128 citations · 277 across the 21 of their papers we have counts for
37 papers
Direct Preference Optimization for Adaptive Concept-based Explanations
Jacopo Teneggi, Zhenzhen Wang, Paul H. Yi +2
Concept-based explanation methods aim at making machine learning models more transparent by finding the most important semantic features of an input (e.g., colors, patterns, shapes…
Multiaccuracy and Multicalibration via Proxy Groups
Beepul Bharti, Mary Versa Clemens-Sewall, Paul H. Yi +1
As the use of predictive machine learning algorithms increases in high-stakes decision-making, it is imperative that these algorithms are fair across sensitive groups. However, mea…
Pivotal Auto-Encoder via Self-Normalizing ReLU
Nelson Goldenstein, Jeremias Sulam, Yaniv Romano
Sparse auto-encoders are useful for extracting low-dimensional representations from high-dimensional data. However, their performance degrades sharply when the input noise at test…
I Bet You Did Not Mean That: Testing Semantic Importance via Betting
Jacopo Teneggi, Jeremias Sulam
Recent works have extended notions of feature importance to semantic concepts that are inherently interpretable to the users interacting with a black-box predictive model. Yet, pre…
What's in a Prior? Learned Proximal Networks for Inverse Problems
Zhenghan Fang, Sam Buchanan, Jeremias Sulam
Proximal operators are ubiquitous in inverse problems, commonly appearing as part of algorithmic strategies to regularize problems that are otherwise ill-posed. Modern deep learnin…
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness
Ambar Pal, Jeremias Sulam, René Vidal
The susceptibility of modern machine learning classifiers to adversarial examples has motivated theoretical results suggesting that these might be unavoidable. However, these resul…