6 citations · 10 across the 4 of their papers we have counts for
3 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…