# HC-Search: Learning Heuristics and Cost Functions for Structured Prediction

@inproceedings{Doppa2013HCSearchLH, title={HC-Search: Learning Heuristics and Cost Functions for Structured Prediction}, author={Janardhan Rao Doppa and Alan Fern and Prasad Tadepalli}, booktitle={AAAI}, year={2013} }

Structured prediction is the problem of learning a function from structured inputs to structured outputs. Inspired by the recent successes of search-based structured prediction, we introduce a new framework for structured prediction called HC-Search. Given a structured input, the framework uses a search procedure guided by a learned heuristic H to uncover high quality candidate outputs and then uses a separate learned cost function C to select a final prediction among those outputs. We can…

## 20 Citations

HC-Search: A Learning Framework for Search-based Structured Prediction

- Computer ScienceJ. Artif. Intell. Res.
- 2014

This work introduces a new framework for structured prediction called HC-Search, which significantly outperforms several state-of-the-art methods and is sensitive to the particular loss function of interest and the time-bound allowed for predictions.

Structured prediction via output space search

- Computer ScienceJ. Mach. Learn. Res.
- 2014

A novel approach to automatically defining an effective search space over structured outputs, which is able to leverage the availability of powerful classification learning algorithms, is described and the limited-discrepancy search space is defined and related to the quality of learned classifiers.

HC-Search for Multi-Label Prediction: An Empirical Study

- Computer ScienceAAAI
- 2014

This paper empirically evaluates the instantiation of the HC-Search framework along with many existing multilabel learning algorithms on a variety of benchmarks by employing diverse task loss functions and demonstrates that the performance of existing algorithms tends to be very similar in most cases and that theHC-Search approach is comparable and often better than all the other algorithms across different loss functions.

Output Feature Augmented Lasso

- Computer Science2014 IEEE International Conference on Data Mining
- 2014

This paper proposes to augment Lasso with output by decoupling the joint feature mapping function of traditional structured learning by using the Augmented Lagrangian Method with Alternating Direction Minimizing to find the optimal model parameters.

Adversarial Structured Output Prediction by

- Computer Science
- 2014

This oral exam studies the state-of-the-art methods for solving the problem of structured learning and output prediction in adversarial settings, and mentions the strengths and weaknesses of the existing methods, and point to the open problems in the field.

AN ABSTRACT OF THE THESIS OF Chao Ma for the degree of Doctor of Philosophy in Computer Science presented on July 15, 2019. Title: New Directions in Search-based Structured Prediction: Multi-Task Learning and Integration of Deep Models Abstract approved:

- Computer Science
- 2019

A search-based learning approach called “Prune-and-Score” to improve the accuracy of greedy policy based structured prediction for search spaces with large action spaces and the HC-Nets framework, which allows to incorporate prior knowledge in the form of constraints.

Extreme classification under limited space and time budget

- Computer Science
- 2017

A new framework for solving extreme classification is discussed, in which the original problem is reduced to a structured prediction problem, and learning algorithms that work under a strict time and space budget are obtained.

Learning to control a structured-prediction decoder for detection of HTTP-layer DDoS attackers

- Computer ScienceMachine Learning
- 2016

An online policy-gradient method is derived that finds the parameters of the controller and of the structured-prediction model in a joint optimization problem and obtains a convergence guarantee for the latter method.

Mixed heuristic search for sketch prediction on chemical structure drawing

- Computer ScienceSBIM '14
- 2014

This approach transforms the sketch prediction problem into a search problem to find a hamiltonian path in the corresponding sub-graph with polynomial time complexity and introduces mixed heuristics to guide the search procedure.

Rectifying Classifier Chains for Multi-Label Classification

- Computer ScienceLWA
- 2013

This work analyzes the influence of a potential pitfall of the learning process, namely the discrepancy between the feature spaces used in training and testing, and proposes two modifications of classifier chains that are meant to overcome this problem.

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