20 citations · 51 across the 11 of their papers we have counts for
11 papers
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
BLUR: A Bi-Level Optimization Approach for LLM Unlearning
Hadi Reisizadeh, Jinghan Jia, Zhiqi Bu +6
Enabling large language models (LLMs) to unlearn knowledge and capabilities acquired during training has proven vital for ensuring compliance with data regulations and promoting et…
SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models
Anil Ramakrishna, Yixin Wan, Xiaomeng Jin +6
We introduce SemEval-2025 Task 4: unlearning sensitive content from Large Language Models (LLMs). The task features 3 subtasks for LLM unlearning spanning different use cases: (1)…
LUME: LLM Unlearning with Multitask Evaluations
Anil Ramakrishna, Yixin Wan, Xiaomeng Jin +6
Unlearning aims to remove copyrighted, sensitive, or private content from large language models (LLMs) without a full retraining. In this work, we develop a multi-task unlearning b…
Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate
Zhiqi Bu, Xiaomeng Jin, Bhanukiran Vinzamuri +4
Machine unlearning has been used to remove unwanted knowledge acquired by large language models (LLMs). In this paper, we examine machine unlearning from an optimization perspectiv…
Fair Representation Learning using Interpolation Enabled Disentanglement
Akshita Jha, Bhanukiran Vinzamuri, Chandan K. Reddy
With the growing interest in the machine learning community to solve real-world problems, it has become crucial to uncover the hidden reasoning behind their decisions by focusing o…
Model Agnostic Multilevel Explanations
Karthikeyan Natesan Ramamurthy, Bhanukiran Vinzamuri, Yunfeng Zhang +1
In recent years, post-hoc local instance-level and global dataset-level explainability of black-box models has received a lot of attention. Much less attention has been given to ob…
Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders
Gowthami Somepalli, Yexin Wu, Yogesh Balaji +2
Detecting out of distribution (OOD) samples is of paramount importance in all Machine Learning applications. Deep generative modeling has emerged as a dominant paradigm to model co…
Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines
Chirag Nagpal, Dennis Wei, Bhanukiran Vinzamuri +4
The dearth of prescribing guidelines for physicians is one key driver of the current opioid epidemic in the United States. In this work, we analyze medical and pharmaceutical claim…
Is Ordered Weighted Regularized Regression Robust to Adversarial Perturbation? A Case Study on OSCAR
Pin-Yu Chen, Bhanukiran Vinzamuri, Sijia Liu
Many state-of-the-art machine learning models such as deep neural networks have recently shown to be vulnerable to adversarial perturbations, especially in classification tasks. Mo…
Structure Learning from Time Series with False Discovery Control
Bernat Guillen Pegueroles, Bhanukiran Vinzamuri, Karthikeyan Shanmugam +3
We consider the Granger causal structure learning problem from time series data. Granger causal algorithms predict a 'Granger causal effect' between two variables by testing if pre…