DAGs with NO TEARS: Continuous Optimization for Structure Learning

4 March 2018

Papers citing "DAGs with NO TEARS: Continuous Optimization for Structure Learning"

50 / 152 papers shown

Title
Rethinking Invariance in In-context Learning Lizhe Fang Yifei Wang Khashayar Gatmiry Lei Fang Y. Wang 54 2 0 08 May 2025
HF4Rec: Human-Like Feedback-Driven Optimization Framework for Explainable Recommendation Jiakai Tang Jingsen Zhang Zihang Tian Xueyang Feng Lei Wang Xu Chen OffRL 126 0 0 19 Apr 2025
Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery Rebecca Herman Jonas Wahl Urmi Ninad Jakob Runge 52 0 0 21 Mar 2025
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions Juan L. Gamella Armeen Taeb C. Heinze-Deml Peter Buhlmann CML 94 7 0 13 Mar 2025
Adapt3R: Adaptive 3D Scene Representation for Domain Transfer in Imitation Learning Albert Wilcox Mohamed Ghanem Masoud Moghani Pierre Barroso Benjamin Joffe Animesh Garg 50 0 0 06 Mar 2025
IGDA: Interactive Graph Discovery through Large Language Model Agents Alex Havrilla David Alvarez-Melis Nicolò Fusi AI4CE 45 0 0 24 Feb 2025
Causal Temporal Regime Structure Learning Abdellah Rahmani Pascal Frossard CML 78 2 0 20 Feb 2025
Unrealized Expectations: Comparing AI Methods vs Classical Algorithms for Maximum Independent Set Yikai Wu Haoyu Zhao Sanjeev Arora 82 0 0 05 Feb 2025
GPO-VAE: Modeling Explainable Gene Perturbation Responses utilizing GRN-Aligned Parameter Optimization Seungheun Baek Soyon Park Y. T. Chok Mogan Gim Jaewoo Kang DRL 42 0 0 31 Jan 2025
Causal Discovery via Bayesian Optimization Bao Duong Sunil Gupta Thin Nguyen 44 0 0 28 Jan 2025
Low-Dimensional Representation-Driven TSK Fuzzy System for Feature Selection Qiong Liu Mingjie Cai Qingguo Li 68 0 0 22 Jan 2025
SSL Framework for Causal Inconsistency between Structures and Representations Hang Chen Xinyu Yang Keqing Du Wenya Wang 52 2 0 03 Jan 2025
LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Data Yue Cheng Jiajun Zhang Weiwei Xing Xiaoyu Guo Yue Cheng Witold Pedrycz CML 32 0 0 25 Oct 2024
A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery Yingyu Lin Yuxing Huang Wenqin Liu Haoran Deng Ignavier Ng Kun Zhang Mingming Gong Yi-An Ma Biwei Huang 33 1 0 08 Oct 2024
Zero-Shot Learning of Causal Models Divyat Mahajan Jannes Gladrow Agrin Hilmkil Cheng Zhang M. Scetbon 39 1 0 08 Oct 2024
Causal Reinforcement Learning for Optimisation of Robot Dynamics in Unknown Environments Julian Gerald Dcruz Sam Mahoney Jia Yun Chua Adoundeth Soukhabandith John Mugabe Weisi Guo Miguel Arana-Catania 24 0 0 20 Sep 2024
Causal Inference with Large Language Model: A Survey Jing Ma CML LRM 91 8 0 15 Sep 2024
Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning Shi Bo Minheng Xiao 36 7 0 11 Aug 2024
ExDAG: Exact learning of DAGs Pavel Rytír Ales Wodecki Jakub Marecek CML 41 1 0 21 Jun 2024
Standardizing Structural Causal Models Weronika Ormaniec Scott Sussex Lars Lorch Bernhard Schölkopf Andreas Krause CML 56 5 0 17 Jun 2024
Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency Alan Nawzad Amin Andrew Gordon Wilson CML 34 1 0 13 Jun 2024
Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning Ashka Shah Adela DePavia Nathaniel Hudson Ian T. Foster Rick L. Stevens CML 31 1 0 10 Jun 2024
Embarrassingly Parallel GFlowNets Tiago da Silva Luiz Max Carvalho Amauri Souza Samuel Kaski Diego Mesquita 42 1 0 05 Jun 2024
Demystifying amortized causal discovery with transformers Francesco Montagna Max Cairney-Leeming Dhanya Sridhar Francesco Locatello CML 57 1 0 27 May 2024
ProDAG: Projection-Induced Variational Inference for Directed Acyclic Graphs Ryan Thompson Edwin V. Bonilla Robert Kohn 40 0 0 24 May 2024
ALCM: Autonomous LLM-Augmented Causal Discovery Framework Elahe Khatibi Mahyar Abbasian Zhongqi Yang Iman Azimi Amir M. Rahmani 64 12 0 02 May 2024
Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models Aneesh Komanduri Chengli Zhao Feng Chen Xintao Wu CML DiffM 35 4 0 27 Apr 2024
Inference of Causal Networks using a Topological Threshold Filipe Barroso Diogo Gomes Gareth J. Baxter 29 0 0 21 Apr 2024
Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes Georg Manten Cecilia Casolo E. Ferrucci Søren Wengel Mogensen C. Salvi Niki Kilbertus CML BDL 44 8 0 28 Feb 2024
Towards Automated Causal Discovery: a case study on 5G telecommunication data Konstantina Biza Antonios Ntroumpogiannis Sofia Triantafillou Ioannis Tsamardinos 31 0 0 22 Feb 2024
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders Christian Toth Christian Knoll Franz Pernkopf Robert Peharz CML 43 1 0 22 Feb 2024
Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach Masayuki Takayama Tadahisa Okuda Thong Pham T. Ikenoue Shingo Fukuma Shohei Shimizu Akiyoshi Sannai 79 16 0 02 Feb 2024
Sample, estimate, aggregate: A recipe for causal discovery foundation models Menghua Wu Yujia Bao Regina Barzilay Tommi Jaakkola CML 49 7 0 02 Feb 2024
Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery Daniel Waxman Kurt Butler P. Djuric 33 3 0 05 Jan 2024
Enhancing the Performance of Neural Networks Through Causal Discovery and Integration of Domain Knowledge Xiaoge Zhang Xiao-Lin Wang Fenglei Fan Yiu-ming Cheung Indranil Bose 34 1 0 29 Nov 2023
Causal Structure Representation Learning of Confounders in Latent Space for Recommendation Hangtong Xu Yuanbo Xu Yongjian Yang Fuzhen Zhuang CML 77 0 0 02 Nov 2023
Recovering Linear Causal Models with Latent Variables via Cholesky Factorization of Covariance Matrix Yunfeng Cai Xu Li Ming Sun Ping Li CML 21 1 0 01 Nov 2023
Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice Damian Machlanski Spyridon Samothrakis Paul Clarke CML 36 1 0 27 Oct 2023
Causal Representation Learning Made Identifiable by Grouping of Observational Variables H. Morioka Aapo Hyvarinen OOD CML BDL 33 9 0 24 Oct 2023
Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery Victor-Alexandru Darvariu Stephen Hailes Mirco Musolesi CML 39 2 0 20 Oct 2023
Discovering Mixtures of Structural Causal Models from Time Series Data Sumanth Varambally Yi-An Ma Rose Yu 20 4 0 10 Oct 2023
CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery Yuxiao Cheng Ziqian Wang Tingxiong Xiao Qin Zhong J. Suo Kunlun He AI4TS CML 30 11 0 03 Oct 2023
Order-based Structure Learning with Normalizing Flows Hamidreza Kamkari Vahid Balazadeh Meresht Vahid Zehtab Rahul G. Krishnan CML 29 1 0 14 Aug 2023
Learning nonparametric DAGs with incremental information via high-order HSIC Yafei Wang Jianguo Liu CML 27 0 0 11 Aug 2023
Sparse Bayesian Estimation of Parameters in Linear-Gaussian State-Space Models Benjamin Cox Victor Elvira 36 10 0 20 Jun 2023
A Bayesian Take on Gaussian Process Networks Enrico Giudice Jack Kuipers G. Moffa GP 29 3 0 20 Jun 2023
$$\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery$ $\texttt{causalAssembly}$ : Generating Realistic Production Data for Benchmarking Causal Discovery Konstantin Göbler Tobias Windisch Mathias Drton T. Pychynski Steffen Sonntag Martin Roth CML 70 9 0 19 Jun 2023
Nonparametric Identifiability of Causal Representations from Unknown Interventions Julius von Kügelgen M. Besserve Wendong Liang Luigi Gresele Armin Kekić Elias Bareinboim David M. Blei Bernhard Schölkopf CML 18 56 0 01 Jun 2023
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network T. Deleu Mizu Nishikawa-Toomey Jithendaraa Subramanian Nikolay Malkin Laurent Charlin Yoshua Bengio BDL 32 43 0 30 May 2023
Learning DAGs from Data with Few Root Causes Panagiotis Misiakos Chris Wendler Markus Püschel CML 40 10 0 25 May 2023