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Bryan Wilder

@brwilder.bsky.social

1.2K followers 220 following 15 posts

Assistant Professor at Carnegie Mellon. Machine Learning and social impact. https://bryanwilder.github.io/

bryanwilder.github.io

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Bryan Wilder @brwilder.bsky.social · 1d

How can synthetic data from LLMs be used, e.g. for social science, in a principled way? Check out Emily's thread on our NeurIPS paper! Generating paired real-synthetic samples and using both in a method-of-moments framework enables valid inference that benefits when synthetic data is informative.

Emily Byun @yewonbyun.bsky.social · 1d

💡Can we trust synthetic data for statistical inference?

We show that synthetic data (e.g., LLM simulations) can significantly improve the performance of inference tasks. The key intuition lies in the interactions between the moment residuals of synthetic data and those of real data

Reposted by Bryan Wilder

Gabriel Agostini @gsagostini.bsky.social · Sep 3

Are you a researcher using computational methods to understand cities?

@mfranchi.bsky.social @jennahgosciak.bsky.social and I organize an EAAMO Bridges working group on Urban Data Science and we are looking for new members!

Fill the interest form on our page: urban-data-science-eaamo.github.io

Urban Data Science & Equitable Cities | EAAMO Bridges

EAAMO Bridges Urban Data Science & Equitable Cities working group: biweekly talks, paper studies, and workshops on computational urban data analysis to explore and address inequities.

urban-data-science-eaamo.github.io

Reposted by Bryan Wilder

Nikhil Garg @nkgarg.bsky.social · Aug 11

New piece, out in the Sigecom Exchanges! It's my first solo-author piece, and the closest thing I've written to being my "manifesto." #econsky #ecsky
arxiv.org/abs/2507.03600

Screenshot of paper abstract, with text: "A core ethos of the Economics and Computation (EconCS) community is that people have complex private preferences and information of which the central planner is unaware, but which an appropriately designed mechanism can uncover to improve collective decisionmaking. This ethos underlies the community’s largest deployed success stories, from stable matching systems to participatory budgeting. I ask: is this choice and information aggregation “worth it”? In particular, I discuss how such systems induce heterogeneous participation: those already relatively advantaged are, empirically, more able to pay time costs and navigate administrative burdens imposed by the mechanisms. I draw on three case studies, including my own work – complex democratic mechanisms, resident crowdsourcing, and school matching. I end with lessons for practice and research, challenging the community to help reduce participation heterogeneity and design and deploy mechanisms that meet a “best of both worlds” north star: use preferences and information from those who choose to participate, but provide a “sufficient” quality of service to those who do not."

Bryan Wilder @brwilder.bsky.social · Jul 16

Submit an abstract to present a poster at EAAMO, deadline July 25! EAAMO is one of my favorite conferences, and a great place for anyone working on ML/algorithms/optimization in social settings. The conference is in Pittsburgh this November.

conference.eaamo.org/cfp/call_for...

Call for Posters

We seek poster contributions from different fields that offer insights into the intersectional design and impacts of algorithms, optimization, and mechanism design with a grounding in the social scien...

conference.eaamo.org

Reposted by Bryan Wilder

Amin Rahimian @rahimian.bsky.social · Jul 15

ACM EAAMO, which is coming to Pitt this Fall, has two events for students: a doctoral consortium and a poster session, both of which are due July 25th
- poster session conference.eaamo.org/cfp/call_for...
- doctoral consortium
conference.eaamo.org/cfp/call_for...

Call for Posters

We seek poster contributions from different fields that offer insights into the intersectional design and impacts of algorithms, optimization, and mechanism design with a grounding in the social scien...

conference.eaamo.org

Bryan Wilder @brwilder.bsky.social · Jul 8

My takeaway is that algorithm designers should think more broadly about the goals for algorithms in policy settings. It's tempting to just train ML models to maximize predictive performance, but services might be improved a lot with even modest alterations for other goals.

Bryan Wilder @brwilder.bsky.social · Jul 8

Using historical data from human services, we then look at how severe learning-targeting tradeoffs really are. It turns out, not that bad! We get most of the possible targeting performance while giving up only a little bit of learning compared to the ideal RCT.

Bryan Wilder @brwilder.bsky.social · Jul 8

We introduce a framework for designing allocation policies that optimally trade off between targeting high-need people and learning a treatment effect as accurately as possible. We give efficient algorithms and finite-sample guarantees using a duality-based characterization of the optimal policy.

Bryan Wilder @brwilder.bsky.social · Jul 8

A big factor is that randomizing conflicts with the targeting goal: running a RCT means that people with high predicted risk won't get prioritized for treatment. We wanted to know how sharp the tradeoff really is: does learning treatment effects require giving up on targeting entirely?

Bryan Wilder @brwilder.bsky.social · Jul 8

These days, public services are often targeted with predictive algorithms. Targeting helps prioritize people who might be most in need. But, we don't typically have good causal evidence about whether the program we're targeting actually improves outcomes. Why not run RCTs?

Bryan Wilder @brwilder.bsky.social · Jul 8

Excited to share that our paper "Learning treatment effects while treating those in need" received the exemplary paper award for AI at EC 2025! This paper grew out collaborations with Allegheny County's human services department and my co-author Pim Welle (at ACDHS).
arxiv.org/abs/2407.07596

Learning treatment effects while treating those in need

Many social programs attempt to allocate scarce resources to people with the greatest need. Indeed, public services increasingly use algorithmic risk assessments motivated by this goal. However, targe...

Bryan Wilder @brwilder.bsky.social · Jul 7

CMU is hosting a workshop on Human-AI Complementarity for Decision Making this September! Abstract submissions due July 15, travel will be covered for accepted presenters.

www.cmu.edu/ai-sdm/resea...

Human-AI Complementarity Workshop - NSF AI Institute for Societal Decision Making - Carnegie Mellon University

Landing page that provides details for the annual AI-SDM workshop on Human-AI Complementarity for Decision Making

Reposted by Bryan Wilder

Nikhil Garg @nkgarg.bsky.social · Jul 3

Excited to have this work out at ICML this year! Do LLMs make correlated errors? Yes, and those by the same company, and also more accurate/later generations are more correlated -- increasing algorithmic monoculture

arxiv.org/abs/2506.07962

Reposted by Bryan Wilder

Ted Underwood @tedunderwood.com · May 28

Still thinking about this post. The broader point, which should resonate way beyond the specific issue of "peer review," is that human disagreement is not friction and waste. It's a load-bearing, functional part of social and intellectual systems.

Bryan Wilder @brwilder.bsky.social · May 26

Should LLMs be used to review papers? AAAI is piloting LLM-generated reviews this year. I wrote a blog post arguing that using LLMs as reviewers can have bad downstream consequences for science by centralizing judgments about what constitutes good research.

bryanwilder.github.io/files/llmrev...

Equilibrium effects of LLM reviewing

Equilibrium effects of LLM reviewing

bryanwilder.github.io

Bryan Wilder @brwilder.bsky.social · May 27

I don't know one way or another, but it's at least a clearer capability to benchmark. And, if a LLM *could* summarize well enough on existing papers, arxiv using it for lower-bar moderation decisions wouldn't distort paper-writing in the future.

Reposted by Bryan Wilder

Sanmay Das @sanmayd.bsky.social · May 26

Thoughtful take on one aspect of the increasing problem of LLMs leading to “centralization” of thought/writing/etc.

Bryan Wilder @brwilder.bsky.social · May 26

Should LLMs be used to review papers? AAAI is piloting LLM-generated reviews this year. I wrote a blog post arguing that using LLMs as reviewers can have bad downstream consequences for science by centralizing judgments about what constitutes good research.

bryanwilder.github.io/files/llmrev...

Equilibrium effects of LLM reviewing

Equilibrium effects of LLM reviewing

bryanwilder.github.io

Bryan Wilder @brwilder.bsky.social · May 27

The arXiv summarization use case sounds a lot more sensible. Clear value judgment specified up-front, not outsourced to the LLM: papers should have easily summarized claims and evidence. Resulting incentives for authors seem ok (making sure LLMs can at least parse the paper probably isn't bad).

Bryan Wilder @brwilder.bsky.social · May 27

I would also prefer that these attempts at introducing LLMs be run as a RCT (like ICLR did) so we can learn something. But the tough thing is that even with RCTs it's hard to study the longer-term impact that new incentives will have.

Reposted by Bryan Wilder

angela zhou @angelamczhou.bsky.social · May 26

I didn't know about this, but this is objectively procedurally terrible. See Bryan's great analysis 👇
Yes, peer review needs help, but not like this.

Bryan Wilder @brwilder.bsky.social · May 26

Should LLMs be used to review papers? AAAI is piloting LLM-generated reviews this year. I wrote a blog post arguing that using LLMs as reviewers can have bad downstream consequences for science by centralizing judgments about what constitutes good research.

bryanwilder.github.io/files/llmrev...

Equilibrium effects of LLM reviewing

Equilibrium effects of LLM reviewing

bryanwilder.github.io

Reposted by Bryan Wilder

Clément Canonne @ccanonne.github.io · May 26

Quite the insightful post about the use of LLMs in peer-review. Since that ship has left the stable, let's understand and mitigate the possible adverse effects.
bryanwilder.github.io/files/llmrev...

E.g., "scientists should demand that any system used as part of peer review be openly accessible"

Equilibrium effects of LLM reviewing

Equilibrium effects of LLM reviewing

bryanwilder.github.io

Reposted by Bryan Wilder

Suresh Venkatasubramanian @geomblog.bsky.social · May 26

This is absolutely frightening.

Bryan Wilder @brwilder.bsky.social · May 26

Should LLMs be used to review papers? AAAI is piloting LLM-generated reviews this year. I wrote a blog post arguing that using LLMs as reviewers can have bad downstream consequences for science by centralizing judgments about what constitutes good research.

bryanwilder.github.io/files/llmrev...

Equilibrium effects of LLM reviewing

Equilibrium effects of LLM reviewing

bryanwilder.github.io

Bryan Wilder @brwilder.bsky.social · May 26

Should LLMs be used to review papers? AAAI is piloting LLM-generated reviews this year. I wrote a blog post arguing that using LLMs as reviewers can have bad downstream consequences for science by centralizing judgments about what constitutes good research.

bryanwilder.github.io/files/llmrev...

Equilibrium effects of LLM reviewing

Equilibrium effects of LLM reviewing

bryanwilder.github.io

Reposted by Bryan Wilder

Lily Xu @lilyxu.bsky.social · Apr 24

Paper:
Deep RL + mixed integer programming to plan for restless bandits with combinatorial (NP-hard) constraints.

with @brwilder.bsky.social, Elias Khalil, @milindtambe-ai.bsky.social

Poster #416 on Friday @ 3–5:30pm

bsky.app/profile/lily...

Lily Xu @lilyxu.bsky.social · Apr 1

Can we use RL to plan with combinatorial constraints?

Our #ICLR2025 paper combines deep RL with mathematical programming to do so! We embed a trained Q-network into a mixed-integer program, into which we can specify NP-hard constraints.

w/ brwilder.bsky.social, Elias Khalil, Milind Tambe

Our method combines deep RL with mixed-integer programming
for sequential planning with combinatorial actions.

Reposted by Bryan Wilder

Nikhil Garg @nkgarg.bsky.social · Apr 22

Updated abstract deadline is this Thursday, with full paper deadline the following Thursday! Please submit your papers. We will support hybrid presentations for those unable to travel. There is also a non-archival option for those who would like to submit the paper to a journal in the future!

Nikhil Garg @nkgarg.bsky.social · Feb 28

🚨 Call for Papers – #EAAMO25 🚨

We invite researchers, practitioners & policymakers to submit work on equity, access, & fairness in algorithms, optimization & mechanism design.

📅 Abstracts due Apr-17
📅 Papers due April-24

🔗 Learn more: conference.eaamo.org/cfp/

Call for Participation

The fifth ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO ‘25) will occur November 5–7, 2025 in University of Pittsburgh, Pittsburgh, PA, USA.

conference.eaamo.org

Reposted by Bryan Wilder

Nikhil Garg @nkgarg.bsky.social · Feb 28

🚨 Call for Papers – #EAAMO25 🚨

We invite researchers, practitioners & policymakers to submit work on equity, access, & fairness in algorithms, optimization & mechanism design.

📅 Abstracts due Apr-17
📅 Papers due April-24

🔗 Learn more: conference.eaamo.org/cfp/

Call for Participation

The fifth ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO ‘25) will occur November 5–7, 2025 in University of Pittsburgh, Pittsburgh, PA, USA.

conference.eaamo.org