
Consistent NonParametric Methods for Adaptive Robustness
Learning classifiers that are robust to adversarial examples has receive...
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Connecting Interpretability and Robustness in Decision Trees through Separation
Recent research has recognized interpretability and robustness as essent...
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Sample Complexity of Adversarially Robust Linear Classification on Separated Data
We consider the sample complexity of learning with adversarial robustnes...
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Close Category Generalization
Outofdistribution generalization is a core challenge in machine learni...
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Revisiting ModelAgnostic Private Learning: Faster Rates and Active Learning
The Private Aggregation of Teacher Ensembles (PATE) framework is one of ...
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Multitask Bandit Learning through Heterogeneous Feedback Aggregation
In many realworld applications, multiple agents seek to learn how to pe...
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Locally Differentially Private Analysis of Graph Statistics
Differentially private analysis of graphs is widely used for releasing s...
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Trustworthy AI Inference Systems: An Industry Research View
In this work, we provide an industry research view for approaching the d...
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The Expressive Power of a Class of Normalizing Flow Models
Normalizing flows have received a great deal of recent attention as they...
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Successive Refinement of Privacy
This work examines a novel question: how much randomness is needed to ac...
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A NonParametric Test to Detect DataCopying in Generative Models
Detecting overfitting in generative models is an important challenge in ...
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When are NonParametric Methods Robust?
A growing body of research has shown that many classifiers are susceptib...
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Approximate Data Deletion from Machine Learning Models: Algorithms and Evaluations
Deleting data from a trained machine learning (ML) model is a critical t...
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Location Trace Privacy Under Conditional Priors
Providing meaningful privacy to users of location based services is part...
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Capacity Bounded Differential Privacy
Differential privacy, a notion of algorithmic stability, is a gold stand...
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Adversarial Examples for NonParametric Methods: Attacks, Defenses and Large Sample Limits
Adversarial examples have received a great deal of recent attention beca...
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The Label Complexity of Active Learning from Observational Data
Counterfactual learning from observational data involves learning a clas...
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An Investigation of Data Poisoning Defenses for Online Learning
We consider data poisoning attacks, where an adversary can modify a smal...
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Model Extraction and Active Learning
Machine learning is being increasingly used by individuals, research ins...
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The Inductive Bias of Restricted fGANs
Generative adversarial networks are a novel method for statistical infer...
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Differentially Private Continual Release of Graph Statistics
Motivated by understanding the dynamics of sensitive social networks ove...
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Data Poisoning Attacks against Online Learning
We consider data poisoning attacks, a class of adversarial attacks on ma...
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Active Learning with Logged Data
We consider active learning with logged data, where labeled examples are...
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Spectral Learning of Binomial HMMs for DNA Methylation Data
We consider learning parameters of Binomial Hidden Markov Models, which ...
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Rényi Differential Privacy Mechanisms for Posterior Sampling
Using a recently proposed privacy definition of Rényi Differential Priva...
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Learning to Blame: Localizing Novice Type Errors with DataDriven Diagnosis
Localizing type errors is challenging in languages with global type infe...
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Composition Properties of Inferential Privacy for TimeSeries Data
With the proliferation of mobile devices and the internet of things, dev...
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Analyzing the Robustness of Nearest Neighbors to Adversarial Examples
Motivated by applications such as autonomous vehicles, testtime attacks...
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Approximation and Convergence Properties of Generative Adversarial Learning
Generative adversarial networks (GAN) approximate a target data distribu...
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Variational Bayes In Private Settings (VIPS)
We provide a general framework for privacypreserving variational Bayes ...
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Active Learning from Imperfect Labelers
We study active learning where the labeler can not only return incorrect...
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Private Topic Modeling
We develop a privatised stochastic variational inference method for Late...
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Bolton Differential Privacy for Scalable Stochastic Gradient Descentbased Analytics
While significant progress has been made separately on analytics systems...
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DPEM: Differentially Private Expectation Maximization
The iterative nature of the expectation maximization (EM) algorithm pres...
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On the Theory and Practice of PrivacyPreserving Bayesian Data Analysis
Bayesian inference has great promise for the privacypreserving analysis...
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Pufferfish Privacy Mechanisms for Correlated Data
Many modern databases include personal and sensitive correlated data, su...
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Active Learning from Weak and Strong Labelers
An active learner is given a hypothesis class, a large set of unlabeled ...
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Convergence Rates of Active Learning for Maximum Likelihood Estimation
An active learner is given a class of models, a large set of unlabeled e...
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Spectral Learning of Large Structured HMMs for Comparative Epigenomics
We develop a latent variable model and an efficient spectral algorithm m...
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Crowdsourcing Feature Discovery via Adaptively Chosen Comparisons
We introduce an unsupervised approach to efficiently discover the underl...
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Beyond Disagreementbased Agnostic Active Learning
We study agnostic active learning, where the goal is to learn a classifi...
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Rates of Convergence for Nearest Neighbor Classification
Nearest neighbor methods are a popular class of nonparametric estimators...
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NearOptimal Algorithms for DifferentiallyPrivate Principal Components
Principal components analysis (PCA) is a standard tool for identifying g...
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Convergence Rates for Differentially Private Statistical Estimation
Differential privacy is a cryptographicallymotivated definition of priv...
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An Online Learningbased Framework for Tracking
We study the tracking problem, namely, estimating the hidden state of an...
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Spectral Methods for Learning Multivariate Latent Tree Structure
This work considers the problem of learning the structure of multivariat...
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Differentially Private Empirical Risk Minimization
Privacypreserving machine learning algorithms are crucial for the incre...
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Tracking using explanationbased modeling
We study the tracking problem, namely, estimating the hidden state of an...
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Kamalika Chaudhuri
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Assistant Professor at University of California at San Diego, Postdoc at University of California at San Diego from 20072010, Intern at Microsoft 2006, Intern at IBM 2005, Intern at HewlettPackard 2004, Intern at INRIA 2001