Search Results for author: Roi Weiss

Found 11 papers, 1 papers with code

A Bayes consistent 1-NN classifier

no code implementations1 Jul 2014 Aryeh Kontorovich, Roi Weiss

We show that a simple modification of the 1-nearest neighbor classifier yields a strongly Bayes consistent learner.

Learning Parametric-Output HMMs with Two Aliased States

no code implementations7 Feb 2015 Roi Weiss, Boaz Nadler

In various applications involving hidden Markov models (HMMs), some of the hidden states are aliased, having identical output distributions.

Vocal Bursts Valence Prediction

Maximum Margin Multiclass Nearest Neighbors

no code implementations30 Jan 2014 Aryeh Kontorovich, Roi Weiss

We prove generalization bounds that match the state of the art in sample size $n$ and significantly improve the dependence on the number of classes $k$.

Generalization Bounds

Universal Bayes consistency in metric spaces

no code implementations24 Jun 2019 Steve Hanneke, Aryeh Kontorovich, Sivan Sabato, Roi Weiss

This is the first learning algorithm known to enjoy this property; by comparison, the $k$-NN classifier and its variants are not generally universally Bayes-consistent, except under additional structural assumptions, such as an inner product, a norm, finite dimension, or a Besicovitch-type property.

Universal consistency and rates of convergence of multiclass prototype algorithms in metric spaces

no code implementations1 Oct 2020 László Györfi, Roi Weiss

We first obtain rates for the standard $k$-NN rule under a margin condition and a new generalized-Lipschitz condition.

Tree density estimation

no code implementations23 Nov 2021 László Györfi, Aryeh Kontorovich, Roi Weiss

data we identify an optimal tree $T^*$ and efficiently construct a tree density estimate $f_n$ such that, without any regularity conditions on the density $f$, one has $\lim_{n\to \infty} \int |f_n(\boldsymbol x)-f_{T^*}(\boldsymbol x)|d\boldsymbol x=0$ a. s. For Lipschitz $f$ with bounded support, $\mathbb E \left\{ \int |f_n(\boldsymbol x)-f_{T^*}(\boldsymbol x)|d\boldsymbol x\right\}=O\big(n^{-1/4}\big)$, a dimension-free rate.

Density Estimation

On Error and Compression Rates for Prototype Rules

no code implementations16 Jun 2022 Omer Kerem, Roi Weiss

We first show that OptiNet achieves non-trivial compression rates while enjoying near minimax-optimal error rates.

Weighted Distance Nearest Neighbor Condensing

no code implementations24 Oct 2023 Lee-Ad Gottlieb, Timor Sharabi, Roi Weiss

The problem of nearest neighbor condensing has enjoyed a long history of study, both in its theoretical and practical aspects.

Generalization Bounds

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