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.
Paper proposes energy objective for training normalizing flows without determinants.
The study examines MCMC methods for arbitrary objectives and finds likelihood sharpness impacts performance and regularization.
Recent work in variational inference (VI) uses ideas from Monte Carlo estimation to tighten the lower bounds on the log-likelihood that are used as objectives. However, there is no systematic understanding of how optimizing different objectives relates to approximating the posterior distribution. Developing such a conn…
Gaussian surrogates improve Poisson imaging performance at low doses.
Generative Adversarial Networks (GANs) can achieve state-of-the-art sample quality in generative modelling tasks but suffer from the mode collapse problem. Variational Autoencoders (VAE) on the other hand explicitly maximize a reconstruction-based data log-likelihood forcing it to cover all modes, but suffer from poore…
We explain SSL objectives as log-likelihoods in a data curation model.
A new method normalizes EBM training by introducing a learnable parameter.
We consider training probabilistic classifiers in the case of a large number of classes. The number of classes is assumed too large to perform exact normalisation over all classes. To account for this we consider a simple approach that directly approximates the likelihood. We show that this simple approach works well o…
Proposes a model to generate 3D-aware images from 2D images.
When used as a surrogate objective for maximum likelihood estimation in latent variable models, the evidence lower bound (ELBO) produces state-of-the-art results. Inspired by this, we consider the extension of the ELBO to a family of lower bounds defined by a particle filter's estimator of the marginal likelihood, the …
Improves hyperparameter learning in GP models with non-conjugate likelihoods.
This work improves likelihood of score-based diffusion ODEs using high-order denoising score matching.
Normalizing flows optimize Jacobian determinant for unique likelihood objective.
Combines VI and EP for better Gaussian process hyperparameter learning.
We consider the problem of training probabilistic conditional random fields (CRFs) in the context of a task where performance is measured using a specific loss function. While maximum likelihood is the most common approach to training CRFs, it ignores the inherent structure of the task's loss function. We describe alte…
Test log-likelihood comparisons can be misleading.
This research improves neural likelihood approximation for Bayesian inverse problems.
Sparse high dimensional graphical model selection is a topic of much interest in modern day statistics. A popular approach is to apply l1-penalties to either (1) parametric likelihoods, or, (2) regularized regression/pseudo-likelihoods, with the latter having the distinct advantage that they do not explicitly assume Ga…
wd1 improves reasoning in dLLMs by optimizing policies without policy ratios.
With an eye towards human-centered automation, we contribute to the development of a systematic means to infer features of human decision-making from behavioral data. Motivated by the common use of softmax selection in models of human decision-making, we study the maximum likelihood parameter estimation problem for sof…
New method for robust distribution alignment using log-likelihood ratio and normalizing flows.
Modern applications and progress in deep learning research have created renewed interest for generative models of text and of images. However, even today it is unclear what objective functions one should use to train and evaluate these models. In this paper we present two contributions. Firstly, we present a critique o…
New MCFOs improve learning generative models and time series inference.
Enhances VAEs for sharper image synthesis.
This work gives an in-depth derivation of the trainable evidence lower bound obtained from the marginal joint log-Likelihood with the goal of training a Multi-Modal Variational Autoencoder (MVAE).
New framework trains Schrödinger Bridge models using SDEs for generative tasks.
Recent progress in deep latent variable models has largely been driven by the development of flexible and scalable variational inference methods. Variational training of this type involves maximizing a lower bound on the log-likelihood, using samples from the variational posterior to compute the required gradients. Rec…
The outlying property detection problem is the problem of discovering the properties distinguishing a given object, known in advance to be an outlier in a database, from the other database objects. In this paper, we analyze the problem within a context where numerical attributes are taken into account, which represents…
Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class f…
In this note we consider setups in which variational objectives for Bayesian neural networks can be computed in closed form. In particular we focus on single-layer networks in which the activation function is piecewise polynomial (e.g. ReLU). In this case we show that for a Normal likelihood and structured Normal varia…
Introduces BPEL for EL, enhancing flexibility and using MCMC for inference.
We propose an expectation-maximization-like(EMlike) method to train Boltzmann machine with unconstrained connectivity. It adopts Monte Carlo approximation in the E-step, and replaces the intractable likelihood objective with efficiently computed objectives or directly approximates the gradient of likelihood objective i…
Score matching fails to train VAEs robustly, revealing autoencoding loss insights.
Maximum likelihood training improves the performance of score-based diffusion models.
Paper proposes efficient training for normalizing flows in Boltzmann generators.
Innovative game theory approach optimizes survival analysis metrics.
This paper is based on a previous publication [29]. Our work extends exception mining and outlier detection to the case of object-relational data. Object-relational data represent a complex heterogeneous network [12], which comprises objects of different types, links among these objects, also of different types, and at…
We present an asymptotic analysis of Viterbi Training (VT) and contrast it with a more conventional Maximum Likelihood (ML) approach to parameter estimation in Hidden Markov Models. While ML estimator works by (locally) maximizing the likelihood of the observed data, VT seeks to maximize the probability of the most lik…
Geodesic descent optimizes likelihood in dually flat spaces.
Improves Gaussian process regression without bias.
Bayesian optimisation framework for multi-objective decision-making from choice data.
Restricted Boltzmann Machines (RBMs) are a class of generative neural network that are typically trained to maximize a log-likelihood objective function. We argue that likelihood-based training strategies may fail because the objective does not sufficiently penalize models that place a high probability in regions where…
Directly estimates Fisher score for likelihood maximization.
Bayesian scores improve structure learning in probabilistic circuits.
Optimizes MMD learning for generative models with theoretical guarantees.
In decision-making systems, it is important to have classifiers that have calibrated uncertainties, with an optimisation objective that can be used for automated model selection and training. Gaussian processes (GPs) provide uncertainty estimates and a marginal likelihood objective, but their weak inductive biases lead…
A new concordance loss improves model performance and reliability in survival prediction.
This paper improves normalizing flows by combining MLE and sliced-Wasserstein distance for better data fidelity.