We lifted 9 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| nataliemaus/lolbo | canonical | 4 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| compute_train_zs | Ran | nataliemaus/lolbo/lolbo/utils/mol_utils/load_data.py code served (permissive licence) · get_code("73e65b4bd30bbd77") |
| gumbel_softmax | Ran | nataliemaus/lolbo/lolbo/utils/mol_utils/selfies_vae/model_positional_unbounded.py code served (permissive licence) · get_code("5b05e8e843deabaf") |
| is_valid_molecule | Ran | nataliemaus/lolbo/lolbo/utils/mol_utils/selfies_vae/model_positional_unbounded.py code served (permissive licence) · get_code("f8e7af52c159f99d") |
| load_molecule_train_data | Ran | nataliemaus/lolbo/lolbo/utils/mol_utils/load_data.py code served (permissive licence) · get_code("8e738b81f68971d1") |
| generate_batch | Not yet run | nataliemaus/lolbo/lolbo/utils/bo_utils/turbo.py code served (permissive licence) · get_code("de6ce50bd4f3d1ca") |
| load_train_z | Not yet run | nataliemaus/lolbo/lolbo/utils/mol_utils/load_data.py code served (permissive licence) · get_code("e00ccb61913dc9fb") |
| update_models_end_to_end | Not yet run | nataliemaus/lolbo/lolbo/utils/utils.py code served (permissive licence) · get_code("d3895f341b61aad6") |
| update_state | Not yet run | nataliemaus/lolbo/lolbo/utils/bo_utils/turbo.py code served (permissive licence) · get_code("ba51f9dac22959af") |
| update_surr_model | Not yet run | nataliemaus/lolbo/lolbo/utils/utils.py code served (permissive licence) · get_code("0dda692b10a19709") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Bayesian optimization over the latent spaces of deep autoencoder models (DAEs) has recently emerged as a promising new approach for optimizing challenging black-box functions over structured, discrete, hard-to-enumerate search spaces (e.g., molecules). Here the DAE dramatically simplifies the search space by mapping inputs into a continuous latent space where familiar Bayesian optimization tools can be more readily applied. Despite this simplification, the latent space typically remains high-dimensional. Thus, even with a well-suited latent space, these approaches do not necessarily provide a complete solution, but may rather shift the structured optimization problem to a high-dimensional one. In this paper, we propose LOL-BO, which adapts the notion of trust regions explored in recent work on high-dimensional Bayesian optimization to the structured setting. By reformulating the encoder to function as both an encoder for the DAE globally and as a deep kernel for the surrogate model within a trust region, we better align the notion of local optimization in the latent space with local optimization in the input space. LOL-BO achieves as much as 20 times improvement over state-of-the-art latent space Bayesian optimization methods across six real-world benchmarks, demonstrating that improvement in optimization strategies is as important as developing better DAE models.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2201.11872")
get_code_for_paper("2201.11872")
have("2201.11872")
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