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07/05/2023
Conditional independence testing under model misspecification
Conditional independence (CI) testing is fundamental and challenging in ...
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05/01/2023
ISAAC Newton: Input-based Approximate Curvature for Newton's Method
We present ISAAC (Input-baSed ApproximAte Curvature), a novel method tha...
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04/25/2023
On Uniform Consistency of Spectral Embeddings
In this paper, we study the convergence of the spectral embeddings obtai...
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02/20/2023
Simple Disentanglement of Style and Content in Visual Representations
Learning visual representations with interpretable features, i.e., disen...
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01/15/2023
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees
We consider the task of training machine learning models with data-depen...
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06/07/2022
How does overparametrization affect performance on minority groups?
The benefits of overparameterization for the overall performance of mode...
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05/26/2022
Understanding new tasks through the lens of training data via exponential tilting
Deploying machine learning models to new tasks is a major challenge desp...
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05/26/2022
Predictor-corrector algorithms for stochastic optimization under gradual distribution shift
Time-varying stochastic optimization problems frequently arise in machin...
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05/01/2022
Domain Adaptation meets Individual Fairness. And they get along
Many instances of algorithmic bias are caused by distributional shifts. ...
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04/13/2022
Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms
Sampling biases in training data are a major source of algorithmic biase...
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11/21/2021
A linear adjustment based approach to posterior drift in transfer learning
We present a new model and methods for the posterior drift problem where...
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10/26/2021
On sensitivity of meta-learning to support data
Meta-learning algorithms are widely used for few-shot learning. For exam...
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10/26/2021
Post-processing for Individual Fairness
Post-processing in algorithmic fairness is a versatile approach for corr...
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03/31/2021
Individually Fair Gradient Boosting
We consider the task of enforcing individual fairness in gradient boosti...
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03/30/2021
Statistical inference for individual fairness
As we rely on machine learning (ML) models to make more consequential de...
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03/19/2021
Individually Fair Ranking
We develop an algorithm to train individually fair learning-to-rank (LTR...
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12/14/2020
Outlier-Robust Optimal Transport
Optimal transport (OT) provides a way of measuring distances between dis...
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12/03/2020
Matrix Completion Methods for the Total Electron Content Video Reconstruction
The total electron content (TEC) maps can be used to estimate the signal...
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11/06/2020
There is no trade-off: enforcing fairness can improve accuracy
One of the main barriers to the broader adoption of algorithmic fairness...
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06/25/2020
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness
In this paper, we cast fair machine learning as invariant machine learni...
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06/19/2020
Two Simple Ways to Learn Individual Fairness Metrics from Data
Individual fairness is an intuitive definition of algorithmic fairness t...
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03/23/2020
Minimax optimal approaches to the label shift problem
We study minimax rates of convergence in the label shift problem. In add...
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03/11/2020
Auditing ML Models for Individual Bias and Unfairness
We consider the task of auditing ML models for individual bias/unfairnes...
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02/15/2020
Federated Learning with Matched Averaging
Federated learning allows edge devices to collaboratively learn a shared...
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12/26/2019
Communication-Efficient Integrative Regression in High-Dimensions
We consider the task of meta-analysis in high-dimensional settings in wh...
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06/28/2019
Learning fair predictors with Sensitive Subspace Robustness
We consider an approach to training machine learning systems that are fa...
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05/27/2019
Dirichlet Simplex Nest and Geometric Inference
We propose Dirichlet Simplex Nest, a class of probabilistic models suita...
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05/20/2019
Uniform bounds for invariant subspace perturbations
For a fixed matrix A and perturbation E we develop purely deterministic ...
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04/07/2019
Precision Matrix Estimation with Noisy and Missing Data
Estimating conditional dependence graphs and precision matrices are some...
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10/09/2018
Statistical Convergence of the EM Algorithm on Gaussian Mixture Models
We study the convergence behavior of the Expectation Maximization (EM) a...
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07/02/2018
Debiasing representations by removing unwanted variation due to protected attributes
We propose a regression-based approach to removing implicit biases in re...
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03/21/2018
Information Theoretic Interpretation of Deep learning
We interpret part of the experimental results of Shwartz-Ziv and Tishby ...
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08/28/2017
An inexact subsampled proximal Newton-type method for large-scale machine learning
We propose a fast proximal Newton-type algorithm for minimizing regulari...
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06/26/2017
On conditional parity as a notion of non-discrimination in machine learning
We identify conditional parity as a general notion of non-discrimination...
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03/14/2015
Communication-efficient sparse regression: a one-shot approach
We devise a one-shot approach to distributed sparse regression in the hi...
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11/25/2013
Exact post-selection inference, with application to the lasso
We develop a general approach to valid inference after model selection. ...
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11/11/2013
Learning Mixtures of Linear Classifiers
We consider a discriminative learning (regression) problem, whereby the ...
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05/31/2013