arXiv research

This paper explores how LLMs can improve pipeline-based conversational agents.

PyKale bridges interdisciplinary ML with Python, enabling accurate predictions.

The generation of artificial data based on existing observations, known as data augmentation, is a technique used in machine learning to improve model accuracy, generalisation, and to control overfitting. Augmentor is a software package, available in both Python and Julia versions, that provides a high level API for th…

Study reveals similarities in knowledge flows between pharmaceutical and AI industries.

Due to the high computational demands executing a rigorous comparison between hyperparameter optimization (HPO) methods is often cumbersome. The goal of this paper is to facilitate a better empirical evaluation of HPO methods by providing benchmarks that are cheap to evaluate, but still represent realistic use cases. W…

Develops a machine learning pipeline for learning causal structure in time-series data.

Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various…

DreamPropeller accelerates text-to-3D generation by 4.7x with minimal loss in quality.

A scalable method for training prediction models in predict-then-optimize

The explosion of digital data has created multiple opportunities for organizations and individuals to leverage machine learning (ML) to transform the way they operate. However, the shortage of experts in the field of machine learning -- data scientists -- is often a setback to the use of ML. In an attempt to alleviate …

KaCGM models provide transparent causal inference from tabular data.

To solve a machine learning problem, one typically needs to perform data preprocessing, modeling, and hyperparameter tuning, which is known as model selection and hyperparameter optimization.The goal of automated machine learning (AutoML) is to design methods that can automatically perform model selection and hyperpara…

New method uses neural networks to infer dark matter subhalo abundance from stellar streams.

Conformal prediction ensures reliable weed spraying guarantees.

Method models other agents' behaviors without requiring direct observation.

Agent-to-agent finance aims to manage payments and trust for AI agents.

AI agents manage portfolios, improving on human oversight.

RL agents outperform baselines in asset allocation.

Agents learn to give rewards to others in a shared learning environment.

New algorithm reduces regret in multi-agent bandits with malicious agents.

Regret analysis is challenging in Multi-Agent Reinforcement Learning (MARL) primarily due to the dynamical environments and the decentralized information among agents. We attempt to solve this challenge in the context of decentralized learning in multi-agent linear-quadratic (LQ) dynamical systems. We begin with a simp…

We formulate and analyze a multi-agent model for the evolution of individual and systemic risk in which the local agents interact with each other through a central agent who, in turn, is influenced by the mean field of the local agents. The central agent is stabilized by a bistable potential, the only stabilizing force…

Algorithm maximizes total reward in multi-agent bandits with adversarial corruptions.

I2C enables agents to learn efficient communication without redundancy.

We present an effective technique for training deep learning agents capable of negotiating on a set of clauses in a contract agreement using a simple communication protocol. We use Multi Agent Reinforcement Learning to train both agents simultaneously as they negotiate with each other in the training environment. We al…

PEAR dynamically reconfigures agent roles to prevent persistent biases in multi-agent debates.

In mix-game which is an extension of minority game, there are two groups of agents; group1 plays the majority game, but the group2 plays the minority game. This paper studies the change of the average winnings of agents and volatilities vs. the change of mixture of agents in mix-game model. It finds that the correlatio…

We propose a method for modeling and learning turn-taking behaviors for accessing a shared resource. We model the individual behavior for each agent in an interaction and then use a multi-agent fusion model to generate a summary over the expected actions of the group to render the model independent of the number of age…

Many learning agents impact a financial market model, showing complex dynamics.

Reinforcement learning (RL) algorithms allow agents to learn skills and strategies to perform complex tasks without detailed instructions or expensive labelled training examples. That is, RL agents can learn, as we learn. Given the importance of learning in our intelligence, RL has been thought to be one of key compone…

Agents collaborate to reduce regret in a multi-agent linear bandit problem with side information.

Adapts agent strategies on-the-fly for better cross-play in cooperative settings.

A novel framework uses goal-conditioned reinforcement learning to generate diverse samples.

Agents need world models to generalize multi-step tasks.

ODC protocol improves learning in asynchronous multi-agent bandits.

The ability of modeling the other agents, such as understanding their intentions and skills, is essential to an agent's interactions with other agents. Conventional agent modeling relies on passive observation from demonstrations. In this work, we propose an interactive agent modeling scheme enabled by encouraging an a…

The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to operate in them. Here we introduce Relational Forward Models (RFM) for multi-agent learning, networks that can learn to make accurate predic…

EPC curriculum improves MARL performance as agent population grows.

Agents learn social skills from each other, improving performance.

This paper presents an analytical treatment of economic systems with an arbitrary number of agents that keeps track of the systems' interactions and agents' complexity. This formalism does not seek to aggregate agents. It rather replaces the standard optimization approach by a probabilistic description of both the enti…

Study uses AI agents to improve equity portfolio management.

Study improves online learning with adaptable agents in various settings.

Kernel method improves cooperative decision-making among agents.

This work formalizes and extends parameter sharing in multi-agent reinforcement learning.

We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions. Causal influence is assessed using counterfactual reasoning. At each timestep, an agent simulates alternate actions t…

A simple learning agent learns to trade in an agent-based market model.

This paper addresses reward estimation and incentive design for agents with hidden rewards.

Study of repeated principal-agent bandit game with self-interested and exploratory learning agents.