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 discusses new stochastic algorithms for sparse signal recovery.
Developed a new thresholding method that connects soft and hard thresholding.
Noise makes learning linear thresholds hard, but algorithms can still learn near-optimal thresholds.
A central problem of random matrix theory is to understand the eigenvalues of spiked random matrix models, introduced by Johnstone, in which a prominent eigenvector (or "spike") is planted into a random matrix. These distributions form natural statistical models for principal component analysis (PCA) problems throughou…
Optimizes search times by resetting agents when a threshold is reached.
We define the information threshold in Bayesian decision-making.
The MBO scheme for data clustering is analyzed in the large data limit, proving convergence to optimal partition problems.
Analyzes biased random walks and corrupted intervals in adversarial settings.
Stop-loss rules are often studied in the financial literature, but the stop-loss levels are seldom constructed systematically. In many papers, and indeed in practice as well, the level of the stops is too often set arbitrarily. Guided by the overarching goal in finance to maximize expected returns given available infor…
We analyze the sample complexity of the thresholding bandit problem, with and without the assumption that the mean values of the arms are increasing. In each case, we provide a lower bound valid for any risk and any -correct algorithm; in addition, we propose an algorithm whose sample complexity is of the same o…
New findings suggest no ensemble averaging for certain black hole observables.
TPM improves medical image segmentation by separating foreground and background.
Class imbalance presents a major hurdle in the application of data mining methods. A common practice to deal with it is to create ensembles of classifiers that learn from resampled balanced data. For example, bagged decision trees combined with random undersampling (RUS) or the synthetic minority oversampling technique…
Estimates natural parameters of p-tensor Ising models efficiently.
Sharp threshold found for metric uniqueness in Riemannian Calderón-type problems.
The paper studies how arm selection in a bandit problem changes with shape constraints.
We study two global structural properties of a graph , denoted AS and CFS, which arise in a natural way from geometric group theory. We study these properties in the Erdös--Rényi random graph model G(n,p), proving a sharp threshold for a random graph to have the AS property asymptotically almost surely, and giving f…
Gradient descent near stability threshold exhibits sharpness oscillations.
A class of heterogeneous agent models is investigated where investors switch trading position whenever their motivation to do so exceeds some critical threshold. These motivations can be psychological in nature or reflect behaviour suggested by the efficient market hypothesis (EMH). By introducing different propensitie…
New insights into binary perceptron reveal phase transitions and algorithmic thresholds.
To overcome the curse of dimensionality and curse of modeling in Dynamic Programming (DP) methods for solving classical Markov Decision Process (MDP) problems, Reinforcement Learning (RL) algorithms are popular. In this paper, we consider an infinite-horizon average reward MDP problem and prove the optimality of the th…
We relax demographic parity in regression by enforcing parity at quantile levels and score thresholds.
We provide high probability finite sample complexity guarantees for hidden non-parametric structure learning of tree-shaped graphical models, whose hidden and observable nodes are discrete random variables with either finite or countable alphabets. We study a fundamental quantity called the (noisy) information threshol…
We study the fundamental limits of detecting the presence of an additive rank-one perturbation, or spike, to a Wigner matrix. When the spike comes from a prior that is i.i.d. across coordinates, we prove that the log-likelihood ratio of the spiked model against the non-spiked one is asymptotically normal below a certai…
ATC predicts target domain accuracy using only labeled and unlabeled data.
Iterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the objective, and thresholding steps that enforce the constraint. This work examines the choice of the thresholding operator, and asks whether it i…
Memory-augmented neural networks (MANNs) are designed for question-answering tasks. It is difficult to run a MANN effectively on accelerators designed for other neural networks (NNs), in particular on mobile devices, because MANNs require recurrent data paths and various types of operations related to external memory a…
New framework assesses LM uncertainty without thresholding.
Optimal control of reserve assets for stablecoins to maintain peg stability.
New RL method learns K-step lookahead Q-functions for fixed-horizon MDPs.
Many applications of AI involve scoring individuals using a learned function of their attributes. These predictive risk scores are then used to take decisions based on whether the score exceeds a certain threshold, which may vary depending on the context. The level of delegation granted to such systems in critical appl…
A central problem of random matrix theory is to understand the eigenvalues of spiked random matrix models, in which a prominent eigenvector is planted into a random matrix. These distributions form natural statistical models for principal component analysis (PCA) problems throughout the sciences. Baik, Ben Arous and Pé…
An algorithmically hard phase was described in a range of inference problems: even if the signal can be reconstructed with a small error from an information theoretic point of view, known algorithms fail unless the noise-to-signal ratio is sufficiently small. This hard phase is typically understood as a metastable bran…
Gradient descent near stability threshold shows sharpness oscillations.
Paper proves first non-trivial PTF testing lower bounds for NGCA.
A physically natural potential energy for simple closed curves in is shown to be invariant under Möbius transformations. This leads to the rapid resolution of several open problems: round circles are precisely the absolute minima for energy; there is a minimum energy threshold below which knotting cannot oc…
New method calculates sensitivity of system failure probability.
New algorithm for reinforcement learning in uncertain environments with unknown thresholds.
A new SSL method uses instance-dependent thresholds to improve accuracy.
Study stability thresholds of big line bundles, proving bounds and generalizing results.
Study robust estimation under varying corruption probabilities in data.
Adaptive algorithm for outlier detection by balancing arm exploration and threshold estimation.
In this paper we studied about the wavelet identification of the thresholds and time delay for more general case without the constraint that the time delay is smaller than the order of the model. Here we composed an empirical wavelet from the SETAR (Self-Exciting Threshold Autoregressive) model and identified the thres…
Meta-gradient D4PG optimizes performance and constraint adherence in RL.
The article examines different thresholding methods for improving PAM algorithm in cancer classification.
In the work of Ammann, Dahl and Humbert it has turned out that the Yamabe invariant on closed manifolds is a bordism invariant below a certain threshold constant. A similar result holds for a spinorial analogon. These threshold constants are characterized through Yamabe-type equations on products of spheres with rescal…
Although the threshold network is one of the most used tools to characterize the underlying structure of a stock market, the identification of the optimal threshold to construct a reliable stock network remains challenging. In this paper, the concept of dynamic consistence between the threshold network and the stock ma…
Proposes a conservative LR estimator for infrequent data near a frequency threshold.