propagation Network for Depth Completion (AAAI 2022)

[25] Pretraining Language Models with Human Preference (ICML 2023)

[26] Retrieval-augmented generation for knowledge-intensive NLP tasks (NeurIPS 2020)

[27] Retrieval augmented language model pre-training (ICML 2020)

[28] Dense passage retrieval for open-domain question answering (EMNLP 2020)

[29] Large Language Models Struggle to Learn Long-Tail Knowledge (ICML 2023)

[30] Cross-Modal Fine-Tuning: Align then Refine (ICML 2023)

[31] Frozen pretrained transformers as universal computation engines (AAAI 2022)

[32] Lift:Language-interfaced fine-tuning for non-language machine learning tasks (NeurIPS 2022)

[33] Gradient Surgery for Multi-Task Learning (NeurIPS 2020)

[34] Gradient Surgery for One-shot Unlearning on Generative Model (ICML 2023 Workshop on Generative AI & Law)

[35] Privacy-Preserving Gradient Surgery for Group Removal on Deep Network (Preprint)

[36] Certified Data Removal from Machine Learning Models (ICML 2020)

[37] Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks (CVPR 2020)

[38] Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations (ECCV 2020)

[39] Deep Unlearning via Randomized Conditionally Independent Hessians (CVPR 2020)

[40] Generative Flow Networks (https://yoshuabengio.org/2022/03/05/generative-flow-networks/)