Team
| Cevher Volkan |
While machine learning (ML) is making extraordinary demonstrations in many scientific and engineering domains with neural networks (NN), ML researchers have no delusions about the emerging weaknesses of the NN paradigm, such as robustness, interpretability, bias, and reproducibility (RISE). To this end, there is growing interest in finding robust and fair training models, where rigorous certificates of correctness can be obtained, reducing (inductive) biases and improving interpretability of the ML models, and understanding as well as overcoming the new-found difficulties in optimizing such models.
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