Contextual Optimization Under Model Misspecification: A Tractable and Generalizable Approach
Authors: Omar Bennouna, Jiawei Zhang, Saurabh Amin, Asuman E. Ozdaglar
ICML 2025 | Venue PDF | Archive PDF | Plain Text | 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. |