Krishnamurthy Dvijotham | DeepAI

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∙ 06/14/2023

Selective Concept Models: Permitting Stakeholder Customisation at Test-Time

Concept-based models perform prediction using a set of concepts that are...

0 Matthew Barker, et al. ∙

∙ 05/28/2023

Training Private Models That Know What They Don't Know

Training reliable deep learning models which avoid making overconfident ...

0 Stephan Rabanser, et al. ∙

∙ 05/23/2023

Expressive Losses for Verified Robustness via Convex Combinations

In order to train networks for verified adversarial robustness, previous...

0 Alessandro De Palma, et al. ∙

∙ 03/22/2023

Human Uncertainty in Concept-Based AI Systems

Placing a human in the loop may abate the risks of deploying AI systems ...

7 Katherine M. Collins, et al. ∙

∙ 02/02/2023

Provably Bounding Neural Network Preimages

Most work on the formal verification of neural networks has focused on b...

0 Suhas Kotha, et al. ∙

∙ 12/14/2022

Interactive Concept Bottleneck Models

Concept bottleneck models (CBMs) (Koh et al. 2020) are interpretable neu...

0 Kushal Chauhan, et al. ∙

∙ 06/29/2022

IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound

Recent works have tried to increase the verifiability of adversarially t...

0 Alessandro De Palma, et al. ∙

∙ 12/13/2021

Role of Human-AI Interaction in Selective Prediction

Recent work has shown the potential benefit of selective prediction syst...

22 Elizabeth Bondi, et al. ∙

∙ 04/14/2021

Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition

We improve the scalability of Branch and Bound (BaB) algorithms for form...

6 Alessandro De Palma, et al. ∙

∙ 12/07/2020

Autoencoding Variational Autoencoder

Does a Variational AutoEncoder (VAE) consistently encode typical samples...

15 A. Taylan Cemgil, et al. ∙

∙ 10/22/2020

Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming

Convex relaxations have emerged as a promising approach for verifying de...

6 Sumanth Dathathri, et al. ∙

∙ 02/24/2020

Lagrangian Decomposition for Neural Network Verification

A fundamental component of neural network verification is the computatio...

30 Rudy Bunel, et al. ∙

∙ 12/06/2019

Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations

Recent research has made the surprising finding that state-of-the-art de...

14 Sven Gowal, et al. ∙

∙ 09/03/2019

Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation

Neural networks are part of many contemporary NLP systems, yet their emp...

1 Po-Sen Huang, et al. ∙

∙ 04/26/2019

Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications

Models such as Sequence-to-Sequence and Image-to-Sequence are widely use...

6 Chenglong Wang, et al. ∙

∙ 12/06/2018

Verification of deep probabilistic models

Probabilistic models are a critical part of the modern deep learning too...

0 Krishnamurthy Dvijotham, et al. ∙

∙ 10/30/2018

On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models

Recent works have shown that it is possible to train models that are ver...

0 Sven Gowal, et al. ∙

∙ 05/25/2018

Training verified learners with learned verifiers

This paper proposes a new algorithmic framework,predictor-verifier train...

0 Krishnamurthy Dvijotham, et al. ∙

∙ 03/17/2018

A Dual Approach to Scalable Verification of Deep Networks

This paper addresses the problem of formally verifying desirable propert...

0 Krishnamurthy Dvijotham, et al. ∙

∙ 01/26/2018

Safe Exploration in Continuous Action Spaces

We address the problem of deploying a reinforcement learning (RL) agent ...

0 Gal Dalal, et al. ∙

∙ 06/21/2016

Graphical Models for Optimal Power Flow

Optimal power flow (OPF) is the central optimization problem in electric...

0 Krishnamurthy Dvijotham, et al. ∙

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