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Multiclass Transductive Online Learning
Steve Hanneke, Vinod Raman, Amirreza Shaeiri +1
We consider the problem of multiclass transductive online learning when the number of labels can be unbounded. Previous works by Ben-David et al. [1997] and Hanneke et al. [2023b]…
Generation through the lens of learning theory
Jiaxun Li, Vinod Raman, Ambuj Tewari
We study generation through the lens of statistical learning theory. First, we abstract and formalize the results of Gold [1967], Angluin [1979], Angluin [1980] and Kleinberg and M…
Smoothed Online Classification can be Harder than Batch Classification
Vinod Raman, Unique Subedi, Ambuj Tewari
We study online classification under smoothed adversaries. In this setting, at each time point, the adversary draws an example from a distribution that has a bounded density with r…
Online Classification with Predictions
Vinod Raman, Ambuj Tewari
We study online classification when the learner has access to predictions about future examples. We design an online learner whose expected regret is never worse than the worst-cas…
The Complexity of Sequential Prediction in Dynamical Systems
Vinod Raman, Unique Subedi, Ambuj Tewari
We study the problem of learning to predict the next state of a dynamical system when the underlying evolution function is unknown. Unlike previous work, we place no parametric ass…