arXiv research

Proposes BSSP to stabilize predictions in biased data.

Designs efficient factorial experiments for product design under budget constraints.

New method uses Rashomon sets to improve Bayesian inference in factorial designs.

CUBE explains models by balanced experiments and contrasts.

DABS uses a policy network to select experiments in high-dimensional design spaces.

New findings show single-treatment effects are unidentifiable in factorial experiments.

A new experimental design method for combinatorial interventions reduces complexity and improves accuracy.

Study optimizes CANN for actuarial tasks using RSM.

Natural image statistics exhibit hierarchical dependencies across multiple scales. Representing such prior knowledge in non-factorial latent tree models can boost performance of image denoising, inpainting, deconvolution or reconstruction substantially, beyond standard factorial "sparse" methodology. We derive a large …

In this paper, we develop a parameter estimation method for factorially parametrized models such as Factorial Gaussian Mixture Model and Factorial Hidden Markov Model. Our contributions are two-fold. First, we show that the emission matrix of the standard Factorial Model is unidentifiable even if the true assignment ma…

Unsupervised machine learning helps design complex experiments more efficiently.

We study a novel spline-like basis, which we name the "falling factorial basis", bearing many similarities to the classic truncated power basis. The advantage of the falling factorial basis is that it enables rapid, linear-time computations in basis matrix multiplication and basis matrix inversion. The falling factoria…

New factorial power constants improve optimization convergence rates.

Bayesian neural networks improve uncertainty estimation in 3D point cloud segmentation for factory planning.

We employ a 2x3 factorial experiment to study two central factors in the design of prediction markets (PMs) for idea evaluation: the overall design of the PM, and the elasticity of market prices set by a market maker. The results show that 'multi-market designs' on which each contract is traded on a separate PM lead to…

Neural networks learn more efficiently with hidden factorial structures.

Factorial moments are convenient tools in nuclear physics to characterize the multiplicity distributions when phase-space resolution ($Δ$) becomes small. For uncorrelated particle production within $Δ$, Gaussian statistics holds and factorial moments $F_q$ are equal to unity for all orders $q$. Correlations between par…

High-throughput 3D control training system achieves 100,000 FPS.

Develops active learning for Jump Gaussian Process models.

The past decade has seen substantial work on the use of non-negative matrix factorization and its probabilistic counterparts for audio source separation. Although able to capture audio spectral structure well, these models neglect the non-stationarity and temporal dynamics that are important properties of audio. The re…

Factorial moments are convenient tools in particle physics to characterize the multiplicity distributions when phase-space resolution ($Δ$) becomes small. They include all correlations within the system of particles and represent integral characteristics of any correlation between these particles. In this letter, we sh…

Existence of Kähler-Einstein metrics on toric varieties proven.

We propose algorithms for approximate filtering and smoothing in high-dimensional Factorial hidden Markov models. The approximation involves discarding, in a principled way, likelihood factors according to a notion of locality in a factor graph associated with the emission distribution. This allows the exponential-in-d…

We prove the factoriality of the following nodal threefolds: a complete intersection of hypersurfaces $F$ and $G\subset\mathbb{P}^{5}$ of degree $n$ and $k$ respectively, where $G$ is smooth, $|\mathrm{Sing}(F\cap G)|\leqslant(n+k-2)(n-1)/5$, $n\geqslant k$; a double cover of a smooth hypersurface $F\subset\mathbb{P}^{…

This paper introduces the factorial marked temporal point process model and presents efficient learning methods. In conventional (multi-dimensional) marked temporal point process models, event is often encoded by a single discrete variable i.e. a marker. In this paper, we describe the factorial marked point processes w…

PAC-MOO optimizes constrained multi-objective problems with preferences.

NoMoPy models noise as HMM/FHMM in Python.

Factorial Hidden Markov Models (FHMMs) are powerful models for sequential data but they do not scale well with long sequences. We propose a scalable inference and learning algorithm for FHMMs that draws on ideas from the stochastic variational inference, neural network and copula literatures. Unlike existing approaches…

Develops nonparametric regression for non-smooth functions using fractional Laplacian.

Bayesian segmentation and uncertainty estimation improve 3D model accuracy for factory planning.

The purpose of this paper is to indicate that the recently proposed Momentum fractional least mean squares (mFLMS) algorithm has some serious flaws in its design and analysis. Our apprehensions are based on the evidence we found in the derivation and analysis in the paper titled: \textquotedblleft \textit{Momentum frac…

MAFLA improves sampling from heavy-tailed distributions using MH-inspired corrections.

Study evaluates discretized arbitrage strategies in fractional financial markets.

In this paper, we present a data science automation system called Prediction Factory. The system uses several key automation algorithms to enable data scientists to rapidly develop predictive models and share them with domain experts. To assess the system's impact, we implemented 3 different interfaces for creating pre…

We study local, global and local-to-global properties of threefolds with certain singularities. We prove criteria for these threefolds to be rational homology manifolds and conditions for threefolds to satisfy rational Poincaré duality. We relate the topological Euler characteristic of elliptic Calabi-Yau threefolds wi…

This work speeds up fHMM analysis by tensor algebra.

We assume that a high-dimensional datum, like an image, is a compositional expression of a set of properties, with a complicated non-linear relationship between the datum and its properties. This paper proposes a factorial mixture prior for capturing latent properties, thereby adding structured compositionality to deep…

Bayesian inference for factorial hidden Markov models is challenging due to the exponentially sized latent variable space. Standard Monte Carlo samplers can have difficulties effectively exploring the posterior landscape and are often restricted to exploration around localised regions that depend on initialisation. We …

The work of Jørgensen and Thurston shows that there is a finite number N(v) of orientable hyperbolic 3-manifolds with any given volume v. In this paper, we construct examples showing that the number of hyperbolic knot complements with a given volume v can grow at least factorially fast with v. A similar statement holds…

New neural operators model turbulence with memory and randomness.

Bayesian CNN estimates uncertainty in bone age prediction.

A new approach to sensitivity analysis without the Sobol decomposition.

Paper offers a simpler solution for managing complex financial options.

The paper describes a cover of strata of k-differentials with a formula for fiber cardinality.

Study simulates liquidity in fractional ownership markets using ABM.

New robust regression method works with fewer data points than previous methods.

Factorial hidden Markov models (FHMMs) are powerful tools of modeling sequential data. Learning FHMMs yields a challenging simultaneous model selection issue, i.e., selecting the number of multiple Markov chains and the dimensionality of each chain. Our main contribution is to address this model selection issue by exte…

Composite likelihood inference of fractional Gaussian processes with sequentially optimal subset selection