About

I'm a second year Computer Science Ph.D. student at UC Irvine advised by Sameer Singh. I also collaborate closely with Hima Lakkaraju. I generally work on machine learning learning safety, from the perspective of explainability, debugging, and fairness. I spent last summer at AWS working on model debugging with Krishnaram Kenthapadi.

dslack@uci.edu / @dylanslack20

Papers

• How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations
  Dylan Slack, Sophie Hilgard, Sameer Singh, and Himabindu Lakkaraju
  arXiv, 2020
  
• Defuse: Debugging Classifiers Through Distilling Unrestricted Adversarial Examples
  Dylan Slack, Nathalie Rauschmayr, and Krishnaram Kenthapadi
  arXiv, 2020
  
• Differentially Private Language Models Benefit from Public Pre-training
  Gavin Kerrigan*, Dylan Slack*, and Jens Tuyls*
  EMNLP PrivateNLP Workshop, 2020
  
• Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods
  Dylan Slack*, Sophie Hilgard*, Emiliy Jia, Sameer Singh, and Himabindu Lakkaraju
  AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2020
  Also accepted at SafeAI Workshop, AAAI, 2020
  [Harvard Business Review, Deeplearning.ai]
• Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data
  Dylan Slack, Sorelle Friedler, and Emile Givental
  ACM Conference on Fairness, Accountability and Transparency (FAccT), 2020,
  Also accepted at NeurIPS HCML Workshop, 2019
  
• Assessing the Local Interpretability of Machine Learning Models
  Dylan Slack, Sorelle A Friedler, Carlos Scheidegger, and Chitradeep Dutta Roy
  Workshop on Human-Centric Machine Learning, NeurIPS, 2019
  

* notes equal contribution