prakharg24 commited on
Commit
086122f
·
verified ·
1 Parent(s): 86d3466

Update my_pages/bibliography.py

Browse files
Files changed (1) hide show
  1. my_pages/bibliography.py +1 -1
my_pages/bibliography.py CHANGED
@@ -24,7 +24,7 @@ def render():
24
  <li>Breiman, Leo. "Statistical modeling: The two cultures (with comments and a rejoinder by the author)." Statistical science 16, no. 3 (2001): 199-231.</li>
25
  <li>Marx, Charles, Flavio Calmon, and Berk Ustun. "Predictive multiplicity in classification." In International conference on machine learning, pp. 6765-6774. PMLR, 2020.</li>
26
  <li>Simson, Jan, Florian Pfisterer, and Christoph Kern. "One model many scores: Using multiverse analysis to prevent fairness hacking and evaluate the influence of model design decisions." In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, pp. 1305-1320. 2024.</li>
27
- <li>Ganesh, Prakhar, Afaf Taik, and Golnoosh Farnadi. "The Curious Case of Arbitrariness in Machine Learning." In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. 2025.</li>
28
  <li>Multiplicity Tutorial Website - https://prakharg24.github.io/multiplicity-tutorial/</li>
29
  <li>Multiplicity Systematic Survey - Ganesh et al. (2025) above</li>
30
  </ul>
 
24
  <li>Breiman, Leo. "Statistical modeling: The two cultures (with comments and a rejoinder by the author)." Statistical science 16, no. 3 (2001): 199-231.</li>
25
  <li>Marx, Charles, Flavio Calmon, and Berk Ustun. "Predictive multiplicity in classification." In International conference on machine learning, pp. 6765-6774. PMLR, 2020.</li>
26
  <li>Simson, Jan, Florian Pfisterer, and Christoph Kern. "One model many scores: Using multiverse analysis to prevent fairness hacking and evaluate the influence of model design decisions." In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, pp. 1305-1320. 2024.</li>
27
+ <li>Ganesh, Prakhar, Afaf Taik, and Golnoosh Farnadi. "Systemizing Multiplicity: The Curious Case of Arbitrariness in Machine Learning." In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. 2025.</li>
28
  <li>Multiplicity Tutorial Website - https://prakharg24.github.io/multiplicity-tutorial/</li>
29
  <li>Multiplicity Systematic Survey - Ganesh et al. (2025) above</li>
30
  </ul>