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07/11/2023
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
The practical utility of causality in decision-making is widespread and ...
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07/04/2023
Generative Flow Networks: a Markov Chain Perspective
While Markov chain Monte Carlo methods (MCMC) provide a general framewor...
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06/26/2023
BatchGFN: Generative Flow Networks for Batch Active Learning
We introduce BatchGFN – a novel approach for pool-based active learning ...
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05/30/2023
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
Generative Flow Networks (GFlowNets), a class of generative models over ...
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02/01/2023
GFlowNets for AI-Driven Scientific Discovery
Tackling the most pressing problems for humanity, such as the climate cr...
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01/30/2023
A theory of continuous generative flow networks
Generative flow networks (GFlowNets) are amortized variational inference...
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11/26/2022
Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective
Although disentangled representations are often said to be beneficial fo...
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11/04/2022
Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes
Bayesian causal structure learning aims to learn a posterior distributio...
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10/24/2022
Learning Latent Structural Causal Models
Causal learning has long concerned itself with the accurate recovery of ...
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10/02/2022
GFlowNets and variational inference
This paper builds bridges between two families of probabilistic algorith...
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03/02/2022
Continuous-Time Meta-Learning with Forward Mode Differentiation
Drawing inspiration from gradient-based meta-learning methods with infin...
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02/28/2022
Bayesian Structure Learning with Generative Flow Networks
In Bayesian structure learning, we are interested in inferring a distrib...
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01/27/2022
Rethinking Learning Dynamics in RL using Adversarial Networks
We present a learning mechanism for reinforcement learning of closely re...
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01/27/2022
The Effect of Diversity in Meta-Learning
Few-shot learning aims to learn representations that can tackle novel ta...
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11/17/2021
GFlowNet Foundations
Generative Flow Networks (GFlowNets) have been introduced as a method to...
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02/07/2021
Structured Sparsity Inducing Adaptive Optimizers for Deep Learning
The parameters of a neural network are naturally organized in groups, so...
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02/19/2020
Curriculum in Gradient-Based Meta-Reinforcement Learning
Gradient-based meta-learners such as Model-Agnostic Meta-Learning (MAML)...
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10/18/2019
The TCGA Meta-Dataset Clinical Benchmark
Machine learning is bringing a paradigm shift to healthcare by changing ...
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09/14/2019
Torchmeta: A Meta-Learning library for PyTorch
The constant introduction of standardized benchmarks in the literature h...
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06/11/2019
Learning Powerful Policies by Using Consistent Dynamics Model
Model-based Reinforcement Learning approaches have the promise of being ...
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06/05/2019
Gradient-Based Neural DAG Learning
We propose a novel score-based approach to learning a directed acyclic g...
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01/30/2019
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
We propose to meta-learn causal structures based on how fast a learner a...
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12/05/2018