Statistical Learning
Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL Y ang Yue
We also give unique insights into its effectiveness.
Supplementary A Properties of the InfoGAIL
I ( x; y; c) can be decomposed as I (x; y; c) = I ( y; x) + I ( c; x) I ( y, c; x) = I ( y; x) + I ( c; x) H (y, c) + H (y, c |x) = I ( y; c) I (y; c |x). I ( s, a; s, a) is finally increased as well. The main parameters for training Ess-InfoGAIL are listed in Table 4. To minimize computational time, we restrict the update of the latent skill distribution to only the first iteration of policy updates. Our experiments demonstrate that this approach does not result in significant performance degradation.
Multi-Object Representation Learning via Feature Connectivity and Object-Centric Regularization
We demonstrate that our approach outperforms state-of-the-art methods in discovering multiple objects from simulated, real-world, complex texture and common object images in a fine-grained manner without supervision. The proposed solution attains sample efficiency and is generalizable to out-of-domain images.
An Efficient Doubly-Robust Test for the Kernel Treatment Effect
Such assumptions allow for identification of causal target parameters. For instance, one may be interested in the average variance-weighted treatment effects (Robins et al., 2008; Li et al., 2011),
Adapting Fairness Interventions to Missing Values
Missing values in real-world data pose a significant and unique challenge to algorithmic fairness.
