Search Results for author:
Found 12 papers, 5 papers with code
Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs
In this context, multi-agent debate (MAD) has emerged as a promising strategy for enhancing the truthfulness of LLMs.
Combinatorial Optimization with Policy Adaptation using Latent Space Search
Combinatorial Optimization underpins many real-world applications and yet, designing performant algorithms to solve these complex, typically NP-hard, problems remains a significant research challenge.
Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX
Open-source reinforcement learning (RL) environments have played a crucial role in driving progress in the development of AI algorithms.
Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization
Applying reinforcement learning (RL) to combinatorial optimization problems is attractive as it removes the need for expert knowledge or pre-solved instances.
Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round
With the lessons learned from the first round and the feedback from the participants, we have designed the second round of our challenge with a new protocol and a new meta-dataset.
More Efficient Exploration with Symbolic Priors on Action Sequence Equivalences
Incorporating prior knowledge in reinforcement learning algorithms is mainly an open question.
Better state exploration using action sequence equivalence
Incorporating prior knowledge in reinforcement learning algorithms is mainly an open question.
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning
We propose to learn to distinguish reversible from irreversible actions for better informed decision-making in Reinforcement Learning (RL).
Interferometric Graph Transform for Community Labeling
We present a new approach for learning unsupervised node representations in community graphs.
Low-Rank Projections of GCNs Laplacian
In this work, we study the behavior of standard models for community detection under spectral manipulations.
A spectral perspective on GCNs
In this work, we study the behavior of standard GCNs under spectral manipulations.
Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling
In this paper, we propose a reinforcement learning approach to solve a realistic scheduling problem, and apply it to an algorithm commonly executed in the high performance computing community, the Cholesky factorization.
