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Adi Botea

4 accepted papers

2022

Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization

AAAI 2022technical

Optimizing a machine learning (ML) pipeline has been an important topic of AI and ML. Despite recent progress, pipeline optimization remains a challenging problem, due to potentially many combinations to consider as well as slow training and validation. We present the BLDS algorithm for optimized al…

Cited by 7Source ↗PDF ↗Scholar ↗
2021

Searching for Machine Learning Pipelines Using a Context-Free Grammar

AAAI 2021technical

AutoML automatically selects, composes and parameterizes machine learning algorithms into a workflow or pipeline of operations that aims at maximizing performance on a given dataset. Although current methods for AutoML achieved impressive results they mostly concentrate on optimizing fixed linear wo…

2019

Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning

NeurIPS 2019poster

Search techniques, such as Monte Carlo Tree Search (MCTS) and Proof-Number Search (PNS), are effective in playing and solving games. However, the understanding of their performance in industrial applications is still limited. We investigate MCTS and Depth-First Proof-Number (DFPN) Search, a PNS va…

Cited by 73Source ↗PDF ↗Scholar ↗
2015

Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models

NeurIPS 2015poster

The paper presents and evaluates the power of parallel search for exact MAP inference in graphical models. We introduce a new parallel shared-memory recursive best-first AND/OR search algorithm, called SPRBFAOO, that explores the search space in a best-first manner while operating with restricted me…

Cited by 4Source ↗PDF ↗Scholar ↗