10 papers
Instance-Adaptive Online Multiclass Multicalibration
Zhiming Huang, Jamie Morgenstern, Aaron Roth +1
We study online multiclass multicalibration over rounds, and give an algorithm with beyond-worst-case guarantees. With possible outcomes, forecasts lie in a simplex of dime…
Defense via Consensus: Mitigating Subliminal Learning through Multi-Teacher Distillation
Adhyyan Narang, Artin Tajdini, Claire Zhang +1
Fine-tuning can introduce unwanted behaviors through deliberate poisoning or unexpected generalization from seemingly benign data. Because these effects may be difficult to anticip…
Instance-Adaptive Online Multicalibration
Zhiming Huang, Jamie Morgenstern, Aaron Roth +1
We study online multicalibration beyond the worst-case. We give a single, efficient algorithm which dynamically interpolates between benign and worst-case sequences by adaptively r…
Semantic Search At LinkedIn
Fedor Borisyuk, Sriram Vasudevan, Muchen Wu +71
Semantic search with large language models (LLMs) enables retrieval by meaning rather than keyword overlap, but scaling it requires major inference efficiency advances. We present…
Efficient Uncoupled Learning Dynamics with Last-Iterate Convergence in Bilinear Saddle-Point Problems over Convex Sets under Bandit Feedback
Arnab Maiti, Claire Jie Zhang, Kevin Jamieson +3
In this paper, we study last-iterate convergence of learning algorithms in bilinear saddle-point problems, a preferable notion of convergence that captures the day-to-day behavior…
CoRPO: Adding a Correctness Bias to GRPO Improves Generalization
Anisha Garg, Claire Zhang, Nishit Neema +3
Group-Relative Policy Optimization (GRPO) has emerged as the standard for training reasoning capabilities in large language models through reinforcement learning. By estimating adv…
When Assisting One Disempowers Another
Claire Yang, Claire Jie Zhang, Maya Cakmak +1
Personal AI agents are increasingly deployed in shared environments, where their actions affect not just the primary user they are assisting, but bystanders who never consented to…
Welfare-Centric Clustering
Claire Jie Zhang, Seyed A. Esmaeili, Jamie Morgenstern
Fair clustering has traditionally focused on ensuring equitable group representation or equalizing group-specific clustering costs. However, Dickerson et al. (2025) recently showed…
Fair Clustering: Critique, Caveats, and Future Directions
John Dickerson, Seyed A. Esmaeili, Jamie Morgenstern +1
Clustering is a fundamental problem in machine learning and operations research. Therefore, given the fact that fairness considerations have become of paramount importance in algor…
Doubly Constrained Fair Clustering
John Dickerson, Seyed A. Esmaeili, Jamie Morgenstern +1
The remarkable attention which fair clustering has received in the last few years has resulted in a significant number of different notions of fairness. Despite the fact that these…