Education
Group-Fair Online Allocation in Continuous Time
The mean (median) normalized rating score for White workers was 0.98 (1), while it is 0.97 (1) for Black
Supplement to " Learning Individualized Treatment Rules with Many Treatments: A Supervised Clustering Approach Using Adaptive Fusion "
Haixu Ma Department of Statistics and Operations Research University of North Carolina at Chapel Hill Chapel Hill, NC 27516 haixuma@live.unc.edu A.1 Estimation of the main effect We briefly discuss how to obtain the estimation of the main effect function M For nonparametric regression, we follow [ 3 ] to divide the training data into M folds based on the assigned treatment. Then p E r Y |Z,A " a s is obtained from the regression forest [ 4 ] on Y Z with the dataset tp y We refer to [ 3 ] for more discussions about the case of misspecifying the main effect, and the corresponding robust and efficient method to solve the misspecification problem. A.2 Implementation details for the adaptive proximal gradient algorithm Recall that U " diag pX The main steps of the proposed algorithm for SCAF are summarized as below. In particular, the experiments were run on a Linux-based computing server.
Online Meta-Learning via Learning with Layer-Distributed Memory
We demonstrate that efficient meta-learning can be achieved via end-to-end training of deep neural networks with memory distributed across layers. The persistent state of this memory assumes the entire burden of guiding task adaptation. Moreover, its distributed nature is instrumental in orchestrating adaptation.
On Batch Teaching with Sample Complexity Bounded by VCD
In machine teaching, a concept is represented by (and inferred from) a small number of labeled examples.
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions
Neural networks are a key component of the machine learning toolbox.
Linear Label Ranking with Bounded Noise
However, the common assumption both in theoretical and in applied works is that the observed rankings are noisy in the sense that they do not always correspond to the ground-truth ranking.
On the Importance of Exploration for Generalization in Reinforcement Learning
We hypothesize that the agent's exploration strategy plays a key role in its ability to generalize to new environments.
A Additional Related Works
We review the recent studies in OOD detection, model reprogramming, and backdoor attack. The classification-based methods use the representations extracted from the well-trained classification models in OOD scoring. Matrix to exploit models' detection capability from embedding features; [ Our methods can also be used in the distance-based methods. The term "attack" lies in the fact that, by reprogramming, an attacker can easily In this paper, we also employ the reprogramming property of deep models for transfer learning. Output: learned watermark w .