- Paper at CVPR:
The paper entitled ‘DARE-GRAM: Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices.’ This work was a collaboration between our Ph.D. student Ismail Nejjar, Dr. Qin Wang, and Prof.Dr. Olga Fink. We proposed a novel domain adaptation for regression method that aligns the inverse Gram matrix of the features, which was motivated by its presence in the OLS solution and the Gram matrix’s ability to capture feature correlations. Our method leverages the pseudo-inverse low-rank property to align the scale and angle in a selected subspace generated by the pseudo-inverse Gram matrix of the two domains. Experimental results on several benchmark datasets demonstrate that our method outperforms state-of-the-art methods.
The paper is available open access: https://lnkd.in/dCXcU4BK.
- New Ph.D. student:
Warmly welcomes our new Ph.D. student, Keivan Faghih!
- Paper in Reliability Engineering & System Safety:
Are you looking for a solution that jointly optimizes revenue, reliability, and safety in the electricity market? Our team’s paper ‘Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units’ . This work was a collaboration between our postdoc Pegah Rokhforoz, visiting Ph.D. mina montazeri, and Prof.Dr. Olga Fink.
Don’t miss out on this research, check out the full paper now! Direct link to the paper: