4 citations · 5 across the 6 of their papers we have counts for
8 papers
FUSE: Ensembling Verifiers with Zero Labeled Data
Joonhyuk Lee, Virginia Ma, Sarah Zhao +4
Verification of model outputs is rapidly emerging as a key primitive for both training and real-world deployment of large language models (LLMs). In practice, this often involves u…
Efficient Evaluation of LLM Performance with Statistical Guarantees
Skyler Wu, Yash Nair, Emmanuel J. Candès
Exhaustively evaluating many large language models (LLMs) on a large suite of benchmarks is expensive. We cast benchmarking as finite-population inference and, under a fixed query…
Bernstein-von Mises for Adaptively Collected Data
Kevin Du, Yash Nair, Lucas Janson
Uncertainty quantification (UQ) for adaptively collected data, such as that coming from adaptive experiments, bandits, or reinforcement learning, is necessary for critical elements…
Diversifying Conformal Selections
Yash Nair, Ying Jin, James Yang +1
When selecting from a list of potential candidates, it is important to ensure not only that the selected items are of high quality, but also that they are sufficiently dissimilar s…
Evaluating the Effectiveness of Index-Based Treatment Allocation
Niclas Boehmer, Yash Nair, Sanket Shah +3
When resources are scarce, an allocation policy is needed to decide who receives a resource. This problem occurs, for instance, when allocating scarce medical resources and is ofte…
Randomization Tests for Adaptively Collected Data
Yash Nair, Lucas Janson
Randomization testing is a fundamental method in statistics, enabling inferential tasks such as testing for (conditional) independence of random variables, constructing confidence…
A Spectral Approach to Off-Policy Evaluation for POMDPs
Yash Nair, Nan Jiang
We consider off-policy evaluation (OPE) in Partially Observable Markov Decision Processes, where the evaluation policy depends only on observable variables but the behavior policy…
PAC Bounds for Imitation and Model-based Batch Learning of Contextual Markov Decision Processes
Yash Nair, Finale Doshi-Velez
We consider the problem of batch multi-task reinforcement learning with observed context descriptors, motivated by its application to personalized medical treatment. In particular,…