1 citations · 1 across the 19 of their papers we have counts for
19 papers
GroupDPO: Memory-Efficient Group-Wise Direct Preference Optimization
Jixuan Leng, Si Si, Hsiang-Fu Yu +2
Preference optimization is widely used to align Large Language Models (LLMs) with preference feedback. However, most existing methods train on a single positive-negative pair per p…
Missing Mass for Differentially Private Domain Discovery
Travis Dick, Matthew Joseph, Vinod Raman
We study several problems in differentially private domain discovery, where each user holds a subset of items from a shared but unknown domain, and the goal is to output an informa…
On Generation in Metric Spaces
Jiaxun Li, Vinod Raman, Ambuj Tewari
We study generation in separable metric instance spaces. We extend the language generation framework from Kleinberg and Mullainathan [2024] beyond countable domains by defining nov…
Learning to Choose or Choosing to Learn: Best-of-N vs. Supervised Fine-Tuning for Bit String Generation
Seamus Somerstep, Vinod Raman, Unique Subedi +1
Using the bit string generation problem as a case study, we theoretically compare two standard methods for adapting large language models to new tasks. The first, referred to as su…
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…
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…
On Proper Learnability between Average- and Worst-case Robustness
Vinod Raman, Unique Subedi, Ambuj Tewari
Recently, Montasser et al. [2019] showed that finite VC dimension is not sufficient for proper adversarially robust PAC learning. In light of this hardness, there is a growing effo…
Online Agnostic Multiclass Boosting
Vinod Raman, Ambuj Tewari
Boosting is a fundamental approach in machine learning that enjoys both strong theoretical and practical guarantees. At a high-level, boosting algorithms cleverly aggregate weak le…