research
∙
04/20/2023
The Dataset Multiplicity Problem: How Unreliable Data Impacts Predictions
We introduce dataset multiplicity, a way to study how inaccuracies, unce...
share
research
∙
02/06/2023
Sketching Robot Programs On the Fly
Service robots for personal use in the home and the workplace require en...
research
∙
01/27/2023
PECAN: A Deterministic Certified Defense Against Backdoor Attacks
Neural networks are vulnerable to backdoor poisoning attacks, where the ...
research
∙
11/17/2022
Synthesizing Quantum-Circuit Optimizers
Near-term quantum computers are expected to work in an environment where...
research
∙
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...
research
∙
08/29/2022
Qubit Mapping and Routing via MaxSAT
Near-term quantum computers will operate in a noisy environment, without...
research
∙
06/07/2022
Certifying Data-Bias Robustness in Linear Regression
Datasets typically contain inaccuracies due to human error and societal ...
research
∙
05/26/2022
BagFlip: A Certified Defense against Data Poisoning
Machine learning models are vulnerable to data-poisoning attacks, in whi...
research
∙
10/08/2021
Certifying Robustness to Programmable Data Bias in Decision Trees
Datasets can be biased due to societal inequities, human biases, under-r...
research
∙
09/21/2021
Introduction to Neural Network Verification
Deep learning has transformed the way we think of software and what it c...
research
∙
02/15/2021
Certified Robustness to Programmable Transformations in LSTMs
Deep neural networks for natural language processing are fragile in the ...
research
∙
01/04/2021
Learning Differentially Private Mechanisms
Differential privacy is a formal, mathematical definition of data privac...
research
∙
07/12/2020
Abstract Universal Approximation for Neural Networks
With growing concerns about the safety and robustness of neural networks...
research
∙
06/11/2020
Backdoors in Neural Models of Source Code
Deep neural networks are vulnerable to a range of adversaries. A particu...
research
∙
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...
research
∙
02/22/2020
Robustness to Programmable String Transformations via Augmented Abstract Training
Deep neural networks for natural language processing tasks are vulnerabl...
research
∙
02/07/2020
Semantic Robustness of Models of Source Code
Deep neural networks are vulnerable to adversarial examples - small inpu...
research
∙
12/02/2019
Proving Data-Poisoning Robustness in Decision Trees
Machine learning models are brittle, and small changes in the training d...
research
∙
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...
research
∙
05/20/2019
Efficient Synthesis with Probabilistic Constraints
We consider the problem of synthesizing a program given a probabilistic ...
research
∙
12/10/2018
Scaling-Up In-Memory Datalog Processing: Observations and Techniques
Recursive query processing has experienced a recent resurgence, as a res...
research
∙
10/29/2018
Trace Abstraction Modulo Probability
We propose trace abstraction modulo probability, a proof technique for v...
research
∙
09/11/2018
Neural-Augmented Static Analysis of Android Communication
We address the problem of discovering communication links between applic...
research
∙
04/11/2018
Constraint-Based Synthesis of Coupling Proofs
Proof by coupling is a classical technique for proving properties about ...
research
∙
09/15/2017
Synthesizing Coupling Proofs of Differential Privacy
Differential privacy has emerged as a promising probabilistic formulatio...
research
∙
02/17/2017
Quantifying Program Bias
With the range and sensitivity of algorithmic decisions expanding at a b...
research
∙
10/19/2016
