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.
Paper introduces Prob-SSI for robust OMA in noisy data.
Bayesian methods reduce variance in subspace identification for small data sets.
Projective DP-SGD reduces privacy error by identifying low-dimensional gradient subspaces.
In this paper, we analyze the finite sample complexity of stochastic system identification using modern tools from machine learning and statistics. An unknown discrete-time linear system evolves over time under Gaussian noise without external inputs. The objective is to recover the system parameters as well as the Kalm…
Paper unifies subspace identification and DMD for dynamical systems.
SIG model identifies invariant variables for MSDA with fewer domain constraints.
We propose a conjugate gradient type optimization technique for the computation of the Karcher mean on the set of complex linear subspaces of fixed dimension, modeled by the so-called Grassmannian. The identification of the Grassmannian with Hermitian projection matrices allows an accessible introduction of the geometr…
It is often the case that, within an online recommender system, multiple users share a common account. Can such shared accounts be identified solely on the basis of the userprovided ratings? Once a shared account is identified, can the different users sharing it be identified as well? Whenever such user identification …
Subspace identification is a classical and very well studied problem in system identification. The problem was recently posed as a convex optimization problem via the nuclear norm relaxation. Inspired by robust PCA, we extend this framework to handle outliers. The proposed framework takes the form of a convex optimizat…
This work presents GROUSE (Grassmanian Rank-One Update Subspace Estimation), an efficient online algorithm for tracking subspaces from highly incomplete observations. GROUSE requires only basic linear algebraic manipulations at each iteration, and each subspace update can be performed in linear time in the dimension of…
Paper analyzes and improves GPSP algorithm for block sparse signal recovery.
Robust PCA, the problem of PCA in the presence of outliers has been extensively investigated in the last few years. Here we focus on Robust PCA in the column sparse outlier model. The existing methods for column sparse outlier model assumes either the knowledge of the dimension of the lower dimensional subspace or the …
Optimizes parameters in high-dimensional spaces for practical applications.
Kernel models learn low-dimensional predictive subspaces from input data.
In this paper we consider the problem of group invariant subspace clustering where the data is assumed to come from a union of group-invariant subspaces of a vector space, i.e. subspaces which are invariant with respect to action of a given group. Algebraically, such group-invariant subspaces are also referred to as su…
Active sampling selects few points for accurate model reduction of high-fidelity systems.
This paper solves matrix blind joint block diagonalization with noise.
Paper recovers multi-subspace matrices from permuted data.
AdaSub optimizes with second-order info in low-dims subspace.
Stochastic Sparse Subspace Clustering improves subspace clustering by reducing over-segmentation through dropout.
New algorithm catches moving subspaces in bandit problems.
Robust PCA, the problem of PCA in the presence of outliers has been extensively investigated in the last few years. Here we focus on Robust PCA in the outlier model where each column of the data matrix is either an inlier or an outlier. Most of the existing methods for this model assumes either the knowledge of the dim…
In this paper, we present GASG21 (Grassmannian Adaptive Stochastic Gradient for norm minimization), an adaptive stochastic gradient algorithm to robustly recover the low-rank subspace from a large matrix. In the presence of column outliers, we reformulate the batch mode matrix norm minimization with…
Motivated by the task of hyperparameter optimization, we introduce the non-stochastic best-arm identification problem. Within the multi-armed bandit literature, the cumulative regret objective enjoys algorithms and analyses for both the non-stochastic and stochastic settings while to the best of our knowledge, the best…
Efficient algorithms for low-rank bandits using subspace recovery.
In this paper we present deterministic conditions for success of sparse subspace clustering (SSC) under missing data, when data is assumed to come from a Union of Subspaces (UoS) model. We consider two algorithms, which are variants of SSC with entry-wise zero-filling that differ in terms of the optimization problems u…
This research improves deep neural networks for parameter identification and prediction in stochastic Volterra integral equations.
This paper considers the problem of completing a matrix with many missing entries under the assumption that the columns of the matrix belong to a union of multiple low-rank subspaces. This generalizes the standard low-rank matrix completion problem to situations in which the matrix rank can be quite high or even full r…
A distributed system identification method for LTI systems using reverse experience replay.
In this paper we present deterministic analysis of sufficient conditions for sparse subspace clustering under missing data, when data is assumed to come from a Union of Subspaces (UoS) model. In this context we consider two cases, namely Case I when all the points are sampled at the same co-ordinates, and Case II when …
Paper tackles good arm identification in stochastic bandits.
The paper tackles drift identification in Lévy α-stable stochastic systems, proposing a Fourier space approach.
Optimal algorithm for identifying best arm in stochastic linear bandits with fixed confidence.
Algorithm selects public datasets for private machine learning.
ADSGD method speeds up model identification in sparse optimization.
Noise causes learning plateaus in neural networks.
This work proposes a new method for simultaneous probabilistic identification and control of an observable, fully-actuated mechanical system. Identification is achieved by conditioning stochastic process priors on observations of configurations and noisy estimates of configuration derivatives. In contrast to previous w…
New method identifies latent components in PNL mixtures without strong assumptions.
Optimal best-arm identification in linear bandits reduces sampling budget.
New algorithm optimally identifies best arm in both stochastic and adversarial settings.
Adaptive stochastic gradient algorithms in the Euclidean space have attracted much attention lately. Such explorations on Riemannian manifolds, on the other hand, are relatively new, limited, and challenging. This is because of the intrinsic non-linear structure of the underlying manifold and the absence of a canonical…
A new algorithm identifies one of several nearly optimal arms in linear bandits.
Studying a softmax-attention model, we show that the learned query converges to the latent signal subspace spanned by the informative direction.
Early detection of Alzheimer's disease (AD) and identification of potential risk/beneficial factors are important for planning and administering timely interventions or preventive measures. In this paper, we learn a disease model for AD that combines genotypic and phenotypic profiles, and cognitive health metrics of pa…
Bayesian neural networks with nonparametric noise models for system identification.
New algorithm learns principal subspace from random samples.
A problem of considerable importance within the field of uncertainty quantification (UQ) is the development of efficient methods for the construction of accurate surrogate models. Such efforts are particularly important to applications constrained by high-dimensional uncertain parameter spaces. The difficulty of accura…
We present SEBOOST, a technique for boosting the performance of existing stochastic optimization methods. SEBOOST applies a secondary optimization process in the subspace spanned by the last steps and descent directions. The method was inspired by the SESOP optimization method for large-scale problems, and has been ada…