arXiv research

Study learns linear utility functions from comparisons, showing learnability gaps between passive and active learning.

This paper provides a block coordinate descent algorithm to solve unconstrained optimization problems. In our algorithm, computation of function values or gradients is not required. Instead, pairwise comparison of function values is used. Our algorithm consists of two steps; one is the direction estimate step and the o…

Paper studies SGD stability and optimization error in pairwise learning.

Paper shows robustness of kernel-based pairwise learning without strict assumptions.

Bayesian active learning finds individual's most preferred choice with deep Gaussian processes.

Pairwise learning usually refers to a learning task which involves a loss function depending on pairs of examples, among which most notable ones include ranking, metric learning and AUC maximization. In this paper, we study an online algorithm for pairwise learning with a least-square loss function in an unconstrained …

We consider the problem of learning regression functions from pairwise data when there exists prior knowledge that the relation to be learned is symmetric or anti-symmetric. Such prior knowledge is commonly enforced by symmetrizing or anti-symmetrizing pairwise kernel functions. Through spectral analysis, we show that …

New gossip algorithms solve decentralized optimization of pairwise functions.

A new cost function for hierarchical clustering improves algorithm precision.

Study shows attention-style models learn pairwise interactions efficiently.

Pairwise methods outperform pseudo-likelihood in high-dimensional Markov network structure learning.

Efficient online learning with pairwise loss functions is a crucial component in building large-scale learning system that maximizes the area under the Receiver Operator Characteristic (ROC) curve. In this paper we investigate the generalization performance of online learning algorithms with pairwise loss functions. We…

Paper proposes Pcomp classification for binary classification with pairwise confidence comparisons.

We investigate a generic problem of learning pairwise exponential family graphical models with pairwise sufficient statistics defined by a global mapping function, e.g., Mercer kernels. This subclass of pairwise graphical models allow us to flexibly capture complex interactions among variables beyond pairwise product. …

The paper uses Heegaard Floer homology to find lower bounds on link genera.

Pairwise quantile regression tackles similarity scoring in biometric systems.

Enhances matrix completion with pairwise penalties for latent features.

We develop efficient methods to approximate maximum entropy distributions for pairwise moments.

This paper tackles bandit optimization with a new pairwise comparison oracle for unknown strongly concave functions.

Sampling a fraction of pairs can match full evaluation in machine learning losses.

Paper tackles interactive learning with noisy labels and comparisons, showing how comparison oracles can improve learning efficiency.

In this paper, we study the generalization properties of online learning based stochastic methods for supervised learning problems where the loss function is dependent on more than one training sample (e.g., metric learning, ranking). We present a generic decoupling technique that enables us to provide Rademacher compl…

A novel method classifies shapes by their square-root velocity function.

One significant challenge to scaling entity resolution algorithms to massive datasets is understanding how performance changes after moving beyond the realm of small, manually labeled reference datasets. Unlike traditional machine learning tasks, when an entity resolution algorithm performs well on small hold-out datas…

In spectral clustering and spectral image segmentation, the data is partioned starting from a given matrix of pairwise similarities S. the matrix S is constructed by hand, or learned on a separate training set. In this paper we show how to achieve spectral clustering in unsupervised mode. Our algorithm starts with a se…

A new method learns from pairwise comparisons to predict sensitive data without making strong assumptions.

Two-hop walks reveal PageRank order in networks.

Active learning optimizes correlation clustering by querying the most informative pairwise comparisons.

Estimates latent positions in 1D torus from noisy pairwise affinities.

The aim of this short note is to draw attention to a method by which the partition function and marginal probabilities for a certain class of random fields on complete graphs can be computed in polynomial time. This class includes Ising models with homogeneous pairwise potentials but arbitrary (inhomogeneous) unary pot…

Hybrid-MST improves preference aggregation from sparse data.

Regularized empirical risk minimization including support vector machines plays an important role in machine learning theory. In this paper regularized pairwise learning (RPL) methods based on kernels will be investigated. One example is regularized minimization of the error entropy loss which has recently attracted qu…

Estimates mixture entropy using pairwise distances.

New model reduces bias in crowdsourced pairwise comparisons.

The Johnson-Lindenstrauss Lemma allows for the projection of $n$ points in $p-$dimensional Euclidean space onto a $k-$dimensional Euclidean space, with $k \ge \frac{24\ln \emph{n}}{3ε^2-2ε^3}$, so that the pairwise distances are preserved within a factor of $1\pmε$. Here, working directly with the distributions of the …

Flexible model predicts sports outcomes over time.

A Markov Chain approach for aligning generative models from pairwise human preferences.

The paper proposes a method to calibrate evidential clustering using bootstrapped finite mixture models.

Geometric framework for consistent pairwise comparisons reduces inconsistency.

A new model for supervised learning to rank using gradient estimation.

This paper extends Median-of-Means to new learning problems involving pairwise comparisons.

Study on exotic smooth embeddings of surfaces in 4-manifolds, revealing different properties and complexities.

Bayesian model infers strengths from noisy tennis match outcomes.

Paper extends knowledge gradient for preferential BO, overcoming computational challenges.

Breaks the hardness conjecture for batch RL with a novel tournament-based approach.

Sparse logistic regression recovers any discrete pairwise graph model.

We present a global optimization algorithm for clustering data given the ratio of likelihoods that each pair of data points is in the same cluster or in different clusters. To define a clustering solution in terms of pairwise relationships, a necessary and sufficient condition is that belonging to the same cluster sati…

Researchers discover phase transitions in estimating object ranks from pairwise interactions.