Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Contextual Optimization Under Model Misspecification: A Tractable and Generalizable Approach
Authors: Omar Bennouna, Jiawei Zhang, Saurabh Amin, Asuman E. Ozdaglar
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide rigorous theoretical analysis and experimental validation, demonstrating superior performance compared to state-of-the-art methods. Our work offers a principled solution to the practically relevant challenge of model misspecification in contextual optimization. |
| Researcher Affiliation | Academia | 1Department of EECS, Massachusetts Institute of Technology 2Department of Computer Sciences, University of Wisconsin Madison. Correspondence to: Omar Bennouna <EMAIL>, Jiawei Zhang <EMAIL>. |
| Pseudocode | No | No pseudocode or algorithm block is explicitly provided in this paper. The paper refers to "Algorithm 3.1 in (Nocedal and Wright, 1999)" but does not include it within the text. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | We have validated our method on synthetic data and plan further experiments on real-world datasets for comparison with existing methods. In every experiment, we sample x N(0, I) while all of its coordinates are conditioned to be between 0 and 10. and the coefficients of A from a standard normal Gaussian distribution, and b to be equal to A |w|... The paper uses synthetically generated data and does not provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes how synthetic data is generated for experiments but does not provide specific training/test/validation split information (percentages, counts, or methodology) for the generated data. |
| Hardware Specification | No | All computational experiments were run on the MIT Super Cloud (Reuther et al., 2018). This names a computing resource but does not provide specific hardware details like GPU/CPU models or memory amounts. |
| Software Dependencies | No | The paper mentions running gradient descent for optimization but does not provide specific software dependencies or their version numbers, such as programming languages, libraries, or solvers. |
| Experiment Setup | Yes | We set (d, j) = (20, 5) and W to be a polyhedron and written as W = {w Rd, Aw = b, 10 w 0} where A Rj d (j d) and b Rj. To optimize ℓβ Pn, we ran gradient descent on its surrogate loss rβ Pn... We chose β by line search. We used βmin,P = E(x,c) Pn c w(c) as a lower bound to β, and βSPO+ = E(x,c) Pn c w ˆcθ SPO+(x) where θ SPO+ is the solution obtained by optimizing the SPO+ loss. For every value of s, we tested 96 evenly spaced values of β in the interval [βmin,P , βSPO+], and picked β yielding the solution with the best decision performance. |