Learning from indirect supervision signals is important in real-world AI applications when, often, gold labels are missing or too costly.

We might hope that when faced with unexpected inputs, well-designed software systems would fire off warnings. Machine learning (ML) systems, however, which depend strongly on properties of their inputs (e.g. the i.i.d.

Although optimization is the longstanding algorithmic backbone of machine learning, new models still require the time-consuming implementation of new solvers. As a result, there are thousands of implementations of optimization algorithms for machine learning problems.

In this paper, we introduce variational semantic memory into meta-learning to acquire long-term knowledge for few-shot learning.

A small perturbation in the embedding space will lead to diverse clustering results since the labels are absent.

Information Processing Systems 34, page to appear.

We develop a universal, parameter-free, and computationally efficient algorithm that starts with hierarchical graph clustering and then iteratively refines estimates both on graph clustering and matrix ratings.

Q2: Please summarize your review in 1-2 sentences The paper proposes a modified SVM learning algorithm in which the loss function is modified by a per-example weight. However, example dependent costs are already widely used in machine learning.

In many situations we have some measurement of confidence on "positiveness" for a binary label. The "positiveness" is a continuous value whose range is a bounded interval.