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.
Enhanced time series forecasting with improved trend and seasonal components.
This paper presents a normalization mechanism called Instance-Level Meta Normalization (ILM~Norm) to address a learning-to-normalize problem. ILM~Norm learns to predict the normalization parameters via both the feature feed-forward and the gradient back-propagation paths. ILM~Norm provides a meta normalization mechanis…
Unified framework for efficient trans-dimensional Bayesian inference using VI and NFs.
We investigate a hybrid quantum-classical solution method to the mean-variance portfolio optimization problems. Starting from real financial data statistics and following the principles of the Modern Portfolio Theory, we generate parametrized samples of portfolio optimization problems that can be related to quadratic b…
Improved KL divergence estimators for normalizing flows lead to faster convergence and better approximations.
A new method normalizes activations to match batch normalization without batch dependence.
Enhances RJMCMC efficiency with non-linear transport-based proposals.
We study the problem of hypothesis testing between two discrete distributions, where we only have access to samples after the action of a known reversible Markov chain, playing the role of noise. We derive instance-dependent minimax rates for the sample complexity of this problem, and show how its dependence in time is…
Develops a martingale expansion for stochastic volatility models.
The electricity market is a very peculiar market due to the large variety of phenomena that can affect the spot price. However, this market still shows many typical features of other speculative (commodity) markets like, for instance, data clustering and mean reversion. We apply the diffusion entropy analysis (DEA) to …
Estimates Markov chain mixing time from a single trajectory.
Classifies orientation-reversing homeomorphisms of even periods on surfaces.
We introduce graph normalizing flows: a new, reversible graph neural network model for prediction and generation. On supervised tasks, graph normalizing flows perform similarly to message passing neural networks, but at a significantly reduced memory footprint, allowing them to scale to larger graphs. In the unsupervis…
Automates defect detection using autoencoders on normal images only.
DFM simplifies CNF training without interpolants.
Study shows TD(0) with linear approx. converges for reversible Markov chains.
Batch Normalization (BN)(Ioffe and Szegedy 2015) normalizes the features of an input image via statistics of a batch of images and hence BN will bring the noise to the gradient of the training loss. Previous works indicate that the noise is important for the optimization and generalization of deep neural networks, but …
mfBm models and forecasts volatility with different Hurst exponents and correlations.
New method determines arrangement combinatorics from Milnor fiber boundary.
Adaptive feature normalization improves model robustness to extraneous variables.
M4L-JMF tackles multi-typed objects learning, improving on M3L.
The paper proposes methods to extract and analyze individual variable information from complex dependencies.
Data-driven anomaly detection methods typically build a model for the normal behavior of the target system, and score each data instance with respect to this model. A threshold is invariably needed to identify data instances with high (or low) scores as anomalies. This presents a practical limitation on the applicabili…
Method uses normalizing flows to efficiently sample from complex target densities.
DDS samples from noisy data by reversing diffusion, providing theoretical guarantees.
We propose a supervised anomaly detection method based on neural density estimators, where the negative log likelihood is used for the anomaly score. Density estimators have been widely used for unsupervised anomaly detection. By the recent advance of deep learning, the density estimation performance has been greatly i…
Generative models using PDMPs with explicit jump rates and kernels.
There are some statistical anomalies in the Chinese stock market, i.e., positive return skewness, anti-leverage effect (positive returns induce higher volatility than negative returns); and reverse volatility asymmetry (contemporaneous return-volatility correlation is positive). In this paper, we first confirm the exis…
Anomaly detection has numerous applications and has been studied vastly. We consider a complementary problem that has a much sparser literature: anomaly description. Interpretation of anomalies is crucial for practitioners for sense-making, troubleshooting, and planning actions. To this end, we present a new approach c…
A new model reconciles rough volatility and jumps.
In 3D space forms, a lens minimizes volume for a fixed surface area.
Flow-based deep generative models learn data distributions by transforming a simple base distribution into a complex distribution via a set of invertible transformations. Due to the invertibility, such models can score unseen data samples by computing their exact likelihood under the learned distribution. This makes fl…
Choosing a reference group in Oaxaca-Blinder decomposition can reverse conclusions.
Spoken language translation (SLT) has become very important in an increasingly globalized world. Machine translation (MT) for automatic speech recognition (ASR) systems is a major challenge of great interest. This research investigates that automatic sentence segmentation of speech that is important for enriching speec…
The process of un-reduction, a sort of reversal of reduction by the Lie group symmetries of a variational problem, is explored in the setting of field theories. This process is applied to the problem of curve matching in the plane, when the curves depend on more than one independent variable. This situation occurs in a…
PDDS samples from unnormalized densities using iterative particle scheme.
Entropy rigidity for Finsler flows but collapse for Reeb flows.
SurVAE Flows combine VAEs and flows using surjective transformations.
The cycling operation is a special kind of conjugation that can be applied to elements in Artin's braid groups, in order to reduce their length. It is a key ingredient of the usual solutions to the conjugacy problem in braid groups. In their seminal paper on braid-cryptography, Ko, Lee et al. proposed the {\it cycling …
In reinforcement learning, temporal difference (TD) is the most direct algorithm to learn the value function of a policy. For large or infinite state spaces, exact representations of the value function are usually not available, and it must be approximated by a function in some parametric family. However, with \emph{no…
A new method relaxes molecules without needing non-equilibrium data.
We propose a simple but effective multi-source domain generalization technique based on deep neural networks by incorporating optimized normalization layers that are specific to individual domains. Our approach employs multiple normalization methods while learning separate affine parameters per domain. For each domain,…
Recent semi-supervised anomaly detection methods that are trained using small labeled anomaly examples and large unlabeled data (mostly normal data) have shown largely improved performance over unsupervised methods. However, these methods often focus on fitting abnormalities illustrated by the given anomaly examples on…
A new method for reconstructing flows from perturbed distributions.
A new method uses MCMC-assisted normalizing flows for efficient Bayesian sampling.
Study on policy testing in MDPs with lower bounds and new algorithm.
SNPLA uses normalizing flows for efficient inference in implicit models.
Quantum tech speeds up financial risk assessment.