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.
Unified analysis for deterministic samplers in diffusion models.
Study on regret minimization in deterministic MDPs.
CGAN fails to improve deterministic sequence predictions, revealing a theoretical limitation.
Deterministic tensor completion using hypergraph expanders with linear sample complexity.
Develops new bounds for deterministic samplers in diffusion models.
Identifies most probable flows for Kunita SDEs in fluid dynamics.
We explain theoretically a curious empirical phenomenon: "Approximating a matrix by deterministically selecting a subset of its columns with the corresponding largest leverage scores results in a good low-rank matrix surrogate". To obtain provable guarantees, previous work requires randomized sampling of the columns wi…
Paper proposes efficient algorithm for recovering sparsity pattern from deterministic missing data.
New algorithm reduces ERM problem size while maintaining accuracy.
SAMoSSA combines mSSA and AR for accurate time series analysis.
We present a mathematical analysis of a non-convex energy landscape for robust subspace recovery. We prove that an underlying subspace is the only stationary point and local minimizer in a specified neighborhood under a deterministic condition on a dataset. If the deterministic condition is satisfied, we further show t…
KL annealing helps VAEs avoid posterior collapse and overfitting.
In this paper, we analyze the fundamental conditions for low-rank tensor completion given the separation or tensor-train (TT) rank, i.e., ranks of unfoldings. We exploit the algebraic structure of the TT decomposition to obtain the deterministic necessary and sufficient conditions on the locations of the samples to ens…
A contraction analysis improves model-based RL's error recovery.
DAOC provides stable clustering for large networks.
New analysis improves convergence guarantees for diffusion-based samplers in Wasserstein distance.
New method predicts and optimizes matrix recovery from noisy measurements.
Designing deterministic denominators for SGLD stabilizes large drifts.
We find a deterministic equivalent for random feature regression's test error, independent of feature map dimension.
Understanding how different information sources together transmit information is crucial in many domains. For example, understanding the neural code requires characterizing how different neurons contribute unique, redundant, or synergistic pieces of information about sensory or behavioral variables. Williams and Beer (…
We consider principal component analysis for contaminated data-set in the high dimensional regime, where the dimensionality of each observation is comparable or even more than the number of observations. We propose a deterministic high-dimensional robust PCA algorithm which inherits all theoretical properties of its ra…
Chatter identification and detection in machining processes has been an active area of research in the past two decades. Part of the challenge in studying chatter is that machining equations that describe its occurrence are often nonlinear delay differential equations. The majority of the available tools for chatter id…
Unified framework for solving fixed-point equations in deterministic and stochastic settings.
Paper proposes a new method to stabilize noisy gradient algorithms.
Supervised learning frequently boils down to determining hidden and bright parameters in a parameterized hypothesis space based on finite input-output samples. The hidden parameters determine the attributions of hidden predictors or the nonlinear mechanism of an estimator, while the bright parameters characterize how h…
This paper analyses the problem of Gaussian process (GP) bandits with deterministic observations. The analysis uses a branch and bound algorithm that is related to the UCB algorithm of (Srinivas et al., 2010). For GPs with Gaussian observation noise, with variance strictly greater than zero, (Srinivas et al., 2010) pro…
In several experimental reports on nonconvex optimization problems in machine learning, stochastic gradient descent (SGD) was observed to prefer minimizers with flat basins in comparison to more deterministic methods, yet there is very little rigorous understanding of this phenomenon. In fact, the lack of such work has…
This paper analyzes the problem of Gaussian process (GP) bandits with deterministic observations. The analysis uses a branch and bound algorithm that is related to the UCB algorithm of (Srinivas et al, 2010). For GPs with Gaussian observation noise, with variance strictly greater than zero, Srinivas et al proved that t…
Randomized classifiers outperform deterministic ones in robustness against adversarial attacks.
New algorithms reduce regret in both stochastic and deterministic environments.
We confirm universal behaviors such as eigenvalue distribution and spacings predicted by Random Matrix Theory (RMT) for the cross correlation matrix of the daily stock prices of Tokyo Stock Exchange from 1993 to 2001, which have been reported for New York Stock Exchange in previous studies. It is shown that the random …
Subspace clustering is the problem of partitioning unlabeled data points into a number of clusters so that data points within one cluster lie approximately on a low-dimensional linear subspace. In many practical scenarios, the dimensionality of data points to be clustered are compressed due to constraints of measuremen…
In many domains, scientists build complex simulators of natural phenomena that encode their hypotheses about the underlying processes. These simulators can be deterministic or stochastic, fast or slow, constrained or unconstrained, and so on. Optimizing the simulators with respect to a set of parameter values is common…
Randomised classifiers outperform deterministic ones in strategic classification.
Low-rank matrix completion (LRMC) problems arise in a wide variety of applications. Previous theory mainly provides conditions for completion under missing-at-random samplings. This paper studies deterministic conditions for completion. An incomplete matrix is finitely rank- completable if there are at …
Analyzes how rough volatility affects stock pricing and risk premium.
We consider the problem of low canonical polyadic (CP) rank tensor completion. A completion is a tensor whose entries agree with the observed entries and its rank matches the given CP rank. We analyze the manifold structure corresponding to the tensors with the given rank and define a set of polynomials based on the sa…
We study the convergence rate of stochastic optimization of exact (NP-hard) objectives, for which only biased estimates of the gradient are available. We motivate this problem in the context of learning the structure and parameters of Ising models. We first provide a convergence-rate analysis of deterministic errors fo…
New bounds explain deterministic non-smooth deep nets without large Lipschitz constants.
Refined BN-S model improves crude oil hedging with machine learning.
The paper improves classification accuracy by leveraging a shared signal across domains in high-dimensional classification.
We present a theoretical analysis of the training process for a single-layer GAN fed by high-dimensional input data. The training dynamics of the proposed model at both microscopic and macroscopic scales can be exactly analyzed in the high-dimensional limit. In particular, we prove that the macroscopic quantities measu…
Paper explains failures of DDPG in simple deterministic environments.
This paper tackles reliability analysis for stochastic systems using surrogate models.
LLMs produce volatile sentence-level sentiment classifications that affect financial decision-making.
In this paper we propose a look at the capital risk problem inspired by deterministic, known from classical mechanics, problem of juggling. We propose capital equivalents to the Newton's laws of motion and on this basis we determine the most secure form of credit repayment with regard to maximisation of profit. Then we…
Paper proposes a new method for exact recovery in robust tensor principal component analysis.
Improved perturbation reduces matrix condition number to O(n) with minimal storage.