Filip Úradník

I’m a Bachelor student of Computer Science at MFF UK. For more info about me, take a look at my CV, either in Czech or English, or visit my personal website.

Contact me

My office is in the CoRE building, room 434.

You can also send me an email or contact me on Matrix.

Truth Learning in Social and Adversarial Setting

At REU 2024, I work on Truth Learning in a Social and Adversarial Setting with Julia Križanová, Rhett Olson, and Amanda Wang. Our mentor is Professor Jie Gao. We further collaborate with Kevin Lu.

Project Description

Let G = \left( V,E \right) be a directed multigraph representing a network of n := |V| agents. Let q \in (0,1) and p \in (\frac 12, 1). We have a ground truth \theta \sim \text{Ber}(q), which the agents are trying to learn. Each agent v has some private information s_v, which can have values 0 or 1, such that \text{Pr}[s_v = 1 \;|\; \theta = 1] = \text{Pr}[s_v = 0 \;|\; \theta = 0] = p. The variables \{s_v \;|\; v \in V\} are independent given \theta. Each agent v makes a prediction a_v of the ground truth according to a decision ordering, where predictions are based on both private information and the predictions of in-neighbors earlier in the ordering. We denote the sequence of all private signals as s = (s_v \;|\; v \in V).

We consider a majority vote setting, where an agent v in some given ordering \sigma can see the neighborhood N = \{u \;|\; uv \in E \land \sigma(u) < \sigma(v) \}, and chooses an action a_v = \begin{cases} 1 & \frac 1{|N|+1}(\sum_{u \in N} a_u + s_v) > \frac 12, \\ 0 & \frac 1{|N|+1}(\sum_{u \in N} a_u + s_v) < \frac 12,\\ s_v & \text{otherwise.} \end{cases}

The goal of our project is to find out, if it is NP-hard to decide, whether there exists an ordering of the agents \sigma, such that the expected error they make is small, or in other words, \mathbb{E}_{\theta, s} \left[ \frac{\sum_{v \in V}^{} {[a_v \neq \theta]}}{n} \right] < \varepsilon.

Week log

• Week 1: 05/29–06/02
  • I did research about information cascades and voting (Easley and Kleinberg 2010).
  • I created a website for this project.
  • I started working on the initial presentation of the project.
• Week 2: 06/03–06/09
  • We have finished and given the initial presentation.
  • I have read a paper about complexity of a decision process in a similar model to ours (Hązła et al. 2019).
  • We have discussed a statement of a complexity problem regarding optimal learning order.
  • I have started to work on a proof of the complexity problem regarding optimal learning order.
• Week 3: 06/10–06/16
  • We have discussed the proof of the complexity problem of deciding the optimal learning order with Kevin Lu.
  • I have started to work on a new problem concerning the comparison of Bayesian and Majority models.
  • We have further discussed adversarial approaches to the sequential learning setting.
• Week 4: 06/17–06/23
  • We have discussed the proof of the complexity problem of deciding the optimal learning order with Prof. Gao.
  • I found (and then successfully removed, yay) a bug in our initial computation of probabilities.
  • We have finished write-up of our complexity proof along with (messy) technical details with Amanda.
• Week 5: 06/24–06/30
  • We discussed another setting of network learning, this time based on learning from crowds with Prof. Gao.
  • We did some minor edits of the write-up and sent it to the others to read.
  • I’ve tried to prove that the expected learning rate is always better when using Bayesian learning, compared to majority vote.
• Week 6: 07/01–07/07
  • We have discussed extending the proof to non-directed graphs without multi-edges.
  • We have discussed the majority vs. Bayesian problem, I have found a counterexample when adversarial players are present.
  • I have examined the possibility of extending the proof to (still directed) graphs without multi-edges.
  • I put together a script for computing the expected learning rate in an arbitrary network.
  • I have found a construction which reduces 3-SAT to our problem without multi-edges, didn’t have time to write it down fully, as it is involved and messy.
• Week 7: 07/08–07/14
  • I have simplified the structure of the write-up.
  • I started making some of the notation a bit clearer.
  • I added bibliography to the write-up.
  • I worked on the format of the write-up—adding the option to render it in the plain, IEEE, and AAMAS format.
  • We have discussed the possibilities of publishing our work.
  • We have decided that we will give the final presentation together as a big group.
• Week 8: 07/15–07/21
  • We made the final presentation.
  • We discussed the generalization of our proof for other settings.
  • We considered notation in our paper.
  • We gave the final presentation.
  • I worked on reports for both DIMACS and CoSP.
• Week 9: 07/22–07/28
  • I continued to work on the final DIMACS report.
  • I made and gave a presentation on Cooperative Game Theory, as a part of the REU Student Workshop seminar in Prague.
  • I attended lectures, which were a part of the REU program at Charles University in Prague.

Acknowledgments

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 823748.

References