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.
Novel approach for estimating conditional expectations using Bayesian quadrature.
A new IC-Connection improves disentanglement in conditional GANs.
Novel boundary conditions for Ricci flow to deform compact manifolds.
We present a novel procedure for scaling relatively high frequency tail probability and quantile estimates for the conditional distribution of returns.
The simplicial condition and other stronger conditions that imply it have recently played a central role in developing polynomial time algorithms with provable asymptotic consistency and sample complexity guarantees for topic estimation in separable topic models. Of these algorithms, those that rely solely on the simpl…
CcGAN tackles conditional image generation for continuous labels.
Generative neural network designs novel 3D molecules with specified properties.
The paper tackles intervention generalization using factor graph models.
We introduce a novel conditional density estimation model termed the conditional density operator (CDO). It naturally captures multivariate, multimodal output densities and shows performance that is competitive with recent neural conditional density models and Gaussian processes. The proposed model is based on a novel …
A novel deep bootstrap framework for nonparametric regression using conditional diffusion models.
A novel k-NN method estimates conditional mean and variance efficiently.
Unified tractability conditions for various compositional inference queries.
Regression models for supervised learning problems with a continuous target are commonly understood as models for the conditional mean of the target given predictors. This notion is simple and therefore appealing for interpretation and visualisation. Information about the whole underlying conditional distribution is, h…
Novel method for time-series prediction with tighter confidence intervals.
A novel approach learns goal-conditioned policies for locomotion using batch RL.
Improved penalty-based methods for bilevel optimization with reduced complexity.
A novel framework uses goal-conditioned reinforcement learning to generate diverse samples.
We consider the problem of learning parameters of latent variable models from mixed (continuous and ordinal) data with missing values. We propose a novel Bayesian Gaussian copula factor (BGCF) approach that is consistent under certain conditions and that is quite robust to the violations of these conditions. In simulat…
Novel method discovers causal relations in time series data, even with autocorrelation.
Although machine learning has been successfully used to propose novel molecules that satisfy desired properties, it is still challenging to explore a large chemical space efficiently. In this paper, we present a conditional molecular design method that facilitates generating new molecules with desired properties. The p…
We introduce a novel encoder-decoder architecture to embed functional processes into latent vector spaces. This embedding can then be decoded to sample the encoded functions over any arbitrary domain. This autoencoder generalizes the recently introduced Conditional Neural Process (CNP) model of random processes. Our ar…
Paper tackles leveraging unlabeled data for PU classification and robust generation.
Prediction of future states of the environment and interacting agents is a key competence required for autonomous agents to operate successfully in the real world. Prior work for structured sequence prediction based on latent variable models imposes a uni-modal standard Gaussian prior on the latent variables. This indu…
ProFITi model forecasts irregular time series with missing values using conditional flows.
We examined the use of modern Generative Adversarial Nets to generate novel images of oil paintings using the Painter By Numbers dataset. We implemented Spectral Normalization GAN (SN-GAN) and Spectral Normalization GAN with Gradient Penalty, and compared their outputs to a Deep Convolutional GAN. Visually, and quantit…
Bayesian model for discrete data with conditional transformations.
Generative model for inferring graph from time series data.
In this study, a novel topology optimization approach based on conditional Wasserstein generative adversarial networks (CWGAN) is developed to replicate the conventional topology optimization algorithms in an extremely computationally inexpensive way. CWGAN consists of a generator and a discriminator, both of which are…
New metric predicts neural network reliability under novel conditions.
Novel approach ensures stability of compact schemes for variable PDEs.
The objectives of this technical report is to provide additional results on the generalized conditional gradient methods introduced by Bredies et al. [BLM05]. Indeed , when the objective function is smooth, we provide a novel certificate of optimality and we show that the algorithm has a linear convergence rate. Applic…
Novel unsupervised scheme for highly imbalanced and overlapping datasets.
Study analyzes portfolio liquidation games influenced by self-exciting order flow.
Generates samples conditioned on labels using optimal transport.
New PG methods tackle nonconvex optimization with auto-conditioned stepsizes.
Auxiliary Tuning adapts pre-trained models for novel tasks efficiently.
Current meta-learning approaches focus on learning functional representations of relationships between variables, i.e. on estimating conditional expectations in regression. In many applications, however, we are faced with conditional distributions which cannot be meaningfully summarized using expectation only (due to e…
Paper proposes a new method for estimating conditional densities using logistic regressions.
The GANs are generative models whose random samples realistically reflect natural images. It also can generate samples with specific attributes by concatenating a condition vector into the input, yet research on this field is not well studied. We propose novel methods of conditioning generative adversarial networks (GA…
Study develops a machine learning-based ramp metering model to improve freeway efficiency.
We propose a novel method for automatic pain intensity estimation from facial images based on the framework of kernel Conditional Ordinal Random Fields (KCORF). We extend this framework to account for heteroscedasticity on the output labels(i.e., pain intensity scores) and introduce a novel dynamic features, dynamic ra…
Improves conditional coverage of regression models using conformal prediction.
We develop necessary and sufficient conditions and a novel provably consistent and efficient algorithm for discovering topics (latent factors) from observations (documents) that are realized from a probabilistic mixture of shared latent factors that have certain properties. Our focus is on the class of topic models in …
Deep NURBS improves PINNs for solving PDEs on arbitrary geometries.
DRMMs enable flexible conditional sampling for interactive machine learning.
Learning a distribution conditional on a set of discrete-valued features is a commonly encountered task. This becomes more challenging with a high-dimensional feature set when there is the possibility of interaction between the features. In addition, many frequently applied techniques consider only prediction of the me…
Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this…
Geometric approach finds correspondences between different conditions.