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08/24/2023
Prediction without Preclusion: Recourse Verification with Reachable Sets
Machine learning models are often used to decide who will receive a loan...
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04/26/2023
Concept-Monitor: Understanding DNN training through individual neurons
In this work, we propose a general framework called Concept-Monitor to h...
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04/12/2023
Label-Free Concept Bottleneck Models
Concept bottleneck models (CBM) are a popular way of creating more inter...
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04/02/2023
Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies
Transformations based on domain expertise (expert transformations), such...
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02/11/2023
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
Deep neural networks (DNN) have shown great capacity of modeling a dynam...
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01/26/2023
Certified Interpretability Robustness for Class Activation Mapping
Interpreting machine learning models is challenging but crucial for ensu...
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10/20/2022
Learning Sample Reweighting for Accuracy and Adversarial Robustness
There has been great interest in enhancing the robustness of neural netw...
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07/28/2022
Quantifying Safety of Learning-based Self-Driving Control Using Almost-Barrier Functions
Path-tracking control of self-driving vehicles can benefit from deep lea...
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04/23/2022
CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks
In this paper, we propose CLIP-Dissect, a new technique to automatically...
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02/07/2022
Evaluating Robustness of Cooperative MARL: A Model-based Approach
In recent years, a proliferation of methods were developed for cooperati...
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11/11/2021
On the Equivalence between Neural Network and Support Vector Machine
Recent research shows that the dynamics of an infinitely wide neural net...
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02/20/2021
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
Model-agnostic meta-learning (MAML) has emerged as one of the most succe...
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02/01/2021
Fast Training of Provably Robust Neural Networks by SingleProp
Recent works have developed several methods of defending neural networks...
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10/13/2020
Higher-Order Certification for Randomized Smoothing
Randomized smoothing is a recently proposed defense against adversarial ...
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08/05/2020
Robust Deep Reinforcement Learning through Adversarial Loss
Deep neural networks, including reinforcement learning agents, have been...
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08/18/2019
Verification of Neural Network Control Policy Under Persistent Adversarial Perturbation
Deep neural networks are known to be fragile to small adversarial pertur...
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06/10/2019
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective
Graph neural networks (GNNs) which apply the deep neural networks to gra...
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05/17/2019
POPQORN: Quantifying Robustness of Recurrent Neural Networks
The vulnerability to adversarial attacks has been a critical issue for d...
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01/22/2019
Optimal Finite-Sum Smooth Non-Convex Optimization with SARAH
The total complexity (measured as the total number of gradient computati...
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12/18/2018
PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach
With deep neural networks providing state-of-the-art machine learning mo...
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11/29/2018
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks
Verifying robustness of neural network classifiers has attracted great i...
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11/02/2018
Efficient Neural Network Robustness Certification with General Activation Functions
Finding minimum distortion of adversarial examples and thus certifying r...
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10/19/2018
On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm
CLEVER (Cross-Lipschitz Extreme Value for nEtwork Robustness) is an Extr...
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04/25/2018
Towards Fast Computation of Certified Robustness for ReLU Networks
Verifying the robustness property of a general Rectified Linear Unit (Re...
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01/31/2018