Search Results for author:
Found 24 papers, 9 papers with code
Efficient Reinforcement Learning with Large Language Model Priors
In sequential decision-making (SDM) tasks, methods like reinforcement learning (RL) and heuristic search have made notable advances in specific cases.
Mixture of Attentions For Speculative Decoding
The growth in the number of parameters of Large Language Models (LLMs) has led to a significant surge in computational requirements, making them challenging and costly to deploy.
Group Robust Preference Optimization in Reward-free RLHF
Our approach builds upon reward-free direct preference optimization methods, but unlike previous approaches, it seeks a robust policy which maximizes the worst-case group performance.
Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization
This paper introduces a modular framework for Mixed-variable and Combinatorial Bayesian Optimization (MCBO) to address the lack of systematic benchmarking and standardized evaluation in the field.
End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions
Reinforcement learning (RL) exhibits impressive performance when managing complicated control tasks for robots.
Structured Q-learning For Antibody Design
Optimizing combinatorial structures is core to many real-world problems, such as those encountered in life sciences.
Effects of Safety State Augmentation on Safe Exploration
We further show that Simmer can stabilize training and improve the performance of safe RL with average constraints.
Sample-Efficient Optimisation with Probabilistic Transformer Surrogates
First, we notice that these models are trained on uniformly distributed inputs, which impairs predictive accuracy on non-uniform data - a setting arising from any typical BO loop due to exploration-exploitation trade-offs.
BOiLS: Bayesian Optimisation for Logic Synthesis
Optimising the quality-of-results (QoR) of circuits during logic synthesis is a formidable challenge necessitating the exploration of exponentially sized search spaces.
Viscos Flows: Variational Schur Conditional Sampling With Normalizing Flows
We present a method for conditional sampling for pre-trained normalizing flows when only part of an observation is available.
Online Double Oracle
Solving strategic games with huge action space is a critical yet under-explored topic in economics, operations research and artificial intelligence.
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems
Multiagent reinforcement learning (MARL) has achieved a remarkable amount of success in solving various types of video games.
HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation
Our results on the Bayesmark benchmark indicate that heteroscedasticity and non-stationarity pose significant challenges for black-box optimisers.
Ranked #1 on
Hyperparameter Optimization
on Bayesmark
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving.
Multi-View Reinforcement Learning
This paper is concerned with multi-view reinforcement learning (MVRL), which allows for decision making when agents share common dynamics but adhere to different observation models.
Derivative-Free & Order-Robust Optimisation
In this paper, we formalise order-robust optimisation as an instance of online learning minimising simple regret, and propose Vroom, a zero'th order optimisation algorithm capable of achieving vanishing regret in non-stationary environments, while recovering favorable rates under stochastic reward-generating processes.
$α^α$-Rank: Practically Scaling $α$-Rank through Stochastic Optimisation
Furthermore, we also show successful results on large joint strategy profiles with a maximum size in the order of $\mathcal{O}(2^{25})$ ($\approx 33$ million joint strategies) -- a setting not evaluable using $\alpha$-Rank with reasonable computational budget.
Wasserstein Robust Reinforcement Learning
Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world.
Distributed Multitask Reinforcement Learning with Quadratic Convergence
Multitask reinforcement learning (MTRL) suffers from scalability issues when the number of tasks or trajectories grows large.
Learning to Communicate Implicitly By Actions
The auxiliary reward for communication is integrated into the learning of the policy module.
Estimating 3D Trajectories from 2D Projections via Disjunctive Factored Four-Way Conditional Restricted Boltzmann Machines
Estimation, recognition, and near-future prediction of 3D trajectories based on their two dimensional projections available from one camera source is an exceptionally difficult problem due to uncertainty in the trajectories and environment, high dimensionality of the specific trajectory states, lack of enough labeled data and so on.
Theoretically-Grounded Policy Advice from Multiple Teachers in Reinforcement Learning Settings with Applications to Negative Transfer
This paper formally defines a setting where multiple teacher agents can provide advice to a student and introduces an algorithm to leverage both autonomous exploration and teacher's advice.
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret
Lifelong reinforcement learning provides a promising framework for developing versatile agents that can accumulate knowledge over a lifetime of experience and rapidly learn new tasks by building upon prior knowledge.
