Kati8/ICLR_2023_2025 huggingface dataset:
authors: Daniel Ward
authors: Mark Beaumont
authors: Matteo Fasiolo
reviews: 
Comments: The paper proposes SoftCVI, a variational inference method based on contrastive learning that uses soft labels, i.e., binary classification probabilities. SoftCVI reframes fitting a variational distribution as finding a classifier to identify actual posterior samples among a set of samples. The paper claims that:
  1. SoftCVI works without having access to samples from the posterior.
  2. SoftCVI forms objectives that are mass-covering.
  3. SoftCVI yields better posterior approximation than baselines with simple and flexible variational distributions.
Strengths: ## Reason for score
- The presentation is lucid.
- The derivations look good,  aside from a couple of technicalities (see questions).
- Using soft labels in contrastive learning for Bayesian inference is exciting and novel.
- The connection to SNIS-fKL is clear, and viewing the gradient of eq. 7 as a control variate is insightful and supported by evidence.
- Experimenting with normal and normalizing flow variational distribution demonstrates SoftCVI's flexibility, and the results support that SoftCVI is more mass-covering than ELBO and SNIS-fKL.
Weaknesses: - It is unclear whether the variational distribution is a biased estimator of the posterior. Please see the questions.
Recommendation: 8
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 4