Recent Developments in Data-Driven Algorithms for Discrete Optimization
Bibliographic record
Abstract
The last few years have witnessed a renewed interest in “data-driven algorithm design” (Balcan 2020), the use of Machine Learning (ML) to tailor an algorithm to a distribution of instances. More than a decade ago, advances in algorithm configuration (Hoos 2011) paved the way for the use of historical data to modify an algorithm’s (typically fixed, static) parameters. In discrete optimization (e.g., satisfiability, integer programming, etc.), exact and inexact algorithms for NP-Hard problems often involve heuristic search decisions (Lodi 2013), abstracted as parameters, that can demonstrably benefit from tuning on historical instances from the application of interest. While useful, algorithm configuration may be insufficient: setting the parameters of an algorithm upfront of solving the input instance is still a static, high-level decision. In contrast, we have been exploring a suite of ML and Reinforcement Learning (RL) approaches that tune iterative optimization algorithms, such as branch-and-bound for integer programming or construction heuristics, at the iteration-level (Khalil et al. 2016, 2017; Dai et al. 2017; Chmiela et al. 2021; Gupta et al. 2022; Chi et al. 2022; Khalil, Vaezipoor, and Dilkina 2022; Khalil, Morris, and Lodi 2022; Alomrani, Moravej, and Khalil 2022; Cappart et al. 2021; Gupta et al. 2020). We will survey our most recent work in this area: 1. New methods for learning in MILP branch-and-bound (Gupta et al. 2020, 2022; Chmiela et al. 2021; Khalil, Vaezipoor, and Dilkina 2022; Khalil, Morris, and Lodi 2022); 2. RL for online combinatorial optimization and largescale linear programming (Alomrani, Moravej, and Khalil 2022; Chi et al. 2022); 3. Neural network approximations for stochastic programming (Dumouchelle et al. 2022).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".