Paul Grigas, Wyame Benslimane, Pascal Van Hentenryck, Tinghan Ye
We lifted 8 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| Joeyetinghan/on-policy-bandit-dfl | canonical | 6 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| build_lr_schedule | Ran | Joeyetinghan/on-policy-bandit-dfl/src/algos/lr_schedules.py code served (permissive licence) · get_code("dc1ae44d02477f4c") |
| build_nuisance_optimizer | Ran | Joeyetinghan/on-policy-bandit-dfl/src/common/nuisance_models.py code served (permissive licence) · get_code("6202d097edebb39a") |
| compute_log_prob_surrogate | Ran | Joeyetinghan/on-policy-bandit-dfl/src/common/generative_models.py code served (permissive licence) · get_code("5aa8934e67b7cbbc") |
| generative_update_objective | Ran | Joeyetinghan/on-policy-bandit-dfl/src/common/generative_dfl.py code served (permissive licence) · get_code("63d84498ef4dc43d") |
| resolve_torch_device | Ran | Joeyetinghan/on-policy-bandit-dfl/src/common/generative_models.py code served (permissive licence) · get_code("f30a6622b4cf953d") |
| resolve_torch_device | Ran | Joeyetinghan/on-policy-bandit-dfl/src/common/nuisance_models.py code served (permissive licence) · get_code("3d143cd1f8994493") |
| build_nuisance_model | Not yet run | Joeyetinghan/on-policy-bandit-dfl/src/common/nuisance_models.py code served (permissive licence) · get_code("eea8f65117d46c59") |
| create_model | Not yet run | Joeyetinghan/on-policy-bandit-dfl/src/common/models.py code served (permissive licence) · get_code("f625dae2acb1036f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Decision-focused learning (DFL) trains predictive models by optimizing downstream decision quality rather than standalone prediction accuracy. For contextual linear optimization, most existing DFL methods assume offline data and full observations of the objective cost vector. We develop an on-policy learning method for sequential contextual linear optimization under partial feedback, generalizing the standard bandit feedback setting. Our method learns a stochastic predict-thenoptimize policy that samples a cost-vector prediction from a conditional distribution and solves the resulting downstream linear optimization problem. To update this distributional model, we introduce a two-component hybrid gradient estimator. The first component is a score function estimator, which provides an unbiased but potentially high-variance policy gradient estimate. The second is a decision-focused plug-in component that uses an auxiliary nuisance estimate of the latent cost vector to exploit the downstream optimization structure, becoming more informative as the estimate improves. We prove an O(T -1/2 ) bound on the average squared policy-gradient norm, matching the standard non-convex SGD rate. Experiments on top-k selection, shortest path, combinatorial pricing, and a real-data energyscheduling benchmark show that the hybrid gradient approach achieves lower cumulative regret than contextual-bandit-style baselines across all benchmarks, using both Gaussian and richer conditional generative models. Code is available at https://github.com/Joeyetinghan/on-policy-bandit-dfl.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2606.01081")
get_code_for_paper("2606.01081")
have("2606.01081")
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