Weekly Updates for Arjun Dahal

Arjun:

Hello Everyone,

Milestone: Nov 29: Complete Pipeline for current approach along with results

Action Items performed this week:

1. Sampling from the multi-variate normal distribution, t-way coverage selection mechanism to get the data points.

1. Compare ATN ( Ankita’s approach ) to approach (1).

Project Page: https://ajdahal.github.io/portfolio/

I will be presenting on new idea session today.

Replies:

Dr. Lei:

the project page does not seem to be in a good state. also please summarize the results of your action items, which would be more informative.

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Arjun:

Hello Group,

Milestone: Baseline for Fairness Testing

Nov 12

Action Item completed this week:

I have been doing some experiments with regards to VAE for generating data points.

Fixed the issue with kl divergence for ‘hours-per-week’ observed in the last meeting.

I have obtained metrics for Reconstruction Loss vs Number of Embedding Dimension.

ATN and Kldivergence for random binning of embedding dimension.

Currently, I am carrying out t-way combination of latent space vectors. I believe I will have results by this afternoon.

Action Items for next week:

1. Complete the implementation of Conditional VAE, thus completing the pipeline.

1. Review all the Fairness definitions, and present them.

1. I have a list of papers related to my current project regarding VAE and it’s use in fairness testing. I will add state of the art papers regarding fairness testing to the list and upload them to the project page.

I would like to book 15 minutes of time for discussion on the results and the approach.

Arjun:

Hello Group,

Milestone: Nov 12

Baseline for Fairness Testing

I am still working on the experiments regarding fairness testing for Variational AutoEncoder.

I will share some results on slack before Nov 12 regarding the process with some metrics.

Replies:

Dr. Lei:

since you are targeting IWCT, whose deadline is fast approaching, it would help you can work out a schedule, with break-down tasks and target dates. the schedule will guide all your efforts toward the paper submission.

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Arjun:

Hello Everyone,

Milestone: Baseline for Fairness Testing ( Nov 12 )

I am working on making changes on the existing library of VAE to encorporate the combinatorial sampling in it’s latent space. Furthermore, I am also looking into creating conditional VAE and recording metrics for ATN and KL divergence along the way. I am currently working on the implementation.

I will briefly discuss the findings this Friday.

Arjun:

Hi Group,

Milestone: Baseline for Fairness Testing

I have collected and reviewed the necessary literature for the fairness testing with variational auto-encoder and implemented the necessary code for conditional variational auto-encoder. I would like to signup for a brief discussion tomorrow regarding the review and the results.

Replies:

Dr. Lei:

do you want to put a target date on your milestone?

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Arjun:

Hi Group,

Milestone: Finalize the baseline approach Oct 18

I have been working on the following action items since last week:

1. Literature review related to fairness testing using data samples that reflect realistic changes when the sensitive attribute is flipped for fairness testing.

1. Conditional VAE for data generation.

I have made some progress with the action items above, but haven’t worked till completion primarily due to travel.

I will update my progress everyday till coming Friday to ensure the baseline approach is finalized.

Replies:

Dr. Lei:

try your best to make the milestone

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Arjun:

Milestone: Finalize the baseline approach for Fairness testing ( 18 / 08 )

Intermediate Goals:

A. Complete literature search for fairness testing using data samples that reflect realistic changes when sensitive attribute is flipped for fairness testing. ( 11 / 08 )

B. Generation of Data samples conditioned on specific sensitive attribute. ( 11 / 08 )

Action Items completed:

1. Compiled a list of papers that lists papers that use counterfactuals for fairness testing . Adding the description to indicate if the paper is relevant to my task.

1. I have basic code template for conditional VAE. I am debugging it for generating better samples.

I will present today in the topic of “LLM and it’s application in Tabular Data”.

Replies:

Dr. Lei:

sounds good. i am very interested in hearing what you found out from your literature review first, in your presentation today. also what are the specific issues with conditional VAE?

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Arjun:

Thank You Dr. Lei for your feedback. I will be able to complete the literature review by Friday. Furthermore, I will sample few datapoints from the CTVAE and present some results this Friday.

To ensure, I make incremental progress to the task, I will update the status on channel related to my research Wednesday evening.

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Arjun:

Hi Group,

I have been following up on action items discussed in the last meeting. I will share some findings regarding LLM producing samples of tabular data for different prompts given to it which will take around ~15 mins. I plan to continue on the action items (sampling in latent space of VAE, generating adversarial examples on top of it ) and present the results in upcoming meetings.

My next milestone would be: Sample instances in latent space of VAE, generate adversarial examples on those samples to produce discriminatory samples and analyze results in each step (Oct 4 ).

Replies:

Dr. Lei:

this is not a good status updates, as I cannot get useful information out of it.

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Arjun:

Hi Group,

I have been looking into the literature regarding Fairness and Clustering since the last meeting

( shared in the channel arjun-research ). Creating Baseline approach with the findings from last few weeks is taking little more time than expected.

I intend to present the summary of findings, approach and summary of relevant literature in the upcoming meeting.

Arjun:

Hello All,

Milestone: Present on literature of all existing work on Fairness testing with clustering ( sep 24 )

Action items for the week: I have been following up on action items ( Divide the data into further sub-clusters to get discriminatory and non-discriminatory clusters, Inspect the datapoints that were discriminatory and non-discriminatory in labels as well as prediction ) discussed in last meeting but haven’t been able to make much progress as I amn’t very well. I will present some findings in upcoming meeting on Friday.

Replies:

Dr. Lei:

it is probably time to look back at what you have found out, and then start on developing your own approach. sign up a technical discussion to summarize your findings and propose some possible ideas for your own approach.

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Arjun:

Hi All,

Milestone:

Improve upon the baseline approach

• Review all the papers regarding Fairness Testing, Active Learning

I have been investigating the clusters that have all the discriminatory samples.

I have some findings that I would like to share in tomorrow’s meeting ( ~15-20 mins ).

Replies:

Dr. Lei:

try to make a presentation on the major approaches to fairness testing, especially on any existing work based on clustering

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