Category
PPO、モデルベースRL、RLHFなど、制御・最適化・エージェント設計に関係する技術を扱います。
クエンティング投資プラットフォームを実現するためにAI技術を活用します。
Pythonでマシンラーニングアプリを作成・共有することができるライブラリです。
ゲーム『明日方舟』の支援ツール。全日常のタスクを一括で実行可能。
LLMの推論 Transparency を高めるために、DiffusionGemmaの計算を分離しVariable Transparency とAlgorithmic Transparencyを評価します。
P
Pythonでマシンラーニングアプリを作成・共有することができるライブラリです。
LLMの推論 Transparency を高めるために、DiffusionGemmaの計算を分離しVariable Transparency とAlgorithmic Transparencyを評価します。
P
Pythonでマシンラーニングアプリを作成・共有することができるライブラリです。
ゲーム『明日方舟』の支援ツール。全日常のタスクを一括で実行可能。
Mathematical Foundations of Reinforcement Learningは、ディープラーニングにおける推論力学習の数学的基礎を網羅している。
An open source quadruped robot pet framework for developing Boston Dynamics-style four-legged robots that are
このリポジトリでは、LLMベースのエージェントアプリケーションのための強化学習の橋渡しを提供しています。
Gymnasiumは、シングルエージェントRLの疑似環境を提供するAPIです。
This study introduces an intelligent framework that integrates machine learning and deep neural network ensemb
Artificial intelligence (AI) is transforming not only what information systems researchers design, but also ho
Designing effective and fiscally sustainable policies for solar photovoltaic (PV) adoption requires balancing
Natural-language-to-SQL systems have ad- vanced rapidly on academic benchmarks, yet production enterprise sche
More than a decade in, explainable AI (XAI) for computer vision has assembled a mature toolbox: attribution, f
Multi-modal learning has demonstrated strong potential in medical applications by integrating heterogeneous da
Robotic hands vary widely in anatomical fidelity and mechanical complexity, and these structural choices influ
Cooperative rehabilitation enhances engagement, task performance, and social-motor interaction, yet it demands
Personalized autonomous packing requires robots to account for resident preferences that cannot be inferred fr
Forecasting-model selection remains difficult in heterogeneous demand because the most suitable decision rule
Distributed AI training involves recurring rounds of data exchange between multiple pairs of GPU nodes. Slowdo
Multi-Task semantic communication (SemCom) prioritizes simultaneous execution of multiple tasks over bit-accur
Offline reinforcement learning (RL) must reconcile two competing requirements: policy updates should stay near
Knowledge graphs describe reality in crisp assertions, while the systems now consuming them, foundation models
The dominance of Neural Networks (NNs) in RL is partially due to their incremental learning capability, which
Tracking depression from multi-session counseling dialogues requires estimating both current symptom severity
This paper analyzes the implementation of blockchain-based integrity mechanisms in Greek Fiscal Electronic Mec
How do people learn to become better conversationalists? This question is especially important in the context
Modern misinformation is often heard before it is read, yet fact-checking systems are still evaluated mainly o
AI reviewers can now produce many specific criticisms, but more criticism is not necessarily a better review.
We investigate what makes synthetic OCR supervision transfer to real Thai documents and use the resulting insi
Personalized assistants should not only comply with user requests but also assess whether those requests are a
Noisy annotations pose a significant challenge for supervised deep learning, as neural networks rely on large-
This technical report is a study of the use of differential game (DG) theory to solve the target-assignment an
This paper presents an energy-based controller for a multiagent robotic system designed to achieve and maintai
Soft eversion robots, also known as vine robots, have attracted growing interest for navigation and inspection
This paper presents an integrated approach to modeling human competencies by combining the theoretical foundat
Highly varying production sets increasing challenges for robotic manufacturing and indoor logistics. New capab
Grasping and holding tools while using them presents a considerable challenge not only for robots but also for
Core stability gives approval-based committee elections a strong form of coalitional proportionality, but dete
Miner extractable value (MEV) in automated market makers allows block builders to profit from transaction orde
We use tools from nonlinear algebra to study the equilibria of small linear translational spring networks. Spe
Future-aware representations and world models are increasingly used in proposal-based autonomous-driving plann
The Mini-Girona Intervention Autonomous Underwater Vehicle (I-AUV) represents an advancement in accessible und
The ability to move stably over terrain with varying slopes and textures is essential for mobile agricultural
Accurate joint encoder offsets are essential for kinematic consistency in humanoid lower limbs, yet existing c
We study the computational complexity of satisfying proportional representation -- in particular proportional,
Faithfully translating research papers into repository-level implementations remains challenging because paper
クエンティング投資プラットフォームを実現するためにAI技術を活用します。
We present SG-AMP, integrating robust depth completion with input-conditioned uncertainty, persistent panoptic
We describe SCARAB--Swarm-Capable Autonomous Robotic Aquatic Bridging. Using distributed swarm control and mul
For noisy real-world environments such as those in open public spaces, spoken dialogue systems for both autono
Standardized benchmarks have played a central role in advancing robot manipulation learning, yet most focus on
At the water-land interface, amphibious fishes encounter wet flowable substrates made of granular solid-water
Animals and robots encounter mud at the water-land interface. Like sand, mud can stay solid or flow like a flu
We study weighted fair division of indivisible mixed manna under additive valuations. First, we resolve the ge
In this work, we study radically uncoupled learning in discounted general-sum Markov games. Assuming ``$\maths
We propose a new design of fair classifiers for multi-class classification problems in the presence of vector-
Gaze-based assistive manipulation typically supports object selection, while arbitrary-position placement requ
The kinematic analysis of a generic 3R robot has been investigated with multiple approaches in the past. The a
The inverse kinematics of generic 3R robots has been investigated through multiple approaches, mainly algebrai
Bridging model-based control and learned policies in long-horizon manipulation has harbored a silent disagreem
This paper considers decentralized contingency MPC for multi-agent control under a state-only information patt
General embodied agents should perceive, predict, act, evaluate, and improve within a unified system. World mo
We seek to understand the effect of adding disruptive highly-capable new technologies to competitions by asses
We study fair allocation of indivisible goods under additive valuations and matroid constraints. A challenging
Bidding games are graph games in which a token is placed on a vertex, each player starts with an initial budge
Fair allocation of indivisible goods has largely been studied under the assumption that no prior allocation ex
We study complete allocations under nonnegative additive valuations through the positive supports of goods, fo
We study violation-feedback learning of proportionally representative approval-based committees. In each round
ゲームの一般的な強化学習用エンドポインティであるEnvironmentおよびアルゴリズムの集合。
This paper introduces a family of multiclass linear Perceptron classifiers with a multiplicative margin mechan
Recent machine learning research has increasingly focused on equilibrium analysis in non-cooperative games rat
Forecast combination is a reliable way to improve predictive performance when several forecasting models are a
This article introduces peer $k$-oversight, a property of sequential collective decision mechanisms requiring
Ensuring the security of complex systems involves the strategic allocation of defensive resources to prevent v
In applications, it is often required to test objects or people to determine their qualities in terms of certa
Approval voting is a simple and well-regarded voting rule: voters submit approval ballots (subsets of the cand
We develop a mathematical theory of superposition in neural networks using tools from frame theory and compres
In ranked-choice voting, a Condorcet-winning set is a group of candidates for which no outside candidate is pr
We study approval-based apportionment, a variant of committee elections in which candidates ("parties") can be
In this paper, we consider Robbins' problem, which is a full information variant of the well-known secretary s
Generative AI is transforming how people access information, challenging traditional advertising mechanisms bu
As electricity market participants increasingly adopt learning-based agents for their bidding strategies, elec
We study metric distortion in randomized social choice under bounded randomness: on every preference profile,
In the subjective divisibility allocation model, all goods are divisible, every agent $i$ has a non-negative a
People's trust in AI advice diverges as they use it, deepening for some and eroding for others. We study this
Compositional generalization is usually evaluated through model accuracy. We instead ask which structural or l
Modern multi-agent systems are increasingly deployed at scale over large populations of agents in settings suc
Paradigmatic interaction models explain how collective behaviors can emerge in complex systems from interactio
Vowpal Wabbitは、機械学習を進歩させるためのオンライン学習、ハッシュ、reduceなどの強力なアルゴリズムを含むシステムです。その結果、さまざまな問題に応じて、高品質な解決策を提供できます。
Mixture-of-Experts (MoE) models provide a flexible framework for partitioning complex prediction problems into
Predictive Coding (PC) is a neural learning paradigm that enables parallelizable neural network layer updates.
We consider fair allocation of indivisible goods in a setting in which agents have subjective valuation functi
Recent studies on social rankings in coalitional settings have introduced methods that rank individuals by lex
Maximizing Nash welfare over indivisible goods is a central problem in resource allocation. For additive valua
We study multilevel fair resource allocation with tree-structured hierarchical relations among agents. At each
In truthful interval covering, each agent has a private interval of unit length, and the goal is to decide whe
This paper proposes the Coronavirus Optimization Algorithm (COA), a SARS-CoV-2-inspired success-history adapti
While contemporary Evolution Strategies handle integer optimization problems effectively, their adaptation mec
Continuous optimisation methods need to balance sharing information and maintaining alternative search directi
Reinforcement learning (RL) algorithms have made strides over the past decade applying them to a wide range of
We introduce multi-winner voting with argumentative ballots (MVArg) and investigate theoretical properties. As
Consider the problem of designing a revenue-optimal auction mechanism when two heterogeneous items are sold to
The house allocation problem is a classical one-sided matching problem that concerns the assignment of a set o
We study randomized strategyproof mechanisms for locating multiple facilities on the real line. We introduce t
We introduce and study an online variant of the multi-agent contract model. In our model, agents arrive one-by
We study a dynamic coalition-formation process in the tradition of Konishi and Ray (2003): players repeatedly
This article surveys emerging directions in the mathematics of democracy. It uses three case studies --- votin
We study the strategyproof placement of \(k\) facilities on the real line for \(n\) agents who privately repor
Proportionality (PROP) is one of the simplest fairness criteria for allocating items among agents with additiv
We consider strategic facility location in Euclidean space $\mathbb R^d$, where a mechanism selects a single f
This paper presents CoupVisor, a decision-support system for the hidden-information card game Coup. It address
The maximin share (MMS) is a central fairness benchmark for allocating indivisible goods and chores. We study
We study approval-based multi-winner elections with justified representation (JR) when voters strategically re
In metric social choice, voters and candidates lie in a common but unknown metric space, voters rank candidate
We introduce the problem of designing mechanisms that incentivize strategic agents to form self-funded marketp
We study the facility location mechanism design problem where $n$ strategic agents report locations in Euclide
We study equilibrium pricing in oligopolistic data markets with budget-constrained buyers (e.g., machine learn
We examine the interplay between ordinal, preference-based solution concepts in games and the long-run behavio
Liquid民主主義の意思決定において、意思決定ネットワークの中断度を考慮した力関係を計算方法を提案。意思決定の力関係を計測し、意思決定の透明性と責任性を高める。
We introduce a repeated dynamic incentive framework for characterizing when "compliance", or full-effort hones
We consider dynamic network flows and study the following question: Which dynamic edge flows can be implemente
この研究では、熱帯気候の商業ビルでの冷房システムの制御に適した、コンテキストベースの品質多様性進化的強化学習を提案します。制御システムは、データドライブの稼働状況、日々の気象と負荷のシナリオ、コンテキスト無関係の行動記述
We introduce the study of \emph{multilateral trade}: a mechanism-design problem in which a single potential tr
We introduce the Dark Souls Learning Environment (DSLE), a containerized platform that presents all 22 boss en
We prove that no randomized integral or fractional algorithm for online vertex cover under general vertex arri
We study strategyproof mechanism design without transfers for the two-facility location problem in metric spac
We consider fair allocation of indivisible items among agents with non-negative and additive valuations. The g
A common problem in social choice is to determine whether there is a social choice procedure, such as a voting
Generative AI is shifting digital commerce from browsing toward agentic search, in which consumers delegate pr
As firms increasingly deploy machine learning for strategic decision-making, understanding algorithmic interac
Sequential allocation mechanisms contain a class of widely studied mechanisms (e.g., round-robin) in the fair
This paper studies ex-post equilibria (EPEs) in simultaneous-move games with parameter uncertainty. We first c
Infrastructure networks increasingly rely on distributed sensing to detect intrusions before attackers reach v
Consider a revenue-maximizing seller who can access a binary signal about two bidders` joint values. We explor
この研究では、既存のAIエージェントをコンプライアンス管理に対応させるためのメカニズムを提案します。この手法は、リソース割り当てを用いて、AIエージェントの行動を管理することを目的としています。
政治投票では、候補者が選出される必要がある。Candidate Resignation Monotonicityは、選出された候補者が辞任すれば、それに応じて選出される条件が変化することを示した。
The El Farol Bar game is a classical model of coordination under uncertainty that traditionally treats the ven
I study Full Justified Representation (FJR) in approval-based multiwinner elections under both the Hare and Dr
We study Evolution Strategies (ES) for continual control, where agents must adapt to changing tasks without fo
The optimal allocation of academic resources to individual students is essential for addressing learner divers