19 citations · 61 across the 16 of their papers we have counts for
17 papers
Muse Glimmer 30B: An On-Device Agentic Model
Arash Ebrahim Abtahizadeh, Muna Aghamelu, Adil Ahmed +327
We introduce Muse Glimmer, an open-source 30B parameter causal language model with perception encoder, purpose-built for autonomous agentic tasks on consumer hardware. Muse Glimmer…
Model-Based and Sample-Efficient AI-Assisted Math Discovery in Sphere Packing
Rasul Tutunov, Alexandre Maraval, Antoine Grosnit +3
Sphere packing, Hilbert's eighteenth problem, asks for the densest arrangement of congruent spheres in n-dimensional Euclidean space. Although relevant to areas such as cryptograph…
Many of Your DPOs are Secretly One: Attempting Unification Through Mutual Information
Rasul Tutnov, Antoine Grosnit, Haitham Bou-Ammar
Post-alignment of large language models (LLMs) is critical in improving their utility, safety, and alignment with human intentions. Direct preference optimisation (DPO) has become…
Kolb-Based Experiential Learning for Generalist Agents with Human-Level Kaggle Data Science Performance
Antoine Grosnit, Alexandre Maraval, Refinath S N +16
Human expertise emerges through iterative cycles of interaction, reflection, and internal model updating, which are central to cognitive theories such as Kolb's experiential learni…
ShortCircuit: AlphaZero-Driven Circuit Design
Dimitrios Tsaras, Antoine Grosnit, Lei Chen +3
Chip design relies heavily on generating Boolean circuits, such as AND-Inverter Graphs (AIGs), from functional descriptions like truth tables. This generation operation is a key pr…
ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning
Christopher E. Mower, Yuhui Wan, Hongzhan Yu +20
We present a framework for intuitive robot programming by non-experts, leveraging natural language prompts and contextual information from the Robot Operating System (ROS). Our sys…
Why Can Large Language Models Generate Correct Chain-of-Thoughts?
Rasul Tutunov, Antoine Grosnit, Juliusz Ziomek +2
This paper delves into the capabilities of large language models (LLMs), specifically focusing on advancing the theoretical comprehension of chain-of-thought prompting. We investig…
Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization
Kamil Dreczkowski, Antoine Grosnit, Haitham Bou Ammar
This paper introduces a modular framework for Mixed-variable and Combinatorial Bayesian Optimization (MCBO) to address the lack of systematic benchmarking and standardized evaluati…
End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit +1
Meta-Bayesian optimisation (meta-BO) aims to improve the sample efficiency of Bayesian optimisation by leveraging data from related tasks. While previous methods successfully meta-…
Contextual Causal Bayesian Optimisation
Vahan Arsenyan, Antoine Grosnit, Haitham Bou-Ammar +1
We introduce a unified framework for contextual and causal Bayesian optimisation, which aims to design intervention policies maximising the expectation of a target variable. Our ap…
Sample-Efficient Optimisation with Probabilistic Transformer Surrogates
Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit +3
Faced with problems of increasing complexity, recent research in Bayesian Optimisation (BO) has focused on adapting deep probabilistic models as flexible alternatives to Gaussian P…
AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation
Asif Khan, Alexander I. Cowen-Rivers, Antoine Grosnit +12
Antibodies are canonically Y-shaped multimeric proteins capable of highly specific molecular recognition. The CDRH3 region located at the tip of variable chains of an antibody domi…
BOiLS: Bayesian Optimisation for Logic Synthesis
Antoine Grosnit, Cedric Malherbe, Rasul Tutunov +3
Optimising the quality-of-results (QoR) of circuits during logic synthesis is a formidable challenge necessitating the exploration of exponentially sized search spaces. While exper…
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval +9
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces. By…
Decentralized Deterministic Multi-Agent Reinforcement Learning
Antoine Grosnit, Desmond Cai, Laura Wynter
[Zhang, ICML 2018] provided the first decentralized actor-critic algorithm for multi-agent reinforcement learning (MARL) that offers convergence guarantees. In that work, policies…
Are we Forgetting about Compositional Optimisers in Bayesian Optimisation?
Antoine Grosnit, Alexander I. Cowen-Rivers, Rasul Tutunov +3
Bayesian optimisation presents a sample-efficient methodology for global optimisation. Within this framework, a crucial performance-determining subroutine is the maximisation of th…
HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation
Alexander I. Cowen-Rivers, Wenlong Lyu, Rasul Tutunov +8
In this work we rigorously analyse assumptions inherent to black-box optimisation hyper-parameter tuning tasks. Our results on the Bayesmark benchmark indicate that heteroscedastic…