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07/07/2023
Programmable Synthetic Tabular Data Generation
Large amounts of tabular data remain underutilized due to privacy, data ...
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06/17/2023
Understanding Certified Training with Interval Bound Propagation
As robustness verification methods are becoming more precise, training c...
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06/05/2023
Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning
Malicious server (MS) attacks have enabled the scaling of data stealing ...
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05/25/2023
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization
Collaborative learning techniques have the potential to enable training ...
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05/25/2023
Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation
Large language models (large LMs) are susceptible to producing text with...
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05/08/2023
TAPS: Connecting Certified and Adversarial Training
Training certifiably robust neural networks remains a notoriously hard p...
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04/03/2023
Abstraqt: Analysis of Quantum Circuits via Abstract Stabilizer Simulation
Stabilizer simulation can efficiently simulate an important class of qua...
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03/09/2023
Efficient Certified Training and Robustness Verification of Neural ODEs
Neural Ordinary Differential Equations (NODEs) are a novel neural archit...
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02/10/2023
Large Language Models for Code: Security Hardening and Adversarial Testing
Large language models (LMs) are increasingly pretrained on massive codeb...
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12/20/2022
Human-Guided Fair Classification for Natural Language Processing
Text classifiers have promising applications in high-stake tasks such as...
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12/12/2022
Prompting Is Programming: A Query Language For Large Language Models
Large language models have demonstrated outstanding performance on a wid...
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10/27/2022
Private and Reliable Neural Network Inference
Reliable neural networks (NNs) provide important inference-time reliabil...
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10/26/2022
Learning to Configure Computer Networks with Neural Algorithmic Reasoning
We present a new method for scaling automatic configuration of computer ...
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10/13/2022
FARE: Provably Fair Representation Learning
Fair representation learning (FRL) is a popular class of methods aiming ...
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10/10/2022
Certified Training: Small Boxes are All You Need
We propose the novel certified training method, SABR, which outperforms ...
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10/04/2022
Data Leakage in Tabular Federated Learning
While federated learning (FL) promises to preserve privacy in distribute...
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06/24/2022
Data Leakage in Federated Averaging
Recent attacks have shown that user data can be recovered from FedSGD up...
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05/27/2022
(De-)Randomized Smoothing for Decision Stump Ensembles
Tree-based models are used in many high-stakes application domains such ...
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04/30/2022
Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound
State-of-the-art neural network verifiers are fundamentally based on one...
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04/21/2022
On Distribution Shift in Learning-based Bug Detectors
Deep learning has recently achieved initial success in program analysis ...
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04/01/2022
Robust and Accurate – Compositional Architectures for Randomized Smoothing
Randomized Smoothing (RS) is considered the state-of-the-art approach to...
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02/17/2022
LAMP: Extracting Text from Gradients with Language Model Priors
Recent work shows that sensitive user data can be reconstructed from gra...
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12/09/2021
The Fundamental Limits of Interval Arithmetic for Neural Networks
Interval analysis (or interval bound propagation, IBP) is a popular tech...
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11/26/2021
Latent Space Smoothing for Individually Fair Representations
Fair representation learning encodes user data to ensure fairness and ut...
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11/08/2021
Bayesian Framework for Gradient Leakage
Federated learning is an established method for training machine learnin...
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10/14/2021
Effective Certification of Monotone Deep Equilibrium Models
Monotone Operator Equilibrium Models (monDEQs) represent a class of mode...
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09/01/2021
Shared Certificates for Neural Network Verification
Existing neural network verifiers compute a proof that each input is han...
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07/01/2021
Scalable Certified Segmentation via Randomized Smoothing
We present a new certification method for image and point cloud segmenta...
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06/13/2021
Boosting Randomized Smoothing with Variance Reduced Classifiers
Randomized Smoothing (RS) is a promising method for obtaining robustness...
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06/10/2021
Fair Normalizing Flows
Fair representation learning is an attractive approach that promises fai...
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03/30/2021
Robustness Certification for Point Cloud Models
The use of deep 3D point cloud models in safety-critical applications, s...
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03/05/2021
Precise Multi-Neuron Abstractions for Neural Network Certification
Formal verification of neural networks is critical for their safe adopti...
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02/23/2021
Automated Discovery of Adaptive Attacks on Adversarial Defenses
Reliable evaluation of adversarial defenses is a challenging task, curre...
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02/12/2021
Certified Defenses: Why Tighter Relaxations May Hurt Training?
Certified defenses based on convex relaxations are an established techni...
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09/19/2020
Efficient Certification of Spatial Robustness
Recent work has exposed the vulnerability of computer vision models to s...
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09/02/2020
zkay v0.2: Practical Data Privacy for Smart Contracts
Recent work introduces zkay, a system for specifying and enforcing data ...
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07/23/2020
Scalable Inference of Symbolic Adversarial Examples
We present a novel method for generating symbolic adversarial examples: ...
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07/20/2020
Neural Network Robustness Verification on GPUs
Certifying the robustness of neural networks against adversarial attacks...
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05/27/2020
Fast and Effective Robustness Certification for Recurrent Neural Networks
We present a precise and scalable verifier for recurrent neural networks...
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04/30/2020
Robustness Certification of Generative Models
Generative neural networks can be used to specify continuous transformat...
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03/08/2020
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models
We develop an effective generation of adversarial attacks on neural mode...
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02/27/2020
Certification of Semantic Perturbations via Randomized Smoothing
We introduce a novel certification method for parametrized perturbations...
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02/24/2020
Learning Certified Individually Fair Representations
To effectively enforce fairness constraints one needs to define an appro...
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02/11/2020
Adversarial Robustness for Code
We propose a novel technique which addresses the challenge of learning a...
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12/31/2019
Learning to Infer User Interface Attributes from Images
We explore a new domain of learning to infer user interface attributes t...
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11/03/2019
Online Robustness Training for Deep Reinforcement Learning
In deep reinforcement learning (RL), adversarial attacks can trick an ag...
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09/30/2019
Universal Approximation with Certified Networks
Training neural networks to be certifiably robust is a powerful defense ...
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03/29/2019
A Provable Defense for Deep Residual Networks
We present a training system, which can provably defend significantly la...
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06/04/2018
