44 citations · 66 across the 9 of their papers we have counts for
9 papers
Sampling for Quality: Training-Free Reward-Guided LLM Decoding via Sequential Monte Carlo
Jelena Markovic-Voronov, Wenhui Zhu, Bo Long +5
We introduce a principled probabilistic framework for reward-guided decoding in large language models, addressing the limitations of standard decoding methods that optimize token-l…
Support Tokens, Stability Margins, and a New Foundation for Robust LLMs
Deepak Agarwal, Dhyey Dharmendrakumar Mavani, Suyash Gupta +2
Self-attention is usually described as a flexible, content-adaptive way to mix a token with information from its past. We reinterpret causal self-attention transformers, the backbo…
Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning
Nihal Balivada, Shrey Gupta, Shashank Shreedhar Bhatt +1
Federated Learning (FL) enables a distributed client-server architecture where multiple clients collaboratively train a global Machine Learning (ML) model without sharing sensitive…
Spatial Transfer Learning for Estimating PM2.5 in Data-poor Regions
Shrey Gupta, Yongbee Park, Jianzhao Bi +4
Air pollution, especially particulate matter 2.5 (PM2.5), is a pressing concern for public health and is difficult to estimate in developing countries (data-poor regions) due to a…
Predictive Inference in Multi-environment Scenarios
John C. Duchi, Suyash Gupta, Kuanhao Jiang +1
We address the challenge of constructing valid confidence intervals and sets in problems of prediction across multiple environments. We investigate two types of coverage suitable f…
Predictive Inference with Weak Supervision
Maxime Cauchois, Suyash Gupta, Alnur Ali +1
The expense of acquiring labels in large-scale statistical machine learning makes partially and weakly-labeled data attractive, though it is not always apparent how to leverage suc…