Category
最適化、確率統計、解釈可能性など、手法選定や評価設計の土台になる理論を扱います。
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learni
Reinforcement Learning from Verifiable Rewards works well when a task has a programmatic checker, but most lon
Learning-augmented algorithms use fallible predictions while retaining formal performance guarantees. This sur
Learning-augmented algorithms use fallible predictions while retaining formal performance guarantees. This sur
In this paper we illustrate a novel architecture generating interpretable behavior and explanations. We refer
We consider safety-critical control problems involving multiple requirements with different priorities and unc
Learning-augmented algorithms use fallible predictions while retaining formal performance guarantees. This sur
In this paper we illustrate a novel architecture generating interpretable behavior and explanations. We refer
We consider safety-critical control problems involving multiple requirements with different priorities and unc
Reinforcement Learning from Verifiable Rewards works well when a task has a programmatic checker, but most lon
Banks need conversational systems that can answer product questions, assist customers with account-related req
The existence of equilibria, which is called the realizability problem in the formal methods community, for pr
We study how far gradient descent (GD) can be accelerated by predetermined stepsizes in smooth convex optimiza
We approximate strongly continuous convex monotone semigroups by learning their Chernoff-type one-step operato
The central flow of Cohen et al. (2025) is an empirically accurate continuous-time model of gradient descent a
We study the empirical Sinkhorn estimator of the entropic optimal transport potentials under the uniform loss.
We determine the sharp restricted isometry threshold for recovery at global minima of the rank-restricted matr
We study the entropy of random graphs generated by piecewise Hölder continuous graphons. We first present a re
Bug localization is a labor-intensive task, particularly in large software systems. When abnormal behavior occ
We study contraction properties of non-stationary continuous-time mean-field games (MFGs) under discounting an
Deep neural networks often exploit spurious associations, a failure known as shortcut learning. Auditing for s
We settle the minimax-optimal alternating regret, a regret notion motivated by alternating learning dynamics i
This paper studies a class of multi-cluster aggregative games characterized by the coexistence of cooperation
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learni
The Traveling Salesman Problem (TSP) is one of the most extensively studied NP-hard optimization problems. Gen
Calibration requires probabilistic reports to be conditionally unbiased and reliably interpretable as probabil
Designing, implementing, and comparing interpretable architectures requires a formal language to represent the
Electric vehicles (EVs) represent a critical intelligent terminal within the Internet of Things (IoT). Despite
エッジ・アセンブリ・クロスオーバーを用いた進化的マルチタスクアルゴリズムを用いて、旅行販売人が最短のルートで目的地へ移動する問題を解決した。
Crash is a widely played casino game that blends strategic decision-making, probability, and computational ana
この研究では、単目標最適化問題とマルチ目標最適化問題の関係についての研究が行われました。研究者たちは、単目標問題とマルチ目標問題のパラメータ空間を考慮し、両者を統合する方法を提案しました。
This paper investigates the role of dynamic population sizes in evolutionary multi-objective optimization. Alt
Closed-loop generative selection has become a workhorse of computational drug discovery: a learned generative