436 citations · 2.1k across the 92 of their papers we have counts for
8 papers · 1 filter
Efficient Adaptive Federated Optimization
Su Hyeong Lee, Sidharth Sharma, Manzil Zaheer +1
Adaptive optimization is critical in federated learning, where enabling adaptivity on both the server and client sides has proven essential for achieving optimal performance. Howev…
A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks
Nicholas Monath, Will Grathwohl, Michael Boratko +3
In dense retrieval, deep encoders provide embeddings for both inputs and targets, and the softmax function is used to parameterize a distribution over a large number of candidate t…
A Statistical Framework for Data-dependent Retrieval-Augmented Models
Soumya Basu, Ankit Singh Rawat, Manzil Zaheer
Modern ML systems increasingly augment input instances with additional relevant information to enhance final prediction. Despite growing interest in such retrieval-augmented models…
Analysis of Plan-based Retrieval for Grounded Text Generation
Ameya Godbole, Nicholas Monath, Seungyeon Kim +3
In text generation, hallucinations refer to the generation of seemingly coherent text that contradicts established knowledge. One compelling hypothesis is that hallucinations occur…
Natural Language Outlines for Code: Literate Programming in the LLM Era
Kensen Shi, Deniz Altınbüken, Saswat Anand +17
We propose using natural language outlines as a novel modality and interaction surface for providing AI assistance to developers throughout the software development process. An NL…
Deep Reinforcement Learning for Sequential Combinatorial Auctions
Sai Srivatsa Ravindranath, Zhe Feng, Di Wang +3
Revenue-optimal auction design is a challenging problem with significant theoretical and practical implications. Sequential auction mechanisms, known for their simplicity and stron…