A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Improved model capacity for graph cut algorithms by relaxing submodularity constraints.
Proposes a novel tensor-based approach for multi-level link prediction.
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…
This work introduces CAET, an algorithm for cost-aware pairwise pure exploration.
Optimal privacy-preserving ranking from noisy comparisons.
Pairwise ranking aligns subjective clinical evaluations with objective indicators.
Structured prediction can be thought of as a simultaneous prediction of multiple labels. This is often done by maximizing a score function on the space of labels, which decomposes as a sum of pairwise and unary potentials. The above is naturally modeled with a graph, where edges and vertices are related to pairwise and…
FastMap-D embeds directed graphs using potential fields.
Proves bounds on spanning two-forests and random cut sizes.
The well known maximum-entropy principle due to Jaynes, which states that given mean parameters, the maximum entropy distribution matching them is in an exponential family, has been very popular in machine learning due to its "Occam's razor" interpretation. Unfortunately, calculating the potentials in the maximum-entro…
Given a graph where vertices represent alternatives and arcs represent pairwise comparison data, the statistical ranking problem is to find a potential function, defined on the vertices, such that the gradient of the potential function agrees with the pairwise comparisons. Our goal in this paper is to develop a method …
Class2Simi reduces noise in noisy label learning by transforming noisy class labels into noisy similarity labels.
In this paper we investigate two variants of association rules for preference data, Label Ranking Association Rules and Pairwise Association Rules. Label Ranking Association Rules (LRAR) are the equivalent of Class Association Rules (CAR) for the Label Ranking task. In CAR, the consequent is a single class, to which th…
Factorization machines (FM) are a popular model class to learn pairwise interactions by a low-rank approximation. Different from existing FM-based approaches which use a fixed rank for all features, this paper proposes a Rank-Aware FM (RaFM) model which adopts pairwise interactions from embeddings with different ranks.…
PDHAMS improves sampling for discrete distributions with quadratic potential functions.
New method uses geometric moments for accurate machine learning potentials.
Belief propagation (BP) is a popular method for performing probabilistic inference on graphical models. In this work, we enhance BP and propose self-guided belief propagation (SBP) that incorporates the pairwise potentials only gradually. This homotopy continuation method converges to a unique solution and increases th…
To understand the empirical success of approximate MAP inference, recent work (Lang et al., 2018) has shown that some popular approximation algorithms perform very well when the input instance is stable. The simplest stability condition assumes that the MAP solution does not change at all when some of the pairwise pote…
A new method combines EMD and diffusion maps for protein shape analysis.
We study the problem of ranking from crowdsourced pairwise comparisons. Answers to pairwise tasks are known to be affected by the position of items on the screen, however, previous models for aggregation of pairwise comparisons do not focus on modeling such kind of biases. We introduce a new aggregation model factorBT …
Gaussian processes have been successful in both supervised and unsupervised machine learning tasks, but their computational complexity has constrained practical applications. We introduce a new approximation for large-scale Gaussian processes, the Gaussian Process Random Field (GPRF), in which local GPs are coupled via…
Derives a new objective to learn from human preferences without approximations.
Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph…
In this study, a pairwise comparison matrix is generalized to the case when coefficients create Lie group , non necessarily abelian. A necessary and sufficient criterion for pairwise comparisons matrices to be consistent is provided. Basic criteria for finding a nearest consistent pairwise comparisons matrix (extend…
Paper introduces differential pairwise privacy for secure metric learning.
Novel ML model predicts solvation free energies from atom interactions.
ROVAE uses noisy pairwise comparisons to disentangle factors in VAEs.
Cross-entropy loss linked to metric learning, outperforming complex pairwise losses.
Develops a statistical framework to measure uncertainty in model rankings based on human preferences.
Knowledge graphs are a versatile framework to encode richly structured data relationships, but it can be challenging to combine these graphs with unstructured data. Methods for retrofitting pre-trained entity representations to the structure of a knowledge graph typically assume that entities are embedded in a connecte…
Improves labeling quality in machine learning with pairwise feedback.
A2I Transformer predicts atom energies from coordinates, avoiding heavy featurization.
The paper explores game-theoretic alignment of LLMs with human preferences, finding limitations and conditions.
As one of the most important types of (weaker) supervised information in machine learning and pattern recognition, pairwise constraint, which specifies whether a pair of data points occur together, has recently received significant attention, especially the problem of pairwise constraint propagation. At least two reaso…
Paper proposes Pcomp classification for binary classification with pairwise confidence comparisons.
Study on pairwise counter-monotonicity, a type of negative dependence.
This paper examines the problem of ranking a collection of objects using pairwise comparisons (rankings of two objects). In general, the ranking of objects can be identified by standard sorting methods using pairwise comparisons. We are interested in natural situations in which relationships among the o…
Conditions for curves on a torus with specific pairwise intersections.
In this paper we study the stability and its trade-off with optimization error for stochastic gradient descent (SGD) algorithms in the pairwise learning setting. Pairwise learning refers to a learning task which involves a loss function depending on pairs of instances among which notable examples are bipartite ranking,…
New method improves neural network classification accuracy and confidence.
Paper characterizes and represents pairwise causal background knowledge for improved causal inference.
Paper establishes statistical inference for pairwise comparison models.
Financial markets are a classical example of complex systems as they comprise many interacting stocks. As such, we can obtain a surprisingly good description of their structure by making the rough simplification of binary daily returns. Spin glass models have been applied and gave some valuable results but at the price…
Automates debiasing for large language model evaluations through Fisher random walk.
S3C2 uses Siamese networks for semi-supervised clustering with pairwise constraints.
Paper develops active learning for clustering unknown pairwise similarities.
PReNet detects seen and unseen anomalies using pairwise relations.
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. …