Official pytorch implementation for our ICLR 2023 paper "Latent State Marginalization as a Low-cost Approach for Improving Exploration".

Marginal act is an independent game studio based in Kharkiv, Ukraine, founded in 2017. Here you can find info about our games, which are different by gameplay (roguelike, adventure, survival-horror, simulator) and visualization (collage, pixel-art, pixel-collage) but similar by strong experimental nature.

We present a framework called TLF that builds a classifier for the target domain having only few labeled training records by transferring knowledge from the source domain having many labeled records. While existing methods often focus on one issue and leave the other one for the further work, TLF is capable of handling both issues simultaneously. In TLF, we alleviate feature discrepancy by identifying shared label distributions that act as the pivots to bridge the domains. We handle distribution divergence by simultaneously optimizing the structural risk functional, joint distributions between domains, and the manifold consistency underlying marginal distributions. Moreover, for the manifold consistency we exploit its intrinsic properties by identifying $k$ nearest neighbors of a record, where the value of k is determined automatically in TLF. Furthermore, since negative transfer is not desired, we consider only the source records that are belonging to the source pivots during the knowledge transfer. We evaluate TLF on seven publicly available natural datasets and compare the performance of TLF against the performance of eleven state-of-the-art techniques. We also evaluate the effectiveness of TLF in some challenging situations. Our experimental results, including statistical sign test and Nemenyi test analyses, indicate a clear superiority of the proposed framework over the state-of-the-art techniques.

Notes, inquiry, and musings from the margins of earth science.

Minami-Komatsu fit for cosmic birefringence in ACT DR6 while marginalizing over instrumental systematics.