Generative Flow Networks: Theory and Applications to Structure Learning.
CoRR, January, 2025
Gymnasium: A Standard Interface for Reinforcement Learning Environments.
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CoRR, 2024
Discrete Probabilistic Inference as Control in Multi-path Environments.
CoRR, 2024
J. Mach. Learn. Res., 2023
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation.
CoRR, 2023
Generative Flow Networks: a Markov Chain Perspective.
CoRR, 2023
BatchGFN: Generative Flow Networks for Batch Active Learning.
CoRR, 2023
GFlowNets for AI-Driven Scientific Discovery.
CoRR, 2023
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
A theory of continuous generative flow networks.
Proceedings of the International Conference on Machine Learning, 2023
Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning.
Proceedings of the International Conference on Machine Learning, 2023
GFlowNets and variational inference.
Proceedings of the Eleventh International Conference on Learning Representations, 2023
The Effect of Diversity in Meta-Learning.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023
Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective.
CoRR, 2022
Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes.
CoRR, 2022
Learning Latent Structural Causal Models.
CoRR, 2022
Rethinking Learning Dynamics in RL using Adversarial Networks.
CoRR, 2022
Bayesian structure learning with generative flow networks.
Proceedings of the Uncertainty in Artificial Intelligence, 2022
Continuous-Time Meta-Learning with Forward Mode Differentiation.
Proceedings of the Tenth International Conference on Learning Representations, 2022
Structured Sparsity Inducing Adaptive Optimizers for Deep Learning.
CoRR, 2021
Predicting Infectiousness for Proactive Contact Tracing.
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Proceedings of the 9th International Conference on Learning Representations, 2021
Curriculum in Gradient-Based Meta-Reinforcement Learning.
CoRR, 2020
Gradient-Based Neural DAG Learning.
Proceedings of the 8th International Conference on Learning Representations, 2020
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms.
Proceedings of the 8th International Conference on Learning Representations, 2020
The TCGA Meta-Dataset Clinical Benchmark.
CoRR, 2019
Torchmeta: A Meta-Learning library for PyTorch.
CoRR, 2019
Learning Powerful Policies by Using Consistent Dynamics Model.
CoRR, 2019
The effects of negative adaptation in Model-Agnostic Meta-Learning.
CoRR, 2018
Learning Operations on a Stack with Neural Turing Machines.
CoRR, 2016