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Rethinking the role of optimization in learning.
Series: Theory Seminar Series
Speaker: Dr. Suriya Gunasekar Senior Researcher ML & Optimization Group Microsoft R
Date/Time: Jan 23 11:30:00
Location: CSA Seminar Hall (Room No. 254, First Floor)
In this talk, I will overview our recent progress towards understanding how we learn large capacity machine learning models. In the modern practice of machine learning, especially deep learning, many successful models have far more trainable parameters compared to the number of training examples. Consequently, the optimization objective for training such models have multiple minimizers that perfectly fit the training data. More problematically, while some of these minimizers generalize well to new examples, most minimizers will simply overfit or memorize the training data and will perform poorly on new examples. In practice though, when such ill-posed objectives are minimized using local search algorithms like (stochastic) gradient descent ((S)GD), the
Suriya Gunasekar is a senior researcher in the ML & Optimization group at Microsoft Research, Redmond. Previously, she was a research assistant professor at the Toyota Technological Institute at Chicago. Her research interests are broadly driven by statistical, algorithmic, and societal aspects of machine learning including topics of optimization, high dimensional learning, and algorithmic fairness.
Host Faculty: Dr. Anand Louis