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.
Investigates statistical properties of perturb-softmax and perturb-argmax distributions.
New method uses small perturbations to improve representation learning from few labels.
This work introduces a geometric approach to probability representation and option pricing.
Representations based on random walks can exploit discrete data distributions for clustering and classification. We extend such representations from discrete to continuous distributions. Transition probabilities are now calculated using a diffusion equation with a diffusion coefficient that inversely depends on the dat…
A central tenet of probabilistic programming is that a model is specified exactly once in a canonical representation which is usable by inference algorithms. We describe JointDistributions, a family of declarative representations of directed graphical models in TensorFlow Probability.
We describe a method to perform functional operations on probability distributions of random variables. The method uses reproducing kernel Hilbert space representations of probability distributions, and it is applicable to all operations which can be applied to points drawn from the respective distributions. We refer t…
The paper introduces new measures for quantifying uncertainty in machine learning.
Proposes DWMD for better matching of hidden representations across domains.
We derive relations between theoretical properties of restricted Boltzmann machines (RBMs), popular machine learning models which form the building blocks of deep learning models, and several natural notions from discrete mathematics and convex geometry. We give implications and equivalences relating RBM-representable …
Efficiently learns disentangled representations using conditional probability differences.
Conditional restricted Boltzmann machines are undirected stochastic neural networks with a layer of input and output units connected bipartitely to a layer of hidden units. These networks define models of conditional probability distributions on the states of the output units given the states of the input units, parame…
The learning of domain-invariant representations in the context of domain adaptation with neural networks is considered. We propose a new regularization method that minimizes the discrepancy between domain-specific latent feature representations directly in the hidden activation space. Although some standard distributi…
Bayesian approach approximates probability functions of Gaussian mixtures.
A new method for distribution regression using sliced Wasserstein distance.
Proposes a new way to represent uncertainty using implied volatility.
A new IPM uses ReLU networks to measure probability discrepancies.
To reduce the large computation and storage cost of a deep convolutional neural network, the knowledge distillation based methods have pioneered to transfer the generalization ability of a large (teacher) deep network to a light-weight (student) network. However, these methods mostly focus on transferring the probabili…
Proposes a new divergence measure for probability distributions.
Kernel embeddings help estimate causal effects from observational data.
Study error bounds in evaluating distributional computational graphs.
A market-maker-based prediction market lets forecasters aggregate information by editing a consensus probability distribution either directly or by trading securities that pay off contingent on an event of interest. Combinatorial prediction markets allow trading on any event that can be specified as a combination of a …
A pivotal problem in Bayesian nonparametrics is the construction of prior distributions on the space M(V) of probability measures on a given domain V. In principle, such distributions on the infinite-dimensional space M(V) can be constructed from their finite-dimensional marginals---the most prominent example being the…
Empower efficient representation of distributions through moment-preserving methods.
A new probability distribution on full rooted trees helps in model selection.
We propose an analytical approach to the computation of tail probabilities of compound distributions whose individual components have heavy tails. Our approach is based on the contour integration method, and gives rise to a representation of the tail probability of a compound distribution in the form of a rapidly conve…
MSRL learns a representation maximizing mutual info with response variables.
We study the mixtures of factorizing probability distributions represented as visible marginal distributions in stochastic layered networks. We take the perspective of kernel transitions of distributions, which gives a unified picture of distributed representations arising from Deep Belief Networks (DBN) and other netw…
Sparse representations have proven their efficiency in solving a wide class of inverse problems encountered in signal and image processing. Conversely, enforcing the information to be spread uniformly over representation coefficients exhibits relevant properties in various applications such as digital communications. A…
Markov networks (MNs) are a powerful way to compactly represent a joint probability distribution, but most MN structure learning methods are very slow, due to the high cost of evaluating candidates structures. Dependency networks (DNs) represent a probability distribution as a set of conditional probability distributio…
A quantum walk-based method for generating precise probability distributions efficiently.
Probability density estimation is a classical and well studied problem, but standard density estimation methods have historically lacked the power to model complex and high-dimensional image distributions. More recent generative models leverage the power of neural networks to implicitly learn and represent probability …
By representing words with probability densities rather than point vectors, probabilistic word embeddings can capture rich and interpretable semantic information and uncertainty. The uncertainty information can be particularly meaningful in capturing entailment relationships -- whereby general words such as "entity" co…
UniNet efficiently learns network representations from large graphs.
Paper proposes a new FRL algorithm for continuous sensitive attributes using EIPM.
We consider the problem of the estimation of a high-dimensional probability distribution from i.i.d. samples of the distribution using model classes of functions in tree-based tensor formats, a particular case of tensor networks associated with a dimension partition tree. The distribution is assumed to admit a density …
Develops a new divergence framework that combines -divergences and IPMs.
Study optimizes tree-based models for better alignment of predicted scores and actual probabilities.
We identify a fundamental problem in policy gradient-based methods in continuous control. As policy gradient methods require the agent's underlying probability distribution, they limit policy representation to parametric distribution classes. We show that optimizing over such sets results in local movement in the actio…
New KQEs improve probability metrics without mean function constraints.
Flow-based models use ODEs to generate complex data distributions.
Paper introduces S3W distance for spherical probability distributions.
A new method for Gaussian filtering using gradient flows and Wasserstein metrics.
Flexible evidential deep learning improves uncertainty quantification in machine learning.
Introduces triangular transport for uncertain data.
The paper evaluates functions of stable Lévy processes and their extrema efficiently.
It has long been agreed by academics that the inversion method is the method of choice for generating random variates, given the availability of the quantile function. However for several probability distributions arising in practice a satisfactory method of approximating these functions is not available. The main focu…
A new method for nonstationary Gaussian processes using Fourier features.
Gaussian Processes enhance financial forecasting by predicting mean-reverting time series with probability distributions.