Search Results for author:
Found 18 papers, 7 papers with code
Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Design
Crucially, this allows us to efficiently break down the complexity of the combinatorial domain into individual decision sets, making $\textbf{GameOpt}$ scalable to large combinatorial spaces.
Gemma 2: Improving Open Language Models at a Practical Size
In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters.
BOND: Aligning LLMs with Best-of-N Distillation
Reinforcement learning from human feedback (RLHF) is a key driver of quality and safety in state-of-the-art large language models.
WARP: On the Benefits of Weight Averaged Rewarded Policies
To tackle the trade-off between KL and reward, in this paper we introduce a novel alignment strategy named Weight Averaged Rewarded Policies (WARP).
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.
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
We introduce RecurrentGemma, a family of open language models which uses Google's novel Griffin architecture.
Gemma: Open Models Based on Gemini Research and Technology
This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models.
DockGame: Cooperative Games for Multimeric Rigid Protein Docking
Most traditional and deep learning methods for docking have focused mainly on binary docking, following either a search-based, regression-based, or generative modeling paradigm.
Distributionally Robust Model-based Reinforcement Learning with Large State Spaces
Three major challenges in reinforcement learning are the complex dynamical systems with large state spaces, the costly data acquisition processes, and the deviation of real-world dynamics from the training environment deployment.
Model-based Causal Bayesian Optimization
We formalize this generalization of CBO as Adversarial Causal Bayesian Optimization (ACBO) and introduce the first algorithm for ACBO with bounded regret: Causal Bayesian Optimization with Multiplicative Weights (CBO-MW).
How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games
We consider the interaction among agents engaging in a driving task and we model it as general-sum game.
Movement Penalized Bayesian Optimization with Application to Wind Energy Systems
Contextual Bayesian optimization (CBO) is a powerful framework for sequential decision-making given side information, with important applications, e. g., in wind energy systems.
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation
We consider model-based multi-agent reinforcement learning, where the environment transition model is unknown and can only be learned via expensive interactions with the environment.
Boosting Search Engines with Interactive Agents
This paper presents first successful steps in designing search agents that learn meta-strategies for iterative query refinement in information-seeking tasks.
Contextual Games: Multi-Agent Learning with Side Information
We formulate the novel class of contextual games, a type of repeated games driven by contextual information at each round.
Learning to Play Sequential Games versus Unknown Opponents
We consider a repeated sequential game between a learner, who plays first, and an opponent who responds to the chosen action.
Mixed Strategies for Robust Optimization of Unknown Objectives
We consider robust optimization problems, where the goal is to optimize an unknown objective function against the worst-case realization of an uncertain parameter.
No-Regret Learning in Unknown Games with Correlated Payoffs
We consider the problem of learning to play a repeated multi-agent game with an unknown reward function.
