Mrinank Sharma

According to our database, Mrinank Sharma authored at least 18 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Incorporating Unlabelled Data into Bayesian Neural Networks.

Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin

Trans. Mach. Learn. Res., 2024

Best-of-N Jailbreaking.

John Hughes, Sara Price, Aengus Lynch, Rylan Schaeffer, Fazl Barez, Sanmi Koyejo, Henry Sleight, Erik Jones, Ethan Perez, Mrinank Sharma

CoRR, 2024

Jailbreak Defense in a Narrow Domain: Limitations of Existing Methods and a New Transcript-Classifier Approach.

Tony T. Wang, John Hughes, Henry Sleight, Rylan Schaeffer, Rajashree Agrawal, Fazl Barez, Mrinank Sharma, Jesse Mu, Nir Shavit, Ethan Perez

CoRR, 2024

Adaptive Deployment of Untrusted LLMs Reduces Distributed Threats.

Jiaxin Wen, Vivek Hebbar, Caleb Larson, Aryan Bhatt, Ansh Radhakrishnan, Mrinank Sharma, Henry Sleight, Shi Feng, He He, Ethan Perez, Buck Shlegeris, Akbir Khan

CoRR, 2024

Rapid Response: Mitigating LLM Jailbreaks with a Few Examples.

Alwin Peng, Julian Michael, Henry Sleight, Ethan Perez, Mrinank Sharma

CoRR, 2024

PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning.

Tingchen Fu, Mrinank Sharma, Philip Torr, Shay B. Cohen, David Krueger, Fazl Barez

CoRR, 2024

When Do Universal Image Jailbreaks Transfer Between Vision-Language Models?

Rylan Schaeffer, Dan Valentine, Luke Bailey, James Chua, Cristóbal Eyzaguirre, Zane Durante, Joe Benton, Brando Miranda, Henry Sleight, John Hughes, Rajashree Agrawal, Mrinank Sharma, Scott Emmons, Sanmi Koyejo, Ethan Perez

CoRR, 2024

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training.

Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M. Ziegler, Tim Maxwell, Newton Cheng, Adam S. Jermyn, Amanda Askell, Ansh Radhakrishnan, Cem Anil, David Duvenaud, Deep Ganguli, Fazl Barez, Jack Clark, Kamal Ndousse, Kshitij Sachan, Michael Sellitto, Mrinank Sharma, Nova DasSarma, Roger Grosse, Shauna Kravec, Yuntao Bai, Zachary Witten, Marina Favaro, Jan Brauner, Holden Karnofsky, Paul F. Christiano, Samuel R. Bowman, Logan Graham, Jared Kaplan, Sören Mindermann, Ryan Greenblatt, Buck Shlegeris, Nicholas Schiefer, Ethan Perez

CoRR, 2024

Towards Understanding Sycophancy in Language Models.

Mrinank Sharma, Meg Tong, Tomasz Korbak, David Duvenaud, Amanda Askell, Samuel R. Bowman, Esin Durmus, Zac Hatfield-Dodds, Scott R. Johnston, Shauna Kravec, Timothy Maxwell, Sam McCandlish, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, Ethan Perez

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Towards Understanding Sycophancy in Language Models.

Mrinank Sharma, Meg Tong, Tomasz Korbak, David Duvenaud, Amanda Askell, Samuel R. Bowman, Newton Cheng, Esin Durmus, Zac Hatfield-Dodds, Scott R. Johnston, Shauna Kravec, Timothy Maxwell, Sam McCandlish, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, Ethan Perez

CoRR, 2023

Understanding and Controlling a Maze-Solving Policy Network.

Ulisse Mini, Peli Grietzer, Mrinank Sharma, Austin Meek, Monte MacDiarmid, Alexander Matt Turner

CoRR, 2023

Do Bayesian Neural Networks Need To Be Fully Stochastic?

Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, Tom Rainforth

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Seasonal variation in SARS-CoV-2 transmission in temperate climates: A Bayesian modelling study in 143 European regions.

Tomas Gavenciak, Joshua Teperowski Monrad, Gavin Leech, Mrinank Sharma, Sören Mindermann, Samir Bhatt, Jan Markus Brauner, Jan Kulveit

PLoS Comput. Biol., 2022

Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt.

Sören Mindermann, Jan Markus Brauner, Muhammed Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N. Gomez, Adrien Morisot, Sebastian Farquhar, Yarin Gal

Proceedings of the International Conference on Machine Learning, 2022

2021

Prioritized training on points that are learnable, worth learning, and not yet learned.

Sören Mindermann, Muhammed Razzak, Winnie Xu, Andreas Kirsch, Mrinank Sharma, Adrien Morisot, Aidan N. Gomez, Sebastian Farquhar, Jan Markus Brauner, Yarin Gal

CoRR, 2021

2020

On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission.

Mrinank Sharma, Sören Mindermann, Jan Markus Brauner, Gavin Leech, Anna B. Stephenson, Tomas Gavenciak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal

CoRR, 2020

How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?

Mrinank Sharma, Sören Mindermann, Jan Markus Brauner, Gavin Leech, Anna B. Stephenson, Tomas Gavenciak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Differentially Private Federated Variational Inference.

Mrinank Sharma, Michael J. Hutchinson, Siddharth Swaroop, Antti Honkela, Richard E. Turner

CoRR, 2019