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.
A new method for 1-bit matrix completion that is faster and more accurate.
Study improves fractional posterior for 1-bit matrix completion.
New method predicts binary matrix entries using empirical Bayes and low-rank structure.
Matrix completion has a long-time history of usage as the core technique of recommender systems. In particular, 1-bit matrix completion, which considers the prediction as a ``Recommended'' or ``Not Recommended'' question, has proved its significance and validity in the field. However, while customers and products aggre…
Paper proposes a 1-bit quantization scheme for high-dimensional statistical estimation.
We consider the problem of noisy 1-bit matrix completion under an exact rank constraint on the true underlying matrix . Instead of observing a subset of the noisy continuous-valued entries of a matrix , we observe a subset of noisy 1-bit (or binary) measurements generated according to a probabilistic model. W…
Social trust prediction addresses the significant problem of exploring interactions among users in social networks. Naturally, this problem can be formulated in the matrix completion framework, with each entry indicating the trustness or distrustness. However, there are two challenges for the social trust problem: 1) t…
We consider in this paper the problem of noisy 1-bit matrix completion under a general non-uniform sampling distribution using the max-norm as a convex relaxation for the rank. A max-norm constrained maximum likelihood estimate is introduced and studied. The rate of convergence for the estimate is obtained. Information…
Due to challenging applications such as collaborative filtering, the matrix completion problem has been widely studied in the past few years. Different approaches rely on different structure assumptions on the matrix in hand. Here, we focus on the completion of a (possibly) low-rank matrix with binary entries, the so-c…
Paper offers robust recovery for 1-bit sensing with partial Gaussian circulant matrices.
New protocols show 1-bit mean estimation can be order-optimal without interaction.
New model analyzes customer churn with tensor completion and binary data.
Paper develops an efficient mean estimator for 1-bit communication constraints.
APGD algorithm efficiently recovers over-parameterized matrices from noisy measurements.
The goal of standard 1-bit compressive sensing is to accurately recover an unknown sparse vector from binary-valued measurements, each indicating the sign of a linear function of the vector. Motivated by recent advances in compressive sensing with generative models, where a generative modeling assumption replaces the u…
Study quantile reward identification with 1-bit feedback constraints.
New algorithm tackles batched stochastic linear bandits with 1-bit communication constraints.
Autoencoders fail to capture sparse structure in 1-bit data compression.
Paper proposes a 1-bit mean estimation method with near-optimal sample complexity.
For fast and energy-efficient deployment of trained deep neural networks on resource-constrained embedded hardware, each learned weight parameter should ideally be represented and stored using a single bit. Error-rates usually increase when this requirement is imposed. Here, we report large improvements in error rates …
Binary Iterative Hard Thresholding converges with optimal number of 1-bit measurements.
Paper analyzes BIHT for noisy 1-bit CS, improving results with up to τ-fraction of incorrect measurements.
Moniqua improves SGD convergence with quantized communication.
Paper studies signal detection in noisy environments with limited communication.
We present DeepFPC, a novel deep neural network designed by unfolding the iterations of the fixed-point continuation algorithm with one-sided l1-norm (FPC-l1), which has been proposed for solving the 1-bit compressed sensing problem. The network architecture resembles that of deep residual learning and incorporates pri…
BiTAT improves neural network quantization for edge devices by focusing on weight dependencies and disentangling them.
Study on recovering sparse linear classifiers from mixed binary responses.
Unified framework for nonconvex matrix completion with linearly parameterized factors.
Study on recovering supports of multiple sparse vectors from mixed linear measurements.
Matrix completion is a problem that arises in many data-analysis settings where the input consists of a partially-observed matrix (e.g., recommender systems, traffic matrix analysis etc.). Classical approaches to matrix completion assume that the input partially-observed matrix is low rank. The success of these methods…
A very simple interpretation of matrix completion problem is introduced based on statistical models. Combined with the well-known results from missing data analysis, such interpretation indicates that matrix completion is still a valid and principled estimation procedure even without the missing completely at random (M…
Recommender systems are widely used to recommend the most appealing items to users. These recommendations can be generated by applying collaborative filtering methods. The low-rank matrix completion method is the state-of-the-art collaborative filtering method. In this work, we show that the skewed distribution of rati…
Matrix completion is often applied to data with entries missing not at random (MNAR). For example, consider a recommendation system where users tend to only reveal ratings for items they like. In this case, a matrix completion method that relies on entries being revealed at uniformly sampled row and column indices can …
New method corrects bias in missing data for matrix completion.
Matrix completion is a modern missing data problem where both the missing structure and the underlying parameter are high dimensional. Although missing structure is a key component to any missing data problems, existing matrix completion methods often assume a simple uniform missing mechanism. In this work, we study ma…
Unified framework for uniform signal recovery in nonlinear GCS with 1-bit/quantized measurements.
Paper develops new patterns for unique matrix completions.
We consider the problem of matrix completion with side information (\textit{inductive matrix completion}). In real-world applications many side-channel features are typically non-informative making feature selection an important part of the problem. We incorporate feature selection into inductive matrix completion by p…
Study shows how fast a specific matrix completion method works.
Proposes a transductive matrix completion method with calibration for multi-task learning.
Paper explores robustness of CCS model for matrix completion.
In this paper, we develop a relative error bound for nuclear norm regularized matrix completion, with the focus on the completion of full-rank matrices. Under the assumption that the top eigenspaces of the target matrix are incoherent, we derive a relative upper bound for recovering the best low-rank approximation of t…
New method solves matrix completion problems to certifiable optimality.
We present a novel algebraic combinatorial view on low-rank matrix completion based on studying relations between a few entries with tools from algebraic geometry and matroid theory. The intrinsic locality of the approach allows for the treatment of single entries in a closed theoretical and practical framework. More s…
In this paper, we review the problem of matrix completion and expose its intimate relations with algebraic geometry, combinatorics and graph theory. We present the first necessary and sufficient combinatorial conditions for matrices of arbitrary rank to be identifiable from a set of matrix entries, yielding theoretical…
Unified approach for robust low rank matrix estimation with adversaries.
Paper proposes a clustering algorithm for nonnegative data.
Paper proposes a novel method to improve matrix completion with median loss for large datasets.