Optimization
Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh Recovery
However, the pretrained model may not provide an ideal optimization starting point for the test-time optimization.
8 Supplementary Material 8.1 Details and Proofs for the Proposed EOC 8.1.1 Calculation of T Given data D
Fourier transform of a power of a Euclidean distance, i.e., According to Jensen's inequality and Lipschitzness assumption, we have X According to the law of total probability and Theorem 4.1, we have P { Y A feasible solution to Equation (1) can be quickly found. Pseudocode for Algorithm 2 The pseudocode for the constrained optimization is detailed in Algorithm 2. 18 Algorithm 2 Robust Optimization Method with EOC Constraint Input: Initiate Array A of shape 2 A M that stores the max possible step. Our proposed algorithm is highly computationally efficient.
Equal Opportunity of Coverage in Fair Regression
The seminal work of "equalized coverage" proposed an uncertainty-aware fairness notion.
Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization
Bayesian optimization (BO) conventionally relies on handcrafted acquisition functions (AFs) to sequentially determine the sample points. However, it has been widely observed in practice that the best-performing AF in terms of regret can vary significantly under different types of black-box functions. It has remained a challenge to design one AF that can attain the best performance over a wide variety of black-box functions.
