Bayesian optimization stops when a solution is within ε of the optimum with high probability.
problem Stopping Bayesian optimization prematurely based on a probabilistic criterion.
method Introducing a (ε,δ)-criterion for stopping Bayesian optimization. result Bayesian optimization satisfies the (ε,δ)-criterion under mild assumptions. The paper solves IRL for Bayesian stopping time problems.
problem Identifying optimal actions in Bayesian stopping time problems.
method Novel IRL framework using Bayesian revealed preferences.
result Identifies optimality and constructs cost function estimates.
Study proposes a stopping criterion for active learning based on error stability.
problem Improving predictive performance in active learning by adaptively annotating samples.
method Proposes a stopping criterion based on error stability for Bayesian active learning.
result Demonstrates the proposed criterion stops active learning at the appropriate timing for various models and datasets.
A new stopping rule based on E-values helps efficiently use sampling in Bayesian Deep Ensembles.
problem How long should sampling continue in Bayesian Deep Ensembles to yield significant improvements?
method Formulated as a sequential anytime-valid hypothesis test, using E-values to decide when to stop sampling.
result Only a fraction of the full-chain budget is often required for significant improvements.
Bayesian optimization has been proposed as a practical and efficient tool through which to tune parameters in many difficult settings. Recently, such techniques have been combined with real-time fMRI to propose a novel framework which turns on its head the conventional functional neuroimaging approach. This closed-loop…
A new stopping criterion for active learning based on deterministic generalization bounds.
problem Determining the optimal stopping point for active learning when data acquisition is costly.
method The proposed stopping criterion is based on the difference in expected generalization errors and hypothesis testing, derived from PAC-Bayesian theory.
result The proposed stopping criterion effectively stops active learning by combining an upper bound with a statistical test.
The paper optimizes LLM accuracy by stopping early based on consistent answers.
problem Improving LLM accuracy in math and reasoning problems.
method Bayesian stopping policy to save on sampling costs, tracking only the L-1 most frequent answer counts.
result The L=3 stopping policy is sufficient for asymptotic optimality and significantly reduces inference costs.
Develops a framework for cost-efficient Bayesian optimization with constraints.
problem Optimizing designs with minimal cost in constrained search spaces.
method Constrained multi-fidelity Bayesian optimization (CMFBO) with automatic stopping criterion.
result Minimizes overall sampling costs while ensuring feasibility.
Variational Laplace improves Bayesian neural network performance without sampling.
problem Improving Bayesian neural network performance and calibration.
method Develops a new variational Laplace method for BNNs, exploiting curvature of likelihood.
result Variational Laplace outperforms standard VI methods in test performance and calibration.
The paper analyzes and proposes a new stopping criterion for recursive Bayesian classification.
problem Limitations of conventional stopping criteria in recursive Bayesian classification.
method Geometric interpretation of state posterior progression and analysis of conventional criteria.
result Proposes a new stopping criterion to overcome limitations of conventional methods.
Bayesian models predict Collatz stopping times with high accuracy.
problem Predicting the total stopping time of Collatz sequences.
method Developed two complementary models: a hierarchical Negative Binomial regression and a mechanistic generative approximation.
result Bayesian models outperform generative approximations in predicting Collatz stopping times.
We develop the first Bayesian Optimization algorithm, BLOSSOM, which selects between multiple alternative acquisition functions and traditional local optimization at each step. This is combined with a novel stopping condition based on expected regret. This pairing allows us to obtain the best characteristics of both lo…
Stop-loss rules are often studied in the financial literature, but the stop-loss levels are seldom constructed systematically. In many papers, and indeed in practice as well, the level of the stops is too often set arbitrarily. Guided by the overarching goal in finance to maximize expected returns given available infor…
Variational Laplace improves Bayesian neural networks performance.
problem Improving Bayesian neural networks performance.
method Develops variational Laplace for BNNs, exploiting curvature of likelihood.
result Variational Laplace outperforms other inference methods.
Semiparametric Bayesian networks combine parametric and nonparametric models for flexible data analysis.
problem Combining the advantages of parametric and nonparametric models for flexible data analysis.
method Semiparametric Bayesian networks combining parametric and nonparametric conditional probability distributions. Modifications of two algorithms for structure learning from data.
result Accurately learns the combination of parametric and nonparametric components, comparable to state-of-the-art methods.
Design of experiments improves validation of biomolecular networks.
problem Efficiently validate non-machine learning designed biomolecular networks.
method Use Gaussian processes and Bayesian optimization to select experimental points.
result Developed a stopping criterion based on discrepancy metric and uncertainty.
Bayesian bandits misspecification affects UX optimization, revealing new models.
problem Misspecification of value models in Bayesian bandits impacts UX optimization.
method Formulated UXO as a restless, sleeping bandit with unobserved confounders and optional stopping. Provided model extensions to address misspecifications.
result Common misspecifications lead to sub-optimal rewards, demonstrating overdispersion's effects on bandit performance.
Study examines strategic exit timing in uncertain competition.
problem Timing of strategic exit decisions in competitive markets with uncertainty.
method Constructs a stochastic game equilibrium for exit strategies involving state variable and posterior belief process.
result Unique equilibrium found for symmetric Bayesian players.
Adaptive stopping in MCMC using classifier-based dynamics
problem Sampling from complex, unnormalized probability densities
method Training state-dependent neural classifiers
result Significant reduction in average trajectory lengths
Proposes a new model for predicting chronic conditions over time.
problem Predicting complex relationships between multiple chronic conditions.
method Continuous time Bayesian network with adaptive regularization for structure and parameter learning.
result Proposed model provides sparse, intuitive representation of chronic condition relationships.
New method to assess uncertainty in Bayesian optimization.
problem Uncertainty quantification in Bayesian optimization.
method Constructing confidence regions of the maximum point or value of the objective function.
result Unified uncertainty quantification framework for various sampling policies and stopping criteria.
SADCBO optimizes contextual variables by balancing relevance and cost.
problem Optimizing contextual variables with varying costs and unknown relevance.
method Adaptive selection of relevant contextual variables using sensitivity analysis and early stopping.
result Consistent improvement in optimization across various examples.
This paper introduces a Bayesian framework for optimizing online experiments to maximize profit.
problem Statistical flaws and reliance on proxy metrics in A/B tests compromise their effectiveness.
method Hierarchical Bayesian model for estimating conversion probability and monetary value, decision-theoretic stopping rule.
result The framework ensures experiments conclude when no variant offers a significant profit improvement, conserving resources.
We study a problem of finding an optimal stopping strategy to liquidate an asset with unknown drift. Taking a Bayesian approach, we model the initial beliefs of an individual about the drift parameter by allowing an arbitrary probability distribution to characterise the uncertainty about the drift parameter. Filtering …
A new matrix factorization method for high-dimensional data.
problem Exploiting sparse structures in complex data for better interpretability.
method Bayesian shrinkage priors and flexible sparse patterns modeled through row and column dependencies.
result Demonstrated practical advantages through simulation and soccer heatmap analysis.
We show that unconverged stochastic gradient descent can be interpreted as a procedure that samples from a nonparametric variational approximate posterior distribution. This distribution is implicitly defined as the transformation of an initial distribution by a sequence of optimization updates. By tracking the change …
Early-stopped aggregation improves computational efficiency in adaptive statistical inference.
problem Computational inefficiency in model selection and aggregation for adaptive inference.
method Early-stopped aggregation (ESA) framework: compute only a few simpler models using early-stopping criteria.
result ESA achieves optimal adaptive contraction rates in variational Bayes and frequentist settings.
New algorithm improves best arm identification in Bayesian settings.
problem Finding the arm with the highest mean in unknown distributions.
method Developed a variant of successive elimination algorithm.
result Achieved optimal performance in Bayesian setting with logarithmic gap.
LC-PFN predicts learning curve performance more accurately and faster than MCMC.
problem Bayesian extrapolation of learning curves is computationally expensive and overly restrictive.
method Prior-Data Fitted Neural Networks (PFNs) for approximate Bayesian inference.
result LC-PFN outperforms MCMC in accuracy and is significantly faster.
New method learns BN structures from data efficiently.
problem Learning sparse DAG structure of BN from continuous data.
method Consistent second-order conic integer programming with early stopping criterion.
result Near-optimal solutions to medium-size problems within reasonable time.
SOBER framework optimizes Bayesian optimization tasks efficiently.
problem Challenges in parallel Bayesian optimization.
method Probabilistic Lifting with Kernel Quadrature.
result Versatile and flexible batch Bayesian optimization.
We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs. The proposed model shows extremely competitive performance when compared to the state-of-the-art graph neural networks on semi-supervised learning benchmark experiments, and outperforms the neural net…
Combining Bayesian nonparametrics and a forward model selection strategy, we construct parsimonious Bayesian deep networks (PBDNs) that infer capacity-regularized network architectures from the data and require neither cross-validation nor fine-tuning when training the model. One of the two essential components of a PB…
We investigate and provide new insights on the sampling rule called Top-Two Thompson Sampling (TTTS). In particular, we justify its use for fixed-confidence best-arm identification. We further propose a variant of TTTS called Top-Two Transportation Cost (T3C), which disposes of the computational burden of TTTS. As our …
Solves optimal stopping problem with Poisson constraints using jumps.
problem Optimal stopping with Poisson constraints and jumps.
method Penalized backward stochastic differential equation (PBSDE) with jumps, decomposition method based on Jacod-Pham, comparison theorem of BSDEs with jumps.
result Solves American option pricing in nonlinear markets with Poisson constraints.
Neural networks optimize stopping boundaries in financial instruments.
problem Optimizing stopping boundaries in financial instruments.
method Deep neural networks and empirical risk minimization for parameterizing stopping boundaries.
result Proved existence of stopping boundary under natural assumptions.
A new BO termination criterion for HPO reduces optimization time without sacrificing test performance.
problem Determining an optimal budget for hyperparameter optimization.
method A new termination criterion based on the discrepancy between predictive and computable target performance.
result The proposed termination criterion achieves a better trade-off between test performance and optimization time.
New method stops experiments early for harm in diverse groups.
problem Early stopping of experiments for harmful treatment effects in diverse populations.
method Causal machine learning approach (CLASH) for early stopping.
result CLASH effectively stops experiments early for harmful treatment effects in diverse groups.
A survey of existing methods for stopping active learning (AL) reveals the needs for methods that are: more widely applicable; more aggressive in saving annotations; and more stable across changing datasets. A new method for stopping AL based on stabilizing predictions is presented that addresses these needs. Furthermo…
In this work we consider optimal stopping problems with conditional convex risk measures called optimised certainty equivalents. Without assuming any kind of time-consistency for the underlying family of risk measures, we derive a novel representation for the solution of the optimal stopping problem. In particular, we …
Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian optimization to adaptively select configurations, we focus on speeding up random search through adaptive resource allocation and early-stopping. We formulate hyperparameter op…
DO-IQS recovers optimal stopping region from expert trajectories, addressing specific challenges.
problem Recovering optimal stopping region from expert trajectories with unknown gain functions.
method Dynamics-Aware Offline Inverse Q-Learning incorporating temporal information and confidence-based oversampling.
result Demonstrated performance on real and artificial data, including optimal intervention for critical events.
The paper tackles optimal stopping problems using reinforcement learning and singular control.
problem Continuous-time and state-space optimal stopping problems.
method Formulated as a singular control problem with randomized stopping times and penalized cumulative residual entropy.
result Identified unique optimal exploratory strategy through dynamic programming.
New algorithm solves complex stopping problems with robust optimization.
problem Solving complex stochastic optimal stopping problems.
method Simulation-based robust optimization with exact reformulation as a zero-one bilinear program.
result Developed polynomial-time heuristics and algorithms for practical solution.
The strategy of early stopping is a regularization technique based on choosing a stopping time for an iterative algorithm. Focusing on non-parametric regression in a reproducing kernel Hilbert space, we analyze the early stopping strategy for a form of gradient-descent applied to the least-squares loss function. We pro…
Solves optimal stopping for Gauss-Markov bridges using time-space transformation.
problem Optimal stopping problem of a Gauss-Markov bridge.
method Time-space transformation approach, Picard iteration algorithm.
result Lipschitz continuity of the optimal stopping boundary and its characterization.
New algorithms improve stopping time for best arm identification.
problem Efficiently identifying the best alternative in experiments.
method Proposed algorithms with exponential-tailed stopping time.
result Proved that some algorithms never stop, leading to new methods.
Trailing stop is a popular stop-loss trading strategy by which the investor will sell the asset once its price experiences a pre-specified percentage drawdown. In this paper, we study the problem of timing buy and then sell an asset subject to a trailing stop. Under a general linear diffusion framework, we study an opt…