511 citations · 968 across the 39 of their papers we have counts for
12 papers · 1 filter
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…
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…
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…
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…
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…
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.…
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…
An inexact subsampled proximal Newton-type method for large-scale machine learning
Xuanqing Liu, Cho-Jui Hsieh, Jason D. Lee +1
We propose a fast proximal Newton-type algorithm for minimizing regularized finite sums that returns an -suboptimal point in $\tilde{\mathcal{O}}(d(n + \sqrt{κd})\log(\frac{1}ε)…
Learning Mixtures of Linear Classifiers
Yuekai Sun, Stratis Ioannidis, Andrea Montanari
We consider a discriminative learning (regression) problem, whereby the regression function is a convex combination of k linear classifiers. Existing approaches are based on the EM…