Characterizes functions in Carnot groups of step 2.
problem Understanding intrinsic Lipschitz functions in Carnot groups.
method Characterization via intrinsic distributional gradients.
result Characterization of locally intrinsic Lipschitz functions in Carnot groups of step 2.
Study finds curves with explicit formulas for curvature and torsion.
problem Finding curves with specific geometric properties.
method Developed a family of curves parametrized by arc length, dependent on angular and intrinsic fraction functions.
result Explicit formulas for curvature, torsion, and geodetic curvature found in terms of angular and intrinsic fraction functions.
h-DQN integrates hierarchical value functions with intrinsic motivation for efficient exploration.
problem Sparse feedback and insufficient exploration in reinforcement learning.
method Hierarchical-DQN framework combining temporal abstraction and intrinsic motivation.
result Demonstrated efficiency in exploration and task-solving on sparse feedback problems.
Paper infers intrinsic dimension from quasi-convex measurements.
problem Inferring intrinsic dimension from measurements by quasi-convex functions.
method Developed a method using filtration of Dowker complexes based on discrete data of point orderings.
result Correct intrinsic dimension can be inferred in the limit of large data under generic assumptions.
Paper proposes a new algorithm to learn intrinsic rewards for policy-gradient agents.
problem Learning intrinsic rewards for policy-gradient agents is an open problem.
method Derives a novel algorithm for learning intrinsic rewards for policy-gradient based learning agents.
result Improved performance on most domains but not all when using the new algorithm.
Introduces Floer functions and Floerfolds for intrinsic properties.
problem Complex transformation of Hessian under chart transition.
method Introduces Floer functions and Floerfolds to address intrinsic properties.
result Floer functions and Floerfolds provide intrinsic conditions for Hessian.
Study on curves in Riemannian surfaces, focusing on total intrinsic curvature.
problem Understanding the total intrinsic curvature of irregular curves in Riemannian surfaces.
method Weak notion of parallel transport, bounded variation of angle, energy functional analysis.
result Total intrinsic curvature of irregular curves matches an energy functional.
Study on random manifolds converging to intrinsic geometry.
problem Understanding the intrinsic geometry of random manifolds.
method Sequence of random embeddings into Euclidean spaces, convergence analysis.
result Many intrinsic functionals converge to deterministic limits.
Bayesian neural networks achieve optimal posterior contraction rates in Besov spaces with intrinsic dimensionality.
problem High-dimensional structured estimation problems with unknown smoothness levels.
method Sparse Bayesian neural networks with either sparse or continuous shrinkage priors.
result Optimal posterior contraction rates are achieved, adapting to the unknown smoothness level of the true function.
Study intrinsic volume forms on complex hypersurfaces.
problem Computing volume functionals on pseudoconvex hypersurfaces.
method Compute first and second variation formulae, explore infinite dimensional aspects.
result Discuss possible analogues of the affine isoperimetric inequality.
In Heisenberg group, certain graphs are stable under contact variations but not area-minimizing.
problem Characterizing stable sub-Riemannian graphs in the Heisenberg group.
method Analyzing intrinsic graphs of smooth functions in the first Heisenberg group under contact variations.
result Intrinsic graphs of smooth functions are stable points of sub-Riemannian perimeter under contact variations, but not area-minimizing.
New covariance function improves climate model accuracy.
problem Non-stationary and non-uniform spatial data in climate modeling.
method Intrinsic non-stationary covariance function for Gaussian process regression.
result Improved regression estimates for relative sea level changes.
The diameter of a disc filling a loop in the universal covering of a Riemannian manifold may be measured extrinsically using the distance function on the ambient space or intrinsically using the induced length metric on the disc. Correspondingly, the diameter of a van Kampen diagram filling a word that represents the i…
The paper studies harmonic graphs in the Heisenberg group and their properties.
problem No analogous theorem exists for H-minimal surfaces in the Heisenberg group. method Introduced intrinsic Dirichlet energy and studied its critical points (contact harmonic graphs).
result Calibration condition and construction of energy-minimizing graphs with various singularities.
This paper explores intrinsic rewards to improve learning from multiple value functions.
problem How to adapt reinforcement learning systems to optimize learning from multiple value functions.
method Investigated and compared 14 different intrinsic reward mechanisms in a new bandit-like parallel-learning testbed.
result Intrinsic rewards based on the amount of learning can generate useful behavior, if each individual learner is introspective.
Formulas for curvature measures of sublevel sets on manifolds derived from function and derivatives.
problem Calculating intrinsic volumes of sublevel sets on Riemannian manifolds.
method Established formulas involving integrals of functionals of function and its derivatives.
result Formulas for intrinsic volumes of sublevel sets on Riemannian manifolds.
Hadwiger's Theorem states that Euclidean-invariant convex-continuous valuations of definable sets are linear combinations of intrinsic volumes. We lift this result from sets to data distributions over sets, specifically, to definable real-valued functions on n-dimensional Euclidean space. This generalizes intrinsic vol…
New RL approach uses future state and action visitation measures for better exploration.
problem Improving exploration in reinforcement learning.
method Intrinsic reward based on future state and action visitation measures, using contraction operators.
result Policies achieve good state-action space coverage and high performance.
Develops intrinsic Gaussian process regression for manifold-valued data.
problem Lack of intrinsic Gaussian process methods for manifold-valued response variables.
method Proposes an intrinsic covariance structure and a novel intrinsic Gaussian process regression model.
result Establishes asymptotic properties and shows posterior consistency.
Paper analyzes a new Hopf-Lax semigroup in metric spaces.
problem Analyzing a new Hopf-Lax semigroup in metric spaces.
method Using continuous sections of quotient maps and variational problems.
result The 'symmetrized' Hopf-Lax semigroup is a subsolution of Hamilton-Jacobi type equation.
Study area and coarea formulas for graphs and submanifolds in Carnot groups.
problem Understanding geometric properties of submanifolds in Carnot groups.
method Developed area and coarea formulas for CH1 intrinsic graphs and submanifolds. result Deduced density properties for Hausdorff measures and coarea formula for Carnot groups.
Study on predicting sequences with Gaussian constraints, linking to intrinsic volumes and metric complexity.
problem Predicting sequences almost as well as the best Gaussian distribution with mean in a given subset.
method Expressed minimax regret in terms of intrinsic volumes, established comparison inequality for Wills functional, characterized global covering numbers and local Gaussian widths.
result Sharp estimates on the log-Laplace transform of intrinsic volume sequence for a general nonconvex set.
Introduces intrinsically Lipschitz graphs in metric spaces.
problem Graphs in metric spaces with Lipschitz conditions.
method Focuses on quotient maps and intrinsically Lipschitz sections.
result Compactness, Ahlfors regularity, and extension theorems.
Deep networks can adapt to intrinsic dimensionality beyond domain constraints.
problem Approximating functions on low-dimensional manifolds with high-dimensional data.
method Two-layer compositions with ReLU activation, using dimensionality reducing feature maps.
result Near optimal approximation rates depend on the complexity of the dimensionality reducing map, not the ambient dimension.
Proves compactness for timed-metric spaces using new distance and maps.
problem Weak convergence of space-times using timed-Hausdorff distance.
method Uses Gromov's original compactness theorem and introduces addresses.
result Establishes compactness theorem for intrinsic timed-Hausdorff convergence.
Catenaries defined on any Riemannian surface using intrinsic distance.
problem Defining catenaries on Riemannian surfaces.
method Defining catenaries as critical points of a potential functional, calculating potential with intrinsic distance, and characterizing using curvature.
result Characterization of catenaries on various Riemannian surfaces.
New method tightens sub-Gaussian concentration inequalities.
problem Estimating variance-type parameters of sub-Gaussian distributions.
method Using sub-Gaussian intrinsic moment norm to maximize normalized moments.
result Provides tighter sub-Gaussian concentration inequalities.
Paper reviews intrinsic motivations and their role in open-ended learning.
problem Understanding intrinsic motivations and their role in open-ended learning.
method Defining intrinsic motivations, presenting psychological/neuroscientific and computational models.
result Links between psychological/neuroscientific and computational models of intrinsic motivations.
Gradient optimization improved for complex agent locomotion with intrinsic reward.
problem Challenges in reinforcement learning for embodied agents, especially rugged reward landscapes.
method Incorporated an intrinsic reward based on mutual information of sensor readings to smooth the optimization landscape.
result Policy gradient optimization significantly improved for locomotion in complex morphology.
Derivative formulas on measure spaces of Riemannian manifolds are characterized.
problem Characterizing derivatives in measure spaces on Riemannian manifolds.
method Introducing and characterizing derivatives in measure spaces for functions on the space of finite measures over a Riemannian manifold.
result Derivatives in measure spaces for functions on Riemannian manifolds are linked and calculated.
New estimators for intrinsic dimension and Wasserstein distance improve OT accuracy.
problem Intrinsic dimension estimation and Wasserstein distance estimation in large-scale OT.
method Introduces novel estimators for intrinsic dimension and Wasserstein distance.
result Simple, tuning-free estimator of OT and fast intrinsic dimension estimator.
A new measure of causal influence quantifies intrinsic contributions in DAGs.
problem Quantifying intrinsic causal contributions in Directed Acyclic Graphs (DAGs).
method Recursive decomposition of node contributions, structure-preserving interventions, Shapley symmetrization.
result A measure of intrinsic causal contribution that is invariant to node relabeling.
In this paper we provide a characterization of intrinsic Lipschitz graphs in the sub-Riemannian Heisenberg groups in terms of their distributional gradients. Moreover, we prove the equivalence of different notions of continuous weak solutions to the equation φ_y+ [φ^{2}/2]_t=w, where w is a bounded function depending o…
Develop intrinsic consensus-based optimization framework on Riemannian manifolds with bounded curvature.
problem Nonconvex optimization on manifolds
method Intrinsic consensus-based optimization on Riemannian manifolds with bounded curvature
result Global convergence of the mean-field equation toward a global minimizer of the objective function.
A new multi-objective RL framework improves intrinsic exploration performance.
problem Sub-optimal exploration performance due to ad-hoc handling of intrinsic exploration.
method A multi-objective RL framework where both exploration and exploitation are optimized as separate objectives.
result EMU-Q method outperforms classic and other intrinsic RL methods on benchmarks.
Null distance metric studies spacetime convergence.
problem Investigate convergence in spacetime geometry.
method Introduced null distance metric for Lorentzian manifolds, proving convergence results.
result Null distance metric leads to distinct limiting behavior under non-uniform convergence of warping functions.
Introduces intrinsic Hopf-Lax semigroup linking to intrinsic slope.
problem Understanding intrinsic Hopf-Lax semigroup and its relation to intrinsic slope.
method Introduces and proves the link between intrinsic Hopf-Lax semigroup and intrinsic slope.
result Intrinsic Hopf-Lax semigroup is a subsolution of Hamilton-Jacobi type equality.
Researchers classify and decompose valuations on convex functions.
problem Classifying valuations on convex functions.
method Geometric decomposition of valuations, using properties of special subspaces and Monge-Ampère-type operators.
result Valuations decompose into subspaces defined by vanishing properties.
The paper introduces a new intrinsic reward method for exploration in reinforcement learning.
problem Improving exploration in reinforcement learning agents.
method Intrinsic rewards proportional to the entropy of future state-action features.
result The new objective leads to improved visitation of features within individual trajectories.
For negatively curved manifolds, a condition is found for intrinsic ultracontractivity of heat semigroups.
problem Investigating intrinsic ultracontractivity for domains in negatively curved manifolds.
method Using volume doubling property, Poincaré inequality, and Li-Yau Gaussian estimate for the Dirichlet heat kernel.
result The reciprocal of the bottom of the spectrum and the supremum of the torsion function are comparable with the square of the capacitary width for small capacitary width.
We use the theory of rectifiable metric spaces to define a Dirichlet energy of Lipschitz functions defined on the support of integral currents. This energy is obtained by integration of the square of the norm of the tangential derivative, or equivalently of the approximate local dilatation, of the Lipschitz functions. …
This paper constructs a function on Gromov-Hausdorff limits of 2-surfaces with curvature constraints.
problem Understanding geometric properties of limits of surfaces with curvature constraints.
method Construction of an integer-valued function on the limit space.
result Existence and classification of functions on Gromov-Hausdorff limits of 2-surfaces.
Study shows curiosity-driven learning can perform well without extrinsic rewards.
problem Lack of scalable methods for intrinsic reward design in reinforcement learning.
method Performed a large-scale study of curiosity-driven learning across 54 environments, using prediction error as reward.
result Curiosity-driven learning can achieve good performance without extrinsic rewards, aligning with hand-designed rewards in many cases.
This paper studies rectifiability in Carnot groups and proves geometric area formulas.
problem The study of rectifiability in Carnot groups and related geometric properties.
method Analysis of rectifiable measures in Carnot groups, geometric area formulas, and rectifiability of geodesic spheres.
result Geometric area formula for the centered Hausdorff measure restricted to intrinsically differentiable graphs in Carnot groups.
Let (M,I) be an almost complex 6-manifold. The obstruction to integrability of almost complex structure (so-called Nijenhuis tensor) maps a 3-dimensional bundle to a 3-dimensional one. We say that Nijenhuis tensor is non-degenerate if it is an isomorphism. An almost complex manifold is called nearly Kaehler if it adm…
This paper proves energy convexity for bi-harmonic maps into spheres, with applications to heat flow and uniqueness.
problem Analyzing the geometric properties of bi-harmonic maps and their heat flow.
method Energy convexity and ε-regularity of bi-harmonic maps.
result Uniqueness of weakly intrinsic bi-harmonic maps and long-time existence of the heat flow.
Generates counterfactuals in target domain from source domain observations.
problem Cross-domain learning with domain shifts and lack of parallel datasets.
method Unsupervised, Neural Causal Models, Joint Causal Graphs, Effect-Intrinsic vs Domain-Intrinsic Variables.
result Framework generates counterfactuals that closely match ground truth.
Agent learns goals and rewards through language and curiosity.
problem Autonomous agents lack intrinsic motivations and reward functions.
method LE2 algorithm using NL interactions and intrinsic motivations.
result Agent autonomously discovers and grounds goals in real behavior.