{"total":243,"rows":[{"id":"2005.01643","title":"Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems","created":"2020-05-04","cited_by_count":1920,"area":"offlinerl"},{"id":"2206.14576","title":"Using cognitive psychology to understand GPT-3","created":"2022-06-21","cited_by_count":398,"area":null},{"id":"2106.04619","title":"Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style","created":"2021-06-08","cited_by_count":312,"area":null},{"id":"2103.02582","title":"D'ya like DAGs? A Survey on Structure Learning and Causal Discovery","created":"2021-03-03","cited_by_count":211,"area":null},{"id":"2102.11107","title":"Towards Causal Representation Learning","created":"2021-02-22","cited_by_count":209,"area":null},{"id":"2109.00725","title":"Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond","created":"2021-09-02","cited_by_count":178,"area":null},{"id":"2111.09794","title":"A Survey of Zero-shot Generalisation in Deep Reinforcement Learning","created":"2021-11-18","cited_by_count":170,"area":null},{"id":"2102.13647","title":"Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game","created":"2021-02-26","cited_by_count":161,"area":null},{"id":"2308.13067","title":"Causal Parrots: Large Language Models May Talk Causality But Are Not Causal","created":"2023-08-24","cited_by_count":150,"area":null},{"id":"2202.05441","title":"Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs","created":"2022-02-11","cited_by_count":144,"area":null},{"id":"2203.14237","title":"Causality Inspired Representation Learning for Domain Generalization","created":"2022-03-27","cited_by_count":138,"area":null},{"id":"2103.01737","title":"Distilling Causal Effect of Data in Class-Incremental Learning","created":"2021-03-02","cited_by_count":117,"area":null},{"id":"2101.06046","title":"Counterfactual Generative Networks","created":"2021-01-15","cited_by_count":111,"area":null},{"id":"2306.00542","title":"Nonparametric Identifiability of Causal Representations from Unknown Interventions","created":"2023-06-01","cited_by_count":106,"area":null},{"id":"2103.00887","title":"Counterfactual Zero-Shot and Open-Set Visual Recognition","created":"2021-03-01","cited_by_count":95,"area":null},{"id":"2205.12934","title":"Amortized Inference for Causal Structure Learning","created":"2022-05-25","cited_by_count":90,"area":null},{"id":"2103.05045","title":"Size-Invariant Graph Representations for Graph Classification Extrapolations","created":"2021-03-08","cited_by_count":84,"area":null},{"id":"2107.00637","title":"Generalization and Robustness Implications in Object-Centric Learning","created":"2021-07-01","cited_by_count":81,"area":null},{"id":"2101.02032","title":"Socially Responsible AI Algorithms: Issues, Purposes, and Challenges","created":"2021-01-01","cited_by_count":56,"area":null},{"id":"2202.03418","title":"Diversify and Disambiguate: Learning From Underspecified Data","created":"2022-02-07","cited_by_count":56,"area":null},{"id":"2306.15764","title":"High Fidelity Image Counterfactuals with Probabilistic Causal Models","created":"2023-06-27","cited_by_count":53,"area":null},{"id":"2109.02986","title":"Instance-dependent Label-noise Learning under a Structural Causal Model","created":"2021-09-07","cited_by_count":50,"area":null},{"id":"2301.08230","title":"Score-based Causal Representation Learning with Interventions","created":"2023-01-19","cited_by_count":49,"area":null},{"id":"2107.02729","title":"AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning","created":"2021-07-06","cited_by_count":48,"area":null},{"id":"2104.05441","title":"Unsuitability of NOTEARS for Causal Graph Discovery","created":"2021-04-12","cited_by_count":47,"area":null},{"id":"2201.09119","title":"A Causal Lens for Controllable Text Generation","created":"2022-01-22","cited_by_count":45,"area":null},{"id":"2203.11528","title":"Out-of-distribution Generalization with Causal Invariant Transformations","created":"2022-03-22","cited_by_count":45,"area":null},{"id":"2211.14666","title":"Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning","created":"2022-11-26","cited_by_count":44,"area":null},{"id":"2303.10112","title":"Causal Discovery from Temporal Data: An Overview and New Perspectives","created":"2023-03-17","cited_by_count":43,"area":null},{"id":"2105.02522","title":"Neural graphical modelling in continuous-time: consistency guarantees and algorithms","created":"2021-05-06","cited_by_count":42,"area":null},{"id":"2204.00607","title":"From Statistical to Causal Learning","created":"2022-04-01","cited_by_count":40,"area":null},{"id":"2211.03553","title":"Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling","created":"2022-11-07","cited_by_count":34,"area":null},{"id":"2107.05762","title":"Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses","created":"2021-07-12","cited_by_count":32,"area":null},{"id":"2402.06665","title":"The Essential Role of Causality in Foundation World Models for Embodied AI","created":"2024-02-06","cited_by_count":32,"area":"wm"},{"id":"2110.05721","title":"Action-Sufficient State Representation Learning for Control with Structural Constraints","created":"2021-10-12","cited_by_count":31,"area":null},{"id":"2206.10591","title":"Can Foundation Models Talk Causality?","created":"2022-06-14","cited_by_count":30,"area":null},{"id":"2203.10261","title":"FaiRR: Faithful and Robust Deductive Reasoning over Natural Language","created":"2022-03-19","cited_by_count":29,"area":"lr"},{"id":"2206.02928","title":"Neuro-Symbolic Procedural Planning with Commonsense Prompting","created":"2022-06-06","cited_by_count":28,"area":null},{"id":"2208.08345","title":"Discovering Agents","created":"2022-08-17","cited_by_count":28,"area":null},{"id":"2205.12331","title":"Certified Robustness Against Natural Language Attacks by Causal Intervention","created":"2022-05-24","cited_by_count":24,"area":null},{"id":"2106.08314","title":"Causal Navigation by Continuous-time Neural Networks","created":"2021-06-15","cited_by_count":23,"area":"offlinerl"},{"id":"2106.13364","title":"CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning","created":"2021-06-25","cited_by_count":23,"area":null},{"id":"2202.02896","title":"Evaluation Methods and Measures for Causal Learning Algorithms","created":"2022-02-07","cited_by_count":23,"area":null},{"id":"2102.10440","title":"Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models","created":"2021-02-20","cited_by_count":22,"area":null},{"id":"2307.09933","title":"Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features","created":"2023-07-19","cited_by_count":21,"area":null},{"id":"2103.08187","title":"Adversarial Training is Not Ready for Robot Learning","created":"2021-03-15","cited_by_count":20,"area":null},{"id":"2210.11557","title":"Learning Attention Propagation for Compositional Zero-Shot Learning","created":"2022-10-20","cited_by_count":20,"area":null},{"id":"2606.20104","title":"Sensorimotor World Models: Perception for Action via Inverse Dynamics","created":"2026-06-18","cited_by_count":20,"area":"wm"},{"id":"2103.12857","title":"Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptation","created":"2021-03-23","cited_by_count":19,"area":null},{"id":"2110.14690","title":"VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queries","created":"2021-10-27","cited_by_count":19,"area":null},{"id":"2208.09590","title":"Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey","created":"2022-08-20","cited_by_count":19,"area":null},{"id":"2310.17405","title":"Causal Modeling with Stationary Diffusions","created":"2023-10-26","cited_by_count":19,"area":null},{"id":"2110.15949","title":"Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains","created":"2021-10-29","cited_by_count":18,"area":null},{"id":"2206.15374","title":"Verification and search algorithms for causal DAGs","created":"2022-06-30","cited_by_count":18,"area":null},{"id":"2110.14118","title":"Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning","created":"2021-10-27","cited_by_count":17,"area":"offlinerl"},{"id":"2209.08541","title":"Distribution inference risks: Identifying and mitigating sources of leakage","created":"2022-09-18","cited_by_count":17,"area":null},{"id":"2406.11601","title":"Standardizing Structural Causal Models","created":"2024-06-17","cited_by_count":17,"area":null},{"id":"2109.11990","title":"Optimization-based Causal Estimation from Heterogenous Environments","created":"2021-09-24","cited_by_count":16,"area":null},{"id":"2111.06721","title":"Causal Multi-Agent Reinforcement Learning: Review and Open Problems","created":"2021-11-12","cited_by_count":16,"area":null},{"id":"2112.15324","title":"Deconfounded Visual Grounding","created":"2021-12-31","cited_by_count":16,"area":null},{"id":"2305.09703","title":"Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting","created":"2023-05-16","cited_by_count":16,"area":null},{"id":"2406.14302","title":"Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning","created":"2024-06-20","cited_by_count":16,"area":null},{"id":"2110.08413","title":"Invariant Language Modeling","created":"2021-10-16","cited_by_count":15,"area":null},{"id":"2102.05242","title":"Patterns, predictions, and actions: A story about machine learning","created":"2021-02-10","cited_by_count":14,"area":null},{"id":"2104.09582","title":"Robust Uncertainty Bounds in Reproducing Kernel Hilbert Spaces: A Convex Optimization Approach","created":"2021-04-19","cited_by_count":14,"area":null},{"id":"2106.06845","title":"Harmonization with Flow-based Causal Inference","created":"2021-06-12","cited_by_count":14,"area":null},{"id":"2204.04606","title":"Towards efficient representation identification in supervised learning","created":"2022-04-10","cited_by_count":14,"area":null},{"id":"2211.00184","title":"FL Games: A Federated Learning Framework for Distribution Shifts","created":"2022-10-31","cited_by_count":14,"area":null},{"id":"2301.08249","title":"Causal conditional hidden Markov model for multimodal traffic prediction","created":"2023-01-19","cited_by_count":14,"area":null},{"id":"2302.06975","title":"A Review of the Role of Causality in Developing Trustworthy AI Systems","created":"2023-02-14","cited_by_count":14,"area":null},{"id":"2304.07638","title":"Causal models in string diagrams","created":"2023-04-15","cited_by_count":14,"area":"lr"},{"id":"2306.10790","title":"Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference","created":"2023-06-19","cited_by_count":13,"area":null},{"id":"2410.22194","title":"ADAM: An Embodied Causal Agent in Open-World Environments","created":"2024-10-29","cited_by_count":13,"area":"agents"},{"id":"2209.07970","title":"Causal Fourier Analysis on Directed Acyclic Graphs and Posets","created":"2022-09-16","cited_by_count":12,"area":null},{"id":"2203.11547","title":"Explainability in reinforcement learning: perspective and position","created":"2022-03-22","cited_by_count":11,"area":null},{"id":"2204.05023","title":"Machine Learning and Deep Learning -- A review for Ecologists","created":"2022-04-11","cited_by_count":11,"area":null},{"id":"2206.14917","title":"Towards out of distribution generalization for problems in mechanics","created":"2022-06-29","cited_by_count":11,"area":null},{"id":"2211.03374","title":"Deep Causal Learning: Representation, Discovery and Inference","created":"2022-11-07","cited_by_count":11,"area":null},{"id":"2405.16225","title":"Local Causal Structure Learning in the Presence of Latent Variables","created":"2024-05-25","cited_by_count":11,"area":null},{"id":"2603.15381","title":"Why AI systems don't learn and what to do about it: Lessons on autonomous learning from cognitive science","created":"2026-03-16","cited_by_count":11,"area":null},{"id":"2106.11560","title":"Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge","created":"2021-06-22","cited_by_count":10,"area":null},{"id":"2107.10703","title":"Typing assumptions improve identification in causal discovery","created":"2021-07-22","cited_by_count":10,"area":null},{"id":"2209.08860","title":"A Survey of Deep Causal Models and Their Industrial Applications","created":"2022-09-19","cited_by_count":10,"area":null},{"id":"2210.03980","title":"Distilling Causal Effect from Miscellaneous Other-Class for Continual Named Entity Recognition","created":"2022-10-08","cited_by_count":10,"area":null},{"id":"2312.12844","title":"Effective Causal Discovery under Identifiable Heteroscedastic Noise Model","created":"2023-12-20","cited_by_count":10,"area":null},{"id":"2411.06890","title":"SPARTAN: A Sparse Transformer World Model Attending to What Matters","created":"2024-11-11","cited_by_count":10,"area":"wm"},{"id":"2507.10689","title":"CWNet: Causal Wavelet Network for Low-Light Image Enhancement","created":"2025-07-14","cited_by_count":10,"area":null},{"id":"2301.07843","title":"Spatio-temporal neural structural causal models for bike flow prediction","created":"2023-01-19","cited_by_count":9,"area":null},{"id":"2406.19049","title":"Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation","created":"2024-06-27","cited_by_count":9,"area":null},{"id":"2602.03018","title":"From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection","created":"2026-02-03","cited_by_count":9,"area":null},{"id":"2104.10105","title":"Neural Networks for Learning Counterfactual G-Invariances from Single Environments","created":"2021-04-20","cited_by_count":8,"area":null},{"id":"2108.11684","title":"Disentangled Generative Models for Robust Prediction of System Dynamics","created":"2021-08-26","cited_by_count":8,"area":null},{"id":"2305.01876","title":"Causality-aware Concept Extraction based on Knowledge-guided Prompting","created":"2023-05-03","cited_by_count":8,"area":null},{"id":"2305.17139","title":"A Measure-Theoretic Axiomatisation of Causality","created":"2023-05-19","cited_by_count":8,"area":null},{"id":"2307.08304","title":"Efficient Computation of Counterfactual Bounds","created":"2023-07-17","cited_by_count":8,"area":null},{"id":"2104.12278","title":"Causal Learning for Socially Responsible AI","created":"2021-04-25","cited_by_count":7,"area":null},{"id":"2107.09546","title":"Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions","created":"2021-07-20","cited_by_count":7,"area":null},{"id":"2301.12930","title":"Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing","created":"2023-01-26","cited_by_count":7,"area":null},{"id":"2407.16975","title":"On the Parameter Identifiability of Partially Observed Linear Causal Models","created":"2024-07-24","cited_by_count":7,"area":null},{"id":"2503.04852","title":"CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Data","created":"2025-03-06","cited_by_count":7,"area":null}],"next_cursor":"100"}