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.
Confidence-based filtering reveals latent structure in diffusion models.
Confidence-based deferral works well in many scenarios but fails in specific cases.
Entropy-based decoding improves DLM sampling efficiency.
While deep learning-based classification is generally tackled using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one particularly popular such technique is that of confidence-based regression, which entails predicting a confidence value for each input-target pai…
CAdam optimizes online learning by adapting to distribution shifts and noise.
Hybrid quantum-classical model boosts S&P 500 prediction accuracy to 60.14%.
State-of-the-art, high capacity deep neural networks not only require large amounts of labelled training data, they are also highly susceptible to label errors in this data, typically resulting in large efforts and costs and therefore limiting the applicability of deep learning. To alleviate this issue, we propose a no…
Efficiently solves exploration-exploitation in LQR using Lagrangian relaxation.
Audio source separation is the process of separating a mixture (e.g. a pop band recording) into isolated sounds from individual sources (e.g. just the lead vocals). Deep learning models are the state-of-the-art in source separation, given that the mixture to be separated is similar to the mixtures the deep model was tr…
DO-IQS recovers optimal stopping region from expert trajectories, addressing specific challenges.
We present a confidence-based single-layer feed-forward learning algorithm SPIRAL (Spike Regularized Adaptive Learning) relying on an encoding of activation spikes. We adaptively update a weight vector relying on confidence estimates and activation offsets relative to previous activity. We regularize updates proportion…
Imitation learning (IL) aims to learn an optimal policy from demonstrations. However, such demonstrations are often imperfect since collecting optimal ones is costly. To effectively learn from imperfect demonstrations, we propose a novel approach that utilizes confidence scores, which describe the quality of demonstrat…
Study improves fraud detection in e-commerce with a stacked model combining CNNs, GNNs, and confidence gating.
This paper proposes a novel policy for a group of agents to, individually as well as collectively, solve a multi armed bandit (MAB) problem. The policy relies solely on the information that an agent has obtained through sampling of the options on its own and through communication with neighbors. The option selection po…
A new algorithm reduces suboptimal arm selection in correlated bandits.
We propose a generic confidence-based approximation that can be plugged in and simplify the auto-regressive generation process with a proved convergence. We first assume that the priors of future samples can be generated in an independently and identically distributed (i.i.d.) manner using an efficient predictor. Given…
This paper focuses on spectral filters on graphs, namely filters defined as elementwise multiplication in the frequency domain of a graph. In many graph signal processing settings, it is important to transfer a filter from one graph to another. One example is in graph convolutional neural networks (ConvNets), where the…
Lifelong learning with deep neural networks is well-known to suffer from catastrophic forgetting: the performance on previous tasks drastically degrades when learning a new task. To alleviate this effect, we propose to leverage a large stream of unlabeled data easily obtainable in the wild. In particular, we design a n…
Optimal learning for parametric prophet inequalities with exponential-type distributions
Modern neural networks are very powerful predictive models, but they are often incapable of recognizing when their predictions may be wrong. Closely related to this is the task of out-of-distribution detection, where a network must determine whether or not an input is outside of the set on which it is expected to safel…
A new SOHP filter improves trend estimation in economic time series.
Deep density methods improve filtering in high-dimensional systems.
The sophisticated structure of Convolutional Neural Network (CNN) allows for outstanding performance, but at the cost of intensive computation. As significant redundancies inevitably present in such a structure, many works have been proposed to prune the convolutional filters for computation cost reduction. Although ex…
Study optimal control in unknown nonlinear systems with near-optimal regret bound.
We endorse the idea, suggested in recent literature, that BitCoin prices are influenced by sentiment and confidence about the underlying technology; as a consequence, an excitement about the BitCoin system may propagate to BitCoin prices causing a Bubble effect, the presence of which is documented in several papers abo…
Gradient filters track moving parameters under noisy data and misspecification.
We simplify Bayesian filtering by framing it as optimization, making it practical for high-dimensional systems.
New algorithm reduces pricing error by a factor of T^2/3.
Develops an inverse particle filter for cognitive systems.
Convolutional neural networks (CNNs) achieve state-of-the-art performance in a wide variety of tasks in computer vision. However, interpreting CNNs still remains a challenge. This is mainly due to the large number of parameters in these networks. Here, we investigate the role of compression and particularly pruning fil…
A novel method reduces dimensionality for filtering SRNs with observed variables.
Kernel learning FBSDE filter improves nonlinear filtering efficiency.
New method filters large networks from financial data to reveal key subnetworks.
Many nonlinear extensions of the Kalman filter, e.g., the extended and the unscented Kalman filter, reduce the state densities to Gaussian densities. This approximation gives sufficient results in many cases. However, this filters only estimate states that are correlated with the observation. Therefore, sequential esti…
Paper proves convergence of Kalman filter on Stiefel manifolds with measurement errors.
Improved Kalman filter for non-linear, non-Gaussian data.
This work analyzes the stability of graph filters under large perturbations.
Advances deep network embedding through multi-filtering GCN.
This work preserves linear invariants in ensemble filters for non-Gaussian data assimilation.
Convolutional Bayesian filtering generalizes state estimation by incorporating inequality conditions.
EnSF improves accuracy in tracking high-dimensional nonlinear systems.
Improved Kalman filter for Stiefel manifold measurements.
Latent FxLMS accelerates ANC by adapting along low-dimensional filter weights.
In this paper we introduce a projection method for the space of probability distributions based on the differential geometric approach to statistics. This method is based on a direct L2 metric as opposed to the usual Hellinger distance and the related Fisher Information metric. We explain how this apparatus can be used…
Robust Kalman filtering method for outlier detection.
Recent work has suggested enhancing Bloom filters by using a pre-filter, based on applying machine learning to determine a function that models the data set the Bloom filter is meant to represent. Here we model such learned Bloom filters,, with the following outcomes: (1) we clarify what guarantees can and cannot be as…
This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in on…
Develops inverse unscented Kalman filter for non-linear systems.