Friday, March 27 — Sujay Sanghavi from UT Austin

The second Foundations of Data Science virtual talk will take place next Friday, March 27th at 11:00 AM Pacific Time (2:00 pm Eastern Time, 20:00 Central European Time, 19:00 UTC).  Sujay Sanghavi from University of Texas at Austin will speak about “Towards Model Agnostic Robustness”.

Abstract: It is now common practice to try and solve machine learning problems by starting with a complex existing model or architecture, and fine-tuning/adapting it to the task at hand. However, outliers, errors or even just sloppiness in training data often lead to drastic drops in performance.

We investigate a simple generic approach to correct for this, motivated by a classic statistical idea: trimmed loss. This advocates jointly (a) selecting which training samples to ignore, and (b) fitting a model on the remaining samples. As such this is computationally infeasible even for linear regression. We propose and study the natural iterative variant that alternates between these two steps (a) and (b) – each of which individually can be easily accomplished in pretty much any statistical setting. We also study the batch-SGD variant of this idea. We demonstrate both theoretically (for generalized linear models) and empirically (for vision and NLP neural network models) that this effectively recovers accuracy in the presence of bad training data.

This work is joint with Yanyao Shen and Vatsal Shah and appears in NeurIPS 2019, ICML 2019 and AISTATS 2020.

Link to join the virtual talk.

The series is supported by the NSF HDR TRIPODS Grant 1934846.

Friday, February 28 — Jon Kleinberg from Cornell University

The first Foundations of Data Science virtual talk will take place this coming Friday, February 28th at 11:00 AM Pacific Time (2:00 pm Eastern Time, 20:00 Central European Time, 19:00 UTC). Jon Kleinberg from Cornell University will speak about “Fairness and Bias in Algorithmic Decision-Making”.

Abstract: As data science has broadened its scope in recent years, a number of domains have applied computational methods for classification and prediction to evaluate individuals in high-stakes settings. These developments have led to an active line of recent discussion in the public sphere about the consequences of algorithmic prediction for notions of fairness and equity. In part, this discussion has involved a basic tension between competing notions of what it means for such classifications to be fair to different groups. We consider several of the key fairness conditions that lie at the heart of these debates, and in particular how these properties operate when the goal is to rank-order a set of applicants by some criterion of interest, and then to select the top-ranking applicants. The talk will be based on joint work with Sendhil Mullainathan and Manish Raghavan.

Link to join the virtual talk.

The series is supported by the NSF HDR TRIPODS Grant 1934846.

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