iiinova — where your digital self keeps evolving

This Wednesday! Don't forget to register! Featuring Keynotes and Discussants (11:00am-1:00pm ET) @djweitzner.bsky.social (MIT) with Kobbi Nissim (Georgetown), Katrina Ligett (HUJI) with Talia Gillis (Columbia)

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ACM SIGecom @acmsigecom.bsky.social · 20/02/2026

Wrapping up with a Fireside Chat (4:15-4:45pm ET) with @jasonhartline.bsky.social (Northwestern) and Nicole Immorlica (Yale and MSR). Plus lots of social events on Gather throughout the day! Join us, but start by registering!

forms.gle

Registration: SIGecom Winter Meeting 2026

February 25, 2026 on Virtual Chair. Event website

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Amin Rahimian @rahimian.bsky.social · 08/12/2025

WINE tutorial on Differential Privacy for Strategic Information Sharing and Learning today at 2 pm EST, in person and on zoom, link: sites.google.com/pitt.edu/win... with @jubaz.bsky.social @papachristoumarios.bsky.social and @yuxin-pitt.bsky.social

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Juba Ziani @jubaz.bsky.social · 02/12/2025

The issue actually seems fixed on my end! Thanks again :)

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Juba Ziani @jubaz.bsky.social · 01/12/2025

I am also unable to submit recommendation letters, also getting a "permission denied"

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TCS+ @tcsplus.bsky.social · 25/11/2025

📢 Our last TCS+ talk of the season will be Wed, Dec 3 (10am PT, 1pm ET, 19:00 CET): Natalie Collina (@ncollina.bsky.social), from UPenn, will tell us about "Swap regret and correlated equilibria beyond normal-form games"! RSVP to receive the link (one day before the talk): forms.gle/utLgSxLpqvpx...

forms.gle

TCS+ RSVP: Natalie Collina (2025/12/03)

Title: Swap regret and correlated equilibria beyond normal-form games

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Juba Ziani @jubaz.bsky.social · 22/10/2025

Hire Natalie right now! She's amazing

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Juba Ziani @jubaz.bsky.social · 15/10/2025

Hi everyone, The TOC4Fairness seminar series are restarted! Our next speaker, this *Monday* October 20th (1pm est, 10am est) is Lalitha Sankar from ASU. More info about her talk here! toc4fairness.org/toc4fairness... If you need the zoom link/mailing list access, please DM me

toc4fairness.org

TOC4Fairness Seminar – Lalitha Sankar

Date: Monday, October 20th; 10:00 am – 11:00 am Pacific Time Location: Zoom meeting

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Juba Ziani @jubaz.bsky.social · 19/09/2025

Hello, this is now accepted at Neurips 2025! Come check out our poster in December :)

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Juba Ziani @jubaz.bsky.social · 19/09/2025

Thanks to Yunzong Xu and Bhaskar Ray Chaudhuri for the invite!

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Juba Ziani @jubaz.bsky.social · 19/09/2025

Can't recommend the Allerton conference at UIUC enough! Went for the first time this year and really enjoyed the smaller scale, being able to talk to so many great researchers, the community, and the great talks! Definitely got quite a bit out of that and a million new ideas

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Clément Canonne @ccanonne.github.io · 25/08/2025

New differential #privacy textbook in town: "DP in Artificial Intelligence: From Theory to Practice", by @nandofioretto.bsky.social and @vanhentenryck.bsky.social. Open access, w/ chapters by @jubaz.bsky.social, @grahamrc.bsky.social, and @stein.ke! www.nowpublishers.com/article/Book...

Front cover: Differential Privacy in Artificial Intelligence: From Theory to Practice, now Publishers

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Juba Ziani @jubaz.bsky.social · 22/08/2025

It's also the tone of the PC. They keep changing the process mid-day, shortening the timeline for ACs, and instead of acknowledging this, blaming the ACs and sending repeatedly threatening emails for ha being one day late on a task that we had a half the original assigned time for

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Juba Ziani @jubaz.bsky.social · 11/08/2025

Check out our @let-all.com blog post on strategic classification and around recent work with Valia and @charapod.bsky.social!

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Juba Ziani @jubaz.bsky.social · 19/07/2025

The tl;dr is that balanced/fair algorithms do not necessarily comes at a cost. When you take incentives around data and network effects into account, fairness (here, through representativeness) can come at no cost. And the paper that started it all: arxiv.org/abs/2501.19294

nsf.gov

NSF Award Search: Award # 2504990

Collaborative Research: III: Medium: Incentives and interventions for robust networked data exchange

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Juba Ziani @jubaz.bsky.social · 19/07/2025

Excited to announce that I just got another NSF grant this week! This collaborative with Prof. Augustin Chaintreau at Columbia University. More info here: www.nsf.gov/awardsearch/...

nsf.gov

NSF Award Search: Award # 2504990

Collaborative Research: III: Medium: Incentives and interventions for robust networked data exchange

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Clément Canonne @ccanonne.github.io · 08/07/2025

Pretty stoked to announce that I won't be presenting my paper at #FOCS2025: but one of my talented coauthors will have to come here 🌏 to do so! "Instance-Optimal Uniformity Testing and Tracking," with Guy Blanc (Stanford) and Erik Waingarten (UPenn). (arXiv coming soon!)

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Juba Ziani @jubaz.bsky.social · 13/06/2025

The cointreau or the apricot? I found that every time I put cointreau in a cocktail, I should have used half.

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Krishna Acharya @kvachai.bsky.social · 09/06/2025

1/8 Happy to share that our paper GLoSS: Generative Language Models with Semantic Search for Sequential Recommendation is accepted at the KDD OARS workshop! 🎉 Paper, code: github.com/krishnachary... This is joint work with my wonderful collaborators @apetrov.bsky.social and @jubaz.bsky.social !

github.com

GitHub - krishnacharya/GLoSS: GLoSS: Generative Language Models with Semantic Search for Sequential Recommendation

GLoSS: Generative Language Models with Semantic Search for Sequential Recommendation - krishnacharya/GLoSS

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ProPublica @propublica.org · 11/04/2025

THREAD: Under a new law, thousands of prisoners in Louisiana have been cut off from ever getting a chance at parole. Why? Because an algorithm said so. 1/

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Juba Ziani @jubaz.bsky.social · 01/05/2025

Hello, I'm off Twitter forever! This is the main place to find me now :)

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Juba Ziani @jubaz.bsky.social · 07/04/2025

Hi everyone! First, wanted to let you all know that the amazing @charapod.bsky.social is on Bluesky! Please make sure to follow her :) Second, she wrote a really cool survey about the state of strategic classification that you should definitely read: www.sigecom.org/exchanges/vo...

sigecom.org

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Juba Ziani @jubaz.bsky.social · 10/03/2025

With this work, we aim to add nuance to the discourse that decentralization on its own may not be the solution---rather, centralized decision-making should be more fine-grained to go beyond naive metrics, and understanding diversity of backgrounds/signals that make up qualified candidates.

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Juba Ziani @jubaz.bsky.social · 10/03/2025

This creates unfairness where *equally qualified* candidates are treated disparately based on, for example, how recognizable their alma matter is.

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Juba Ziani @jubaz.bsky.social · 10/03/2025

The easy way out here is spectacularly bad: hire candidates that they fully understand/have more information about, rather than candidates that are riskier (i.e., a top student from a small, not well-known high school).

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Juba Ziani @jubaz.bsky.social · 10/03/2025

Our main insight is as follows: while decentralization is useful to score candidates beyond just generic characteristics/take into account how successful they are expected to be for a specific team, our mathematical model shows that such decentralized designer will always take the easy way out.

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Juba Ziani @jubaz.bsky.social · 10/03/2025

In stage 2, decentralized entities (professors in a university or specific teams in a company) make admissions/hiring decisions based not only on "quality" (again, which is imperfectly perceived), but also how their specific attributes contribute to the specific team they aim to join.

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Juba Ziani @jubaz.bsky.social · 10/03/2025

We propose a mathematical model of 2-stage admission/hiring that questions this conventional wisdom. In stage 1, a centralized designer makes initial hiring/admissions decisions based solely on a candidate's perceived "quality" (a noisy signal of their competences).

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Juba Ziani @jubaz.bsky.social · 10/03/2025

In particular, applicants that look atypical may be ignored (e.g., top and highly qualified applicants from lesser known schools, or applicants with less access to extracurricular activities to add to their CV).

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Juba Ziani @jubaz.bsky.social · 10/03/2025

The classical wisdom of the crowd is that centralization is bad: centralization tends to rely on generic decision-making rules and AI tools that optimize for historical admissions criteria without considering the diversity of applicant backgrounds.

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Juba Ziani @jubaz.bsky.social · 10/03/2025

Here, we study the impact of centralization vs decentralization in hiring and admissions.

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Juba Ziani @jubaz.bsky.social · 10/03/2025

Interestingly, and likely because of the special structure of our game, we see that very few samples are needed each time step for the dynamics to converge properly.

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Juba Ziani @jubaz.bsky.social · 10/03/2025

We derive results (theoretically and experimentally) both in the full-information setting where all players fully now the distribution of customers, and in the in-sample setting where this must be derived from samples.

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Juba Ziani @jubaz.bsky.social · 10/03/2025

One thing that we lose, compared to the original performative prediction paper, is *uniqueness*. Our game has several equilibria, some symmetric, some asymmetric, some pure, some mixed. We show that the equilibrium we converge to heavily depends on how the algorithm is initialized

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Juba Ziani @jubaz.bsky.social · 10/03/2025

This may be surprising---we would expect no-regret dynamics by both players to converge to a coarse correlated equilibrium, but we in fact show convergence to a significantly smaller set of equilibria

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Juba Ziani @jubaz.bsky.social · 10/03/2025

Our setting, being a Bertrand-type model, clearly does not enjoy nice insensitivity properties. Yet, despite this, we are able to show experimentally and theoretically convergence to a stable solution. Further, this stable solution happens to be one of the Nash equilibria of our game.

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Juba Ziani @jubaz.bsky.social · 10/03/2025

We aim to study the convergence of simple no-regret dynamics (here, each player playing exponential weights) in this setting

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Juba Ziani @jubaz.bsky.social · 10/03/2025

Finally getting to our work: we propose a simple model of 2-player competition for mortgages. We consider two firms that can set two parameters: the minimum credit score they require, and the mortgage rate.

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