Search Results for author: Adam Smith

Found 43 papers, 10 papers with code

Auditing Privacy Mechanisms via Label Inference Attacks

no code implementations4 Jun 2024 Róbert István Busa-Fekete, Travis Dick, Claudio Gentile, Andrés Muñoz Medina, Adam Smith, Marika Swanberg

Across a range of experimental settings, we find that differentially private schemes dominate or match the privacy-utility tradeoff of more heuristic approaches.

Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares

no code implementations23 Apr 2024 Gavin Brown, Jonathan Hayase, Samuel Hopkins, Weihao Kong, Xiyang Liu, Sewoong Oh, Juan C. Perdomo, Adam Smith

We present a sample- and time-efficient differentially private algorithm for ordinary least squares, with error that depends linearly on the dimension and is independent of the condition number of $X^\top X$, where $X$ is the design matrix.

Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation

no code implementations21 Feb 2024 Gavin Brown, Krishnamurthy Dvijotham, Georgina Evans, Daogao Liu, Adam Smith, Abhradeep Thakurta

We provide an improved analysis of standard differentially private gradient descent for linear regression under the squared error loss.

Metalearning with Very Few Samples Per Task

no code implementations21 Dec 2023 Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Nathan Srebro, Jonathan Ullman

In multitask learning, we are given a fixed set of related learning tasks and need to output one accurate model per task, whereas in metalearning we are given tasks that are drawn i. i. d.

Binary Classification

A rigorous benchmarking of methods for SARS-CoV-2 lineage abundance estimation in wastewater

no code implementations29 Sep 2023 Viorel Munteanu, Victor Gordeev, Michael Saldana, Eva Aßmann, Justin Maine Su, Nicolae Drabcinski, Oksana Zlenko, Maryna Kit, Felicia Iordachi, Khooshbu Kantibhai Patel, Abdullah Al Nahid, Likhitha Chittampalli, Yidian Xu, Pavel Skums, Shelesh Agrawal, Martin Hölzer, Adam Smith, Alex Zelikovsky, Serghei Mangul

Here, we perform comprehensive benchmarking of 18 bioinformatics methods for estimating the relative abundance of SARS-CoV-2 (sub)lineages in wastewater by using data from 36 in vitro mixtures of synthetic lineage and sublineage genomes.

Benchmarking

Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions

no code implementations28 Jan 2023 Gavin Brown, Samuel B. Hopkins, Adam Smith

Our algorithm runs in time $\tilde{O}(nd^{\omega - 1} + nd/\varepsilon)$, where $\omega < 2. 38$ is the matrix multiplication exponent.

Open-Ended Question Answering

Differentially Private Sampling from Distributions

no code implementations NeurIPS 2021 Sofya Raskhodnikova, Satchit Sivakumar, Adam Smith, Marika Swanberg

We demonstrate that, in some parameter regimes, private sampling requires asymptotically fewer observations than learning a description of $P$ nonprivately; in other regimes, however, private sampling proves to be as difficult as private learning.

Fully Adaptive Composition for Gaussian Differential Privacy

no code implementations31 Oct 2022 Adam Smith, Abhradeep Thakurta

We show that Gaussian Differential Privacy, a variant of differential privacy tailored to the analysis of Gaussian noise addition, composes gracefully even in the presence of a fully adaptive analyst.

Instance-Optimal Differentially Private Estimation

no code implementations28 Oct 2022 Audra McMillan, Adam Smith, Jon Ullman

In this work, we study local minimax convergence estimation rates subject to $\epsilon$-differential privacy.

Strong Memory Lower Bounds for Learning Natural Models

no code implementations9 Jun 2022 Gavin Brown, Mark Bun, Adam Smith

We give lower bounds on the amount of memory required by one-pass streaming algorithms for solving several natural learning problems.

Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams

1 code implementation16 Feb 2022 Sergey Denisov, Brendan Mcmahan, Keith Rush, Adam Smith, Abhradeep Guha Thakurta

Motivated by recent applications requiring differential privacy over adaptive streams, we investigate the question of optimal instantiations of the matrix mechanism in this setting.

Federated Learning

Covariance-Aware Private Mean Estimation Without Private Covariance Estimation

no code implementations NeurIPS 2021 Gavin Brown, Marco Gaboardi, Adam Smith, Jonathan Ullman, Lydia Zakynthinou

Each of our estimators is based on a simple, general approach to designing differentially private mechanisms, but with novel technical steps to make the estimator private and sample-efficient.

Non-parametric Differentially Private Confidence Intervals for the Median

1 code implementation18 Jun 2021 Joerg Drechsler, Ira Globus-Harris, Audra McMillan, Jayshree Sarathy, Adam Smith

Differential privacy is a restriction on data processing algorithms that provides strong confidentiality guarantees for individual records in the data.

valid

Unlocking capacities of viral genomics for the COVID-19 pandemic response

no code implementations28 Apr 2021 Sergey Knyazev, Karishma Chhugani, Varuni Sarwal, Ram Ayyala, Harman Singh, Smruthi Karthikeyan, Dhrithi Deshpande, Zoia Comarova, Angela Lu, Yuri Porozov, Aiping Wu, Malak Abedalthagafi, Shivashankar Nagaraj, Adam Smith, Pavel Skums, Jason Ladner, Tommy Tsan-Yuk Lam, Nicholas Wu, Alex Zelikovsky, Rob Knight, Keith Crandall, Serghei Mangul

More than any other infectious disease epidemic, the COVID-19 pandemic has been characterized by the generation of large volumes of viral genomic data at an incredible pace due to recent advances in high-throughput sequencing technologies, the rapid global spread of SARS-CoV-2, and its persistent threat to public health.

Orthogonal Quantum Many-body Scars

no code implementations15 Feb 2021 Hongzheng Zhao, Adam Smith, Florian Mintert, Johannes Knolle

Quantum many-body scars have been put forward as counterexamples to the Eigenstate Thermalization Hypothesis.

Statistical Mechanics Quantum Gases Strongly Correlated Electrons Quantum Physics

When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?

1 code implementation11 Dec 2020 Gavin Brown, Mark Bun, Vitaly Feldman, Adam Smith, Kunal Talwar

Our problems are simple and fairly natural variants of the next-symbol prediction and the cluster labeling tasks.

Memorization

The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space

no code implementations NeurIPS 2020 Adam Smith, Shuang Song, Abhradeep Thakurta

We propose an $(\epsilon,\delta)$-differentially private algorithm that approximates $\dist$ within a factor of $(1\pm\gamma)$, and with additive error of $O(\sqrt{\ln(1/\delta)}/\epsilon)$, using space $O(\ln(\ln(u)/\gamma)/\gamma^2)$.

Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy

no code implementations11 Nov 2020 Di Wang, Marco Gaboardi, Adam Smith, Jinhui Xu

In our second attempt, we show that for any $1$-Lipschitz generalized linear convex loss function, there is an $(\epsilon, \delta)$-LDP algorithm whose sample complexity for achieving error $\alpha$ is only linear in the dimensionality $p$.

Real- and imaginary-time evolution with compressed quantum circuits

no code implementations24 Aug 2020 Sheng-Hsuan Lin, Rohit Dilip, Andrew G. Green, Adam Smith, Frank Pollmann

The current generation of noisy intermediate scale quantum computers introduces new opportunities to study quantum many-body systems.

Quantum Physics Mesoscale and Nanoscale Physics Strongly Correlated Electrons

Differentially Private Simple Linear Regression

no code implementations10 Jul 2020 Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam Smith, Salil Vadhan

Economics and social science research often require analyzing datasets of sensitive personal information at fine granularity, with models fit to small subsets of the data.

regression

Crossing a topological phase transition with a quantum computer

no code implementations11 Oct 2019 Adam Smith, Bernhard Jobst, Andrew G. Green, Frank Pollmann

The simulation that we perform is easily scalable and is a practical demonstration of the utility of near-term quantum computers for the study of quantum phases of matter and their transitions.

Strongly Correlated Electrons Mesoscale and Nanoscale Physics Quantum Physics

Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis

1 code implementation21 Jun 2019 Ryan Rogers, Aaron Roth, Adam Smith, Nathan Srebro, Om Thakkar, Blake Woodworth

We design a general framework for answering adaptive statistical queries that focuses on providing explicit confidence intervals along with point estimates.

valid

Simulating quantum many-body dynamics on a current digital quantum computer

1 code implementation14 Jun 2019 Adam Smith, M. S. Kim, Frank Pollmann, Johannes Knolle

Universal quantum computers are potentially an ideal setting for simulating many-body quantum dynamics that is out of reach for classical digital computers.

Quantum Physics Mesoscale and Nanoscale Physics Strongly Correlated Electrons

Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations

no code implementations17 Dec 2018 Di Wang, Adam Smith, Jinhui Xu

For the case of \emph{generalized linear losses} (such as hinge and logistic losses), we give an LDP algorithm whose sample complexity is only linear in the dimensionality $p$ and quasipolynomial in other terms (the privacy parameters $\epsilon$ and $\delta$, and the desired excess risk $\alpha$).

The Structure of Optimal Private Tests for Simple Hypotheses

no code implementations27 Nov 2018 Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam Smith, Jonathan Ullman

Specifically, we characterize this sample complexity up to constant factors in terms of the structure of $P$ and $Q$ and the privacy level $\varepsilon$, and show that this sample complexity is achieved by a certain randomized and clamped variant of the log-likelihood ratio test.

Change Point Detection Generalization Bounds +2

From Soft Classifiers to Hard Decisions: How fair can we be?

1 code implementation3 Oct 2018 Ran Canetti, Aloni Cohen, Nishanth Dikkala, Govind Ramnarayan, Sarah Scheffler, Adam Smith

We study the feasibility of achieving various fairness properties by post-processing calibrated scores, and then show that deferring post-processors allow for more fairness conditions to hold on the final decision.

Decision Making Fairness

The Limits of Post-Selection Generalization

no code implementations NeurIPS 2018 Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, Jonathan Ullman

While statistics and machine learning offers numerous methods for ensuring generalization, these methods often fail in the presence of adaptivity---the common practice in which the choice of analysis depends on previous interactions with the same dataset.

Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization

no code implementations NeurIPS 2018 Blake Woodworth, Jialei Wang, Adam Smith, Brendan Mcmahan, Nathan Srebro

We suggest a general oracle-based framework that captures different parallel stochastic optimization settings described by a dependency graph, and derive generic lower bounds in terms of this graph.

Stochastic Optimization

Information, Privacy and Stability in Adaptive Data Analysis

no code implementations2 Jun 2017 Adam Smith

This assumption breaks downs when data are re-used across analyses and the analysis to be performed at a given stage depends on the results of earlier stages.

valid

Stability selection for component-wise gradient boosting in multiple dimensions

1 code implementation30 Nov 2016 Janek Thomas, Andreas Mayr, Bernd Bischl, Matthias Schmid, Adam Smith, Benjamin Hofner

We apply this new algorithm to a study to estimate abundance of common eider in Massachusetts, USA, featuring excess zeros, overdispersion, non-linearity and spatio-temporal structures.

Additive models

Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing

no code implementations13 Apr 2016 Ryan Rogers, Aaron Roth, Adam Smith, Om Thakkar

In this paper, we initiate a principled study of how the generalization properties of approximate differential privacy can be used to perform adaptive hypothesis testing, while giving statistically valid $p$-value corrections.

Two-sample testing valid

When is Nontrivial Estimation Possible for Graphons and Stochastic Block Models?

no code implementations7 Apr 2016 Audra McMillan, Adam Smith

We provide a lower bound on the accuracy of estimators for block graphons with a large number of blocks.

Graphon Estimation

Algorithmic Stability for Adaptive Data Analysis

no code implementations8 Nov 2015 Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, Jonathan Ullman

Specifically, suppose there is an unknown distribution $\mathbf{P}$ and a set of $n$ independent samples $\mathbf{x}$ is drawn from $\mathbf{P}$.

Local, Private, Efficient Protocols for Succinct Histograms

no code implementations18 Apr 2015 Raef Bassily, Adam Smith

Moreover, we show that this much error is necessary, regardless of computational efficiency, and even for the simple setting where only one item appears with significant frequency in the data set.

Computational Efficiency

More General Queries and Less Generalization Error in Adaptive Data Analysis

no code implementations16 Mar 2015 Raef Bassily, Adam Smith, Thomas Steinke, Jonathan Ullman

However, generalization error is typically bounded in a non-adaptive model, where all questions are specified before the dataset is drawn.

Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds

1 code implementation27 May 2014 Raef Bassily, Adam Smith, Abhradeep Thakurta

We provide a separate set of algorithms and matching lower bounds for the setting in which the loss functions are known to also be strongly convex.

What Can We Learn Privately?

no code implementations6 Mar 2008 Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, Adam Smith

Therefore, almost anything learnable is learnable privately: specifically, if a concept class is learnable by a (non-private) algorithm with polynomial sample complexity and output size, then it can be learned privately using a polynomial number of samples.

Sublinear Algorithms for Approximating String Compressibility

1 code implementation8 Jun 2007 Sofya Raskhodnikova, Dana Ron, Ronitt Rubinfeld, Adam Smith

We raise the question of approximating the compressibility of a string with respect to a fixed compression scheme, in sublinear time.

Data Structures and Algorithms

Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data

no code implementations4 Feb 2006 Yevgeniy Dodis, Rafail Ostrovsky, Leonid Reyzin, Adam Smith

A "secure sketch" produces public information about its input w that does not reveal w, and yet allows exact recovery of w given another value that is close to w. Thus, it can be used to reliably reproduce error-prone biometric inputs without incurring the security risk inherent in storing them.

Cryptography and Security Information Theory Information Theory

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