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Found 8 papers, 2 papers with code
Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation
We study the problem of model selection in causal inference, specifically for the case of conditional average treatment effect (CATE) estimation under binary treatments.
RealCause: Realistic Causal Inference Benchmarking
However, the best causal estimators on synthetic data are unlikely to be the best causal estimators on real data.
In Search of Robust Measures of Generalization
A large volume of work aims to close this gap, primarily by developing bounds on generalization error, optimization error, and excess risk.
On the Bias-Variance Tradeoff: Textbooks Need an Update
Through extensive experiments and analysis, we show a lack of a bias-variance tradeoff in neural networks when increasing network width.
In Support of Over-Parametrization in Deep Reinforcement Learning: an Empirical Study
There is significant recent evidence in supervised learning that, in the over-parametrized setting, wider networks achieve better test error.
A Modern Take on the Bias-Variance Tradeoff in Neural Networks
The bias-variance tradeoff tells us that as model complexity increases, bias falls and variances increases, leading to a U-shaped test error curve.
Learning Generative Models with Locally Disentangled Latent Factors
One of the most successful techniques in generative models has been decomposing a complicated generation task into a series of simpler generation tasks.
How well does your sampler really work?
We present a new data-driven benchmark system to evaluate the performance of new MCMC samplers.
