Jason S. Hartford

According to our database, Jason S. Hartford authored at least 16 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Object-centric architectures enable efficient causal representation learning.

Amin Mansouri, Jason S. Hartford, Yan Zhang, Yoshua Bengio

CoRR, 2023

DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks.

Lazar Atanackovic, Alexander Tong, Jason S. Hartford, Leo J. Lee, Bo Wang, Yoshua Bengio

CoRR, 2023

GFlowNets for AI-Driven Scientific Discovery.

Moksh Jain, Tristan Deleu, Jason S. Hartford, Cheng-Hao Liu, Alex Hernández-García, Yoshua Bengio

CoRR, 2023

DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets.

Lazar Atanackovic, Alexander Tong, Bo Wang, Leo J. Lee, Yoshua Bengio, Jason S. Hartford

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Sequential Underspecified Instrument Selection for Cause-Effect Estimation.

Elisabeth Ailer, Jason S. Hartford, Niki Kilbertus

Proceedings of the International Conference on Machine Learning, 2023

2022

Monte Carlo Forest Search: UNSAT Solver Synthesis via Reinforcement learning.

Chris Cameron, Jason S. Hartford, Taylor Lundy, Tuan Truong, Alan Milligan, Rex Chen, Kevin Leyton-Brown

CoRR, 2022

Weakly Supervised Representation Learning with Sparse Perturbations.

Kartik Ahuja, Jason S. Hartford, Yoshua Bengio

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Properties from mechanisms: an equivariance perspective on identifiable representation learning.

Kartik Ahuja, Jason S. Hartford, Yoshua Bengio

Proceedings of the Tenth International Conference on Learning Representations, 2022

The Perils of Learning Before Optimizing.

Chris Cameron, Jason S. Hartford, Taylor Lundy, Kevin Leyton-Brown

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Valid Causal Inference with (Some) Invalid Instruments.

Jason S. Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Exemplar Guided Active Learning.

Jason S. Hartford, Kevin Leyton-Brown, Hadas Raviv, Dan Padnos, Shahar Lev, Barak Lenz

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Predicting Propositional Satisfiability via End-to-End Learning.

Chris Cameron, Rex Chen, Jason S. Hartford, Kevin Leyton-Brown

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2018

Deep Models of Interactions Across Sets.

Jason S. Hartford, Devon R. Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Deep IV: A Flexible Approach for Counterfactual Prediction.

Jason S. Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Counterfactual Prediction with Deep Instrumental Variables Networks.

Jason S. Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy

CoRR, 2016

Deep Learning for Predicting Human Strategic Behavior.

Jason S. Hartford, James R. Wright, Kevin Leyton-Brown

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016