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08/02/2023
Causal Inference with Differentially Private (Clustered) Outcomes
Estimating causal effects from randomized experiments is only feasible i...
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04/12/2023
Measuring Re-identification Risk
Compact user representations (such as embeddings) form the backbone of p...
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03/28/2023
Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model
We consider dynamic pricing strategies in a streamed longitudinal data s...
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03/27/2023
Learning Rate Schedules in the Presence of Distribution Shift
We design learning rate schedules that minimize regret for SGD-based onl...
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10/30/2022
Prediction Sets for High-Dimensional Mixture of Experts Models
Large datasets make it possible to build predictive models that can capt...
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09/05/2022
GRASP: A Goodness-of-Fit Test for Classification Learning
Performance of classifiers is often measured in terms of average accurac...
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10/29/2021
Pearson Chi-squared Conditional Randomization Test
Conditional independence (CI) testing arises naturally in many scientifi...
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10/22/2021
Adversarial robustness for latent models: Revisiting the robust-standard accuracies tradeoff
Over the past few years, several adversarial training methods have been ...
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08/11/2021
Controlling the False Split Rate in Tree-Based Aggregation
In many domains, data measurements can naturally be associated with the ...
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01/15/2021
Fundamental Tradeoffs in Distributionally Adversarial Training
Adversarial training is among the most effective techniques to improve t...
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12/04/2020
Near-Optimal Model Discrimination with Non-Disclosure
Let θ_0,θ_1 ∈ℝ^d be the population risk minimizers associated to some lo...
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10/21/2020
Precise Statistical Analysis of Classification Accuracies for Adversarial Training
Despite the wide empirical success of modern machine learning algorithms...
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02/25/2020
Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctions
Motivated by pricing in ad exchange markets, we consider the problem of ...
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02/24/2020
Precise Tradeoffs in Adversarial Training for Linear Regression
Despite breakthrough performance, modern learning models are known to be...
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11/04/2019
Online Debiasing for Adaptively Collected High-dimensional Data
Adaptive collection of data is increasingly commonplace in many applicat...
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04/10/2019
New Computational and Statistical Aspects of Regularized Regression with Application to Rare Feature Selection and Aggregation
Prior knowledge on properties of a target model often come as discrete o...
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01/05/2019
Analysis of a Two-Layer Neural Network via Displacement Convexity
Fitting a function by using linear combinations of a large number N of `...
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01/04/2019
Multi-Product Dynamic Pricing in High-Dimensions with Heterogenous Price Sensitivity
We consider the problem of multi-product dynamic pricing in a contextual...
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10/22/2018
Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning
One of the common tasks in unsupervised learning is dimensionality reduc...
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03/12/2018
False Discovery Rate Control via Debiased Lasso
We consider the problem of variable selection in high-dimensional statis...
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07/16/2017
Theoretical insights into the optimization landscape of over-parameterized shallow neural networks
In this paper we study the problem of learning a shallow artificial neur...
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04/26/2017
A Flexible Framework for Hypothesis Testing in High-dimensions
Hypothesis testing in the linear regression model is a fundamental stati...
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01/13/2017
Perishability of Data: Dynamic Pricing under Varying-Coefficient Models
We consider a firm that sells a large number of products to its customer...
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09/24/2016
Dynamic Pricing in High-dimensions
We study the pricing problem faced by a firm that sells a large number o...
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03/30/2016
Performance of a community detection algorithm based on semidefinite programming
The problem of detecting communities in a graph is maybe one the most st...
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03/29/2016
Online Rules for Control of False Discovery Rate and False Discovery Exceedance
Multiple hypothesis testing is a core problem in statistical inference a...
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08/11/2015
De-biasing the Lasso: Optimal Sample Size for Gaussian Designs
Performing statistical inference in high-dimension is an outstanding cha...
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02/24/2015
1-Bit Matrix Completion under Exact Low-Rank Constraint
We consider the problem of noisy 1-bit matrix completion under an exact ...
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05/02/2013
Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition
In the high-dimensional regression model a response variable is linearly...
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03/24/2013
Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems
We study the problem of adaptive control of a high dimensional linear qu...
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01/17/2013
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory
We consider linear regression in the high-dimensional regime where the n...
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09/24/2012