Maximum Mean Discrepancy for Class Ratio Estimation: Convergence Bounds and Kernel Selection

77 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Multi-Source Domain Adaptation: A Causal View (2015, background): “Furthermore, another future line of research is to derive convergence bounds and learning guarantees for the proposed domain adaptation approaches, following (Cortes, Mansour, and Mohri 2010; Iyer, Nath, and Sarawagi 2014).”
Causal Generative Domain Adaptation Networks (2018, methodology): “(Storkey, 2009; Zhang et al., 2013; Iyer et al., 2014) assumed only changes in PY and proposed principled methods to estimate PY in the target domain by matching marginal distributions.”
LTF: A Label Transformation Framework for Correcting Label Shift (2020, background): “Target shift assumes that the label distribution P Y changes but the conditional distribution P X | Y stays the same (Zhang et al., 2013; Iyer et al., 2014; Lipton et al., 2018; Azizzadenesheli et al., 2019).”
Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings (2018, methodology): “, 2015), goodness of fit tests, and class ratio estimation (Iyer et al., 2014) (see (Muandet et al.”
Transfer Learning with Label Noise (2017, methodology): “Directly applying the methods in [27, 8] on the noisy data will lead to wrong results.”