2 citations · 3 across the 20 of their papers we have counts for
8 papers · 1 filter
Apple Tasting: Combinatorial Dimensions and Minimax Rates
Vinod Raman, Unique Subedi, Ananth Raman +1
In online binary classification under \emph{apple tasting} feedback, the learner only observes the true label if it predicts ``1". First studied by \cite{helmbold2000apple}, we rev…
Online Infinite-Dimensional Regression: Learning Linear Operators
Vinod Raman, Unique Subedi, Ambuj Tewari
We consider the problem of learning linear operators under squared loss between two infinite-dimensional Hilbert spaces in the online setting. We show that the class of linear oper…
Multiclass Online Learnability under Bandit Feedback
Ananth Raman, Vinod Raman, Unique Subedi +2
We study online multiclass classification under bandit feedback. We extend the results of Daniely and Helbertal [2013] by showing that the finiteness of the Bandit Littlestone dime…
A Combinatorial Characterization of Supervised Online Learnability
Vinod Raman, Unique Subedi, Ambuj Tewari
We study the online learnability of hypothesis classes with respect to arbitrary, but bounded loss functions. No characterization of online learnability is known at this level of g…
Online Learning with Set-Valued Feedback
Vinod Raman, Unique Subedi, Ambuj Tewari
We study a variant of online multiclass classification where the learner predicts a single label but receives a \textit{set of labels} as feedback. In this model, the learner is pe…
On the Learnability of Multilabel Ranking
Vinod Raman, Unique Subedi, Ambuj Tewari
Multilabel ranking is a central task in machine learning. However, the most fundamental question of learnability in a multilabel ranking setting with relevance-score feedback remai…
Multiclass Online Learning and Uniform Convergence
Steve Hanneke, Shay Moran, Vinod Raman +2
We study multiclass classification in the agnostic adversarial online learning setting. As our main result, we prove that any multiclass concept class is agnostically learnable if…
A Characterization of Multioutput Learnability
Vinod Raman, Unique Subedi, Ambuj Tewari
We consider the problem of learning multioutput function classes in the batch and online settings. In both settings, we show that a multioutput function class is learnable if and o…