Statistical Learning
Learning the Linear Quadratic Regulator from Nonlinear Observations
The learner's goal is to P AC-learn an
Learning the Linear Quadratic Regulator from Nonlinear Observations
The learner's goal is to P AC-learn an
Online Control of Unknown Time-Varying Dynamical Systems
In fact, our algorithm enjoys sublinear adaptive regret bounds, which is a strictly stronger metric than standard regret and is more appropriate for time-varying systems.
A Summary of the Appendix
We repeat Theorem 1 as follows. Theorem 2. Suppose the encoder
Leveraging Predictions in Smoothed Online Convex Optimization via Gradient-based Algorithms
We consider online convex optimization with time-varying stage costs and additional switching costs.
KFC: A Scalable Approximation Algorithm for k-center Fair Clustering
However, there is an additional constraint that each cluster needs to be fair, under some notion of fairness. This ensures that no group is either "over-represented" or "under-represented" in any cluster. Our work builds on the work of Chierichetti et al. (NIPS 2017), Bera et al. (NeurIPS 2019), Ahmadian et al. (KDD 2019), and Bercea et al. (APPROX 2019).
On Inductive Biases for Heterogeneous Treatment Effect Estimation Appendix
Here, we present a detailed overview of existing model-agnostic "meta-learner" strategies for CA TE Unfortunately, good performance on estimation of the POs is not sufficient. Note that, as we discuss in section C.2, we fixed all hyperparameters throughout all experiments as tuning Input: Testing data X Trained FlexTENet flex for i 1: flex.n_layers We retrieve the data from https://jenniferhill7.wixsite.com/acic-2016/competition "D" we change only the response surface of the treated to As stated in the main text, we fixed equivalent hyperparameters across all methods within any experiments to not conflate hyperparameter tuning with the value of the different strategies. B (D.3), present additional results on PO estimation (D.4), and then move to analyzing the learned We also consider the effect of using our approaches as first-stage (nuisance) estimators for two-step learners (D.6).