Publications By LightOn

Technical Reports and Preprints – Machine Learning
The Sliced-Wasserstein distance (SW) is a computationally efficient and theoretically grounded alternative to the Wasserstein distance. Yet, the literature on its statistical properties with respect to the distribution of slices, beyond the uniform measure, is scarce. To bring new contributions to this line of research, we leverage the PAC-Bayesian theory and the central observation that SW actually hinges on a slice-distribution-dependent Gibbs risk, the kind of quantity PAC-Bayesian bounds have been designed to characterize. We provide four types of results: i) PAC-Bayesian generalization bounds that hold on what we refer as adaptive Sliced-Wasserstein distances, i.e. distances defined with respect to any distribution of slices, ii) a procedure to learn the distribution of slices that yields a maximally discriminative SW, by optimizing our PAC-Bayesian bounds, iii) an insight on how the performance of the so-called distributional Sliced-Wasserstein distance may be explained through our theory, and iv) empirical illustrations of our findings.

Technical Reports and Preprints – Machine Learning
Reconfigurable linear optical networks are a key component for the development of optical quantum information processing platforms in the NISQ era and beyond. We report the implementation of such a device based on an innovative design that uses the mode mixing of a multimode fiber in combination with the programmable wavefront shaping of an SLM. The capabilities of the platform are explored in the classical regime. For up to a record number of 8~inputs and 38~outputs we achieve fidelities in excess of 93%, week-long stability and losses below 6.5dB. The device was built inside a standard server rack to allow for real-world use.

Conference proceedings – Machine Learning – November 2021
Optical Processing Units (OPUs) are computing devices that perform random projections of input vectors by exploiting the physical phenomenon of scattering a light source through an opaque medium. OPUs have successfully been proposed to carry out approximate kernel ridge regression at scale and with low power consumption by the means of optical random features. OPUs require input vectors to be binary, and this work proposes a novel way to perform supervised data binarization. The main difficulty to develop a solution is that the OPU projection matrices are unknown which poses a challenge in deriving a binarization approach in an end-to-end fashion. Our approach is based on the REINFORCE gradient estimator, which allows us to estimate the gradient of the loss function with respect to binarization parameters by treating the OPU as a black box. Through experiments on several UCI classification and regression problems, we show that our method outperforms alternative unsupervised and supervised binarization techniques.