2 citations · 3 across the 20 of their papers we have counts for
5 papers · 1 filter
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]…
On the Benefits of Active Data Collection in Operator Learning
Unique Subedi, Ambuj Tewari
We study active data collection strategies for operator learning when the target operator is linear and the input functions are drawn from a mean-zero stochastic process with conti…
Controlling Statistical, Discretization, and Truncation Errors in Learning Fourier Linear Operators
Unique Subedi, Ambuj Tewari
We study learning-theoretic foundations of operator learning, using the linear layer of the Fourier Neural Operator architecture as a model problem. First, we identify three main e…
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…
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…