Kamalika Chaudhuri

research

∙ 06/15/2023

ViP: A Differentially Private Foundation Model for Computer Vision

Artificial intelligence (AI) has seen a tremendous surge in capabilities...

0 Yaodong Yu, et al. ∙

research

∙ 06/02/2023

Agnostic Multi-Group Active Learning

Inspired by the problem of improving classification accuracy on rare or ...

0 Nick Rittler, et al. ∙

research

∙ 05/18/2023

Data Redaction from Conditional Generative Models

Deep generative models are known to produce undesirable samples such as ...

0 Zhifeng Kong, et al. ∙

research

∙ 04/26/2023

Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning

Self-supervised learning (SSL) algorithms can produce useful image repre...

0 Casey Meehan, et al. ∙

research

∙ 03/07/2023

Can Membership Inferencing be Refuted?

Membership inference (MI) attack is currently the most popular test for ...

0 Zhifeng Kong, et al. ∙

research

∙ 02/25/2023

Data-Copying in Generative Models: A Formal Framework

There has been some recent interest in detecting and addressing memoriza...

0 Robi Bhattacharjee, et al. ∙

research

∙ 11/19/2022

A Two-Stage Active Learning Algorithm for k-Nearest Neighbors

We introduce a simple and intuitive two-stage active learning algorithm ...

0 Nick Rittler, et al. ∙

research

∙ 11/08/2022

The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning

We consider private federated learning (FL), where a server aggregates d...

0 Chuan Guo, et al. ∙

research

∙ 10/25/2022

Robustness of Locally Differentially Private Graph Analysis Against Poisoning

Locally differentially private (LDP) graph analysis allows private analy...

0 Jacob Imola, et al. ∙

research

∙ 06/29/2022

Forgetting Data from Pre-trained GANs

Large pre-trained generative models are known to occasionally provide sa...

0 Zhifeng Kong, et al. ∙

research

∙ 06/17/2022

Thompson Sampling for Robust Transfer in Multi-Task Bandits

We study the problem of online multi-task learning where the tasks are p...

0 Zhi Wang, et al. ∙

research

∙ 06/09/2022

A Learning-Theoretic Framework for Certified Auditing of Machine Learning Models

Responsible use of machine learning requires that models be audited for ...

6 Chhavi Yadav, et al. ∙

research

∙ 05/23/2022

Throwing Away Data Improves Worst-Class Error in Imbalanced Classification

Class imbalances pervade classification problems, yet their treatment di...

0 Martin Arjovsky, et al. ∙

research

∙ 05/10/2022

Sentence-level Privacy for Document Embeddings

User language data can contain highly sensitive personal content. As suc...

0 Casey Meehan, et al. ∙

research

∙ 05/03/2022

Differentially Private Triangle and 4-Cycle Counting in the Shuffle Model

Subgraph counting is fundamental for analyzing connection patterns or cl...

0 Jacob Imola, et al. ∙

research

∙ 03/15/2022

Privacy-Aware Compression for Federated Data Analysis

Federated data analytics is a framework for distributed data analysis wh...

0 Kamalika Chaudhuri, et al. ∙

research

∙ 02/10/2022

Understanding Rare Spurious Correlations in Neural Networks

Neural networks are known to use spurious correlations for classificatio...

34 Yao-Yuan Yang, et al. ∙

research

∙ 01/28/2022

Bounding Training Data Reconstruction in Private (Deep) Learning

Differential privacy is widely accepted as the de facto method for preve...

0 Chuan Guo, et al. ∙

research

∙ 01/13/2022

Privacy Amplification by Subsampling in Time Domain

Aggregate time-series data like traffic flow and site occupancy repeated...

6 Tatsuki Koga, et al. ∙

research

∙ 12/11/2021

Privacy Amplification via Shuffling for Linear Contextual Bandits

Contextual bandit algorithms are widely used in domains where it is desi...

0 Evrard Garcelon, et al. ∙

research

∙ 10/13/2021

Communication-Efficient Triangle Counting under Local Differential Privacy

Triangle counting in networks under LDP (Local Differential Privacy) is ...

0 Jacob Imola, et al. ∙

research

∙ 09/14/2021

Behavior of k-NN as an Instance-Based Explanation Method

Adoption of DL models in critical areas has led to an escalating demand ...

0 Chhavi Yadav, et al. ∙

research

∙ 06/11/2021

A Shuffling Framework for Local Differential Privacy

ldp deployments are vulnerable to inference attacks as an adversary can ...

2 Casey Meehan, et al. ∙

research

∙ 05/29/2021

Understanding Instance-based Interpretability of Variational Auto-Encoders

Instance-based interpretation methods have been widely studied for super...

46 Zhifeng Kong, et al. ∙

research

∙ 05/21/2021

Privacy Amplification Via Bernoulli Sampling

Balancing privacy and accuracy is a major challenge in designing differe...

0 Jacob Imola, et al. ∙

research

∙ 03/10/2021

Universal Approximation of Residual Flows in Maximum Mean Discrepancy

Normalizing flows are a class of flexible deep generative models that of...

0 Zhifeng Kong, et al. ∙

research

∙ 02/18/2021

Consistent Non-Parametric Methods for Adaptive Robustness

Learning classifiers that are robust to adversarial examples has receive...

0 Robi Bhattacharjee, et al. ∙

research

∙ 02/14/2021

Connecting Interpretability and Robustness in Decision Trees through Separation

Recent research has recognized interpretability and robustness as essent...

11 Michal Moshkovitz, et al. ∙

research

∙ 12/19/2020

Sample Complexity of Adversarially Robust Linear Classification on Separated Data

We consider the sample complexity of learning with adversarial robustnes...

6 Robi Bhattacharjee, et al. ∙

research

∙ 11/17/2020

Close Category Generalization

Out-of-distribution generalization is a core challenge in machine learni...

13 Yao-Yuan Yang, et al. ∙

research

∙ 11/06/2020

Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning

The Private Aggregation of Teacher Ensembles (PATE) framework is one of ...

0 Chong Liu, et al. ∙

research

∙ 10/29/2020

Multitask Bandit Learning through Heterogeneous Feedback Aggregation

In many real-world applications, multiple agents seek to learn how to pe...

0 Zhi Wang, et al. ∙

research

∙ 10/17/2020

Locally Differentially Private Analysis of Graph Statistics

Differentially private analysis of graphs is widely used for releasing s...

0 Jacob Imola, et al. ∙

research

∙ 08/10/2020

Trustworthy AI Inference Systems: An Industry Research View

In this work, we provide an industry research view for approaching the d...

37 Rosario Cammarota, et al. ∙

research

∙ 05/31/2020

The Expressive Power of a Class of Normalizing Flow Models

Normalizing flows have received a great deal of recent attention as they...

0 Zhifeng Kong, et al. ∙

research

∙ 05/24/2020

Successive Refinement of Privacy

This work examines a novel question: how much randomness is needed to ac...

0 Antonious M. Girgis, et al. ∙

research

∙ 04/12/2020

A Non-Parametric Test to Detect Data-Copying in Generative Models

Detecting overfitting in generative models is an important challenge in ...

6 Casey Meehan, et al. ∙

research

∙ 03/13/2020

When are Non-Parametric Methods Robust?

A growing body of research has shown that many classifiers are susceptib...

0 Robi Bhattacharjee, et al. ∙

research

∙ 02/24/2020

Approximate Data Deletion from Machine Learning Models: Algorithms and Evaluations

Deleting data from a trained machine learning (ML) model is a critical t...

0 Zachary Izzo, et al. ∙

research

∙ 12/09/2019

Location Trace Privacy Under Conditional Priors

Providing meaningful privacy to users of location based services is part...

0 Casey Meehan, et al. ∙

research

∙ 07/03/2019

Capacity Bounded Differential Privacy

Differential privacy, a notion of algorithmic stability, is a gold stand...

0 Kamalika Chaudhuri, et al. ∙

research

∙ 06/07/2019

Adversarial Examples for Non-Parametric Methods: Attacks, Defenses and Large Sample Limits

Adversarial examples have received a great deal of recent attention beca...

0 Yao-Yuan Yang, et al. ∙

research

∙ 05/29/2019

The Label Complexity of Active Learning from Observational Data

Counterfactual learning from observational data involves learning a clas...

0 Songbai Yan, et al. ∙

research

∙ 05/28/2019

An Investigation of Data Poisoning Defenses for Online Learning

We consider data poisoning attacks, where an adversary can modify a smal...

0 Yizhen Wang, et al. ∙

research

∙ 11/05/2018

Model Extraction and Active Learning

Machine learning is being increasingly used by individuals, research ins...

14 Varun Chandrasekaran, et al. ∙

research

∙ 09/12/2018

The Inductive Bias of Restricted f-GANs

Generative adversarial networks are a novel method for statistical infer...

0 Shuang Liu, et al. ∙

research

∙ 09/07/2018

Differentially Private Continual Release of Graph Statistics

Motivated by understanding the dynamics of sensitive social networks ove...

0 Shuang Song, et al. ∙

research

∙ 08/27/2018

Data Poisoning Attacks against Online Learning

We consider data poisoning attacks, a class of adversarial attacks on ma...

0 Yizhen Wang, et al. ∙

research

∙ 02/25/2018

Active Learning with Logged Data

We consider active learning with logged data, where labeled examples are...

0 Songbai Yan, et al. ∙

research

∙ 02/07/2018

Spectral Learning of Binomial HMMs for DNA Methylation Data

We consider learning parameters of Binomial Hidden Markov Models, which ...

0 Chicheng Zhang, et al. ∙

Kamalika Chaudhuri

Featured Co-authors