2 citations · 3 across the 20 of their papers we have counts for
20 papers
Is Zero-Shot Super-Resolution Possible in Operator Learning?
Unique Subedi, Ambuj Tewari
Neural operators are often reported to exhibit zero-shot super-resolution, a phenomenon in which a model trained on coarse grids produces accurate predictions on finer testing grid…
Operator Learning for Schrödinger Equation: Unitarity, Error Bounds, and Time Generalization
Yash Patel, Unique Subedi, Ambuj Tewari
We consider the problem of learning the evolution operator for the time-dependent Schrödinger equation, where the Hamiltonian may vary with time. Existing neural network-based surr…
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…
Operator Learning: A Statistical Perspective
Unique Subedi, Ambuj Tewari
Operator learning has emerged as a powerful tool in scientific computing for approximating mappings between infinite-dimensional function spaces. A primary application of operator…
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…
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…
A conjectural asymptotic formula for multiplicative chaos in number theory
Daksh Aggarwal, Unique Subedi, William Verreault +2
We investigate a special sequence of random variables defined by an exponential power series with independent standard complex Gaussians . Introduced by H…
Sums of random multiplicative functions over function fields with few irreducible factors
Daksh Aggarwal, Unique Subedi, William Verreault +2
We establish a normal approximation for the limiting distribution of partial sums of random Rademacher multiplicative functions over function fields, provided the number of irreduc…