542 citations · 1.6k across the 85 of their papers we have counts for
64 papers · 1 filter
FairProof : Confidential and Certifiable Fairness for Neural Networks
Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh +1
Machine learning models are increasingly used in societal applications, yet legal and privacy concerns demand that they very often be kept confidential. Consequently, there is a gr…
Agnostic Multi-Group Active Learning
Nick Rittler, Kamalika Chaudhuri
Inspired by the problem of improving classification accuracy on rare or hard subsets of a population, there has been recent interest in models of learning where the goal is to gene…
Data Redaction from Conditional Generative Models
Zhifeng Kong, Kamalika Chaudhuri
Deep generative models are known to produce undesirable samples such as harmful content. Traditional mitigation methods include re-training from scratch, filtering, or editing; how…
Can Membership Inferencing be Refuted?
Zhifeng Kong, Amrita Roy Chowdhury, Kamalika Chaudhuri
Membership inference (MI) attack is currently the most popular test for measuring privacy leakage in machine learning models. Given a machine learning model, a data point and some…
Data-Copying in Generative Models: A Formal Framework
Robi Bhattacharjee, Sanjoy Dasgupta, Kamalika Chaudhuri
There has been some recent interest in detecting and addressing memorization of training data by deep neural networks. A formal framework for memorization in generative models, cal…
A Two-Stage Active Learning Algorithm for -Nearest Neighbors
Nick Rittler, Kamalika Chaudhuri
-nearest neighbor classification is a popular non-parametric method because of desirable properties like automatic adaption to distributional scale changes. Unfortunately, it ha…
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design
Chuan Guo, Kamalika Chaudhuri, Pierre Stock +1
In private federated learning (FL), a server aggregates differentially private updates from a large number of clients in order to train a machine learning model. The main challenge…
Robust Empirical Risk Minimization with Tolerance
Robi Bhattacharjee, Max Hopkins, Akash Kumar +2
Developing simple, sample-efficient learning algorithms for robust classification is a pressing issue in today's tech-dominated world, and current theoretical techniques requiring…
Thompson Sampling for Robust Transfer in Multi-Task Bandits
Zhi Wang, Chicheng Zhang, Kamalika Chaudhuri
We study the problem of online multi-task learning where the tasks are performed within similar but not necessarily identical multi-armed bandit environments. In particular, we stu…
Data Redaction from Pre-trained GANs
Zhifeng Kong, Kamalika Chaudhuri
Large pre-trained generative models are known to occasionally output undesirable samples, which undermines their trustworthiness. The common way to mitigate this is to re-train the…
XAudit : A Theoretical Look at Auditing with Explanations
Chhavi Yadav, Michal Moshkovitz, Kamalika Chaudhuri
Responsible use of machine learning requires models to be audited for undesirable properties. While a body of work has proposed using explanations for auditing, how to do so and wh…
Sentence-level Privacy for Document Embeddings
Casey Meehan, Khalil Mrini, Kamalika Chaudhuri
User language data can contain highly sensitive personal content. As such, it is imperative to offer users a strong and interpretable privacy guarantee when learning from their dat…
Privacy-Aware Compression for Federated Data Analysis
Kamalika Chaudhuri, Chuan Guo, Mike Rabbat
Federated data analytics is a framework for distributed data analysis where a server compiles noisy responses from a group of distributed low-bandwidth user devices to estimate agg…
Understanding Rare Spurious Correlations in Neural Networks
Yao-Yuan Yang, Chi-Ning Chou, Kamalika Chaudhuri
Neural networks are known to use spurious correlations such as background information for classification. While prior work has looked at spurious correlations that are widespread i…
Bounding Training Data Reconstruction in Private (Deep) Learning
Chuan Guo, Brian Karrer, Kamalika Chaudhuri +1
Differential privacy is widely accepted as the de facto method for preventing data leakage in ML, and conventional wisdom suggests that it offers strong protection against privacy…
Privacy Amplification via Shuffling for Linear Contextual Bandits
Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet +1
Contextual bandit algorithms are widely used in domains where it is desirable to provide a personalized service by leveraging contextual information, that may contain sensitive inf…
Behavior of k-NN as an Instance-Based Explanation Method
Chhavi Yadav, Kamalika Chaudhuri
Adoption of DL models in critical areas has led to an escalating demand for sound explanation methods. Instance-based explanation methods are a popular type that return selective i…
A Shuffling Framework for Local Differential Privacy
Casey Meehan, Amrita Roy Chowdhury, Kamalika Chaudhuri +1
ldp deployments are vulnerable to inference attacks as an adversary can link the noisy responses to their identity and subsequently, auxiliary information using the order of the da…
Understanding Instance-based Interpretability of Variational Auto-Encoders
Zhifeng Kong, Kamalika Chaudhuri
Instance-based interpretation methods have been widely studied for supervised learning methods as they help explain how black box neural networks predict. However, instance-based i…
Privacy Amplification Via Bernoulli Sampling
Jacob Imola, Kamalika Chaudhuri
Balancing privacy and accuracy is a major challenge in designing differentially private machine learning algorithms. One way to improve this tradeoff for free is to leverage the no…
Universal Approximation of Residual Flows in Maximum Mean Discrepancy
Zhifeng Kong, Kamalika Chaudhuri
Normalizing flows are a class of flexible deep generative models that offer easy likelihood computation. Despite their empirical success, there is little theoretical understanding…
Connecting Interpretability and Robustness in Decision Trees through Separation
Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri
Recent research has recognized interpretability and robustness as essential properties of trustworthy classification. Curiously, a connection between robustness and interpretabilit…
Consistent Non-Parametric Methods for Maximizing Robustness
Robi Bhattacharjee, Kamalika Chaudhuri
Learning classifiers that are robust to adversarial examples has received a great deal of recent attention. A major drawback of the standard robust learning framework is there is a…
Sample Complexity of Adversarially Robust Linear Classification on Separated Data
Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri
We consider the sample complexity of learning with adversarial robustness. Most prior theoretical results for this problem have considered a setting where different classes in the…