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.
This paper improves DNN generalization by accurately estimating mutual information.
CMDNet simplifies MAP detection for large systems with probabilistic relaxation.
We present sparse topical coding (STC), a non-probabilistic formulation of topic models for discovering latent representations of large collections of data. Unlike probabilistic topic models, STC relaxes the normalization constraint of admixture proportions and the constraint of defining a normalized likelihood functio…
Efficiently solves MRF inference problems with semidefinite programming.
We present a probabilistic viewpoint to multiple kernel learning unifying well-known regularised risk approaches and recent advances in approximate Bayesian inference relaxations. The framework proposes a general objective function suitable for regression, robust regression and classification that is lower bound of the…
Unified framework for differentiable graph partitioning with probabilistic cuts.
This work interprets SFA through variational inference, relaxing linearity constraints.
VaSST uses soft symbolic trees for probabilistic symbolic regression.
We give a novel formal theoretical framework for unsupervised learning with two distinctive characteristics. First, it does not assume any generative model and based on a worst-case performance metric. Second, it is comparative, namely performance is measured with respect to a given hypothesis class. This allows to avo…
In this paper, we present a local information theoretic approach to explicitly learn probabilistic clustering of a discrete random variable. Our formulation yields a convex maximization problem for which it is NP-hard to find the global optimum. In order to algorithmically solve this optimization problem, we propose tw…
Variable selection is a fundamental task in statistical data analysis. Sparsity-inducing regularization methods are a popular class of methods that simultaneously perform variable selection and model estimation. The central problem is a quadratic optimization problem with an l0-norm penalty. Exactly enforcing the l0-no…
The paper develops sum-of-squares relaxations for computing -divergences.
This text explores strategies for learning discrete latent structures in neural networks.
We consider a network scenario in which agents can evaluate each other according to a score graph that models some interactions. The goal is to design a distributed protocol, run by the agents, that allows them to learn their unknown state among a finite set of possible values. We propose a Bayesian framework in which …
Study probabilistic safety of BNNs under adversarial attacks.
Bayesian hierarchical clustering (BHC) is an agglomerative clustering method, where a probabilistic model is defined and its marginal likelihoods are evaluated to decide which clusters to merge. While BHC provides a few advantages over traditional distance-based agglomerative clustering algorithms, successive evaluatio…
The paper studies batch decompositions of random datasets with probabilistic similarity constraints.
Proposes a new Gaussian factor for probabilistic inference with degenerate settings.
Multilabel classification is an important problem in a wide range of domains such as text categorization and music annotation. In this paper, we present a probabilistic model, Multilabel Logistic Regression with Hidden variables (MLRH), which extends the standard logistic regression by introducing hidden variables. Hid…
In Bayesian classification, it is important to establish a probabilistic model for each class for likelihood estimation. Most of the previous methods modeled the probability distribution in the whole sample space. However, real-world problems are usually too complex to model in the whole sample space; some fundamental …
Matching correlated VAR time series databases by recovering matching permutations.
Proposes a recursive MPC scheme with probabilistic safety guarantees for uncertain dynamic systems.
Using the linear Gaussian latent variable model as a starting point we relax some of the constraints it imposes by deriving a nonparametric latent feature Gaussian variable model. This model introduces additional discrete latent variables to the original structure. The Bayesian nonparametric nature of this new model al…
Discrete random variables are natural components of probabilistic clustering models. A number of VAE variants with discrete latent variables have been developed. Training such methods requires marginalizing over the discrete latent variables, causing training time complexity to be linear in the number clusters. By appl…
We provide a dynamic programming principle for stochastic optimal control problems with expectation constraints. A weak formulation, using test functions and a probabilistic relaxation of the constraint, avoids restrictions related to a measurable selection but still implies the Hamilton-Jacobi-Bellman equation in the …
Efficiently certifies global robustness of large neural networks with probabilistic guarantees.
Deep-PrAE improves rare-event simulation for black-box systems.
Geometric approach improves probabilistic robustness in neural networks.
Paper introduces PRMs to learn non-Markovian stochastic rewards for reinforcement learning.
This work improves understanding of dimension reduction algorithms and their probabilistic embeddings.
The probabilistic bisection algorithm (PBA) solves a class of stochastic root-finding problems in one dimension by successively updating a prior belief on the location of the root based on noisy responses to queries at chosen points. The responses indicate the direction of the root from the queried point, and are incor…
Improved hierarchical discrete VAEs for better stability and performance.
Enhances traffic forecasting with dynamic regression incorporating error modeling.
Generative AI improves surrogate models by blending LF and HF data.
New method improves robust point matching under probabilistic settings.
Paper proposes DAG-DB for learning discrete DAGs via backpropagation.
Normalizing flows are shown to be equivalent to Bayesian networks, revealing new insights.
A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks that are complicated by nuisance variation. For instance, visual object recognition involves the unknown object position, orientation, and scale in object recognition while …
Paper studies Adam's convergence under relaxed assumptions, proving a rate of O(poly(log T)/sqrt(T)).
Method improves microbial biomass yield estimation from noisy data.
Unified framework for information-theoretic bounds on learning algorithms.
Proposes DILATE and STRIPE++ for precise time series forecasting.
Improves convergence speed in compressive sensing with a new probabilistic approach.
In the analysis of machine learning models, it is often convenient to assume that the parameters are IID. This assumption is not satisfied when the parameters are updated through training processes such as SGD. A relaxation of the IID condition is a probabilistic symmetry known as exchangeability. We show the sense in …
PRAE identifies outliers and reconstructs inliers in autoencoders.
Sparse versions of principal component analysis (PCA) have imposed themselves as simple, yet powerful ways of selecting relevant features of high-dimensional data in an unsupervised manner. However, when several sparse principal components are computed, the interpretation of the selected variables is difficult since ea…
This review synthesizes uncertainty modeling in probabilistic image segmentation.
Using the theory of group action, we first introduce the concept of the automorphism group of an exponential family or a graphical model, thus formalizing the general notion of symmetry of a probabilistic model. This automorphism group provides a precise mathematical framework for lifted inference in the general expone…