Structured Prediction with Stronger Consistency Guarantees
Authors: Anqi Mao, Mehryar Mohri, Yutao Zhong
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present an extensive study of surrogate losses for structured prediction supported by H-consistency bounds. These are recently introduced guarantees that are more relevant to learning than Bayes-consistency, since they are not asymptotic and since they take into into account the hypothesis set H used. We first show that no nontrivial H-consistency bound can be derived for widely used surrogate structured prediction losses. We then define several new families of surrogate losses, including structured comp-sum losses and structured constrained losses, for which we prove H-consistency bounds and thus Bayes-consistency. These loss functions readily lead to new structured prediction algorithms with stronger theoretical guarantees, based on their minimization. We describe efficient algorithms for minimizing several of these surrogate losses, including a new structured logistic loss. In upcoming work, we will report an extensive empirical analysis of our algorithms. |
| Researcher Affiliation | Collaboration | Anqi Mao Courant Institute New York, NY 10012 aqmao@cims.nyu.edu Mehryar Mohri Google Research & CIMS New York, NY 10011 mohri@google.com Yutao Zhong Courant Institute New York, NY 10012 yutao@cims.nyu.edu |
| Pseudocode | No | The paper describes algorithms and gradient computations through mathematical equations and text, but it does not include any formal pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper states, 'In upcoming work, we will report an extensive empirical analysis of our algorithms.' This indicates that the code for the described methods is not yet available and will be released in future work. No links or statements of immediate availability are provided. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical proofs and definitions of loss functions. It explicitly states, 'In upcoming work, we will report an extensive empirical analysis of our algorithms,' indicating that no datasets were used for training in this paper. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical experiments with datasets. Therefore, no dataset splits for training, validation, or testing are provided. |
| Hardware Specification | No | The paper is purely theoretical, focusing on mathematical foundations and algorithms. No experiments are conducted, and thus, no hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and algorithm design. It does not mention any specific software dependencies with version numbers, as no empirical experiments are described. |
| Experiment Setup | No | The paper is theoretical and does not present empirical experiments. Therefore, no details on experimental setup, such as hyperparameters or training settings, are provided. |