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07/22/2023
Collaboratively Learning Linear Models with Structured Missing Data
We study the problem of collaboratively learning least squares estimates...
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07/21/2023
Differentially Private Heavy Hitter Detection using Federated Analytics
In this work, we study practical heuristics to improve the performance o...
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01/17/2023
A Fast Algorithm for Adaptive Private Mean Estimation
We design an (ε, δ)-differentially private algorithm to estimate the mea...
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11/18/2022
Private Federated Statistics in an Interactive Setting
Privately learning statistics of events on devices can enable improved u...
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10/31/2022
Private optimization in the interpolation regime: faster rates and hardness results
In non-private stochastic convex optimization, stochastic gradient metho...
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10/24/2022
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise
Recovering linear subspaces from data is a fundamental and important tas...
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06/24/2022
How many labelers do you have? A closer look at gold-standard labels
The construction of most supervised learning datasets revolves around co...
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06/15/2022
Query-Adaptive Predictive Inference with Partial Labels
The cost and scarcity of fully supervised labels in statistical machine ...
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02/20/2022
Memorize to Generalize: on the Necessity of Interpolation in High Dimensional Linear Regression
We examine the necessity of interpolation in overparameterized models, t...
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01/20/2022
Predictive Inference with Weak Supervision
The expense of acquiring labels in large-scale statistical machine learn...
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12/29/2021
A comment and erratum on "Excess Optimism: How Biased is the Apparent Error of an Estimator Tuned by SURE?"
We identify and correct an error in the paper "Excess Optimism: How Bias...
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08/16/2021
Fine-tuning is Fine in Federated Learning
We study the performance of federated learning algorithms and their vari...
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08/05/2021
Adapting to Function Difficulty and Growth Conditions in Private Optimization
We develop algorithms for private stochastic convex optimization that ad...
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06/25/2021
Private Adaptive Gradient Methods for Convex Optimization
We study adaptive methods for differentially private convex optimization...
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01/13/2021
On Misspecification in Prediction Problems and Robustness via Improper Learning
We study probabilistic prediction games when the underlying model is mis...
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08/24/2020
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems
Learning-based methodologies increasingly find applications in safety-cr...
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07/28/2020
Distributionally Robust Losses for Latent Covariate Mixtures
While modern large-scale datasets often consist of heterogeneous subpopu...
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04/21/2020
Knowing what you know: valid confidence sets in multiclass and multilabel prediction
We develop conformal prediction methods for constructing valid predictiv...
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03/09/2020
FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis
Balancing performance and safety is crucial to deploying autonomous vehi...
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02/25/2020
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy
Adversarial training augments the training set with perturbations to imp...
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12/05/2019
Element Level Differential Privacy: The Right Granularity of Privacy
Differential Privacy (DP) provides strong guarantees on the risk of comp...
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03/01/2019
Proximal algorithms for constrained composite optimization, with applications to solving low-rank SDPs
We study a family of (potentially non-convex) constrained optimization p...
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02/01/2019
Lower Bounds for Locally Private Estimation via Communication Complexity
We develop lower bounds for estimation under local privacy constraints--...
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12/03/2018
Protection Against Reconstruction and Its Applications in Private Federated Learning
Federated learning has become an exciting direction for both research an...
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10/31/2018
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation
While recent developments in autonomous vehicle (AV) technology highligh...
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10/20/2018
Learning Models with Uniform Performance via Distributionally Robust Optimization
A common goal in statistics and machine learning is to learn models that...
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08/28/2018
Bounds on the conditional and average treatment effect in the presence of unobserved confounders
The causal effect of an intervention can not be consistently estimated w...
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05/30/2018
Generalizing to Unseen Domains via Adversarial Data Augmentation
We are concerned with learning models that generalize well to different ...
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10/29/2017
Certifiable Distributional Robustness with Principled Adversarial Training
Neural networks are vulnerable to adversarial examples and researchers h...
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10/11/2016
Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach
We study statistical inference and robust solution methods for stochasti...
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10/08/2016
Variance-based regularization with convex objectives
We develop an approach to risk minimization and stochastic optimization ...
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08/10/2016
Estimation from Indirect Supervision with Linear Moments
In structured prediction problems where we have indirect supervision of ...
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05/24/2016