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Veeranjaneyulu Sadhanala

5 accepted papers

2024

USTAD: Unified Single-model Training Achieving Diverse Scores for Information Retrieval

ICML 2024poster

Modern information retrieval (IR) systems consists of multiple stages like retrieval and ranking, with Transformer-based models achieving state-of-the-art performance at each stage. In this paper, we challenge the tradition of using separate models for different stages and ask if a single Transforme…

Cited by 0SourcePDFScholar
2019

A Higher-Order Kolmogorov-Smirnov Test

AISTATS 2019poster

We present an extension of the Kolmogorov-Smirnov (KS) two-sample test, which can be more sensitive to differences in the tails. Our test statistic is an integral probability metric (IPM) defined over a higher-order total variation ball, recovering the original KS test as its simplest case. We giv…

Cited by 18SourcePDFScholar
2017

Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods

NeurIPS 2017poster

We consider the problem of estimating the values of a function over $n$ nodes of a $d$-dimensional grid graph (having equal side lengths $n^{1/d}$) from noisy observations. The function is assumed to be smooth, but is allowed to exhibit different amounts of smoothness at different regions in the gri…

Cited by 39SourcePDFScholar
2016

Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms

ICML 2016poster

We study parallel and distributed Frank-Wolfe algorithms; the former on shared memory machines with mini-batching, and the latter in a delayed update framework. In both cases, we perform computations asynchronously whenever possible. We assume block-separable constraints as in Block-Coordinate Frank…

Cited by 56SourcePDFScholar
2016

Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers

NeurIPS 2016poster

We consider the problem of estimating a function defined over $n$ locations on a $d$-dimensional grid (having all side lengths equal to $n^{1/d}$). When the function is constrained to have discrete total variation bounded by $C_n$, we derive the minimax optimal (squared) $\ell_2$ estimation error r…

Cited by 92SourcePDFScholar