30 citations · 52 across the 10 of their papers we have counts for
12 papers
Make Every Expansion Count: Learning to Search Efficiently in LLM Code Repair
Elad Sharony, Ido Greenberg, Jialin Song +3
Large language model (LLM) code-repair systems fix faulty programs by repeatedly generating and testing revisions. Each revision is costly, making the choice of which repair to pur…
COMPASS: Ordered Clustered Routing at 100K Scale
Ido Greenberg, Hugo Linsenmaier, Piotr Sielski +4
Large-scale routing often requires visiting clusters of nodes in a prescribed order, giving rise to the Ordered Clustered Traveling Salesman Problem (OCTSP). Optimizing each cluste…
Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms
Ido Greenberg, Piotr Sielski, Hugo Linsenmaier +5
Vehicle Routing Problems (VRP) are an extension of the Traveling Salesperson Problem and are a fundamental NP-hard challenge in combinatorial optimization. Solving VRP in real-time…
Optimization or Architecture: How to Hack Kalman Filtering
Ido Greenberg, Netanel Yannay, Shie Mannor
In non-linear filtering, it is traditional to compare non-linear architectures such as neural networks to the standard linear Kalman Filter (KF). We observe that this mixes the eva…
Individualized Dosing Dynamics via Neural Eigen Decomposition
Stav Belogolovsky, Ido Greenberg, Danny Eytan +1
Dosing models often use differential equations to model biological dynamics. Neural differential equations in particular can learn to predict the derivative of a process, which per…
Train Hard, Fight Easy: Robust Meta Reinforcement Learning
Ido Greenberg, Shie Mannor, Gal Chechik +1
A major challenge of reinforcement learning (RL) in real-world applications is the variation between environments, tasks or clients. Meta-RL (MRL) addresses this issue by learning…
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning
Deborah Cohen, Moonkyung Ryu, Yinlam Chow +8
Despite recent advances in natural language understanding and generation, and decades of research on the development of conversational bots, building automated agents that can carr…
Efficient Risk-Averse Reinforcement Learning
Ido Greenberg, Yinlam Chow, Mohammad Ghavamzadeh +1
In risk-averse reinforcement learning (RL), the goal is to optimize some risk measure of the returns. A risk measure often focuses on the worst returns out of the agent's experienc…
Continuous Forecasting via Neural Eigen Decomposition
Stav Belogolovsky, Ido Greenberg, Danny Eitan +1
Neural differential equations predict the derivative of a stochastic process. This allows irregular forecasting with arbitrary time-steps. However, the expressive temporal flexibil…
The Fragility of Noise Estimation in Kalman Filter: Optimization Can Handle Model-Misspecification
Ido Greenberg, Shie Mannor, Netanel Yannay
The Kalman Filter (KF) parameters are traditionally determined by noise estimation, since under the KF assumptions, the state prediction errors are minimized when the parameters co…
Detecting Rewards Deterioration in Episodic Reinforcement Learning
Ido Greenberg, Shie Mannor
In many RL applications, once training ends, it is vital to detect any deterioration in the agent performance as soon as possible. Furthermore, it often has to be done without modi…
Common lines modeling for reference free ab-initio reconstruction in cryo-EM
Ido Greenberg, Yoel Shkolnisky
We consider the problem of estimating an unbiased and reference-free ab-inito model for non-symmetric molecules from images generated by single-particle cryo-electron microscopy. T…