Aws Albarghouthi | DeepAI

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∙ 04/20/2023

The Dataset Multiplicity Problem: How Unreliable Data Impacts Predictions

We introduce dataset multiplicity, a way to study how inaccuracies, unce...

0 Anna P. Meyer, et al. ∙

∙ 02/06/2023

Sketching Robot Programs On the Fly

Service robots for personal use in the home and the workplace require en...

0 David Porfirio, et al. ∙

∙ 01/27/2023

PECAN: A Deterministic Certified Defense Against Backdoor Attacks

Neural networks are vulnerable to backdoor poisoning attacks, where the ...

0 Yuhao Zhang, et al. ∙

∙ 11/17/2022

Synthesizing Quantum-Circuit Optimizers

Near-term quantum computers are expected to work in an environment where...

0 Amanda Xu, et al. ∙

∙ 08/30/2022

AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels

Weak supervision (WS) is a powerful method to build labeled datasets for...

2 Nicholas Roberts, et al. ∙

∙ 08/29/2022

Qubit Mapping and Routing via MaxSAT

Near-term quantum computers will operate in a noisy environment, without...

0 Abtin Molavi, et al. ∙

∙ 06/07/2022

Certifying Data-Bias Robustness in Linear Regression

Datasets typically contain inaccuracies due to human error and societal ...

0 Anna P. Meyer, et al. ∙

∙ 05/26/2022

BagFlip: A Certified Defense against Data Poisoning

Machine learning models are vulnerable to data-poisoning attacks, in whi...

0 Yuhao Zhang, et al. ∙

∙ 10/08/2021

Certifying Robustness to Programmable Data Bias in Decision Trees

Datasets can be biased due to societal inequities, human biases, under-r...

0 Anna P. Meyer, et al. ∙

∙ 09/21/2021

Introduction to Neural Network Verification

Deep learning has transformed the way we think of software and what it c...

0 Aws Albarghouthi, et al. ∙

∙ 02/15/2021

Certified Robustness to Programmable Transformations in LSTMs

Deep neural networks for natural language processing are fragile in the ...

0 Yuhao Zhang, et al. ∙

∙ 01/04/2021

Learning Differentially Private Mechanisms

Differential privacy is a formal, mathematical definition of data privac...

0 Subhajit Roy, et al. ∙

∙ 07/12/2020

Abstract Universal Approximation for Neural Networks

With growing concerns about the safety and robustness of neural networks...

9 Zi Wang, et al. ∙

∙ 06/11/2020

Backdoors in Neural Models of Source Code

Deep neural networks are vulnerable to a range of adversaries. A particu...

0 Goutham Ramakrishnan, et al. ∙

∙ 05/18/2020

A Comparative Exploration of ML Techniques for Tuning Query Degree of Parallelism

There is a large body of recent work applying machine learning (ML) tech...

0 Zhiwei Fan, et al. ∙

∙ 02/22/2020

Robustness to Programmable String Transformations via Augmented Abstract Training

Deep neural networks for natural language processing tasks are vulnerabl...

0 Yuhao Zhang, et al. ∙

∙ 02/07/2020

Semantic Robustness of Models of Source Code

Deep neural networks are vulnerable to adversarial examples - small inpu...

1 Goutham Ramakrishnan, et al. ∙

∙ 12/02/2019

Proving Data-Poisoning Robustness in Decision Trees

Machine learning models are brittle, and small changes in the training d...

0 Samuel Drews, et al. ∙

∙ 06/18/2019

A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning

Porting code from CPU to GPU is costly and time-consuming; Unless much t...

0 Newsha Ardalani, et al. ∙

∙ 05/20/2019

Efficient Synthesis with Probabilistic Constraints

We consider the problem of synthesizing a program given a probabilistic ...

0 Samuel Drews, et al. ∙

∙ 12/10/2018

Scaling-Up In-Memory Datalog Processing: Observations and Techniques

Recursive query processing has experienced a recent resurgence, as a res...

0 Zhiwei Fan, et al. ∙

∙ 10/29/2018

Trace Abstraction Modulo Probability

We propose trace abstraction modulo probability, a proof technique for v...

0 Calvin Smith, et al. ∙

∙ 09/11/2018

Neural-Augmented Static Analysis of Android Communication

We address the problem of discovering communication links between applic...

0 Jinman Zhao, et al. ∙

∙ 04/11/2018

Constraint-Based Synthesis of Coupling Proofs

Proof by coupling is a classical technique for proving properties about ...

0 Aws Albarghouthi, et al. ∙

∙ 09/15/2017

Synthesizing Coupling Proofs of Differential Privacy

Differential privacy has emerged as a promising probabilistic formulatio...

0 Aws Albarghouthi, et al. ∙

∙ 02/17/2017

Quantifying Program Bias

With the range and sensitivity of algorithmic decisions expanding at a b...

0 Aws Albarghouthi, et al. ∙

∙ 10/19/2016

Fairness as a Program Property

We explore the following question: Is a decision-making program fair, fo...

0 Aws Albarghouthi, et al. ∙

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