A.1 Linear mappings between zand x Usually, we have data x PRNˆD1 and latent representation z PRNˆD2 with N the number of neurons, D1 the dimensionality of the data, D2 the dimensionality of the latent space and, usually, D1 " D2. In cases where a method mdoes only produce some latent representation zm, we fit a reconstruction ˆxm "Wzm with a least squares projection W "pzTmzmq 1zTmx. In cases where a method mdoes only produce some reconstruction ˆxm, we produce a simple latent representation zm by extracting the first D2 columns of the left singular vectors U from the singular value decomposition x"USVT. Both of these projections are fitted on the training data, then fixed and also used on the validation and test data. We used three datasets, where the first two (dataset A [2] n=8417 cells; B [54] n=4600) are two-photon recordings of mouse retinal bipolar cell (BC) responses to the chirp stimuli (local and full-field, see [2] for details).

Model-free reinforcement learning aims to offer off-the-shelf solutions for controlling dynamical systems without requiring models of the system dynamics.

Recently, the Deep Learning community has become interested in evolutionary optimization (EO) as a means to address hard optimization problems, e.g.

This appendix contains additional material for the paper "Optimistic tree search strategies forblack-box combinatorial optimization".

A very promising direction in the field of black-box adversarial attacks are randomized search schemes for crafting adversarial examples [1, 23, 24]. Combining random search with specific update proposal distributions allows to achieve state-of-the-art black-box efficiency for different threat models such as` and `2 [1], `1 [25], `0, adversarial patches, and adversarial frames [24].

Moreover,tofurther accelerate overgreedy descent methods, wepresent a new accelerated random search (ARS) algorithm that incorporates prior information, together with aconvergence analysis.

The red lines in the bottom plot indicate linear fits and the red axis labels show the rank correlation coefficients ρ and p values. The matrix is orthogonal, thus avoiding a singular design. As scGen returns corrected input data, we performed PCA on the output data, which were used for further evaluation (cf. Appendix Section A.1). Here, we used the same number of principle components (PCs) as used for Embedded cells are colored by dataset. In Figure 9, we present the results of the simulation experiments discussed in the main text.