arXiv research

Deep networks reveal colour opponent cells under retinal constraints.

Algorithm learns to play against unknown opponents in sequential games.

Novel approach models opponent learning dynamics in multi-agent reinforcement learning.

This paper proposes using variational autoencoders to model opponents in multi-agent systems.

LAFF algorithm balances adaptability and non-exploitability in repeated games.

This work tackles learning Markov games with adversarial opponents and achieves both average reward and exploitation.

Study online learning in unknown Markov games with sublinear regret.

Opponent modeling improves auction market performance.

This paper investigates generalisation in multi-agent games, where the generality of the agent can be evaluated by playing against opponents it hasn't seen during training. We propose two new games with concealed information and complex, non-transitive reward structure (think rock/paper/scissors). It turns out that mos…

Algorithm improves RL model selection for repeated games with utility maximization.

New algorithm reduces online learning regret in uninformed Markov games.

DORIS algorithm achieves no-regret learning in Markov games with adversarial opponents.

We introduce Threatened Markov Decision Processes (TMDPs) as an extension of the classical Markov Decision Process framework for Reinforcement Learning (RL). TMDPs allow suporting a decision maker against potential opponents in a RL context. We also propose a level-k thinking scheme resulting in a novel learning approa…

Humans are capable of attributing latent mental contents such as beliefs or intentions to others. The social skill is critical in daily life for reasoning about the potential consequences of others' behaviors so as to plan ahead. It is known that humans use such reasoning ability recursively by considering what others …

This work improves autonomous racing by creating diverse opponents and adapting risk.

A new method simulates large, diverse populations of learning agents evolving in games.

Study best-response learning dynamics in zero-sum polymatrix games under full and minimal information settings.

The study classifies and imitates trading agents in financial markets.

New algorithm improves self-play reinforcement learning for competitive games.

AI beats 95% of humans in Rock-Paper-Scissors.

New algorithms improve on bandit feedback in matrix games with unknown payoff matrices.

Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers. However, an attacker is not usually able to directly modify another agent's observations. This might lead one to wonder: is it possible to attack an R…

Expanding models in neural fictitious play improves reinforcement learning efficiency and robustness.

Algorithm converges to Nash equilibria in competitive games.

It is now well known that decentralised optimisation can be formulated as a potential game, and game-theoretical learning algorithms can be used to find an optimum. One of the most common learning techniques in game theory is fictitious play. However fictitious play is founded on an implicit assumption that opponents' …

Optimistic Mirror Descent framework improves bidding strategies in non-stationary first-price auctions.

Deep RL drone trained to compete against classical path planning in drone racing.

Kernel testing compares cell states in single-cell data.

Study optimal policy regret in partially observable Markov games with adaptive opponents.

Automatically finds effective security strategies through reinforcement learning and self-play.

Framework detects and classifies multi-label RBC images from microscopic images.

This study reviews and evaluates clustering methods for single-cell RNA-seq data.

Forest Fire Clustering discovers cell types from single-cell data.

This paper simplifies complex game dynamics by using a recursive representation.

Proposes CCCVAE for better single-cell clustering with cell-cell communication.

Matching cells over time has long been the most difficult step in cell tracking. In this paper, we approach this problem by recasting it as a classification problem. We construct a feature set for each cell, and compute a feature difference vector between a cell in the current frame and a cell in a previous frame. Then…

Improved GPLVM model for single-cell RNA-seq data.

The study identifies all possible vector field structures on specific 2D shapes.

New model identifies cell-specific genes for cancer prognosis.

MarkerMap selects key genes for cell type analysis in single-cell RNA-seq.

Proposes CXNs for neural network computations on cell complexes.

In this paper, we employ machine learning techniques to analyze seventeen seasons (1999-2000 to 2015-2016) of NBA regular season data from every team to determine the common characteristics among NBA playoff teams. Each team was characterized by 26 predictor variables and one binary response variable taking on a value …

New metric scores perturbations across populations, not cells, improving model comparison.

Cataloging the neuronal cell types that comprise circuitry of individual brain regions is a major goal of modern neuroscience and the BRAIN initiative. Single-cell RNA sequencing can now be used to measure the gene expression profiles of individual neurons and to categorize neurons based on their gene expression profil…

The process of morphogenesis, which can be defined as an evolution of the form of an organism, is one of the most intriguing mysteries in the life sciences. It is clear, that gene expression patterns cannot explain the development of the precise geometry of an organism and its parts in space. Here, we suggest a set of …

Hippocampal dentate granule cells are among the few neuronal cell types generated throughout adult life in mammals. In the normal brain, new granule cells are generated from progenitors in the subgranular zone and integrate in a typical fashion. During the development of epilepsy, granule cell integration is profoundly…

Bayesian methods improve adversarial machine learning robustness.

NESS improves neighbor embedding for smooth cell-state transitions in single-cell data.