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.
A new method avoids partition function computation for Gibbs density estimation.
Quantum algorithm speeds up Gibbs partition function estimation.
Exchangeable graphs arise via a sampling procedure from measurable functions known as graphons. A natural estimation problem is how well we can recover a graphon given a single graph sampled from it. One general framework for estimating a graphon uses step-functions obtained by partitioning the nodes of the graph accor…
New bounds for estimating partition functions under bounded f-divergence.
Novel method recursively partitions sample space for density estimation.
New method reduces Gibbs partition function estimation complexity.
Partition functions of probability distributions are important quantities for model evaluation and comparisons. We present a new method to compute partition functions of complex and multimodal distributions. Such distributions are often sampled using simulated tempering, which augments the target space with an auxiliar…
Greedy training of recursive partitioning estimators faces a computational barrier when the true function doesn't satisfy a specific property.
Given observations from an unknown absolute continuous distribution defined on some domain , we propose a nonparametric method to learn a piecewise constant function to approximate the underlying probability density function. Our density estimate is a piecewise constant function defined on a binary partition o…
Paper proposes a new method to learn EBMs and their partition function.
Log-linear models are arguably the most successful class of graphical models for large-scale applications because of their simplicity and tractability. Learning and inference with these models require calculating the partition function, which is a major bottleneck and intractable for large state spaces. Importance Samp…
The output scores of a neural network classifier are converted to probabilities via normalizing over the scores of all competing categories. Computing this partition function, , is then linear in the number of categories, which is problematic as real-world problem sets continue to grow in categorical types, such as …
AIS method improves estimation of RBM partition function with reduced computational cost.
Develops an MS-inspired algorithm for regression mode finding and space partitioning.
A new method for high-dimensional functional regression reduces multicollinearity and improves interpretability.
New algorithms improve convergence rates for non-log-concave sampling and log-partition estimation.
MPF method improves parameter estimation in probabilistic models.
The aim of this short note is to draw attention to a method by which the partition function and marginal probabilities for a certain class of random fields on complete graphs can be computed in polynomial time. This class includes Ising models with homogeneous pairwise potentials but arbitrary (inhomogeneous) unary pot…
New methods improve Monte Carlo estimation of partition functions.
The Restricted Boltzmann Machines (RBM) can be used either as classifiers or as generative models. The quality of the generative RBM is measured through the average log-likelihood on test data. Due to the high computational complexity of evaluating the partition function, exact calculation of test log-likelihood is ver…
Localized transfer learning improves nonparametric regression performance.
Markov random fields (MRFs) are difficult to evaluate as generative models because computing the test log-probabilities requires the intractable partition function. Annealed importance sampling (AIS) is widely used to estimate MRF partition functions, and often yields quite accurate results. However, AIS is prone to ov…
In this paper, we introduce a novel method to generate interpretable regression function estimators. The idea is based on called data-dependent coverings. The aim is to extract from the data a covering of the feature space instead of a partition. The estimator predicts the empirical conditional expectation over the cel…
Paper recovers lattice signal partitions efficiently.
Recent research has made significant progress on the problem of bounding log partition functions for exponential family graphical models. Such bounds have associated dual parameters that are often used as heuristic estimates of the marginal probabilities required in inference and learning. However these variational est…
Paper presents an efficient algorithm for estimating Lipschitz functions from noisy data.
Graphical models represent multivariate and generally not normalized probability distributions. Computing the normalization factor, called the partition function, is the main inference challenge relevant to multiple statistical and optimization applications. The problem is of an exponential complexity with respect to t…
We propose a black-box algorithm called {\it Adversarial Variational Inference and Learning} (AdVIL) to perform inference and learning on a general Markov random field (MRF). AdVIL employs two variational distributions to approximately infer the latent variables and estimate the partition function of an MRF, respective…
In this paper, we investigate a divide and conquer approach to Kernel Ridge Regression (KRR). Given n samples, the division step involves separating the points based on some underlying disjoint partition of the input space (possibly via clustering), and then computing a KRR estimate for each partition. The conquering s…
Topological recursion recovers a specific partition function for colored knots.
GPU-accelerated particle methods outperform neural samplers in LFT benchmarks.
Partition Tree estimates conditional densities for mixed continuous and categorical variables.
Proposes SPFB method for optimizing partition functions in stochastic learning.
Divide-and-conquer framework speeds up black-box inference for large data.
Bayesian approach for multifile record linkage and duplicate detection.
ConvNets improve nonstationary covariance estimation for large-scale spatial data.
In this paper we relate the partition function to the max-statistics of random variables. In particular, we provide a novel framework for approximating and bounding the partition function using MAP inference on randomly perturbed models. As a result, we can use efficient MAP solvers such as graph-cuts to evaluate the c…
Study on estimating Gaussian mean from coarse data, resolving identifiability and computational efficiency questions.
Annealed importance sampling (AIS) is a common algorithm to estimate partition functions of useful stochastic models. One important problem for obtaining accurate AIS estimates is the selection of an annealing schedule. Conventionally, an annealing schedule is often determined heuristically or is simply set as a linear…
We study 4-dimensional higher-derivative conformal higher spin (CHS) fields generalising Weyl graviton and conformal gravitino. They appear, in particular, as "induced" theories in the AdS/CFT context. We consider their partition function on curved Einstein-space backgrounds like (A)dS or sphere and Ricci-flat spaces. …
A new method for state space partitioning in block particle filtering reduces bias and variance.
Improved supervised EM learning for shared kernel models with feature space partitioning.
We perform a resurgence analysis of the Chern-Simons partition function on a Brieksorn homology sphere . Starting from an exact Chern-Simons partition function, we study the Borel resummation of its perturbative expansion.
A new method for density estimation using mixture discrepancy and moments.
RBM and DBM are represented as 2D tensor networks, revealing their expressive power and efficiency.
A new algorithm, Regular Tree Search, tackles non-convex simulation optimization problems.
The abstract conjectures a link between knot homologies and quiver partition functions.
We study approximations of the partition function of dense graphical models. Partition functions of graphical models play a fundamental role is statistical physics, in statistics and in machine learning. Two of the main methods for approximating the partition function are Markov Chain Monte Carlo and Variational Method…