1 citations · 1 across the 17 of their papers we have counts for
19 papers
Classification of minimal-mass blowup solutions to the mass-critical Hartree equation at the -critical level
Zuyu Ma, Changxing Miao, Jiqiang Zheng
In this paper, we study the focusing mass-critical Hartree equation \[ iu_t+Δu=-\bigl(|x|^{-2}*|u|^2\bigr)u \] at the ground-state mass . We prove that, up to the symmetries…
Risk-Averse Online POMDP Planning via CVaR of the Immediate Cost with Performance Guarantees
Yaacov Pariente, Vadim Indelman
Online POMDP planners optimize the expected cumulative cost, which can mask dangerous states when the belief places significant mass on high-cost states. Existing risk-averse metho…
Graph Sparse Sampling: Breaking the Curse of the Horizon in Continuous MDP Planning
Idan Lev-Yehudi, Vadim Indelman
Planning under uncertainty in continuous domains is essential for autonomous systems, yet computationally demanding. Tree-based search methods such as Monte Carlo Tree Search (MCTS…
Finite-Time Analysis of MCTS in Continuous POMDP Planning
Da Kong, Vadim Indelman
This paper presents a finite-time analysis for Monte Carlo Tree Search (MCTS) in Partially Observable Markov Decision Processes (POMDPs), with probabilistic concentration bounds in…
Open-loop POMDP Simplification and Safe Skipping of Replanning with Formal Performance Guarantees
Da Kong, Vadim Indelman
Partially Observable Markov Decision Processes (POMDPs) provide a principled mathematical framework for decision-making under uncertainty. However, the exact solution to POMDPs is…
Accelerated Online Risk-Averse Policy Evaluation in POMDPs with Theoretical Guarantees and Novel CVaR Bounds
Yaacov Pariente, Vadim Indelman
Risk-averse decision-making under uncertainty in partially observable domains is a central challenge in artificial intelligence and is essential for developing reliable autonomous…
Online Risk-Averse Planning in POMDPs Using Iterated CVaR Value Function
Yaacov Pariente, Vadim Indelman
We study risk-sensitive planning under partial observability using the dynamic risk measure Iterated Conditional Value-at-Risk (ICVaR). A policy evaluation algorithm for ICVaR is d…
Towards Optimal Performance and Action Consistency Guarantees in Dec-POMDPs with Inconsistent Beliefs and Limited Communication
Moshe Rafaeli Shimron, Vadim Indelman
Multi-agent decision-making under uncertainty is fundamental for effective and safe autonomous operation. In many real-world scenarios, each agent maintains its own belief over the…
Online Robust Planning under Model Uncertainty: A Sample-Based Approach
Tamir Shazman, Idan Lev-Yehudi, Ron Benchetit +1
Online planning in Markov Decision Processes (MDPs) enables agents to make sequential decisions by simulating future trajectories from the current state, making it well-suited for…
Bounding Conditional Value-at-Risk via Auxiliary Distributions with Bounded Discrepancies
Yaacov Pariente, Vadim Indelman
In this paper, we develop a theoretical framework for bounding the CVaR of a random variable using another related random variable , under assumptions on their cumulative an…
Action-Gradient Monte Carlo Tree Search for Non-Parametric Continuous (PO)MDPs
Idan Lev-Yehudi, Michael Novitsky, Moran Barenboim +2
Online planning in continuous state, action, and observation spaces remains challenging for autonomous systems. While Monte Carlo Tree Search (MCTS) scales effectively via sampling…
Anytime Incremental POMDP Planning in Continuous Spaces
Ron Benchetrit, Idan Lev-Yehudi, Andrey Zhitnikov +1
Partially Observable Markov Decision Processes (POMDPs) provide a robust framework for decision-making under uncertainty in applications such as autonomous driving and robotic expl…
Online Hybrid-Belief POMDP with Coupled Semantic-Geometric Models
Tuvy Lemberg, Vadim Indelman
Robots operating in complex and unknown environments frequently require geometric-semantic representations of the environment to safely perform their tasks. While inferring the env…
Previous Knowledge Utilization In Online Anytime Belief Space Planning
Michael Novitsky, Moran Barenboim, Vadim Indelman
Online planning under uncertainty remains a critical challenge in robotics and autonomous systems. While tree search techniques are commonly employed to construct partial future tr…
Anytime Probabilistically Constrained Provably Convergent Online Belief Space Planning
Andrey Zhitnikov, Vadim Indelman
Taking into account future risk is essential for an autonomously operating robot to find online not only the best but also a safe action to execute. In this paper, we build upon th…
Simplified POMDP Planning with an Alternative Observation Space and Formal Performance Guarantees
Da Kong, Vadim Indelman
Online planning under uncertainty in partially observable domains is an essential capability in robotics and AI. The partially observable Markov decision process (POMDP) is a mathe…
Simplification of Risk Averse POMDPs with Performance Guarantees
Yaacov Pariente, Vadim Indelman
Risk averse decision making under uncertainty in partially observable domains is a fundamental problem in AI and essential for reliable autonomous agents. In our case, the problem…
No Compromise in Solution Quality: Speeding Up Belief-dependent Continuous POMDPs via Adaptive Multilevel Simplification
Andrey Zhitnikov, Ori Sztyglic, Vadim Indelman
Continuous POMDPs with general belief-dependent rewards are notoriously difficult to solve online. In this paper, we present a complete provable theory of adaptive multilevel simpl…
Online POMDP Planning with Anytime Deterministic Optimality Guarantees
Moran Barenboim, Vadim Indelman
Decision-making under uncertainty is a critical aspect of many practical autonomous systems due to incomplete information. Partially Observable Markov Decision Processes (POMDPs) o…