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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 0Source ↗PDF ↗Scholar ↗
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 18Source ↗PDF ↗Scholar ↗
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 39Source ↗PDF ↗Scholar ↗
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 56Source ↗PDF ↗Scholar ↗
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 92Source ↗PDF ↗Scholar ↗