arXiv research

The paper analyzes systemic risk in an insurance model with multiple business lines and heterogeneous claims.

Study on deep neural networks for reward modeling with pairwise comparison data.

New framework extends ICA for non-independent variables, identifying pairwise mean independence.

The main goal of this paper is a detailed study of asymptotic cones of the mapping class groups. In particular, we prove that every asymptotic cone of a mapping class group has a bi-Lipschitz equivariant embedding into a product of real trees, sending limits of hierarchy paths onto geodesics, and with image a median su…

Paper establishes statistical inference for pairwise comparison models.

We propose three measures of mutual dependence between multiple random vectors. All the measures are zero if and only if the random vectors are mutually independent. The first measure generalizes distance covariance from pairwise dependence to mutual dependence, while the other two measures are sums of squared distance…

Optimizes identifying top-k items from comparisons with minimal comparisons.

Thompson Sampling with bilateral uncertainty improves performance in Bayesian Optimization.

A new model for sparse networks improves consistency of score estimators.

Low-rank framework for task-specific LLM ranking from sparse comparisons.

Exact pairwise ranking is achievable but not possible under noisy comparisons.

In this paper, we propose a listwise approach for constructing user-specific rankings in recommendation systems in a collaborative fashion. We contrast the listwise approach to previous pointwise and pairwise approaches, which are based on treating either each rating or each pairwise comparison as an independent instan…

In this paper, we unify the Markov theory of a variety of different types of graphs used in graphical Markov models by introducing the class of loopless mixed graphs, and show that all independence models induced by $m$-separation on such graphs are compositional graphoids. We focus in particular on the subclass of rib…

Paper introduces differential pairwise privacy for secure metric learning.

New estimators reduce computation for Kendall's tau and conditional Kendall's tau matrices under structural assumptions.

Develops a statistical framework to measure uncertainty in model rankings based on human preferences.

Study shows attention-style models learn pairwise interactions efficiently.

New algorithm ranks players from partial comparisons with optimal rate.

Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction or network inference problems. During the last decade kernel methods have played a dominant role in pairwise learning. They still obtain a stat…

Paper quantifies uncertainty in pairwise comparison models.

To date, testing interactions in high dimensions has been a challenging task. Existing methods often have issues with sensitivity to modeling assumptions and heavily asymptotic nominal p-values. To help alleviate these issues, we propose a permutation-based method for testing marginal interactions with a binary respons…

Pairwise quantile regression tackles similarity scoring in biometric systems.

This work introduces CAET, an algorithm for cost-aware pairwise pure exploration.

EPFGNN models graph connections for better node classification.

We show that the asymptotic growth rate for the minimal cardinality of a set of simple closed curves on a closed surface of genus $g$ which fill and pairwise intersect at most $K\ge 1$ times is $2\sqrt{g}/\sqrt{K}$ as $g \to \infty$ . We then bound from below the cardinality of a filling set of systoles by $g/\log(g)$.…

A new method for binary ICA using non-stationary sources.

There is a growing need for discrete choice models that account for the complex nature of human choices, escaping traditional behavioral assumptions such as the transitivity of pairwise preferences. Recently, several parametric models of intransitive comparisons have been proposed, but in all cases the maximum likeliho…

Several classification methods assume that the underlying distributions follow tree-structured graphical models. Indeed, trees capture statistical dependencies between pairs of variables, which may be crucial to attain low classification errors. The resulting classifier is linear in the log-transformed univariate and b…

Paper develops methods for estimating and forecasting integer-valued trawl processes.

This note has been updated (April, 2020) to respond to "Towards Clarifying the Theory of the Deconfounder" by Yixin Wang, David M. Blei (arXiv:2003.04948). This original note, posted in January, 2020, is meant to complement our previous comment on "The Blessings of Multiple Causes" by Wang and Blei (2019). We provide a…

The study examines the independence of GKM manifolds and symmetric spaces.

We introduce a family of pairwise stochastic gradient estimators for gradients of expectations, which are related to the log-derivative trick, but involve pairwise interactions between samples. The simplest example of our new estimator, dubbed the fundamental trick estimator, is shown to arise from either a) introducin…

The functions of proteins and RNAs are determined by a myriad of interactions between their constituent residues, but most quantitative models of how molecular phenotype depends on genotype must approximate this by simple additive effects. While recent models have relaxed this constraint to also account for pairwise in…

New test for conditional independence using kernel embeddings.

We adapt a Markov Random Field learning algorithm for continuous variables.

The Viterbi process can be extended indefinitely in a pairwise Markov model.

Formula derived for a magnetic line invariant.

We prove non-asymptotic lower bounds on the expectation of the maximum of $d$ independent Gaussian variables and the expectation of the maximum of $d$ independent symmetric random walks. Both lower bounds recover the optimal leading constant in the limit. A simple application of the lower bound for random walks is an (…

The independence clustering problem is considered in the following formulation: given a set $S$ of random variables, it is required to find the finest partitioning $\{U_1,\dots,U_k\}$ of $S$ into clusters such that the clusters $U_1,\dots,U_k$ are mutually independent. Since mutual independence is the target, pairwise …

We consider two systems of curves $(α_1,...,α_m)$ and $(β_1,...,β_n)$ drawn on a compact two-dimensional surface $M$ with boundary. Each $α_i$ and each $β_j$ is either an arc meeting the boundary of $M$ at its two endpoints, or a closed curve. The $α_i$ are pairwise disjoint except for possibly sharing endpoints, and s…

This paper optimizes the number of comparisons needed to find the best k items from pairwise comparisons.

SyncRank recovers global ranking from noisy comparisons with theoretical guarantees.

Deep model learns protein interfaces from high-order interactions.

GTMs model complex multivariate data with varying conditional independencies.

We decompose the squared price-of-risk premium into three components: intervention-stable premium, confounding wedge, and information loss.

The study examines uniqueness and non-uniqueness of minimal surfaces in hyperbolic space.

RL-LOW algorithm achieves exponential simple regret in offline RLHF with pairwise comparisons.

We consider the predictive problem of supervised ranking, where the task is to rank sets of candidate items returned in response to queries. Although there exist statistical procedures that come with guarantees of consistency in this setting, these procedures require that individuals provide a complete ranking of all i…