BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems...
Information Retrieval (IR) systems used in search and recommendation platforms frequently employ Learning-to-Rank (LTR) models to rank items in response to user queries....
Evaluating Gender Bias Transfer between Pre-trained and Prompt-Adapted Language Models
*Equal Contributors
Large language models (LLMs) are increasingly being adapted to achieve task-specificity for deployment in real-world decision systems. Several previous works have investigated...
Momentum Approximation in Asynchronous Private Federated Learning
This paper was accepted for presentation at the International Workshop on Federated Foundation Models (FL@FM-NeurIPS'24), held in conjunction with NeurIPS 2024.
Asynchronous protocols have...
How Easy is It to Fool Your Multimodal LLMs? An Empirical Analysis on Deceptive...
The remarkable advancements in Multimodal Large Language Models (MLLMs) have not rendered them immune to challenges, particularly in the context of handling deceptive...
Apple Machine Learning Research at NeurIPS 2024
Apple researchers are advancing the field of ML through fundamental research that improves the world’s understanding of this technology and helps to redefine...
Neural Information Processing Systems (NeurIPS) 2024
Neural Information Processing Systems (NeurIPS) 2024
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Memory-Retaining Finetuning via Distillation
This paper was accepted at the Fine-Tuning in Modern Machine Learning: Principles and Scalability (FITML) Workshop at NeurIPS 2024.
Large language models (LLMs) pretrained...
Kaleido Diffusion: Improving Conditional Diffusion Models with Autoregressive Latent Modeling
Diffusion models have emerged as a powerful tool for generating high-quality images from textual descriptions. Despite their successes, these models often exhibit limited...
Towards Time-Series Reasoning with LLMs
Multi-modal large language models (MLLMs) have enabled numerous advances in understanding and reasoning in domains like vision, but we have not yet seen...
Private and Personalized Frequency Estimation in a Federated Setting
*Equal Contributors
Motivated by the problem of next word prediction on user devices we introduce and study the problem of personalized frequency histogram estimation...
