arXiv research

New methods use vector search and nearest-neighbor matching for policy learning in causal inference.

Biased sampling and missing data complicates statistical problems ranging from causal inference to reinforcement learning. We often correct for biased sampling of summary statistics with matching methods and importance weighting. In this paper, we study nearest neighbor matching (NNM), which makes estimates of populati…

Study proves NN matching is equivalent to Riesz regression for debiased machine learning.

We establish the first nonasymptotic error bounds for Kaplan-Meier-based nearest neighbor and kernel survival probability estimators where feature vectors reside in metric spaces. Our bounds imply rates of strong consistency for these nonparametric estimators and, up to a log factor, match an existing lower bound for c…

Matching datasets of multiple modalities has become an important task in data analysis. Existing methods often rely on the embedding and transformation of each single modality without utilizing any correspondence information, which often results in sub-optimal matching performance. In this paper, we propose a nonlinear…

New matching estimators correct bias in multivariate settings without smoothing parameters.

Optimizes sample reweighting to match laws under covariate shift using Wasserstein distance.

The article proposes modified Gower's coefficients for handling mixed type variables in nearest neighbor methods.

Generative models use kernel smoothing for conditioning on small example sets.

A new method for covariate shift adaptation using nearest neighbors.

Improves k-NN for monotonic data with robustness against noise.

New method uses optimal transport for better covariate matching in causal effect estimation.

Riemannian metric matching learns the geometry of high-dimensional datasets using neural networks.

In this paper, we propose deep learning techniques for econometrics, specifically for causal inference and for estimating individual as well as average treatment effects. The contribution of this paper is twofold: 1. For generalized neighbor matching to estimate individual and average treatment effects, we analyze the …

Unified theory for causal inference using various methods.

A growing interest has been witnessed recently from both academia and industry in building nearest neighbor search (NNS) solutions on top of full-text search engines. Compared with other NNS systems, such solutions are capable of effectively reducing main memory consumption, coherently supporting multi-model search and…

Adaptive NN method improves matrix completion for non-smooth data.

k Nearest Neighbor (kNN) method is a simple and popular statistical method for classification and regression. For both classification and regression problems, existing works have shown that, if the distribution of the feature vector has bounded support and the probability density function is bounded away from zero in i…

We consider the problem of diversifying automated reply suggestions for a commercial instant-messaging (IM) system (Skype). Our conversation model is a standard matching based information retrieval architecture, which consists of two parallel encoders to project messages and replies into a common feature representation…

The Earth Mover's Distance (EMD) is a state-of-the art metric for comparing discrete probability distributions, but its high distinguishability comes at a high cost in computational complexity. Even though linear-complexity approximation algorithms have been proposed to improve its scalability, these algorithms are eit…

Python package automates causal parameter estimation using Riesz regression.

We improve density-based distances using normalizing flows and score matching.

Study nearest-neighbor radii under dependent sampling, finding they remain informative.

Deep nearest neighbors outperform self-supervised methods in anomaly detection.

We introduce an evolutionary algorithm called recombinator-$k$-means for optimizing the highly non-convex kmeans problem. Its defining feature is that its crossover step involves all the members of the current generation, stochastically recombining them with a repurposed variant of the $k$-means++ seeding algorithm. Th…

Characterizes Lebesgue points using nearest neighbor methods.

The paper explains how nearest neighbor methods succeed in prediction.

AWNN improves matrix completion by adaptively weighting nearest neighbors.

A new method uses nearest neighbors for importance weighting.

Nearest neighbor methods are a popular class of nonparametric estimators with several desirable properties, such as adaptivity to different distance scales in different regions of space. Prior work on convergence rates for nearest neighbor classification has not fully reflected these subtle properties. We analyze the b…

Paper uses K-NN resampling to simulate and evaluate LOB markets.

VNNGP uses nearest neighbors to approximate GPs, improving scalability and performance.

The estimation of optimal treatment regimes is of considerable interest to precision medicine. In this work, we propose a causal $k$-nearest neighbor method to estimate the optimal treatment regime. The method roots in the framework of causal inference, and estimates the causal treatment effects within the nearest neig…

Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of trait-matching for determining species in…

Java implementation improves nearest neighbor algorithm complexity.

The paper tackles time series data by applying conformal prediction with nearest neighbors.

Paper solves nonparametric contextual bandit with unbounded contexts.

K-Nearest neighbor classifier (k-NNC) is simple to use and has little design time like finding k values in k-nearest neighbor classifier, hence these are suitable to work with dynamically varying data-sets. There exists some fundamental improvements over the basic k-NNC, like weighted k-nearest neighbors classifier (wh…

Paper introduces k-DTW for robust curve comparison.

The paper studies empirical processes from nearest neighbors in regression.

This paper proposes a new hashing-based KNN technique for faster nearest neighbor selection.

GAPA method provides efficient uncertainty quantification for pretrained networks.

The nearest neighbor problem is defined as follows: Given a set $P$ of $n$ points in some metric space $(X,D)$, build a data structure that, given any point $q$, returns a point in $P$ that is closest to $q$ (its "nearest neighbor" in $P$). The data structure stores additional information about the set $P$, which is th…

Locally adaptive nearest neighbors improve automated systems' performance and are easier to interpret.

An implicit goal in works on deep generative models is that such models should be able to generate novel examples that were not previously seen in the training data. In this paper, we investigate to what extent this property holds for widely employed variational autoencoder (VAE) architectures. VAEs maximize a lower bo…

Quantum machine learning improves satellite image alignment.

Both supervised and unsupervised machine learning algorithms have been used to learn partition-based index structures for approximate nearest neighbor (ANN) search. Existing supervised algorithms formulate the learning task as finding a partition in which the nearest neighbors of a training set point belong to the same…

New method speeds up k-means clustering for large k by improving nearest-neighbor search.