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.
Proposes a new classifier for causal discovery in categorical data.
The task of aggregating and denoising crowd-labeled data has gained increased significance with the advent of crowdsourcing platforms and massive datasets. We propose a permutation-based model for crowd labeled data that is a significant generalization of the classical Dawid-Skene model, and introduce a new error metri…
Label switching is a phenomenon arising in mixture model posterior inference that prevents one from meaningfully assessing posterior statistics using standard Monte Carlo procedures. This issue arises due to invariance of the posterior under actions of a group; for example, permuting the ordering of mixture components …
HistNetQ improves quantification tasks by optimizing loss functions and eliminating label requirements.
Machine understanding of complex images is a key goal of artificial intelligence. One challenge underlying this task is that visual scenes contain multiple inter-related objects, and that global context plays an important role in interpreting the scene. A natural modeling framework for capturing such effects is structu…
Many applications, including rank aggregation, crowd-labeling, and graphon estimation, can be modeled in terms of a bivariate isotonic matrix with unknown permutations acting on its rows and/or columns. We consider the problem of estimating an unknown matrix in this class, based on noisy observations of (possibly, a su…
Single-microphone, speaker-independent speech separation is normally performed through two steps: (i) separating the specific speech sources, and (ii) determining the best output-label assignment to find the separation error. The second step is the main obstacle in training neural networks for speech separation. Recent…
The roles played by learning and memorization represent an important topic in deep learning research. Recent work on this subject has shown that the optimization behavior of DNNs trained on shuffled labels is qualitatively different from DNNs trained with real labels. Here, we propose a novel permutation approach that …
Random permutations can offer faster convergence than with-replacement sampling for some functions.
New method learns optimal prediction strategies in adversarial games.
Paper tackles sparse recovery with shuffled labels, establishing statistical and computational limits.
We study the sample complexity of semi-supervised learning (SSL) and introduce new assumptions based on the mismatch between a mixture model learned from unlabeled data and the true mixture model induced by the (unknown) class conditional distributions. Under these assumptions, we establish an labeled samp…
New graph foundation models respect symmetries for broader applicability.
Cheap permutation tests speed up distribution testing without sacrificing accuracy.
Proposes PEMI for online selective conformal prediction with asymmetric rules.
Generative model for set-valued data using permutation invariant flows.
Bayesian optimization method for permutations accelerates combinatorial search.
The paper examines properties of GW optimal transport plans, showing they can be sparse and permutation-supported.
Paper develops deep neural networks for wireless tasks with reduced complexity.
A new method reduces memory requirements for sorting high-dimensional data.
Representations of sets are challenging to learn because operations on sets should be permutation-invariant. To this end, we propose a Permutation-Optimisation module that learns how to permute a set end-to-end. The permuted set can be further processed to learn a permutation-invariant representation of that set, avoid…
New algorithm learns permutations mixtures with optimal sample complexity.
The paper proposes an efficient estimator for linear regression with shuffled labels.
Paper tackles noisy labels in deep learning networks.
Many machine learning problems can be characterized by mutual contamination models. In these problems, one observes several random samples from different convex combinations of a set of unknown base distributions. It is of interest to decontaminate mutual contamination models, i.e., to recover the base distributions ei…
End-to-end deep learning for multi-view clustering improves accuracy across various data types.
Proposes neuron alignment to optimize mode connectivity in neural networks.
Randomization is minimax-optimal for variance in experimental design, even with structure.
Permutation-equivariant neural networks improve auction mechanisms by reducing regret and sample complexity.
We consider a simple and overarching representation for permutation-invariant functions of sequences (or multiset functions). Our approach, which we call Janossy pooling, expresses a permutation-invariant function as the average of a permutation-sensitive function applied to all reorderings of the input sequence. This …
We tackle tensor denoising with unknown permutations, achieving optimal recovery with polynomial estimators.
We show that, in a resource allocation problem, the ex ante aggregate utility of players with cumulative-prospect-theoretic preferences can be increased over deterministic allocations by implementing lotteries. We formulate an optimization problem, called the system problem, to find the optimal lottery allocation. The …
The paper models financial correlation matrices using permutation invariant Gaussian models and predicts market anomalies.
Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances. In this paper, we state the MIL problem as learning the Bernoulli distribution of the bag label where the bag label probability is fully parameterized by neural networks. Furthermore, we …
Identifies latent actions and dynamics from offline data with diverse demonstrators.
ShuffleNet is a state-of-the-art light weight convolutional neural network architecture. Its basic operations include group, channel-wise convolution and channel shuffling. However, channel shuffling is manually designed empirically. Mathematically, shuffling is a multiplication by a permutation matrix. In this paper, …
There has been a recent surge of interest in studying permutation-based models for ranking from pairwise comparison data. Despite being structurally richer and more robust than parametric ranking models, permutation-based models are less well understood statistically and generally lack efficient learning algorithms. In…
Estimates isotonic functions under unknown permutations, achieving optimal statistical and computational efficiency.
We propose new positive definite kernels for permutations. First we introduce a weighted version of the Kendall kernel, which allows to weight unequally the contributions of different item pairs in the permutations depending on their ranks. Like the Kendall kernel, we show that the weighted version is invariant to rela…
A permutation-based SW test achieves minimax-optimal power for two-sample testing.
Many problems at the intersection of combinatorics and computer science require solving for a permutation that optimally matches, ranks, or sorts some data. These problems usually have a task-specific, often non-differentiable objective function that data-driven algorithms can use as a learning signal. In this paper, w…
Graph neural networks improve systemic risk measures for financial networks.
Many applications, including rank aggregation and crowd-labeling, can be modeled in terms of a bivariate isotonic matrix with unknown permutations acting on its rows and columns. We consider the problem of estimating such a matrix based on noisy observations of a subset of its entries, and design and analyze a polynomi…
New method for regression in high-dimensional space using mixture modeling and optimal transport.
We consider the problem of noisy matrix completion, in which the goal is to reconstruct a structured matrix whose entries are partially observed in noise. Standard approaches to this underdetermined inverse problem are based on assuming that the underlying matrix has low rank, or is well-approximated by a low rank matr…
This research tackles multiclass classification by introducing a method for label ranking.
Deep generative models trained with large amounts of unlabelled data have proven to be powerful within the domain of unsupervised learning. Many real life data sets contain a small amount of labelled data points, that are typically disregarded when training generative models. We propose the Cluster-aware Generative Mod…
This work refines claims about neural network connectivity, showing that simultaneous linear connectivity is possible under certain conditions.