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.
The paper introduces a method to probabilistically select inducing points in sparse Gaussian processes.
A new method selects inducing points to optimize high-throughput Bayesian optimisation.
Local GP approach improves simulation efficiency for large datasets.
Study investigates induced geometry on surfaces in 3D contact manifolds.
Global inducing points improve Bayesian neural network performance.
HIP-GP improves GP inference for inter-domain observations with millions of inducing points.
We introduce a kernel approximation strategy that enables computation of the Gaussian process log marginal likelihood and all hyperparameter derivatives in time. Our GRIEF kernel consists of eigenfunctions found using a Nystrom approximation from a dense Cartesian product grid of inducing points. B…
VNNGP uses nearest neighbors to approximate GPs, improving scalability and performance.
Sparse Gaussian Processes improve scalability by learning inducing points from data.
New method optimizes Gaussian process allocation for BO.
New method DDVI improves posterior inference for deep Gaussian processes.
Kernel methods on discrete domains have shown great promise for many challenging data types, for instance, biological sequence data and molecular structure data. Scalable kernel methods like Support Vector Machines may offer good predictive performances but do not intrinsically provide uncertainty estimates. In contras…
Proposes FIPO-BC for efficient online calibration of complex models.
Proposes IGN for scalable Gaussian process networks.
New method combines spectral and sparse methods for Gaussian processes.
We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs). SKI methods produce kernel approximations for fast computations through kernel interpolation. The SKI framework clarifies how the quality of an inducing point a…
Adaptive selection of IPs improves online GP performance.
New method improves stability of Gaussian process approximations.
Proposes efficient Gaussian process approximations for large datasets.
VAR-GPs solve continual learning by updating posteriors sequentially.
Learning new representations of input observations in machine learning is often tackled using a factorization of the data. For many such problems, including sparse coding and matrix completion, learning these factorizations can be difficult, in terms of efficiency and to guarantee that the solution is a global minimum.…
New algorithms improve GP inference without approximations, achieving better results.
Paper tightens variational GP approximations for large datasets.
We present Blitzkriging, a new approach to fast inference for Gaussian processes, applicable to regression, optimisation and classification. State-of-the-art (stochastic) inference for Gaussian processes on very large datasets scales cubically in the number of 'inducing inputs', variables introduced to factorise the mo…
ASkotch solves large-scale KRR faster and better than existing methods.
PerfGD solves model-induced data shifts by finding optimal points.
We find a new relation among right-handed Dehn twists in the mapping class group of a -holed torus for . This relation induces an elliptic Lefschetz pencil structure on the four-manifold \cp $#(9-k)$ \cpb with base points and twelve singular fibers. By blowing up the base points we get an el…
The paper proposes a method to detect credit card fraud using sparse Gaussian approximations.
We establish a product formula for Gromov-Witten invariants for closed, connected, relatively semi-positive Hamiltonian fibrations over any symplectic base. Furthermore, we show that the fibration projection induces a locally trivial (orbi-)fibration map from the moduli space of pseudo-holomorphic maps with marked poin…
Real slices of parabolic opers on Riemann surfaces are studied.
Sparse GPs improved with nearest neighbor inducing variables.
Efficient poisoning attack converges to any target classifier with provable convergence.
We introduce a representation theory for risk operations on locally compact groups in a partition of unity on a topological manifold for Markowitz-Tversky-Kahneman (MTK) reference points. We identify (1) risk torsion induced by the flip rate for risk averse and risk seeking behaviour, and (2) a structure constant or co…
Variational inference techniques based on inducing variables provide an elegant framework for scalable posterior estimation in Gaussian process (GP) models. Besides enabling scalability, one of their main advantages over sparse approximations using direct marginal likelihood maximization is that they provide a robust a…
DGKIP extends KIP for dataset distillation without bi-level optimization.
Inference for GP models with non-Gaussian noises is computationally expensive when dealing with large datasets. Many recent inference methods approximate the posterior distribution with a simpler distribution defined on a small number of inducing points. The inference is accurate only when data points have strong corre…
Gaussian processes (GPs) are flexible models that can capture complex structure in large-scale dataset due to their non-parametric nature. However, the usage of GPs in real-world application is limited due to their high computational cost at inference time. In this paper, we introduce a new framework, \textit{kernel di…
This paper analyzes error in SKI for Gaussian Processes, providing conditions for linear time inference.
This paper constructs a family of coordinate systems about a point on a quaternionic contact manifold, called quaternionic contact pseudohermitian normal coordinates. Once defined, conformal variations of the quaternionic contact structure induce changes on the coordinates which are studied in an effort to simplify the…
The fine curve graph is hyperbolic and contains all countable graphs as induced subgraphs.
CDP reduces point cloud dimensions by preserving detour-induced local non-convexity.
Extends Khovanov homology to surfaces with singularities.
Determinantal point process have recently been used as models in machine learning and this has raised questions regarding the characterizations of conditional independence. In this paper we investigate characterizations of conditional independence. We describe some conditional independencies through the conditions on t…
On the space of positive 3-forms on a seven-manifold, we study a natural functional whose critical points induce metrics with holonomy contained in . We prove short-time existence and uniqueness for its negative gradient flow. Furthermore, we show that the flow exists for all times and converges modulo diffeomorph…
Quantizes the standard hyperkähler space R^(4n) without a point.
Gaussian process classification is a popular method with a number of appealing properties. We show how to scale the model within a variational inducing point framework, outperforming the state of the art on benchmark datasets. Importantly, the variational formulation can be exploited to allow classification in problems…
We construct bosonic and fermionic locally covariant quantum field theories on curved backgrounds for large classes of fields. We investigate the quantum field and n-point functions induced by suitable states.
A connected regular surface in Lorentz-Minkowski 3-space is called a mixed type surface if the spacelike, timelike and lightlike point sets are all non-empty. Lightlike points on mixed type surfaces may be regarded as singular points of the induced metrics. In this paper, we introduce the L-Gauss map around non-degener…