EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Making decisions requires information relevant to the task at hand. Many real-life decision-making situations allow acquiring further relevant information at a specific cost. For example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment. More information that is relevant allows for better decisions but it may be costly to acquire all of this information. How can we trade off the desire to make good decisions with the option to acquire further information at a cost? To this end, we propose a principled framework, named EDDI (Efficient Dynamic Discovery of high-value Information), based on the theory of Bayesian experimental design. In EDDI we propose a novel partial variational autoencoder (Partial VAE), to efficiently handle missing data over varying subsets of known information. EDDI combines this Partial VAE with an acquisition function that maximizes expected information gain on a set of target variables. EDDI is efficient and demonstrates that dynamic discovery of high-value information is possible; we show cost reduction at the same decision quality and improved decision quality at the same cost in benchmarks and in two health-care applications.. We believe there is great potential for realizing these gains in real-world decision support systems.
 ----I acknowledge that the authors have made improvements to the paper and have increased my score to 6 This is still definitely not my area of expertise and so I am leaving my confidence score low. --- The paper presents an algorithm EDDI that uses a a partial VAE and does active feature selection. The authors show quite a bit of experiments that seem to indicate the approach gives positive results. However, since this is not my main area of expertise I do not know if these tasks are standard evaluation for this task. For instance in Section 4.3, 4.4 why don*t the authors plot accuracy as a function of steps/number of variables observed. That would seem much more useful than log likelihood. In general, I found the methodology in the paper to be difficult to understand and not enough background was given. I think the paper would be clearer if it was more self contained. -For instance, I found much of Section 3 to not have enough background. The authors use lots of terminology around VAEs but don*t give enough rigorous background so the paper doesn*t feel self contained. -The same is true regarding "amortized inference" which I also feel isn*t rigorously defined anywhere but often discussed. -The task for Section 4.1 (image inpainting) is not quite defined.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Making decisions requires information relevant to the task at hand. Many real-life decision-making situations allow acquiring further relevant information at a specific cost. For example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment. More information that is relevant allows for better decisions but it may be costly to acquire all of this information. How can we trade off the desire to make good decisions with the option to acquire further information at a cost? To this end, we propose a principled framework, named EDDI (Efficient Dynamic Discovery of high-value Information), based on the theory of Bayesian experimental design. In EDDI we propose a novel partial variational autoencoder (Partial VAE), to efficiently handle missing data over varying subsets of known information. EDDI combines this Partial VAE with an acquisition function that maximizes expected information gain on a set of target variables. EDDI is efficient and demonstrates that dynamic discovery of high-value information is possible; we show cost reduction at the same decision quality and improved decision quality at the same cost in benchmarks and in two health-care applications.. We believe there is great potential for realizing these gains in real-world decision support systems.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 ----I acknowledge that the authors have made improvements to the paper and have increased my score to 6 This is still definitely not my area of expertise and so I am leaving my confidence score low. --- The paper presents an algorithm EDDI that uses a a partial VAE and does active feature selection. The authors show quite a bit of experiments that seem to indicate the approach gives positive results. However, since this is not my main area of expertise I do not know if these tasks are standard evaluation for this task. For instance in Section 4.3, 4.4 why don*t the authors plot accuracy as a function of steps/number of variables observed. That would seem much more useful than log likelihood. In general, I found the methodology in the paper to be difficult to understand and not enough background was given. I think the paper would be clearer if it was more self contained. -For instance, I found much of Section 3 to not have enough background. The authors use lots of terminology around VAEs but don*t give enough rigorous background so the paper doesn*t feel self contained. -The same is true regarding "amortized inference" which I also feel isn*t rigorously defined anywhere but often discussed. -The task for Section 4.1 (image inpainting) is not quite defined.
assistant
 ----I acknowledge that the authors have made improvements to the paper and have increased my score to 6 This is still definitely not my area of expertise and so I am leaving my confidence score low. --- The paper presents an algorithm EDDI that uses a a partial VAE and does active feature selection. The authors show quite a bit of experiments that seem to indicate the approach gives positive results. However, since this is not my main area of expertise I do not know if these tasks are standard evaluation for this task. For instance in Section 4.3, 4.4 why don*t the authors plot accuracy as a function of steps/number of variables observed. That would seem much more useful than log likelihood. In general, I found the methodology in the paper to be difficult to understand and not enough background was given. I think the paper would be clearer if it was more self contained. -For instance, I found much of Section 3 to not have enough background. The authors use lots of terminology around VAEs but don*t give enough rigorous background so the paper doesn*t feel self contained. -The same is true regarding "amortized inference" which I also feel isn*t rigorously defined anywhere but often discussed. -The task for Section 4.1 (image inpainting) is not quite defined.