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Found 8 papers, 0 papers with code
Optimal Design for Human Feedback
Learning of preference models from human feedback has been central to recent advances in artificial intelligence.
Experimental Design for Active Transductive Inference in Large Language Models
We design the LLM prompt by adaptively choosing few-shot examples for a given inference query.
Fixed-Budget Best-Arm Identification with Heterogeneous Reward Variances
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.
Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams
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.
Bayesian Algorithms for Decentralized Stochastic Bandits
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.
Decentralized Bayesian Learning over Graphs
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.
Peer-to-peer Federated Learning on Graphs
We consider the problem of training a machine learning model over a network of nodes in a fully decentralized framework.
Automatic Grammar Augmentation for Robust Voice Command Recognition
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.
