511 citations · 968 across the 39 of their papers we have counts for
41 papers
Distributionally Robust Performative Prediction
Songkai Xue, Yuekai Sun
Performative prediction aims to model scenarios where predictive outcomes subsequently influence the very systems they target. The pursuit of a performative optimum (PO) -- minimiz…
ISAAC Newton: Input-based Approximate Curvature for Newton's Method
Felix Petersen, Tobias Sutter, Christian Borgelt +4
We present ISAAC (Input-baSed ApproximAte Curvature), a novel method that conditions the gradient using selected second-order information and has an asymptotically vanishing comput…
On Uniform Consistency of Spectral Embeddings
Ruofei Zhao, Songkai Xue, Yuekai Sun
In this paper, we study the convergence of the spectral embeddings obtained from the leading eigenvectors of certain similarity matrices to their population counterparts. We opt to…
Simple Disentanglement of Style and Content in Visual Representations
Lilian Ngweta, Subha Maity, Alex Gittens +2
Learning visual representations with interpretable features, i.e., disentangled representations, remains a challenging problem. Existing methods demonstrate some success but are ha…
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…
How does overparametrization affect performance on minority groups?
Subha Maity, Saptarshi Roy, Songkai Xue +2
The benefits of overparameterization for the overall performance of modern machine learning (ML) models are well known. However, the effect of overparameterization at a more granul…
Understanding new tasks through the lens of training data via exponential tilting
Subha Maity, Mikhail Yurochkin, Moulinath Banerjee +1
Deploying machine learning models to new tasks is a major challenge despite the large size of the modern training datasets. However, it is conceivable that the training data can be…
Predictor-corrector algorithms for stochastic optimization under gradual distribution shift
Subha Maity, Debarghya Mukherjee, Moulinath Banerjee +1
Time-varying stochastic optimization problems frequently arise in machine learning practice (e.g. gradual domain shift, object tracking, strategic classification). Although most pr…
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…
A linear adjustment based approach to posterior drift in transfer learning
Subha Maity, Diptavo Dutta, Jonathan Terhorst +2
We present a new model and methods for the posterior drift problem where the regression function in the target domain is modeled as a linear adjustment (on an appropriate scale) of…
On sensitivity of meta-learning to support data
Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun
Meta-learning algorithms are widely used for few-shot learning. For example, image recognition systems that readily adapt to unseen classes after seeing only a few labeled examples…
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…
Individually Fair Gradient Boosting
Alexander Vargo, Fan Zhang, Mikhail Yurochkin +1
We consider the task of enforcing individual fairness in gradient boosting. Gradient boosting is a popular method for machine learning from tabular data, which arise often in appli…
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…
Outlier-Robust Optimal Transport
Debarghya Mukherjee, Aritra Guha, Justin Solomon +2
Optimal transport (OT) measures distances between distributions in a way that depends on the geometry of the sample space. In light of recent advances in computational OT, OT dista…
Matrix Completion Methods for the Total Electron Content Video Reconstruction
Hu Sun, Zhijun Hua, Jiaen Ren +3
The total electron content (TEC) maps can be used to estimate the signal delay of GPS due to the ionospheric electron content between a receiver and satellite. This delay can resul…
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…
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness
Mikhail Yurochkin, Yuekai Sun
In this paper, we cast fair machine learning as invariant machine learning. We first formulate a version of individual fairness that enforces invariance on certain sensitive sets.…
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…
Minimax optimal approaches to the label shift problem in non-parametric settings
Subha Maity, Yuekai Sun, Moulinath Banerjee
We study the minimax rates of the label shift problem in non-parametric classification. In addition to the unsupervised setting in which the learner only has access to unlabeled ex…
Federated Learning with Matched Averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun +2
Federated learning allows edge devices to collaboratively learn a shared model while keeping the training data on device, decoupling the ability to do model training from the need…