no code implementations • 31 Oct 2023 • Ernesto Araya Valdivia, Hemant Tyagi
We use this condition to show exact one-step recovery of the ground truth (holding almost surely) via the mirror descent scheme, in the noiseless setting.
no code implementations • 27 Mar 2023 • Hemant Tyagi, Denis Efimov
We consider the problem of finite-time identification of linear dynamical systems from $T$ samples of a single trajectory.
1 code implementation • 4 Jul 2022 • Ernesto Araya, Eglantine Karlé, Hemant Tyagi
In this setup, we are given a sequence of comparison graphs $(G_t)_{t\in \mathcal{T}}$, where $\mathcal{T} \subset [0, 1]$ is a grid representing the time domain, and for each item $i$ and time $t\in \mathcal{T}$ there is an associated unknown strength parameter $z^*_{t, i}\in \mathbb{R}$.
no code implementations • 8 Apr 2022 • Ernesto Araya, Guillaume Braun, Hemant Tyagi
In the \emph{graph matching} problem we observe two graphs $G, H$ and the goal is to find an assignment (or matching) between their vertices such that some measure of edge agreement is maximized.
1 code implementation • 20 Dec 2021 • Guillaume Braun, Hemant Tyagi, Christophe Biernacki
Our algorithm can be applied to general Contextual Stochastic Block Models and avoids hyperparameter tuning in contrast to previously proposed methods.
1 code implementation • 2 Dec 2021 • Michaël Fanuel, Hemant Tyagi
We consider a fixed design setting where the modulo samples are given on a regular grid.
1 code implementation • 28 Sep 2021 • Eglantine Karlé, Hemant Tyagi
When $(G_{t'})_{t' \in \mathcal{T}}$ is a sequence of Erd\"os-Renyi graphs, we provide non-asymptotic $\ell_2$ and $\ell_{\infty}$ error bounds for estimating $w_t^*$ which in particular establishes the consistency of this method in terms of $n$, and the grid size $\lvert\mathcal{T}\rvert$.
no code implementations • 4 Mar 2021 • Guillaume Braun, Hemant Tyagi, Christophe Biernacki
When these relationships have different modalities, they are better modelled by multilayer graphs where each layer is associated with one modality.
no code implementations • 29 Dec 2020 • Mihai Cucuringu, Hemant Tyagi
This can be thought of as a natural extension of the angular synchronization problem to the heterogeneous setting of multiple groups of angles, where the measurement graph has an unknown edge-disjoint decomposition $G = G_1 \cup G_2 \ldots \cup G_k$, where the $G_i$'s denote the subgraphs of edges corresponding to each group.
no code implementations • 3 Nov 2020 • Mihai Cucuringu, Apoorv Vikram Singh, Déborah Sulem, Hemant Tyagi
We study the problem of $k$-way clustering in signed graphs.
1 code implementation • 10 Sep 2020 • Michaël Fanuel, Hemant Tyagi
The estimates of the samples $f(x_i)$ can be subsequently utilized to construct an estimate of the function $f$, with the aforementioned uniform error rate.
no code implementations • 10 Sep 2020 • Hemant Tyagi
The analysis is performed in a general setting where $G$ is any connected graph.
no code implementations • 6 Jun 2019 • Alexandre d'Aspremont, Mihai Cucuringu, Hemant Tyagi
Given a measurement graph $G= (V, E)$ and an unknown signal $r \in \mathbb{R}^n$, we investigate algorithms for recovering $r$ from pairwise measurements of the form $r_i - r_j$; $\{i, j\} \in E$.
1 code implementation • 18 Apr 2019 • Mihai Cucuringu, Peter Davies, Aldo Glielmo, Hemant Tyagi
We introduce a principled and theoretically sound spectral method for $k$-way clustering in signed graphs, where the affinity measure between nodes takes either positive or negative values.
no code implementations • 9 Mar 2018 • Mihai Cucuringu, Hemant Tyagi
Given the samples $(x_i, y_i)_{i=1}^{n}$, our goal is to recover smooth, robust estimates of the clean samples $f(x_i) \bmod 1$.
no code implementations • 27 Oct 2017 • Mihai Cucuringu, Hemant Tyagi
Given the samples $(x_i, y_i)_{i=1}^{n}$ our goal is to recover smooth, robust estimates of the clean samples $f(x_i) \bmod 1$.
no code implementations • 2 May 2016 • Hemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner, Andreas Krause
A function $f: \mathbb{R}^d \rightarrow \mathbb{R}$ is a Sparse Additive Model (SPAM), if it is of the form $f(\mathbf{x}) = \sum_{l \in \mathcal{S}}\phi_{l}(x_l)$ where $\mathcal{S} \subset [d]$, $|\mathcal{S}| \ll d$.
no code implementations • 18 Apr 2016 • Hemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner, Andreas Krause
For some $\mathcal{S}_1 \subset [d], \mathcal{S}_2 \subset {[d] \choose 2}$, the function $f$ is assumed to be of the form: $$f(\mathbf{x}) = \sum_{p \in \mathcal{S}_1}\phi_{p} (x_p) + \sum_{(l, l^{\prime}) \in \mathcal{S}_2}\phi_{(l, l^{\prime})} (x_{l}, x_{l^{\prime}}).$$ Assuming $\phi_{p},\phi_{(l, l^{\prime})}$, $\mathcal{S}_1$ and, $\mathcal{S}_2$ to be unknown, we provide a randomized algorithm that queries $f$ and exactly recovers $\mathcal{S}_1,\mathcal{S}_2$.
no code implementations • NeurIPS 2014 • Hemant Tyagi, Bernd Gärtner, Andreas Krause
We consider the problem of learning sparse additive models, i. e., functions of the form: $f(\vecx) = \sum_{l \in S} \phi_{l}(x_l)$, $\vecx \in \matR^d$ from point queries of $f$.
no code implementations • 1 Dec 2013 • Hemant Tyagi, Sebastian Stich, Bernd Gärtner
We consider a stochastic continuum armed bandit problem where the arms are indexed by the $\ell_2$ ball $B_{d}(1+\nu)$ of radius $1+\nu$ in $\mathbb{R}^d$.
no code implementations • 7 Oct 2013 • Hemant Tyagi, Volkan Cevher
We consider the problem of learning multi-ridge functions of the form f(x) = g(Ax) from point evaluations of f. We assume that the function f is defined on an l_2-ball in R^d, g is twice continuously differentiable almost everywhere, and A \in R^{k \times d} is a rank k matrix, where k << d. We propose a randomized, polynomial-complexity sampling scheme for estimating such functions.
no code implementations • 21 Apr 2013 • Hemant Tyagi, Bernd Gärtner
We consider the stochastic and adversarial settings of continuum armed bandits where the arms are indexed by [0, 1]^d.