5 papers · 1 filter
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…