no code implementations • 22 Apr 2024 • Subhojyoti Mukherjee, Anusha Lalitha, Kousha Kalantari, Aniket Deshmukh, Ge Liu, Yifei Ma, Branislav Kveton
Learning of preference models from human feedback has been central to recent advances in artificial intelligence.
no code implementations • 12 Apr 2024 • Subhojyoti Mukherjee, Ge Liu, Aniket Deshmukh, Anusha Lalitha, Yifei Ma, Branislav Kveton
We design the LLM prompt by adaptively choosing few-shot examples for a given inference query.
no code implementations • 13 Jun 2023 • Anusha Lalitha, Kousha Kalantari, Yifei Ma, Anoop Deoras, Branislav Kveton
Our algorithms rely on non-uniform budget allocations among the arms where the arms with higher reward variances are pulled more often than those with lower variances.
no code implementations • 2 Oct 2021 • Erdem Biyik, Anusha Lalitha, Rajarshi Saha, Andrea Goldsmith, Dorsa Sadigh
Our results show that the proposed partner-aware strategy outperforms other known methods, and our human subject studies suggest humans prefer to collaborate with AI agents implementing our partner-aware strategy.
no code implementations • 20 Oct 2020 • Anusha Lalitha, Andrea Goldsmith
Specifically, we study an information assimilation algorithm that can be combined with existing Bayesian algorithms, and using this, we propose a decentralized Thompson Sampling algorithm and decentralized Bayes-UCB algorithm.
no code implementations • 24 May 2019 • Anusha Lalitha, Xinghan Wang, Osman Kilinc, Yongxi Lu, Tara Javidi, Farinaz Koushanfar
The proposed algorithm can be viewed as a Bayesian and peer-to-peer variant of federated learning in which each agent keeps a "posterior probability distribution" over a global model parameters.
no code implementations • 31 Jan 2019 • Anusha Lalitha, Osman Cihan Kilinc, Tara Javidi, Farinaz Koushanfar
We consider the problem of training a machine learning model over a network of nodes in a fully decentralized framework.
no code implementations • 14 Nov 2018 • Yang Yang, Anusha Lalitha, Jinwon Lee, Chris Lott
For a given grammar set, a set of potential grammar expressions (candidate set) for augmentation is constructed from an AM-specific statistical pronunciation dictionary that captures the consistent patterns and errors in the decoding of AM induced by variations in pronunciation, pitch, tempo, accent, ambiguous spellings, and noise conditions.