A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper optimizes Bayesian optimization for complex functions with macro-actions.
New methods improve global optimisation for expensive functions using lookahead strategies.
Finite-horizon sequential experimental design (SED) arises naturally in many contexts, including hyperparameter tuning in machine learning among more traditional settings. Computing the optimal policy for such problems requires solving Bellman equations, which are generally intractable. Most existing work resorts to se…
Active search is a learning paradigm for actively identifying as many members of a given class as possible. A critical target scenario is high-throughput screening for scientific discovery, such as drug or materials discovery. In this paper, we approach this problem in Bayesian decision framework. We first derive the B…
This paper presents a novel nonmyopic adaptive Gaussian process planning (GPP) framework endowed with a general class of Lipschitz continuous reward functions that can unify some active learning/sensing and Bayesian optimization criteria and offer practitioners some flexibility to specify their desired choices for defi…
Efficiently optimizes expensive functions with multi-step lookahead using one-shot optimization.
New method optimizes costly evaluations in Bayesian optimization.
A new Bayesian method optimizes time-dependent expensive functions with lookahead.
A new method optimizes spatial sampling for level set estimation in one dimension.