198 citations · 225 across the 7 of their papers we have counts for
7 papers
E-Scores for (In)Correctness Assessment of Generative Model Outputs
Guneet S. Dhillon, Javier González, Teodora Pandeva +1
While generative models, especially large language models (LLMs), are ubiquitous in today's world, principled mechanisms to assess their (in)correctness are limited. Using the conf…
L3Ms -- Lagrange Large Language Models
Guneet S. Dhillon, Xingjian Shi, Yee Whye Teh +1
Supervised fine-tuning (SFT) and alignment of large language models (LLMs) are key steps in providing a good user experience. However, the concept of an appropriate alignment is in…
On the Expected Size of Conformal Prediction Sets
Guneet S. Dhillon, George Deligiannidis, Tom Rainforth
While conformal predictors reap the benefits of rigorous statistical guarantees on their error frequency, the size of their corresponding prediction sets is critical to their pract…
Uniform Sampling over Episode Difficulty
Sébastien M. R. Arnold, Guneet S. Dhillon, Avinash Ravichandran +1
Episodic training is a core ingredient of few-shot learning to train models on tasks with limited labelled data. Despite its success, episodic training remains largely understudied…
Erratum Concerning the Obfuscated Gradients Attack on Stochastic Activation Pruning
Guneet S. Dhillon, Nicholas Carlini
Stochastic Activation Pruning (SAP) (Dhillon et al., 2018) is a defense to adversarial examples that was attacked and found to be broken by the "Obfuscated Gradients" paper (Athaly…
A Baseline for Few-Shot Image Classification
Guneet S. Dhillon, Pratik Chaudhari, Avinash Ravichandran +1
Fine-tuning a deep network trained with the standard cross-entropy loss is a strong baseline for few-shot learning. When fine-tuned transductively, this outperforms the current sta…
Stochastic Activation Pruning for Robust Adversarial Defense
Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton +4
Neural networks are known to be vulnerable to adversarial examples. Carefully chosen perturbations to real images, while imperceptible to humans, induce misclassification and threa…