updated rebuttals and gitignore

.gitignore

@@ -4,6 +4,7 @@
 /Manifest.toml
 /replicated/
 */.CondaPkg/
+/dev/rebuttal/www
 
 # Tex
 

dev/rebuttal/6zGr.md

 Thank you! In this individual response, we will refer back to the main points discussed in the global response where relevant and discuss any other specific points the reviewer has raised. Below we will go through individual points where quotations trace back to reviewer remarks.
 
-#### Low plausibility (real-world data)
+### Low plausibility (real-world data)
 
 > "The major weakness of this work is that plausibility for non-JEM-based classifiers is very low on 'real-world' datasets (Table 2)."
 
 Please refer to **Point 3** (and to some extent also **Point 2**) of the global rebuttal. 
 
-#### Visual quality (MNIST)
+### Visual quality (MNIST)
 
 > "[...] visual quality of generated counterfactuals seems to be low. [Results] hint to low diversity of generated counterfactuals."
 
@@ -17,7 +17,7 @@ Again, we kindly point to the global rebuttal (**Point 2** and **Point 3**) in t
 
 We will discuss this more thoroughly in the paper. 
 
-#### Closeness desideratum
+### Closeness desideratum
 
 > "ECCCos seems to generate counterfactuals that heavily change the initial image [...] thereby violating the closeness desideratum."
 
@@ -28,19 +28,19 @@ We will discuss this more thoroughly in the paper.
 
 We will highlight this tradeoff in section 7. 
 
-#### Datasets
+### Datasets
 
 > "The experiments are only conducted on small-scale datasets."
 
 In short, we have relied on illustrative datasets commonly used in similar studies. Please refer to our global rebuttal (in particular **Point 1**) for additional details.
 
-#### Conformal Prediction (ablation)
+### Conformal Prediction (ablation)
 
 > "[...] it is unclear if conformal prediction is actually required for ECCCos."
 
 Please refer to **Point 4** in the global rebuttal.
 
-#### Bias towards faithfulness
+### Bias towards faithfulness
 
 > "Experimental results for faithfulness are biased since (un)faithfulness is already used during counterfactual optimization as regularizer."
 
@@ -49,7 +49,7 @@ Please refer to **Point 4** in the global rebuttal.
 
 We will make this point more explicit in section 7. 
 
-#### Other questions
+### Other questions
 
 Finally, let us try to answer the specific questions that were raised:
 

dev/rebuttal/ZaU8.md

 Thank you! In this individual response, we will refer back to the main points discussed in the global response where relevant and discuss any other specific points the reviewer has raised. Below we will go through individual points where quotations trace back to reviewer remarks.
 
-#### Summary
+### Summary
 
 The reviewer has nicely summarised our work and we are happy to see that the main messages of the paper evidently came across. We also appreciate the mentioning of "honest acknowledgment of method limitations" as one of the strengths of the paper, which has indeed been important to us. 
 
-#### Citation of Welling \& Teh (2011)
+### Citation of Welling \& Teh (2011)
 
 > "It may be good to add a citation to [Welling & Teh, 2011] for SGLD on line 144."
 
 Regarding the specific question/suggestion raised by the reviewer, we do actually cite Welling \& Teh (2011) in line 145, but we can move that up to line 144 to make it clearer. 
 
-#### Need for gradient-access
+### Need for gradient-access
 
 > "Need for gradient access, e.g. through autodiff, for black-box model under investigation."
 

dev/rebuttal/pekM.md

 Thank you! In this individual response, we will refer back to the main points discussed in the global response where relevant and discuss any other specific points the reviewer has raised. Below we will go through individual points where quotations trace back to reviewer remarks.
 
-#### Mathematical notation
+### Mathematical notation
 
 > "Some notions are lacking descriptions and explanations"
 
 We will make a full pass over all notation, and improve where needed. 
-#### Conditional distribution
+### Conditional distribution
 
 > "[...] the class-condition distribution $p(\mathbf{x}|\mathbf{y^{+}})$ is existed but unknown and learning this distribution is very challenging especially for structural data"
 
@@ -17,14 +17,14 @@ We do not see this as a weakness of our paper. Instead:
 
 We will revisit section 2 to clarify this.
 
-#### Implausibility metric
+### Implausibility metric
 
 > "Additionally, the implausibility metric seems not general and rigorous [...]"
 
 - We agree it is not perfect and speak to this in the paper (e.g. lines 297 to 299). But we think that it is an improved, more robust version of the metric that was previously proposed and used in the literature (lines 159 to 166). Nonetheless, we will make this limitation clearer also in section 7.
 - The rule-based unary constraint metric proposed in Vo et al. (2023) looks interesting, but the paper will be presented for the first time at KDD in August 2023 and we were not aware of it at the time of writing. Thanks for bringing it to our attention, we will mention it in the same context in section 7. 
 
-#### Definiton of "faithfulness"
+### Definiton of "faithfulness"
 
 > "Faithfulness [...] can be understood as the validity and fidelity of counterfactual examples. [...] The definition 4.1 is fine but missing of the details of $p_{\theta}(\mathbf{x}|\mathbf{y^{+}})$. [...] However, it is not clear to me how to [...] use it in [SGLD]."
 
@@ -36,7 +36,7 @@ We wish to highlight a possible reviewer misunderstanding with regard to a funda
 
 We will revisit sections 3 and 4 of the paper to better explain this.
 
-#### Conformal Prediction (CP) 
+### Conformal Prediction (CP) 
 
 CP in this context is mentioned both as a strength
 
@@ -54,6 +54,6 @@ We reiterate our motivation here:
 
 Following the suggestion from reviewer 6zGr we will smoothen the introduction Conformal Prediction and better motivate it beforehand.
 
-#### Experiments
+### Experiments
 
-Please see **Points 1** and **4** of our global rebuttal
+Please see **Points 1** and **4** of our global rebuttal.
\ No newline at end of file

dev/rebuttal/uCjw.md

 Thank you! In this individual response, we will refer back to the main points discussed in the global response where relevant and discuss any other specific points the reviewer has raised. Below we will go through individual points where quotations trace back to reviewer remarks.
 
-#### Q1 and Q3: Data and models
+### Q1 and Q3: Data and models
 
 Please refer to **Point 1** and **Point 2** in the global response, respectively. 
 
-#### Q2: Generalisability
+### Q2: Generalisability
 
 > "Is the ECCCos approach adaptable to a broad range of black-box models beyond those discussed?"
 
 Our approach should generalise to any classifier that is differentiable with respect to inputs, consistent with other gradient-based counterfactual generators (Equation 1). Our actual implementation is currently compatible with neural networks trained in Julia and has experimental support for `torch` trained in either Python or R. Even though it is possible to generate counterfactuals for non-differentiable models, it is not immediately obvious to us how SGLD can be applied in this context. An interesting question for future research would be if other scalable and gradient-free methods can be used to sample from the conditional distribution learned by the model. 
 
-#### Q4: Link to causality
+### Q4: Link to causality
 
 > "There’s a broad literature on causal abstractions and causal model explanations that seems related."