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.
A new algorithm, Regular Tree Search, tackles non-convex simulation optimization problems.
A hybrid model for Bayesian optimization handles mixed variables using MCTS for categorical and GP for continuous.
The design of neural network architectures for a new data set is a laborious task which requires human deep learning expertise. In order to make deep learning available for a broader audience, automated methods for finding a neural network architecture are vital. Recently proposed methods can already achieve human expe…
This paper proposes using a linear function approximator, rather than a deep neural network (DNN), to bias a Monte Carlo tree search (MCTS) player for general games. This is unlikely to match the potential raw playing strength of DNNs, but has advantages in terms of generality, interpretability and resources (time and …
Bayesian methods improve drug discovery experiment design.
Decision makers, such as doctors and judges, make crucial decisions such as recommending treatments to patients, and granting bails to defendants on a daily basis. Such decisions typically involve weighting the potential benefits of taking an action against the costs involved. In this work, we aim to automate this task…
Decision makers, such as doctors and judges, make crucial decisions such as recommending treatments to patients, and granting bails to defendants on a daily basis. Such decisions typically involve weighting the potential benefits of taking an action against the costs involved. In this work, we aim to automate this task…
Monte Carlo Tree Search (MCTS) algorithms have achieved great success on many challenging benchmarks (e.g., Computer Go). However, they generally require a large number of rollouts, making their applications costly. Furthermore, it is also extremely challenging to parallelize MCTS due to its inherent sequential nature:…
In this work, we consider the popular tree-based search strategy within the framework of reinforcement learning, the Monte Carlo Tree Search (MCTS), in the context of infinite-horizon discounted cost Markov Decision Process (MDP). While MCTS is believed to provide an approximate value function for a given state with en…
This paper studies effective parallelization of MCTS for computer games.
Monte-Carlo Tree Search (MCTS) methods are drawing great interest after yielding breakthrough results in computer Go. This paper proposes a Bayesian approach to MCTS that is inspired by distributionfree approaches such as UCT [13], yet significantly differs in important respects. The Bayesian framework allows potential…
Paper proposes MCTSPO for better reinforcement learning policy optimization.
New framework uses tree ensembles for contextual bandits.
The paper considers the problem of global optimization in the setup of stochastic process bandits. We introduce an UCB algorithm which builds a cascade of discretization trees based on generic chaining in order to render possible his operability over a continuous domain. The theoretical framework applies to functions u…
New algorithm improves materials discovery using max K-Armed Bandit.
Combines MCTS and neural networks for efficient multi-period financial planning.
We study an original problem of pure exploration in a strategic bandit model motivated by Monte Carlo Tree Search. It consists in identifying the best action in a game, when the player may sample random outcomes of sequentially chosen pairs of actions. We propose two strategies for the fixed-confidence setting: Maximin…
Decision tree classifiers are a widely used tool in data stream mining. The use of confidence intervals to estimate the gain associated with each split leads to very effective methods, like the popular Hoeffding tree algorithm. From a statistical viewpoint, the analysis of decision tree classifiers in a streaming setti…
The paper honors Lai's contributions to multi-armed bandits and establishes new regret bounds.
This paper analyzes regret bounds for Gaussian process Thompson sampling.
New bounds for Bayesian bandits show prior improves performance.
InfoTree improves reinforcement learning by optimizing tool use with a greedy submodular approach.
Upper bound on withdrawal success for geometric Levy alpha-stable wealth process.
Bayesian bandit algorithms with approximate inference improve regret bounds in stochastic linear bandits.
Paper improves regret bounds for Gaussian process upper confidence bound in Bayesian optimization.
Statistical model checking for PCTL on MDPs using reinforcement learning.
New method uses random decompositions for high-dimensional Bayesian optimization.
Paper shows MCTS approximates policy optimization, proposing an improved variant.
A new differentiable UCB algorithm for linear bandits learns adaptive confidence bounds.
We consider the Lipschitz bandit optimization problem with an emphasis on practical efficiency. Although there is rich literature on regret analysis of this type of problem, e.g., [Kleinberg et al. 2008, Bubeck et al. 2011, Slivkins 2014], their proposed algorithms suffer from serious practical problems including extre…
The estimation of probabilities of default (PDs) for low default portfolios by means of upper confidence bounds is a well established procedure in many financial institutions. However, there are often discussions within the institutions or between institutions and supervisors about which confidence level to use for the…
This paper improves GP-UCB by using a shifted exponential distribution for confidence parameters.
A new algorithm for better decision-making in recommendation systems.
We present an extension of Monte Carlo Tree Search (MCTS) that strongly increases its efficiency for trees with asymmetry and/or loops. Asymmetric termination of search trees introduces a type of uncertainty for which the standard upper confidence bound (UCB) formula does not account. Our first algorithm (MCTS-T), whic…
Two batch Bayesian optimization algorithms with regret guarantees.
This note gives a short, self-contained, proof of a sharp connection between Gittins indices and Bayesian upper confidence bound algorithms. I consider a Gaussian multi-armed bandit problem with discount factor . The Gittins index of an arm is shown to equal the -quantile of the posterior distribution of the arm'…
Improved Bayesian optimisation method using randomised Gaussian process UCB.
One-bit feedback suffices for a bandit problem's optimal strategy.
In this paper, the method UCB-RS, which resorts to recommendation system (RS) for enhancing the upper-confidence bound algorithm UCB, is presented. The proposed method is used for dealing with non-stationary and large-state spaces multi-armed bandit problems. The proposed method has been targeted to the problem of the …
A new method optimizes robustness measures under input uncertainty using randomized Gaussian process upper confidence bound.
Improved BO algorithms reduce prediction error under Gaussian noise.
New algorithm reduces sample complexity for planning in MDPs.
Paper aims to ensure reliable detection of out-of-distribution data with certifiable worst-case guarantees.
Bayesian sequence prediction is a simple technique for predicting future symbols sampled from an unknown measure on infinite sequences over a countable alphabet. While strong bounds on the expected cumulative error are known, there are only limited results on the distribution of this error. We prove tight high-probabil…
Upper Confidence Bound (UCB) method is arguably the most celebrated one used in online decision making with partial information feedback. Existing techniques for constructing confidence bounds are typically built upon various concentration inequalities, which thus lead to over-exploration. In this paper, we propose a n…
Sharp bounds for spanning tree entropy in planar lattices.
A new k-means algorithm using cover trees accelerates clustering.
New algorithms for GLMs adapt to non-stationary contexts.