Modern software-based systems are highly configurable and come with a number of configuration options that impact the performance of the systems. However, selecting inappropriate values for these options can cause long response time, high CPU load, downtime, RAM exhaustion, resulting in performance degradation and poor software reliability. Consequently, considerable effort has been carried out to predict key performance metrics (execution time, program size, energy consumption, etc.) from the user's choice of configuration options values. The selection of inputs (e.g., JavaScript scripts embedded in a web page interpreted by Node.js or input videos encoded with x264 by a streaming platform) also impacts software performance, and there is a complex interplay between inputs and configurations. Unfortunately, owing to the huge variety of existing inputs, it is yet challenging to automate the prediction of software performance whatever their configuration and input. In this article, we empirically evaluate how supervised and transfer learning methods can be leveraged to efficiently learn performance models based on configuration options and input data. Our study over 1,941,075 data points empirically shows that measuring the performance of configurations on multiple inputs allows one to reuse this knowledge and train performance models robust to the change of input data. To the best of our knowledge, this is the first domain-agnostic empirical evaluation of machine learning methods addressing the input-aware performance prediction problem.