arXiv research

We quantify content availability and user discovery opportunities in recommender systems.

Influence maximization (IM) is the problem of finding for a given $s\geq 1$ a set $S$ of $|S|=s$ nodes in a network with maximum influence. With stochastic diffusion models, the influence of a set $S$ of seed nodes is defined as the expectation of its reachability over simulations, where each simulation specifies a det…

Polynomial-time reachability for LTI systems with TLL NN controllers is achieved.

In finance industry portfolio construction deals with how to divide the investors' wealth across an asset-classes' menu in order to maximize the investors' gain. Main approaches in use at the present are based on variations of the classical Markowitz model. However, recent evolutions of the world market showed limitati…

Verifying correctness of deep neural networks (DNNs) is challenging. We study a generic reachability problem for feed-forward DNNs which, for a given set of inputs to the network and a Lipschitz-continuous function over its outputs, computes the lower and upper bound on the function values. Because the network and the …

Lumbermark clusters data robustly, slicing limbs of mutual reachability trees.

Guaranteed reachable set for unknown nonlinear systems on manifolds.

GoTube verifies neural networks over time, scaling to large horizons.

This paper tackles data-efficient nonlinear control in Hamiltonian systems using symplectic geometry.

GraphReach improves GNN performance by incorporating node positions.

Study on Heisenberg group's Lorentzian problems using Pontryagin's principle.

CIfly simplifies causal inference tasks with linear-time reachability primitives.

Procedure verifies if machine learning models assign fixed predictions that preclude access.

We present a novel technique to solve the problem of managing optimally a pumped hydroelectric storage system. This technique relies on representing the system as a stochastic optimal control problem with state constraints, these latter corresponding to the finite volume of the reservoirs. Following the recent level-se…

Study analyzes and enhances robustness of neural networks for classification and regression.

Deep learning method proves convergence for solving HJI equations.

Study of 2D Lorentzian anti-de Sitter plane using geometric control theory.

Improves data-driven reachability estimation for complex systems.

Quantum neural networks need both data-dependent and trainable unitaries for effective geometric deformation.

Compact reachability embeddings for hierarchical data

Paper identifies reductive MDPs, solving them in polynomial time.

CAST improves spectral clustering for multi-scale data by integrating reachability similarity.

Recommender systems often rely on models which are trained to maximize accuracy in predicting user preferences. When the systems are deployed, these models determine the availability of content and information to different users. The gap between these objectives gives rise to a potential for unintended consequences, co…

Improved exploration algorithm for unknown MDPs with reduced sample complexity.

New RL algorithm ensures stable, replicable policies.

How can we design safe reinforcement learning agents that avoid unnecessary disruptions to their environment? We show that current approaches to penalizing side effects can introduce bad incentives, e.g. to prevent any irreversible changes in the environment, including the actions of other agents. To isolate the source…

New distances for causal graphs improve evaluation of learned structures.

We construct normal forms for Lorentzian metrics on Engel distributions under the assumption that abnormal curves are timelike future directed Hamiltonian geodesics. Then we indicate some cases in which the abnormal timelike future directed curve initiating at the origin is geometrically optimal. We also give certain e…

The staircase property aids deep learning by guiding hierarchical feature learning.

Safe imitation learning with a safety layer for flexible training.

A new clustering algorithm considers data smoothness for better performance.

In classical reinforcement learning, when exploring an environment, agents accept arbitrary short term loss for long term gain. This is infeasible for safety critical applications, such as robotics, where even a single unsafe action may cause system failure. In this paper, we address the problem of safely exploring fin…

CUDC collects diverse data for offline RL by predicting future states.

Improves RL from historical data by stitching trajectories.

We consider examples of the $\mathbb H$-type groups with the natural horizontal distribution generated by the commutation relations of the group. In the contrast with the previous studies we furnish the horizontal distribution with the Lorentzian metric, which is nondegenerate metric of index 1 instead of a positive de…

C-Learning estimates reachability over time to solve multi-goal tasks.

DARLING tackles non-stationary RL with guarantees, improving dynamic regret.

We address the problem of verifying neural-based perception systems implemented by convolutional neural networks. We define a notion of local robustness based on affine and photometric transformations. We show the notion cannot be captured by previously employed notions of robustness. The method proposed is based on re…

Theoretical study explains grokking in neural networks.

Study improves understanding of why agentic theorem provers succeed.

The paper certifies neural network-based control barrier functions efficiently.

Deep neural networks are widely used for nonlinear function approximation with applications ranging from computer vision to control. Although these networks involve the composition of simple arithmetic operations, it can be very challenging to verify whether a particular network satisfies certain input-output propertie…

We prove a lower bound for feature dimension in linear MDPs and propose a novel dynamics aggregation framework.

ViTaX provides formal guarantees for targeted explanations in safety-critical systems.

While designing the state space of an MDP, it is common to include states that are transient or not reachable by any policy (e.g., in mountain car, the product space of speed and position contains configurations that are not physically reachable). This leads to defining weakly-communicating or multi-chain MDPs. In this…

Active sampling selects few points for accurate model reduction of high-fidelity systems.

Framework for dynamic node embeddings from graph streams.

New complexity measure helps in agnostic reinforcement learning with or without access to MDP dynamics.