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06/11/2023
Differentially Private Conditional Independence Testing
Conditional independence (CI) tests are widely used in statistical data ...
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04/20/2023
Debiasing Conditional Stochastic Optimization
In this paper, we study the conditional stochastic optimization (CSO) pr...
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02/02/2023
Interventional and Counterfactual Inference with Diffusion Models
We consider the problem of answering observational, interventional, and ...
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01/12/2023
Thompson Sampling with Diffusion Generative Prior
In this work, we initiate the idea of using denoising diffusion models t...
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12/14/2022
Sequential Kernelized Independence Testing
Independence testing is a fundamental and classical statistical problem ...
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06/08/2022
Uplifting Bandits
We introduce a multi-armed bandit model where the reward is a sum of mul...
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07/07/2021
On Codomain Separability and Label Inference from (Noisy) Loss Functions
Machine learning classifiers rely on loss functions for performance eval...
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06/06/2021
Collaborative Causal Discovery with Atomic Interventions
We introduce a new Collaborative Causal Discovery problem, through which...
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05/18/2021
Label Inference Attacks from Log-loss Scores
Log-loss (also known as cross-entropy loss) metric is ubiquitously used ...
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05/24/2020
Efficient Intervention Design for Causal Discovery with Latents
We consider recovering a causal graph in presence of latent variables, w...
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04/11/2019
Restricted Isometry Property under High Correlations
Matrices satisfying the Restricted Isometry Property (RIP) play an impor...
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10/21/2017
Deep Neural Network Approximation using Tensor Sketching
Deep neural networks are powerful learning models that achieve state-of-...
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09/19/2017
Verifying Properties of Binarized Deep Neural Networks
Understanding properties of deep neural networks is an important challen...
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07/25/2017
Compressed Sparse Linear Regression
High-dimensional sparse linear regression is a basic problem in machine ...
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01/04/2017
Private Incremental Regression
Data is continuously generated by modern data sources, and a recent chal...
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12/19/2016
Simple Black-Box Adversarial Perturbations for Deep Networks
Deep neural networks are powerful and popular learning models that achie...
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04/22/2015
