no code implementations • 14 Feb 2024 • Mohammadreza M. Kalan, Samory Kpotufe
We consider the problem of Neyman-Pearson classification which models unbalanced classification settings where error w. r. t.
no code implementations • 24 Dec 2023 • Gan Yuan, Mingyue Xu, Samory Kpotufe, Daniel Hsu
We consider the problem of sufficient dimension reduction (SDR) for multi-index models.
no code implementations • 7 Oct 2023 • Mohammadreza M. Kalan, Samory Kpotufe
A critical barrier to learning an accurate decision rule for outlier detection is the scarcity of outlier data.
no code implementations • 20 Jul 2023 • Dimitri Meunier, Zhu Li, Arthur Gretton, Samory Kpotufe
Many recent theoretical works on \emph{meta-learning} aim to achieve guarantees in leveraging similar representational structures from related tasks towards simplifying a target task.
no code implementations • 7 May 2023 • Nicholas Galbraith, Samory Kpotufe
We consider the problem of \emph{pruning} a classification tree, that is, selecting a suitable subtree that balances bias and variance, in common situations with inhomogeneous training data.
no code implementations • 29 Apr 2023 • Steve Hanneke, Samory Kpotufe, Yasaman Mahdaviyeh
Theoretical studies on transfer learning or domain adaptation have so far focused on situations with a known hypothesis class or model; however in practice, some amount of model selection is usually involved, often appearing under the umbrella term of hyperparameter-tuning: for example, one may think of the problem of tuning for the right neural network architecture towards a target task, while leveraging data from a related source task.
no code implementations • 27 Dec 2021 • Joe Suk, Samory Kpotufe
In bandit with distribution shifts, one aims to automatically adapt to unknown changes in reward distribution, and restart exploration when necessary.
no code implementations • 16 Oct 2021 • Samory Kpotufe, Gan Yuan, Yunfan Zhao
We consider nonparametric classification with smooth regression functions, where it is well known that notions of margin in $E[Y|X]$ determine fast or slow rates in both active and passive learning.
no code implementations • 22 Apr 2021 • Kun Yang, Samory Kpotufe, Nick Feamster
Insecure Internet of things (IoT) devices pose significant threats to critical infrastructure and the Internet at large; detecting anomalous behavior from these devices remains of critical importance, but fast, efficient, accurate anomaly detection (also called "novelty detection") for these classes of devices remains elusive.
no code implementations • 16 Jul 2020 • Joseph Suk, Samory Kpotufe
Bandits with covariates, a. k. a.
no code implementations • 30 Jun 2020 • Kun Yang, Samory Kpotufe, Nick Feamster
To facilitate such exploration, we develop a systematic framework, open-source toolkit, and public Python library that makes it both possible and easy to extract and generate features from network traffic and perform and end-to-end evaluation of these representations across most prevalent modern novelty detection models.
no code implementations • 29 Jun 2020 • Steve Hanneke, Samory Kpotufe
A perplexing fact remains in the evolving theory on the subject: while we would hope for performance bounds that account for the contribution from multiple tasks, the vast majority of analyses result in bounds that improve at best in the number $n$ of samples per task, but most often do not improve in $N$.
no code implementations • NeurIPS 2019 • Steve Hanneke, Samory Kpotufe
We aim to understand the value of additional labeled or unlabeled target data in transfer learning, for any given amount of source data; this is motivated by practical questions around minimizing sampling costs, whereby, target data is usually harder or costlier to acquire than source data, but can yield better accuracy.
no code implementations • 16 Aug 2019 • Samory Kpotufe, Bharath K. Sriperumbudur
The main contribution of the paper is to show that Gaussian sketching of a kernel-Gram matrix $\boldsymbol K$ yields an operator whose counterpart in an RKHS $\mathcal H$, is a \emph{random projection} operator---in the spirit of Johnson-Lindenstrauss (J-L) lemma.
no code implementations • NeurIPS 2018 • Tin D. Nguyen, Samory Kpotufe
We present a weighted-majority classification approach over subtrees of a fixed tree, which provably achieves excess-risk of the same order as the best tree-pruning.
1 code implementation • ICML 2018 • Heinrich Jiang, Jennifer Jang, Samory Kpotufe
We provide initial seedings to the Quick Shift clustering algorithm, which approximate the locally high-density regions of the data.
no code implementations • 5 Mar 2018 • Samory Kpotufe, Guillaume Martinet
We present new minimax results that concisely capture the relative benefits of source and target labeled data, under covariate-shift.
1 code implementation • 6 Dec 2017 • Lirong Xue, Samory Kpotufe
The approach consists of aggregating denoised $1$-NN predictors over a small number of distributed subsamples.
no code implementations • 25 Nov 2017 • Andrea Locatelli, Alexandra Carpentier, Samory Kpotufe
The problem of adaptivity (to unknown distributional parameters) has remained opened since the seminal work of Castro and Nowak (2007), which first established (active learning) rates for this setting.
no code implementations • 16 Mar 2017 • Andrea Locatelli, Alexandra Carpentier, Samory Kpotufe
This work addresses various open questions in the theory of active learning for nonparametric classification.
1 code implementation • 13 Jun 2016 • Heinrich Jiang, Samory Kpotufe
We present a first procedure that can estimate -- with statistical consistency guarantees -- any local-maxima of a density, under benign distributional conditions.
no code implementations • NeurIPS 2014 • Sanjoy Dasgupta, Samory Kpotufe
We present two related contributions of independent interest: (1) high-probability finite sample rates for $k$-NN density estimation, and (2) practical mode estimators -- based on $k$-NN -- which attain minimax-optimal rates under surprisingly general distributional conditions.
no code implementations • 5 Jun 2014 • Kamalika Chaudhuri, Sanjoy Dasgupta, Samory Kpotufe, Ulrike Von Luxburg
For a density $f$ on ${\mathbb R}^d$, a {\it high-density cluster} is any connected component of $\{x: f(x) \geq \lambda\}$, for some $\lambda > 0$.
no code implementations • 19 Dec 2013 • Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schölkopf
We analyze a family of methods for statistical causal inference from sample under the so-called Additive Noise Model.
no code implementations • NeurIPS 2013 • Samory Kpotufe, Vikas Garg
We present the first result for kernel regression where the procedure adapts locally at a point $x$ to both the unknown local dimension of the metric and the unknown H\{o}lder-continuity of the regression function at $x$.
no code implementations • NeurIPS 2013 • Samory Kpotufe, Francesco Orabona
We consider the problem of maintaining the data-structures of a partition-based regression procedure in a setting where the training data arrives sequentially over time.
no code implementations • NeurIPS 2012 • Samory Kpotufe, Abdeslam Boularias
In regression problems over $\real^d$, the unknown function $f$ often varies more in some coordinates than in others.
no code implementations • NeurIPS 2011 • Samory Kpotufe
Many nonparametric regressors were recently shown to converge at rates that depend only on the intrinsic dimension of data.
no code implementations • NeurIPS 2009 • Samory Kpotufe
It was recently shown that certain nonparametric regressors can escape the curse of dimensionality in the sense that their convergence rates adapt to the intrinsic dimension of data (\cite{BL:65, SK:77}).