Feedback in Imitation Learning: Confusion on Causality and Covariate Shift
https://arxiv.org/abs/2102.02872
https://arxiv.org/abs/2102.02872
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge
http://arxiv.org/abs/2102.06271
http://arxiv.org/abs/2102.06271
Efficient Learning with Arbitrary Covariate Shift
http://arxiv.org/abs/2102.07802
http://arxiv.org/abs/2102.07802
Forwarded from Data Science by ODS.ai 🦜
Towards Causal Representation Learning
Work on how neural networks derive casual variables from low-level observations.
Link: https://arxiv.org/abs/2102.11107
#casuallearning #bengio #nn #DL
Work on how neural networks derive casual variables from low-level observations.
Link: https://arxiv.org/abs/2102.11107
#casuallearning #bengio #nn #DL
The Deconfounded Recommender: A Causal Inference Approach to Recommendation
https://arxiv.org/abs/1808.06581
https://arxiv.org/abs/1808.06581
arXiv.org
The Deconfounded Recommender: A Causal Inference Approach to Recommendation
The goal of recommendation is to show users items that they will like. Though usually framed as a prediction, the spirit of recommendation is to answer an interventional question---for each user...
#study_materials
Introduction to Causal Inference by Brady Neal
(PhD student from MILA)
[videos, slides, a list of literature]
https://www.bradyneal.com/causal-inference-course
Introduction to Causal Inference by Brady Neal
(PhD student from MILA)
[videos, slides, a list of literature]
https://www.bradyneal.com/causal-inference-course
Bradyneal
Introduction to Causal Inference
Introduction to Causal Inference. A free online course on causal inference from a machine learning perspective.
Forwarded from Just links
Counterfactual VQA: A Cause-Effect Look at Language Bias https://arxiv.org/abs/2006.04315
Forwarded from Arxiv
- Probing Causal Common Sense in Dialogue Response Generation. (arXiv:2104.09574v1 [cs.CL])
http://arxiv.org/abs/2104.09574
http://arxiv.org/abs/2104.09574
#offtopic
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020
https://arxiv.org/abs/2104.10201
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020
https://arxiv.org/abs/2104.10201
Forwarded from Just links
Causal Inference Q-Network: Toward Resilient Reinforcement Learning https://arxiv.org/abs/2102.09677
Causal Effect Inference with Deep Latent-Variable Models
https://arxiv.org/abs/1705.08821
https://arxiv.org/abs/1705.08821
Adapting Neural Networks for the Estimation of Treatment Effects
https://arxiv.org/abs/1906.02120
https://arxiv.org/abs/1906.02120
arXiv.org
Adapting Neural Networks for the Estimation of Treatment Effects
This paper addresses the use of neural networks for the estimation of treatment effects from observational data. Generally, estimation proceeds in two stages. First, we fit models for the expected...
The Importance of Pessimism in Fixed-Dataset Policy Optimization
https://arxiv.org/abs/2009.06799
https://arxiv.org/abs/2009.06799
Nonlinear Invariant Risk Minimization: A Causal Approach
https://arxiv.org/abs/2102.12353
https://arxiv.org/abs/2102.12353
Causal Discovery with Reinforcement Learning
https://arxiv.org/abs/1906.04477
https://arxiv.org/abs/1906.04477
arXiv.org
Causal Discovery with Reinforcement Learning
Discovering causal structure among a set of variables is a fundamental problem in many empirical sciences. Traditional score-based casual discovery methods rely on various local heuristics to...
A causal framework for distribution generalization
https://arxiv.org/abs/2006.07433
https://arxiv.org/abs/2006.07433
In Search of Lost Domain Generalization
https://arxiv.org/abs/2007.01434
https://arxiv.org/abs/2007.01434
arXiv.org
In Search of Lost Domain Generalization
The goal of domain generalization algorithms is to predict well on distributions different from those seen during training. While a myriad of domain generalization algorithms exist,...
Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning
http://proceedings.mlr.press/v144/sonar21a.html
http://proceedings.mlr.press/v144/sonar21a.html
PMLR
Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning
A fundamental challenge in reinforcement learning is to learn policies that generalize beyond the operating domains experienced during training. In this pape...