1 code implementation • 3 Nov 2022 • Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis
We study the problem of model selection in causal inference, specifically for conditional average treatment effect (CATE) estimation.
no code implementations • 30 Nov 2020 • Brady Neal, Chin-wei Huang, Sunand Raghupathi
However, the best causal estimators on synthetic data are unlikely to be the best causal estimators on real data.
1 code implementation • NeurIPS 2020 • Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy
A large volume of work aims to close this gap, primarily by developing bounds on generalization error, optimization error, and excess risk.
no code implementations • 17 Dec 2019 • Brady Neal
Through extensive experiments and analysis, we show a lack of a bias-variance tradeoff in neural networks when increasing network width.
no code implementations • ICML Workshop Deep_Phenomen 2019 • Brady Neal, Ioannis Mitliagkas
There is significant recent evidence in supervised learning that, in the over-parametrized setting, wider networks achieve better test error.
no code implementations • 19 Oct 2018 • Brady Neal, Sarthak Mittal, Aristide Baratin, Vinayak Tantia, Matthew Scicluna, Simon Lacoste-Julien, Ioannis Mitliagkas
The bias-variance tradeoff tells us that as model complexity increases, bias falls and variances increases, leading to a U-shaped test error curve.
no code implementations • ICLR 2018 • Brady Neal, Alex Lamb, Sherjil Ozair, Devon Hjelm, Aaron Courville, Yoshua Bengio, Ioannis Mitliagkas
One of the most successful techniques in generative models has been decomposing a complicated generation task into a series of simpler generation tasks.
no code implementations • 16 Dec 2017 • Ryan Turner, Brady Neal
We present a new data-driven benchmark system to evaluate the performance of new MCMC samplers.