General bounds on the quality of Bayesian coresets
Authors: Trevor Campbell
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The paper includes empirical validation of the main theoretical claims on two models that violate common assumptions made in the literature: a multimodal, unidentifiable Cauchy location model with a heavy-tailed prior, and an unidentifiable logistic regression model with a heavy-tailed prior and persisting posterior heavy tails. Experiments were performed on a computer with an Intel Core i7-8700K and 32GB of RAM. Figure 2: Importance-weighted coreset quality... Figure 3: Subsample-optimize coreset quality... |
| Researcher Affiliation | Academia | Trevor Campbell Department of Statistics University of British Columbia trevor@stat.ubc.ca |
| Pseudocode | Yes | Algorithm 1 Importance-weighted coreset construction and Algorithm 2 Scaled importance-weighted coreset construction and Algorithm 3 Subsample-optimize coreset construction are present on pages 4 and 6. |
| Open Source Code | No | From the Neur IPS Paper Checklist, section "5. Open access to data and code": "Answer: [No] Justification: There are no new algorithms presented in this work; the experiments involve only existing methods for which public code is available. The code is not central to the contributions of the paper." |
| Open Datasets | No | The paper specifies models and data generation processes (e.g., "Xn iid Cauchy(θ2, 1)") rather than referring to or providing access information for public datasets. |
| Dataset Splits | No | The paper mentions "validation experiments" but does not explicitly provide details on data splits (e.g., percentages or counts) for training, validation, or test sets. |
| Hardware Specification | Yes | Experiments were performed on a computer with an Intel Core i7-8700K and 32GB of RAM. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., "Python 3.8, PyTorch 1.9, and CUDA 11.1") needed to replicate the experiments. |
| Experiment Setup | Yes | Sampling probabilities pn for both models are set proportional to X2 n, thresholded to lie between 0.1/N and 10/N. (Figure 2 caption) and Sampling probabilities are uniform pn = 1/N, and coreset weights were optimized by nonnegative least squares for log-likelihoods discretized via samples from π [34, Eq. 4]. (Figure 3 caption). |