Search Results for author: Ashwin Nayak

Found 7 papers, 0 papers with code

Proper vs Improper Quantum PAC learning

no code implementations5 Mar 2024 Ashwin Nayak, Pulkit Sinha

In the classical case, there are examples of concept classes with VC dimension $d$ that have sample complexity $\Omega\left(\frac d\epsilon\log\frac1\epsilon\right)$ for proper learning with error $\epsilon$, while the complexity for improper learning is O$\!\left(\frac d\epsilon\right)$.

PAC learning

Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicine

no code implementations13 Aug 2023 Thomas Savage, Ashwin Nayak, Robert Gallo, Ekanath Rangan, Jonathan H Chen

One of the major barriers to using large language models (LLMs) in medicine is the perception they use uninterpretable methods to make clinical decisions that are inherently different from the cognitive processes of clinicians.

Language Modelling Large Language Model

Optimal lower bounds for Quantum Learning via Information Theory

no code implementations5 Jan 2023 Shima Bab Hadiashar, Ashwin Nayak, Pulkit Sinha

In this paper, we derive optimal lower bounds for quantum sample complexity in both the PAC and agnostic models via an information-theoretic approach.

Learning Theory PAC learning

Lower bounds for learning quantum states with single-copy measurements

no code implementations29 Jul 2022 Angus Lowe, Ashwin Nayak

This leads to stronger lower bounds when the learner uses measurements with a constant number of outcomes.

Online Learning of Quantum States

no code implementations NeurIPS 2018 Scott Aaronson, Xinyi Chen, Elad Hazan, Satyen Kale, Ashwin Nayak

Even in the "non-realizable" setting---where there could be arbitrary noise in the measurement outcomes---we show how to output hypothesis states that do significantly worse than the best possible states at most $\operatorname{O}\!\left(\sqrt {Tn}\right) $ times on the first $T$ measurements.

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