3 accepted papers
This paper proposes a new approach to accelerate spectral norm estimation for a kernel matrix of n data points. Our key intuition is that, by applying the seminal random feature technique, we can well estimate the norm without computing or operating on the n-by-n kernel matrix but only an n-by-q ran…
We propose a model-agnostic randomized learning framework based on Random Hypothesis Subspace Sampling (RHSS). Given any hypothesis class, it randomly samples $k$ hypotheses and learns a near-optimal model from their span by simply solving a linear least square problem in $O(n k^2)$ time, where $n$…
Existing studies on individual fairness focus on the passive setting and typically require $O(\frac{1}{\varepsilon^2})$ labeled instances to achieve an $\varepsilon$ bias budget. In this paper, we build on the elegant Approximately Metric-Fair (AMF) learning framework and propose an active AMF learn…