TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: Self-paced learning (SPL) has been attracting increasing attention in machine learning and computer vision. Albeit empirically substantiated to be effective, the investigation on its theoretical insight is still a blank. It is even unknown that what objective a general SPL regime converges to. To this issue, this study attempts to initially provide some new insights under this “heuristic” learning scheme. Specifically, we prove that the solving strategy on SPL exactly accords with a majorization minimization algorithm, a well known technique in optimization and machine learning, implemented on a latent objective. A more interesting finding is that, the loss function contained in this latent objective has a similar configuration with non-convex regularized penalty, an attractive topic in statistics and machine learning. In particular, we show that the previous hard and linear self-paced regularizers are equivalent to the capped norm and minimax concave plus penalties, respectively, both being widely investigated in statistics. Such connections between SPL and previous known researches enhance new insightful comprehension on SPL, like convergence and parameter setting rationality. The correctness of the proposed theory is substantiated by experimental results on synthetic and UCI data sets.

SOLUTION: What Objective Does Self-paced Learning Indeed Optimize?

PROBLEM: Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or inconsistent statements about the data, rendering practical applications, especially in scientific domains, futile. Therefore, we here extend the optimistic estimator framework for optimal subgroup discovery to a new class of objective functions: we show how tight estimators can be computed efficiently for all functions that are determined by subgroup size (non-decreasing dependence), the subgroup median value, and a dispersion measure around the median (nonincreasing dependence). In the important special case when dispersion is measured using the mean absolute deviation from the median, this novel approach yields a linear time algorithm. Empirical evaluation on a wide range of datasets shows that, when used within branch-and-bound search, this approach is highly efficient and indeed discovers subgroups with much smaller errors.

SOLUTION: Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery

PROBLEM: We consider the problem of multiple agents sensing and acting in environments<lb>with the goal of maximising their shared utility. In these environments, agents must<lb>learn communication protocols in order to share information that is needed to solve<lb>the tasks. By embracing deep neural networks, we are able to demonstrate end-<lb>to-end learning of protocols in complex environments inspired by communication<lb>riddles and multi-agent computer vision problems with partial observability. We<lb>propose two approaches for learning in these domains: Reinforced Inter-Agent<lb>Learning (RIAL) and Differentiable Inter-Agent Learning (DIAL). The former uses<lb>deep Q-learning, while the latter exploits the fact that, during learning, agents can<lb>backpropagate error derivatives through (noisy) communication channels. Hence,<lb>this approach uses centralised learning but decentralised execution. Our experi-<lb>ments introduce new environments for studying the learning of communication<lb>protocols and present a set of engineering innovations that are essential for success<lb>in these domains.

SOLUTION:
Learning to Communicate with Deep Multi-Agent Reinforcement Learning