Search Results for author:
Found 7 papers, 2 papers with code
Deep Concept Removal
We address the problem of concept removal in deep neural networks, aiming to learn representations that do not encode certain specified concepts (e. g., gender etc.)
Post-hoc Bias Scoring Is Optimal For Fair Classification
We consider a binary classification problem under group fairness constraints, which can be one of Demographic Parity (DP), Equalized Opportunity (EOp), or Equalized Odds (EO).
Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
However, a major challenge faced by practitioners is the lack of clear guidance on evaluating whether LLM outputs align with social norms, values, and regulations.
Robustifying Markowitz
Markowitz mean-variance portfolios with sample mean and covariance as input parameters feature numerous issues in practice.
Stability and Deviation Optimal Risk Bounds with Convergence Rate $O(1/n)$
The sharpest known high probability generalization bounds for uniformly stable algorithms (Feldman, Vondr\'{a}k, 2018, 2019), (Bousquet, Klochkov, Zhivotovskiy, 2020) contain a generally inevitable sampling error term of order $\Theta(1/\sqrt{n})$.
Robust $k$-means Clustering for Distributions with Two Moments
We consider the robust algorithms for the $k$-means clustering problem where a quantizer is constructed based on $N$ independent observations.
Sharper bounds for uniformly stable algorithms
In a series of recent breakthrough papers by Feldman and Vondrak (2018, 2019), it was shown that the best known high probability upper bounds for uniformly stable learning algorithms due to Bousquet and Elisseef (2002) are sub-optimal in some natural regimes.
