Affine-invariant Cubic Newton with Weak Learners
Modern gradient boosting algorithms are based on Newton's method, which excels empirically but lacks global convergence guarantees. While recent work on Gradient-Regularized Newton (GRN) boosting…
nlp
MLINFO COLLECTION
材料探索
材料候補探索、分子生成、実験条件探索、ベイズ最適化に関係する技術です。
記事一覧
5 / 18Planned Restlessness: Delayed Meta Bandits with Budgets
We study episodic finite-horizon Bayesian bandits, or meta-bandits, with independent arms. Each episode draws fresh arm qualities from unknown recurring priors.
computer-vision
Simultaneous Gradient Learning in First-Price Auctions
We study equilibrium learning in discretized Bayesian games, focusing on first-price auctions as a testbed for gradient-based learning dynamics under private information. While the symmetric…
reinforcement-learning
Scalable Bayesian Quadrature via Operator-Adapted Features
Kernel methods face two recurrent bottlenecks: the cubic cost of inverting an $n\times n$ kernel matrix, as in kernel ridge regression, and the evaluation of kernel integrals, as required in kernel…
deep-learning
MiniMMD: Minimization of MMD for Sampling, Inference and Integration
Many problems in computational statistics and machine learning can be formulated as approximating a target distribution by minimizing a discrepancy over a restricted class of candidate distributions.…
deep-learning
Blind Random Search with Noisy Measurements: Almost Sure Convergence and Convergence Rates
Blind random search is a simple global optimization method that only requires the value of the loss function, without needing gradient information or an explicit loss model. However, its classical…
nlp
Efficient Deep Gaussian Process Surrogates via Mini-Batch Latent MAP Estimation
Deep Gaussian processes (DGPs) are flexible surrogate models for computer experiments with nonstationary response surfaces, but inference over their latent representations remains computationally…
deep-learning
Active Constraints for Simultaneous Guidance and Safety in Handheld Micromanipulation
In micromanipulation, erroneous hand motion limits unaided manipulation accuracy, which can affect task performance and safety in critical applications like microsurgery. Handheld robots…
math-theory
An $Ω(T^{3/4})$ lower bound for projection-free online learning with linear minimization oracles
This work considers projection-free online learning algorithms with linear minimization oracles for handling the decision set. For Online Frank--Wolfe (OFW), regret upper bounds were already known…
math-theory
GNBG-C: A Unified and Configurable Benchmark Generator for Constrained Continuous Optimization
Many real-world optimization tasks are governed not only by what should be optimized, but also by what is allowed: a solution must first satisfy physical, operational, safety, budgetary, or…
mlops
Autonomous fixed-wing interception using reinforcement learning
Medium altitude, long endurance unmanned aerial systems such as Shahed-type strike drones are an unanswered threat in modern conflicts. Conventional air defense systems are too expensive to provide a…
reinforcement-learning
Electric-Field Induction Motor with Discrete Circuit Elements and Improved Rotor Dynamics
Electric-field-based actuators and machines lead the design space at the nanoscale and in biological systems, while having the potential to be used in favorable macroscale applications which…
deep-learning
Game-Theoretic Solvers with Bayesian Opponent Modeling and Moral Rhetoric Framing for GLEE
We describe the agent we submitted to the GLEE Competition, which plays bargaining, negotiation, and persuasion games against a shared pool of human and AI opponents. The agent is built around three…
computer-vision
A Bi-level Co-design Framework for Lunar Monoped with 2D Jump and Landing Control
Legged robots for lunar exploration must be designed for low gravity, where reduced contact forces and long flight phases make leg morphology and controller choice interdependent. We study this for a…
deep-learning
PDE-Based Bounds on Optimal Exponents in Active Simple Hypothesis Testing
We present general upper and lower bounds on the optimal error exponent for the Active Simple Hypothesis Testing (ASHT) problem. ASHT has been extensively studied across diverse fields including…
math-theory
Wasserstein approximation of measures via orthogonal frames: statistical and computational complexities
Dimension-reduction methods such as PCA identify a subspace capturing most of the variation in the data, but not a distinguished basis within it. Selecting a basis in which the samples are sparsely…
computer-vision
Bayesian Propensity Score Predictive Inference for Extending Trial Findings to Target Populations
The CODIACS Vanguard randomized trial compared a depression care intervention with usual care for patients with persistent depressive symptoms following acute coronary syndrome. We assess whether its…
nlp
PYTHIA, a transferable machine learning framework for simultaneous defect and adsorption site optimization
Machine learning has permeated material optimization, providing high value. Nevertheless, its re- liance on a high number of samples limits its applicability to relatively explored material families.
deep-learning
Prediction Uncertainty Controlled Computer Model Calibration
Computer-model calibration is usually undertaken in service of prediction, yet neither Bayesian Kennedy-O'Hagan (KOH) calibration nor amortized simulation-based inference is built to control the…
nlp
SCOPE: Compiling Natural-Language Policies into Neurosymbolic Predicate Programs
Policy enforcement for LLM agents has shifted from single-turn content moderation to compound, application-specific policies evaluated over multi-turn transcripts and tool-call trajectories.…
deep-learning
In-Context Learning World Models for Robot Dual-Arm Manipulation of Soft Garment with Partial Observations
Robust manipulation of soft objects, such as garments, is a critical skill for service and household robots to adopt, but remains challenging due to its high-dimensional state space, and…
deep-learning
Synthetic-to-Real Code-Switched ASR: A Spontaneous Nepali-English Podcast Benchmark
Nepali-English code-switched conversational speech presents severe challenges for automatic speech recognition (ASR), chiefly through script confusion (inappropriate mixing of Devanagari and Latin…
nlp
Adaptive Deadline- and Energy-Aware Caching Algorithms in Dynamic Multi-UAV Edge Networks
Content caching via unmanned aerial vehicles (UAVs) becomes one of the solutions to maintain content delivery when terrestrial infrastructure is unavailable or disrupted. However, existing caching…
deep-learning
The Complexity of Strategizing Against No-Regret Learners: Pure When It Is Easy, Hard Even When Mixed
We study the computational complexity of clairvoyant strategy optimization against a known online learner in finite-horizon repeated games. In general-sum games with bounded payoffs, unless…
deep-learning