PaperMetrixEvidence-aware discovery
EN
Interface language English EN العربية AR Türkçe TR
Create account
Researcher profile

Richard S. Zemel

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. A Divergence Minimization Perspective on Imitation Learning Methods

    2019 · arXiv (Cornell University)

    In many settings, it is desirable to learn decision-making and control policies through learning or bootstrapping from expert demonstrations. The most common approaches under this Imitation Learning (IL) framework are Behavioural Cloning (BC), and Inverse …

  2. Understanding the Limitations of Conditional Generative Models

    2019 · arXiv (Cornell University)

    Class-conditional generative models hold promise to overcome the shortcomings of their discriminative counterparts. They are a natural choice to solve discriminative tasks in a robust manner as they jointly optimize for predictive performance and accurate …

  3. Theoretical bounds on estimation error for meta-learning

    2020 · arXiv (Cornell University)

    Machine learning models have traditionally been developed under the assumption that the training and test distributions match exactly. However, recent success in few-shot learning and related problems are encouraging signs that these models can be …

  4. Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic

    2022

    As natural language processing systems become more widespread, it is necessary to address fairness issues in their implementation and deployment to ensure that their negative impacts on society are understood and minimized. However, there is …

  5. Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models

    2023 · arXiv (Cornell University)

    The recent explosion in the capabilities of large language models has led to a wave of interest in how best to prompt a model to perform a given task. While it may be tempting to …

  6. Skip-Thought Vectors

    2015 · arXiv (Cornell University)

    We describe an approach for unsupervised learning of a generic, distributed sentence encoder. Using the continuity of text from books, we train an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded …