arXiv research

Proves robust transitivity for geodesic flows from metrics with conjugate points.

Generic 3D vector fields have singularly hyperbolic transitive sets.

Study shows strong min-max principle for phase transitions.

We consider 3-dimensional pseudo-manifolds M with a given set of marked point V such that M-V is the interior of a compact 3-manifold with boundary. An ideal triangulation T of (M, V ) has V as its set of vertices. A branching (T, b) enhances T to a Delta-complex. Branched triangulations of (M, V ) are considered up to…

Study analyzes stock market dynamics using recurrence measures and transitions.

We develop a transitional geometry, that is, a family of geometries of constant curvatures which makes a continuous connec-tion between the hyperbolic, Euclidean and spherical geometries. In this transitional setting, several geometric entities like points, lines, dis-tances, triangles, angles, area, curvature, etc. as…

This paper shows semi-equivelar toroidal maps are vertex-transitive covers.

Study non-transitive pseudo-Anosov flows using group actions.

4 flat 3-manifolds realized in hyperbolic 4-space.

Efficient algorithm for learning MDPs with unknown transitions and bandit feedback.

We consider the problem of estimating the transition rate matrix of a continuous-time Markov chain from a finite-duration realisation of this process. We approach this problem in an imprecise probabilistic framework, using a set of prior distributions on the unknown transition rate matrix. The resulting estimator is a …

The paper studies phase transitions in Information Bottleneck for representation learning.

We present a representation for describing transition models in complex uncertain domains using relational rules. For any action, a rule selects a set of relevant objects and computes a distribution over properties of just those objects in the resulting state given their properties in the previous state. An iterative g…

Study of isometries on hyperbolic 3-manifold cusps.

Semisimple Lie groups act transitively on pseudo-Riemannian manifolds, making them flat.

4-manifolds show every flat 3-manifold as cusp sections.

Given a linearly ordered set I, every surjective map p: A --> I endows the set A with a structure of set of preferences by "replacing" the elements of I with their inverse images via p considered as "balloons" (sets endowed with an equivalence relation), lifting the linear order on A, and "agglutinating" this structure…

Study optimal holomorphic extensions on complex manifolds with transitivity property.

New method improves robustness of deep learning with noisy labels.

A new method for ILO with transition model disparity using an intermediary policy.

Study shows zero level sets of solutions to Allen-Cahn equation are minimal surfaces with zero mean curvature.

Estimates stationary distribution from batch transitions without access to the underlying process.

Several methods exist to infer causal networks from massive volumes of observational data. However, almost all existing methods require a considerable length of time series data to capture cause and effect relationships. In contrast, memory-less transition networks or Markov Chain data, which refers to one-step transit…

High-dimensional random geometry shows phase transitions in various problems.

Characterizes RFF regression in large $n,p,N$ setting, providing precise learning phases and double descent curve.

New relation on paths is not transitive.

A multi-task GP model tracks time-varying transition probabilities between two states.

New transitions found in Spin(7) holonomy metrics related to a dynamical system.

Learning with noisy labels, which aims to reduce expensive labors on accurate annotations, has become imperative in the Big Data era. Previous noise transition based method has achieved promising results and presented a theoretical guarantee on performance in the case of class-conditional noise. However, this type of a…

GDM models time series with smoother transitions and interpretable states.

Develops CLTs for Markov chain transition probabilities and policies.

TempLe learns transition templates for efficient multi-task RL.

Mathematically proves SYZ conjecture for conifold transition.

Unsupervised learning is a discipline of machine learning which aims at discovering patterns in big data sets or classifying the data into several categories without being trained explicitly. We show that unsupervised learning techniques can be readily used to identify phases and phases transitions of many body systems…

A vertex-transitive map $X$ is a map on a closed surface on which the automorphism group ${\rm Aut}(X)$ acts transitively on the set of vertices. If the face-cycles at all the vertices in a map are of same type then the map is said to be a semi-equivelar map. Clearly, a vertex-transitive map is semi-equivelar. Converse…

Manually authoring transition animations for a complete locomotion system can be a tedious and time-consuming task, especially for large games that allow complex and constrained locomotion movements, where the number of transitions grows exponentially with the number of states. In this paper, we present a novel approac…

We propose a method for non-projective dependency parsing by incrementally predicting a set of edges. Since the edges do not have a pre-specified order, we propose a set-based learning method. Our method blends graph, transition, and easy-first parsing, including a prior state of the parser as a special case. The propo…

New algorithm reduces suboptimality in imitation learning to nearly optimal levels.

The paper establishes pressure gaps for manifolds with flat subtori singularities.

Model improves robustness of neural network sequences without transition failures.

We study a reinforcement learning setting, where the state transition function is a convex combination of a stochastic continuous function and a deterministic function. Such a setting generalizes the widely-studied stochastic state transition setting, namely the setting of deterministic policy gradient (DPG). We firstl…

New algorithms learn in complex decision-making problems with smooth transitions.

In recent years, non-parametric methods utilizing random walks on graphs have been used to solve a wide range of machine learning problems, but in their simplest form they do not scale well due to the quadratic complexity. In this paper, a new dual-tree based variational approach for approximating the transition matrix…

The focus of these lectures is the Gopakumar-Vafa's insight that ``Large N dualities'' (relating gauge theories and closed strings) are realized, in certain cases, by "transition in geometry". In their pivotal 1998 example, the gauge theory is SU(N) Chern-Simons theory on S^3, for large N, and the transition is the "co…

Generative diffusion models exhibit phase transitions in statistical mechanics, impacting their performance.

New RL algorithm optimizes policies with bandit feedback, matching previous bounds.

Faster sampling in discrete diffusion models with predetermined transition time.

Persistent entropy detects phase transitions in complex systems.