RHC-UCRL is the first algorithm for safety-constrained RL under explicit adversarial dynamics, providing sub-linear regret and constraint violation guarantees by maintaining optimism over both agent and adversary policies.
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Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
21 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multi-armed bandit problem, where the payoff function is either sampled from a Gaussian process (GP) or has low RKHS norm. We resolve the important open problem of deriving regret bounds for this setting, which imply novel convergence rates for GP optimization. We analyze GP-UCB, an intuitive upper-confidence based algorithm, and bound its cumulative regret in terms of maximal information gain, establishing a novel connection between GP optimization and experimental design. Moreover, by bounding the latter in terms of operator spectra, we obtain explicit sublinear regret bounds for many commonly used covariance functions. In some important cases, our bounds have surprisingly weak dependence on the dimensionality. In our experiments on real sensor data, GP-UCB compares favorably with other heuristical GP optimization approaches.
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representative citing papers
STEMGym benchmark demonstrates that perception pipelines dominate dose efficiency in autonomous STEM over navigation methods across 33 agent setups.
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AB-SID-iVAR enables Gaussian process active learning for self-induced Boltzmann distributions by closed-form approximation of the target, with high-probability error vanishing guarantees and empirical gains on PES and drug discovery tasks.
FORGE reformulates molecular optimization as context-aware fragment ranking and replacement using mined low-to-high edit pairs, outperforming larger language models and graph methods on standard benchmarks.
Probability-of-Hit acquisition function ranks perturbation candidates by posterior probability of threshold exceedance, with asymptotic optimality proof and up to 6.4% gains on real immunology data.
A myopic MINMPC framework learns a value function offline via inverse optimization from expert data, allowing short horizons with near-optimal performance and strict integer feasibility online for hybrid systems.
Spectral bandits achieve scalable regret in graph-structured recommendation by using an effective dimension to learn good policies from few node evaluations.
LGBO integrates LLM semantic preferences continuously into Bayesian optimization iterations, with a theoretical worst-case guarantee and empirical gains including 90% of best value in 6 iterations on a wet-lab battery task.
NEON provides uncertainty-aware operator learning for composite Bayesian optimization in function spaces using a single network, achieving claimed SOTA with orders of magnitude fewer parameters than ensembles.
A mixed-variable Bayesian optimization framework based on latent variable Gaussian processes is developed and demonstrated on optimizing composition and morphology for insulating polymer nanocomposites, with an extension to multi-objective Pareto optimization.
ADKO is a decentralized framework where agents share compact GP-derived tokens and LM insights to achieve collaborative Bayesian optimization with a decomposed regret bound that includes compression and approximation losses.
Decoupled PFNs use controllable synthetic priors to train separate latent-signal and noise heads, making epistemic-aleatoric decomposition identifiable and improving acquisition in noisy settings.
Lightweight numerical bandits on text embeddings match or exceed LLM accuracy in contextual bandits at a fraction of the cost, with an embedding-based diagnostic to choose between them.
MAPFZ extends classical MAPF to non-unit integer costs on graphs with finite states, solved efficiently by CBS-NIC and Bayesian-optimized discretization, outperforming prior methods on benchmarks.
The paper derives provably tighter instantaneous regret bounds for GP-UCB and proposes (ε,δ)-optimal stopping criteria for Bayesian optimization based on those bounds.
A restarting-based nonparametric online learning method for dynamic pricing with one-point revenue feedback that achieves regret bounds scaling with time horizon and total market variation.
A dual-ranking strategy improves offline data-driven multi-objective optimization by prioritizing solutions that score well on both predicted performance and low uncertainty across different surrogate models.
Formalizes budget-constrained posterior dialog orchestration as CABO and evaluates the approach on simulated and proprietary conversational datasets.
A multi-objective Bayesian optimization framework co-optimizes CIM crossbar hardware and DNN parameters for VGG8/CIFAR-10 and VGG16/Tiny-ImageNet, achieving comparable accuracy with up to 65% smaller area and 52% lower energy.
A review of LECO in silicon photovoltaics that frames it as a multiphysics process and outlines a predictive workflow using regime maps and reduced state metrics for stable contact optimization.
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Bayesian Optimization of Crossbar-Based Compute-In-Memory System Design for Efficient DNN Inference
A multi-objective Bayesian optimization framework co-optimizes CIM crossbar hardware and DNN parameters for VGG8/CIFAR-10 and VGG16/Tiny-ImageNet, achieving comparable accuracy with up to 65% smaller area and 52% lower energy.