Search Results for author:
Found 25 papers, 8 papers with code
Al-Khwarizmi: Discovering Physical Laws with Foundation Models
Inferring physical laws from data is a central challenge in science and engineering, including but not limited to healthcare, physical sciences, biosciences, social sciences, sustainability, climate, and robotics.
Many of Your DPOs are Secretly One: Attempting Unification Through Mutual Information
However, the vast number of DPO variants in the literature has made it increasingly difficult for researchers to navigate and fully grasp the connections between these approaches.
A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics
Despite the many challenges associated with combining machine learning technology with robotics, robot learning remains one of the most promising directions for enhancing the capabilities of robots.
Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level
We introduce Agent K v1. 0, an end-to-end autonomous data science agent designed to automate, optimise, and generalise across diverse data science tasks.
SparsePO: Controlling Preference Alignment of LLMs via Sparse Token Masks
Preference Optimization (PO) has proven an effective step for aligning language models to human-desired behaviors.
ShortCircuit: AlphaZero-Driven Circuit Design
Chip design relies heavily on generating Boolean circuits, such as AND-Inverter Graphs (AIGs), from functional descriptions like truth tables.
Human-like Episodic Memory for Infinite Context LLMs
Large language models (LLMs) have shown remarkable capabilities, but still struggle with processing extensive contexts, limiting their ability to maintain coherence and accuracy over long sequences.
ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning
Key features of the framework include: integration of ROS with an AI agent connected to a plethora of open-source and commercial LLMs, automatic extraction of a behavior from the LLM output and execution of ROS actions/services, support for three behavior modes (sequence, behavior tree, state machine), imitation learning for adding new robot actions to the library of possible actions, and LLM reflection via human and environment feedback.
Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications
Indeed, safety specifications, often represented as constraints, can be complex and non-linear, making safety challenging to guarantee in learning systems.
Bayesian Reward Models for LLM Alignment
To ensure that large language model (LLM) responses are helpful and non-toxic, a reward model trained on human preference data is usually used.
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
Why Can Large Language Models Generate Correct Chain-of-Thoughts?
This paper delves into the capabilities of large language models (LLMs), specifically focusing on advancing the theoretical comprehension of chain-of-thought prompting.
Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?
Learning decompositions of expensive-to-evaluate black-box functions promises to scale Bayesian optimisation (BO) to high-dimensional problems.
Contextual Causal Bayesian Optimisation
Causal Bayesian optimisation (CaBO) combines causality with Bayesian optimisation (BO) and shows that there are situations where the optimal reward is not achievable if causal knowledge is ignored.
Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks
Motion planning is a mature area of research in robotics with many well-established methods based on optimization or sampling the state space, suitable for solving kinematic motion planning.
Reinforcement Learning in Presence of Discrete Markovian Context Evolution
We consider a context-dependent Reinforcement Learning (RL) setting, which is characterized by: a) an unknown finite number of not directly observable contexts; b) abrupt (discontinuous) context changes occurring during an episode; and c) Markovian context evolution.
Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation
Satisfying safety constraints almost surely (or with probability one) can be critical for the deployment of Reinforcement Learning (RL) in real-life applications.
Self-consistent Gradient-like Eigen Decomposition in Solving Schrödinger Equations
The Schr\"odinger equation is at the heart of modern quantum mechanics.
AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation
software suite as a black-box oracle to score the target specificity and affinity of designed antibodies \textit{in silico} in an unconstrained fashion~\citep{robert2021one}.
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces.
Ranked #1 on
Molecular Graph Generation
on ZINC
Are we Forgetting about Compositional Optimisers in Bayesian Optimisation?
Bayesian optimisation presents a sample-efficient methodology for global optimisation.
Compositional ADAM: An Adaptive Compositional Solver
In this paper, we present C-ADAM, the first adaptive solver for compositional problems involving a non-linear functional nesting of expected values.
Learning High-level Representations from Demonstrations
A common approach to HL, is to provide the agent with a number of high-level skills that solve small parts of the overall problem.
Balancing Two-Player Stochastic Games with Soft Q-Learning
Within the context of video games the notion of perfectly rational agents can be undesirable as it leads to uninteresting situations, where humans face tough adversarial decision makers.
An Information-Theoretic Optimality Principle for Deep Reinforcement Learning
Different learning outcomes can be demonstrated by tuning a Lagrange multiplier accordingly.
