Statistical Learning
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability
Estimating personalized effects of treatments is a complex, yet pervasive problem.
Estimating the intrinsic dimensionality using Normalizing Flows - Supplementary A Theoretical appendix A.1 Singular value evolution under normal space noise In [ 4
The complexity of a) depends on the choosen architecture for the NFs. The complexity of b) depends on the operational complexity of the NF i.e. the number of operations We repeat this experiment and report the result in Figure S2. The rows show the datasets we trained on, while the columns represent the datasets on which we estimate the ID. All the intrinsic dimensionalities are correctly retrieved. Though, when the flow is not expressive enough, we failed to estimate the true ID. 4 Figure S2: Samples from the lolipop dataset.
In Defense of the Unitary Scalarization for Deep Multi-Task Learning
SMTO s usually require access to per-task gradients either with respect to the shared parameters, or to the shared representation.
Dual Parameterization of Sparse Variational Gaussian Processes A Tighter Bound for the M-step We here study the role of parameterizations ξ
For a matched optimal E-step, i.e. We here detail the computations required to perform inference and learning using the dual parame-terization. To perform inference, the variational expectations need to be evaluated. Eq. (8) needs to be evaluated which requires the computation of a KL divergence. We used a softmax likelihood with 10 latent GPs, one for each digit.
