arXiv research

Functor connects sheaves on Lagrangian cobordisms, proving equivalence and action decreasing properties.

Study personalizes user experience to maximize rewards with patience budget.

Given a closed connected Riemannian manifold M and a connected Riemannian manifold N, we study fiberwise volume decreasing diffeomorphisms on the product M x N. Our main theorem shows that in the presence of certain cohomological condition on M and N such diffeomorphisms must map a fiber diffeomorphically onto another …

Branched covers of orbit cylinders are the basic examples of holomorphic curves studied in symplectic field theory. Since all curves with Fredholm index one can never be regular for any choice of cylindrical almost complex structure, we generalize the obstruction bundle technique of Taubes for determining multiple cove…

Study examines CSO algorithm for 3D swarming and tracking multiple targets.

We consider the Markov Decision Process (MDP) of selecting a subset of items at each step, termed the Select-MDP (S-MDP). The large state and action spaces of S-MDPs make them intractable to solve with typical reinforcement learning (RL) algorithms especially when the number of items is huge. In this paper, we present …

New algorithm handles both decaying and non-decaying bandit problems.

Optimal strategy proposed for maximizing cumulative reward in continuum-armed bandits.

The invariant measured foliations of a pseudo-Anosov homeomorphism induce a natural (singular) Sol structure on mapping tori of surfaces with pseudo-Anosov monodromy. We show that when the pseudo-Anosov $φ:S\rightarrow S$ has orientable foliations and does not have 1 as an eigenvalue of the induced cohomology action on…

Novel evolutionary strategy solves stochastic constrained optimization problems.

The paper extends a learning heuristic to high-dimensional contexts, reducing the risk of unusual actions.

Investment decisions shift earlier as patience decreases, with implications for pasting conditions.

Characterizes a general range decreasing group homomorphism.

RONM method reduces regret in stochastic convex bandits with decreasing noise.

A new algorithm for bandits with hierarchical rewards.

We study the evolution of the renormalized volume functional for asymptotically Poincare-Einstein metrics (M,g) which are evolving by normalized Ricci flow. In particular, we prove that the time derivative of the renormalized volume along the flow is the negative integral of scal(g(t)) + n(n-1) over the manifold. This …

We propose a method to decrease the number of hidden units of the restricted Boltzmann machine while avoiding decrease of the performance measured by the Kullback-Leibler divergence. Then, we demonstrate our algorithm by using numerical simulations.

Optimization geometrodynamics simplifies adaptive optimizer dynamics.

This paper presents a case study of a recommender system that can be used to save energy in smart homes without lowering the comfort of the inhabitants. We present an algorithm that uses consumer behavior data only and uses machine learning to suggest actions for inhabitants to reduce the energy consumption of their ho…

Sharp estimate for flow in any dimension.

In this paper, we propose a novel sufficient decrease technique for stochastic variance reduced gradient descent methods such as SVRG and SAGA. In order to make sufficient decrease for stochastic optimization, we design a new sufficient decrease criterion, which yields sufficient decrease versions of stochastic varianc…

In this paper, we propose a novel sufficient decrease technique for variance reduced stochastic gradient descent methods such as SAG, SVRG and SAGA. In order to make sufficient decrease for stochastic optimization, we design a new sufficient decrease criterion, which yields sufficient decrease versions of variance redu…

Paper proves genus of surfaces decreases in mean curvature flow.

Learning optimal feedback control laws capable of executing optimal trajectories is essential for many robotic applications. Such policies can be learned using reinforcement learning or planned using optimal control. While reinforcement learning is sample inefficient, optimal control only plans an optimal trajectory fr…

AutoSGD automatically adjusts learning rates for SGD.

GD monotonically decreases GFS sharpness in neural networks and scalar models.

In this paper, we propose an active perception method for recognizing object categories based on the multimodal hierarchical Dirichlet process (MHDP). The MHDP enables a robot to form object categories using multimodal information, e.g., visual, auditory, and haptic information, which can be observed by performing acti…

Life-expectancy is a complex outcome driven by genetic, socio-demographic, environmental and geographic factors. Increasing socio-economic and health disparities in the United States are propagating the longevity-gap, making it a cause for concern. Earlier studies have probed individual factors but an integrated pictur…

Numerical observations on martingale couplings are confirmed under certain conditions.

Paper improves policy updates in reinforcement learning to speed up learning.

DRAG decreases regularization to accelerate semi-discrete OT convergence.

We discuss a special class of solutions to the minimal surface system. These are vector-valued functions that "decrease area" and are natural generalization of scalar functions. After defining area-decreasing maps, we show several classical results for the minimal surface equation can be generalized. We also conjecture…

Attackers can significantly reduce team rewards in cooperative multi-agent reinforcement learning.

Paper generalizes Schwarz Lemma for VT harmonic maps with conditions.

In this paper a neural network heuristic dynamic programing (HDP) is used for optimal control of the virtual inertia based control of grid connected three phase inverters. It is shown that the conventional virtual inertia controllers are not suited for non inductive grids. A neural network based controller is proposed …

The study quantifies how many objects can be linearly classified under all views.

Paper solves pendulum swing-up problem using RL.

Improved Bayesian regret bound for linear Thompson sampling with general distributions.

Quantum channels' contraction under privacy constraints studied.

We consider the mean curvature flow of the graph of a smooth map $f:\mathbb{R}^2\to\mathbb{R}^2$ between two-dimensional Euclidean spaces. If $f$ satisfies an area-decreasing property, the solution exists for all times and the evolving submanifold stays the graph of an area-decreasing map $f_t$. Further, we prove unifo…

New SAGA algorithm with decreasing step for stochastic optimization.

Algorithm balances online and offline data for linear bandits.

In this paper, a MIMO simulated annealing SA based Q learning method is proposed to control a line follower robot. The conventional controller for these types of robots is the proportional P controller. Considering the unknown mechanical characteristics of the robot and uncertainties such as friction and slippery surfa…

Study geometrically characterizes piecewise circular curves with decreasing curvature.

Improved option pricing model with transaction costs.

Policy gradient converges linearly with Hadamard parameterization in tabular settings.

In this work we classify the stable regions (second order minima of perimeter under an area constraint) in tori of revolution with piecewise continuous decreasing Gauss curvature from the longest parallel and with a horizontal symmetry. Some applications to isoperimetric problems are also given.

Optimal insurance minimizes ruin probability with non-decreasing functions.