511 citations · 968 across the 39 of their papers we have counts for
15 papers · 1 filter
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees
Songkai Xue, Yuekai Sun, Mikhail Yurochkin
We consider the task of training machine learning models with data-dependent constraints. Such constraints often arise as empirical versions of expected value constraints that enfo…
Domain Adaptation meets Individual Fairness. And they get along
Debarghya Mukherjee, Felix Petersen, Mikhail Yurochkin +1
Many instances of algorithmic bias are caused by distributional shifts. For example, machine learning (ML) models often perform worse on demographic groups that are underrepresente…
Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms
Laura Niss, Yuekai Sun, Ambuj Tewari
Sampling biases in training data are a major source of algorithmic biases in machine learning systems. Although there are many methods that attempt to mitigate such algorithmic bia…
Post-processing for Individual Fairness
Felix Petersen, Debarghya Mukherjee, Yuekai Sun +1
Post-processing in algorithmic fairness is a versatile approach for correcting bias in ML systems that are already used in production. The main appeal of post-processing is that it…
Statistical inference for individual fairness
Subha Maity, Songkai Xue, Mikhail Yurochkin +1
As we rely on machine learning (ML) models to make more consequential decisions, the issue of ML models perpetuating or even exacerbating undesirable historical biases (e.g., gende…
Individually Fair Ranking
Amanda Bower, Hamid Eftekhari, Mikhail Yurochkin +1
We develop an algorithm to train individually fair learning-to-rank (LTR) models. The proposed approach ensures items from minority groups appear alongside similar items from major…
Does enforcing fairness mitigate biases caused by subpopulation shift?
Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin +1
Many instances of algorithmic bias are caused by subpopulation shifts. For example, ML models often perform worse on demographic groups that are underrepresented in the training da…
Two Simple Ways to Learn Individual Fairness Metrics from Data
Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee +1
Individual fairness is an intuitive definition of algorithmic fairness that addresses some of the drawbacks of group fairness. Despite its benefits, it depends on a task specific f…
Auditing ML Models for Individual Bias and Unfairness
Songkai Xue, Mikhail Yurochkin, Yuekai Sun
We consider the task of auditing ML models for individual bias/unfairness. We formalize the task in an optimization problem and develop a suite of inferential tools for the optimal…
Training individually fair ML models with Sensitive Subspace Robustness
Mikhail Yurochkin, Amanda Bower, Yuekai Sun
We consider training machine learning models that are fair in the sense that their performance is invariant under certain sensitive perturbations to the inputs. For example, the pe…
Dirichlet Simplex Nest and Geometric Inference
Mikhail Yurochkin, Aritra Guha, Yuekai Sun +1
We propose Dirichlet Simplex Nest, a class of probabilistic models suitable for a variety of data types, and develop fast and provably accurate inference algorithms by accounting f…
Precision Matrix Estimation with Noisy and Missing Data
Roger Fan, Byoungwook Jang, Yuekai Sun +1
Estimating conditional dependence graphs and precision matrices are some of the most common problems in modern statistics and machine learning. When data are fully observed, penali…
On conditional parity as a notion of non-discrimination in machine learning
Ya'acov Ritov, Yuekai Sun, Ruofei Zhao
We identify conditional parity as a general notion of non-discrimination in machine learning. In fact, several recently proposed notions of non-discrimination, including a few coun…
Communication-efficient sparse regression: a one-shot approach
Jason D. Lee, Yuekai Sun, Qiang Liu +1
We devise a one-shot approach to distributed sparse regression in the high-dimensional setting. The key idea is to average "debiased" or "desparsified" lasso estimators. We show th…
Proximal Newton-type methods for minimizing composite functions
Jason D. Lee, Yuekai Sun, Michael A. Saunders
We generalize Newton-type methods for minimizing smooth functions to handle a sum of two convex functions: a smooth function and a nonsmooth function with a simple proximal mapping…