Varun Chandrasekaran | DeepAI

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∙ 03/22/2023

Sparks of Artificial General Intelligence: Early experiments with GPT-4

Artificial intelligence (AI) researchers have been developing and refini...

6 Sébastien Bubeck, et al. ∙

∙ 10/17/2022

Verifiable and Provably Secure Machine Unlearning

Machine unlearning aims to remove points from the training dataset of a ...

5 Thorsten Eisenhofer, et al. ∙

∙ 08/06/2022

On the Fundamental Limits of Formally (Dis)Proving Robustness in Proof-of-Learning

Proof-of-learning (PoL) proposes a model owner use machine learning trai...

3 Congyu Fang, et al. ∙

∙ 07/26/2022

Generative Extraction of Audio Classifiers for Speaker Identification

It is perhaps no longer surprising that machine learning models, especia...

0 Tejumade Afonja, et al. ∙

∙ 06/10/2022

Hierarchical Federated Learning with Privacy

Federated learning (FL), where data remains at the federated clients, an...

9 Varun Chandrasekaran, et al. ∙

∙ 01/08/2022

CONFIDANT: A Privacy Controller for Social Robots

As social robots become increasingly prevalent in day-to-day environment...

0 Brian Tang, et al. ∙

∙ 09/27/2021

Unrolling SGD: Understanding Factors Influencing Machine Unlearning

Machine unlearning is the process through which a deployed machine learn...

4 Anvith Thudi, et al. ∙

∙ 09/20/2021

SoK: Machine Learning Governance

The application of machine learning (ML) in computer systems introduces ...

5 Varun Chandrasekaran, et al. ∙

∙ 08/03/2021

On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examples

Machine learning (ML) models are known to be vulnerable to adversarial e...

8 Adelin Travers, et al. ∙

∙ 03/09/2021

Proof-of-Learning: Definitions and Practice

Training machine learning (ML) models typically involves expensive itera...

12 Hengrui Jia, et al. ∙

∙ 07/29/2020

Detecting Anomalous Inputs to DNN Classifiers By Joint Statistical Testing at the Layers

Detecting anomalous inputs, such as adversarial and out-of-distribution ...

16 Jayaram Raghuram, et al. ∙

∙ 03/19/2020

Face-Off: Adversarial Face Obfuscation

Advances in deep learning have made face recognition increasingly feasib...

0 Chuhan Gao, et al. ∙

∙ 02/26/2020

On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping

Machine learning algorithms are vulnerable to data poisoning attacks. Pr...

0 Sanghyun Hong, et al. ∙

∙ 12/09/2019

Machine Unlearning

Once users have shared their data online, it is generally difficult for ...

53 Lucas Bourtoule, et al. ∙

∙ 10/02/2019

Generating Semantic Adversarial Examples with Differentiable Rendering

Machine learning (ML) algorithms, especially deep neural networks, have ...

23 Lakshya Jain, et al. ∙

∙ 05/26/2019

Enhancing ML Robustness Using Physical-World Constraints

Recent advances in Machine Learning (ML) have demonstrated that neural n...

0 Varun Chandrasekaran, et al. ∙

∙ 12/01/2018

Characterizing Privacy Perceptions of Voice Assistants: A Technology Probe Study

The increasing pervasiveness of voice assistants in the home poses sever...

0 Varun Chandrasekaran, et al. ∙

∙ 11/05/2018

Model Extraction and Active Learning

Machine learning is being increasingly used by individuals, research ins...

14 Varun Chandrasekaran, et al. ∙

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