6 citations · 6 across the 4 of their papers we have counts for
5 papers
What to Forget in Unlearning? Forget Set Curation for Language Models
Animesh Jha, Arpandeep Khatua, Youssef Allouah +1
Machine unlearning aims to remove targeted data or behaviors from a trained model without retraining from scratch. Yet most evaluations assume that the examples to forget are alrea…
MAEB: Massive Audio Embedding Benchmark
Adnan El Assadi, Isaac Chung, Chenghao Xiao +15
We introduce the Massive Audio Embedding Benchmark (MAEB), a large-scale benchmark covering 30 tasks across speech, music, environmental sounds, and cross-modal audio-text reasonin…
RL-Guided Data Selection for Language Model Finetuning
Animesh Jha, Harshit Gupta, Ananjan Nandi
Data selection for finetuning Large Language Models (LLMs) can be framed as a budget-constrained optimization problem: maximizing a model's downstream performance under a strict tr…
Certified Unlearning for Neural Networks
Anastasia Koloskova, Youssef Allouah, Animesh Jha +2
We address the problem of machine unlearning, where the goal is to remove the influence of specific training data from a model upon request, motivated by privacy concerns and regul…
Local NMPC on Global Optimised Path for Autonomous Racing
Dvij Kalaria, Parv Maheshwari, Animesh Jha +4
The paper presents a strategy for the control of anautonomous racing car on a pre-mapped track. Using a dynamic model of the vehicle, the optimal racing line is computed, taking tr…