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.
Unified tractability conditions for various compositional inference queries.
We present a novel tractable generative model that extends Sum-Product Networks (SPNs) and significantly boosts their power. We call it Sum-Product-Quotient Networks (SPQNs), whose core concept is to incorporate conditional distributions into the model by direct computation using quotient nodes, e.g. $P(A|B) = \frac{P(…
New tractable density models from squaring neural networks.
A non-Euclidean generalization of conditional expectation is introduced and characterized as the minimizer of expected intrinsic squared-distance from a manifold-valued target. The computational tractable formulation expresses the non-convex optimization problem as transformations of Euclidean conditional expectation. …
Study shows tractable generalization in RL is impossible but possible with Strong Proximity.
Hybrid model combines continuous and tractable probabilistic models.
TRUST improves structure learning with tractable uncertainty.
Paper introduces md-vtrees for efficient probabilistic and causal inference.
Proposes Exogenous Matching for efficient counterfactual estimation.
A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and…
New method for fitting graphical models with latent variables using regularized conditional likelihood.
We introduce RNADE, a new model for joint density estimation of real-valued vectors. Our model calculates the density of a datapoint as the product of one-dimensional conditionals modeled using mixture density networks with shared parameters. RNADE learns a distributed representation of the data, while having a tractab…
New framework for conditional risk minimization using optimal transport.
Study efficient interactive learning for structured outputs with reliable computation.
Probabilistic graphical models are a central tool in AI; however, they are generally not as expressive as deep neural models, and inference is notoriously hard and slow. In contrast, deep probabilistic models such as sum-product networks (SPNs) capture joint distributions in a tractable fashion, but still lack the expr…
MDMA provides closed-form marginals and conditionals for deep networks.
Variational inference (VI) is a widely used framework in Bayesian estimation. For most of the non-Gaussian statistical models, it is infeasible to find an analytically tractable solution to estimate the posterior distributions of the parameters. Recently, an improved framework, namely the extended variational inference…
ACNML method improves uncertainty estimation for deep networks.
LatentFlow simplifies conditioning of stochastic processes without training.
This paper advances sample-efficient learning for partially observable RL by introducing B-stability and new algorithms.
Unified framework for tractable inference scenarios in machine learning models.
We present a class of models that, via a simple construction, enables exact, incremental, non-parametric, polynomial-time, Bayesian inference of conditional measures. The approach relies upon creating a sequence of covers on the conditioning variable and maintaining a different model for each set within a cover. Infere…
Active learning methods, like uncertainty sampling, combined with probabilistic prediction techniques have achieved success in various problems like image classification and text classification. For more complex multivariate prediction tasks, the relationships between labels play an important role in designing structur…
This paper develops tools for nonreversible MCMC with convergence guarantees.
The discrete-time multifactor Vasiček model is a tractable Gaussian spot rate model. Typically, two- or three-factor versions allow one to capture the dependence structure between yields with different times to maturity in an appropriate way. In practice, re-calibration of the model to the prevailing market conditions …
Flexible selective inference using flow-based transport maps.
OMLE combines optimism and MLE for efficient sequential decision making.
Proves efficient learning of hierarchical structure in meta-reinforcement learning.
PNCs balance tractability and expressiveness in probabilistic modeling.
Generates samples conditioned on labels using optimal transport.
Variational inference is a popular technique to approximate a possibly intractable Bayesian posterior with a more tractable one. Recently, boosting variational inference has been proposed as a new paradigm to approximate the posterior by a mixture of densities by greedily adding components to the mixture. However, as i…
Sharp policy value estimation for contextual bandits with unobserved confounders.
Algorithmic fairness involves expressing notions such as equity, or reasonable treatment, as quantifiable measures that a machine learning algorithm can optimise. Most work in the literature to date has focused on classification problems where the prediction is categorical, such as accepting or rejecting a loan applica…
A new method trains and samples from energy-based models using diffusion recovery likelihood.
Proposes a method to generate text that adheres to logical constraints.
We study discretizations of polynomial processes using finite state Markov processes satisfying suitable moment matching conditions. The states of these Markov processes together with their transition probabilities can be interpreted as Markov cubature rules. The polynomial property allows us to study such rules using …
We consider the class of affine LIBOR models with multiple curves, which is an analytically tractable class of discrete tenor models that easily accommodates positive or negative interest rates and positive spreads. By introducing an interpolating function, we extend the affine LIBOR models to a continuous tenor and de…
We consider the problem of transforming samples from one continuous source distribution into samples from another target distribution. We demonstrate with optimal transport theory that when the source distribution can be easily sampled from and the target distribution is log-concave, this can be tractably solved with c…
Alternating minimization represents a widely applicable and empirically successful approach for finding low-rank matrices that best fit the given data. For example, for the problem of low-rank matrix completion, this method is believed to be one of the most accurate and efficient, and formed a major component of the wi…
Optimized variable orderings improve autoregressive model performance.
Gradient flows on distributions of distributions for machine learning tasks.
New neural process models produce correlated predictions for better estimation tasks.
Fixed-parameter tractability of private synthetic data generation
Probabilistic models learned as density estimators can be exploited in representation learning beside being toolboxes used to answer inference queries only. However, how to extract useful representations highly depends on the particular model involved. We argue that tractable inference, i.e. inference that can be compu…
New findings on flatness for specific driftless systems.
Quantitatively assessing relationships between latent variables and observed variables is important for understanding and developing generative models and representation learning. In this paper, we propose latent-observed dissimilarity (LOD) to evaluate the dissimilarity between the probabilistic characteristics of lat…
New method identifies causal relationships in presence of hidden variables.
Proposes second-order Esscher transform for Lévy models in financial markets.