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- Leila Wehbe, Brian Murphy, Partha Talukdar, Alona Fyshe, Aaditya Ramdas, Tom Mitchell
- PloS one
- 2014

Story understanding involves many perceptual and cognitive subprocesses, from perceiving individual words, to parsing sentences, to understanding the relationships among the story characters. We present an integrated computational model of reading that incorporates these and additional subprocesses, simultaneously discovering their fMRI signatures. Our… (More)

This paper is about two related decision theoretic problems , nonparametric two-sample testing and independence testing. There is a belief that two recently proposed solutions, based on kernels and distances between pairs of points, behave well in high-dimensional settings. We identify different sources of misconception that give rise to the above belief.… (More)

- Aaditya Ramdas, Ryan J. Tibshirani
- ArXiv
- 2014

This paper presents a fast and robust algorithm for trend filtering, a recently developed nonparametric regression tool. It has been shown that, for estimating functions whose derivatives are of bounded variation, trend filtering achieves the minimax optimal error rate, while other popular methods like smoothing splines and kernels do not. Standing in the… (More)

- Aaditya Ramdas, Javier Peña
- ICML
- 2014

We focus on the problem of finding a non-linear classification function that lies in a Reproducing Kernel Hilbert Space (RKHS) both from the primal point of view (finding a perfect separator when one exists) and the dual point of view (giving a certificate of non-existence), with special focus on generalizations of two classical schemes-the Perceptron… (More)

We deal with two independent but related problems, those of graph similarity and subgraph matching, which are both important practical problems useful in several fields of science, engineering and data analysis. For the problem of graph similarity, we develop and test a new framework for solving the problem using belief propagation and related ideas. For… (More)

We propose a class of nonparametric two-sample tests with a cost linear in the sample size. Two tests are given, both based on an ensemble of distances between analytic functions representing each of the distributions. The first test uses smoothed empirical characteristic functions to represent the distributions, the second uses distribution embeddings in a… (More)

- Aaditya Ramdas, Aarti Singh
- ALT
- 2013

Interesting theoretical associations have been established by recent papers between the fields of active learning and stochastic convex optimization due to the common role of feedback in sequential querying mechanisms. In this paper, we continue this thread in two parts by exploiting these relations for the first time to yield novel algorithms in both… (More)

- Aaditya Ramdas, Aarti Singh
- ICML
- 2013

We focus on the problem of minimizing a convex function f over a convex set S given T queries to a stochastic first order oracle. We argue that the complexity of convex minimization is only determined by the rate of growth of the function around its minimizer x * f,S , as quantified by a Tsybakov-like noise condition. Specifically, we prove that if f grows… (More)

- Anna Ma, Deanna Needell, Aaditya Ramdas
- SIAM J. Matrix Analysis Applications
- 2015

The Kaczmarz and Gauss-Seidel methods both solve a linear system Xβ = y by iteratively refining the solution estimate. Recent interest in these methods has been sparked by a proof of Strohmer and Vershynin which shows the randomized Kaczmarz method converges linearly in expectation to the solution. Lewis and Leventhal then proved a similar result for the… (More)

- Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
- AISTATS
- 2015

Nonparametric two sample testing deals with the question of consistently deciding if two distributions are different, given samples from both, without making any parametric assumptions about the form of the distributions. The current literature is split into two kinds of tests-those which are consistent without any assumptions about how the distributions… (More)