Neural variance-aware dueling bandit algorithms achieve sublinear regret with network width m = Omega~(T^6), an improvement over the previous Omega~(T^14), under both UCB and Thompson sampling.
Neural contextual bandits with deep representation and shallow exploration.arXiv preprint arXiv:2012.01780
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EE-Net is a contextual bandit algorithm that pairs an exploitation neural net with a separate exploration neural net and proves an instance-dependent Õ(√T) regret bound while beating linear and neural baselines on real data.
Proposes projected quantum kernels with misspecified GP bandit algorithms and regret bounds to trade off expressivity against learnability in quantum kernel optimization.
VP2O maps PPO to SVGD in a MoE architecture using functional kernels and expert orthogonalization, claiming +179 ELO on Codeforces and 32% token reduction on AIME for a 33B/4B model.
citing papers explorer
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Neural Variance-aware Dueling Bandits with Deep Representation and Shallow Exploration
Neural variance-aware dueling bandit algorithms achieve sublinear regret with network width m = Omega~(T^6), an improvement over the previous Omega~(T^14), under both UCB and Thompson sampling.
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Neural Exploitation and Exploration of Contextual Bandits
EE-Net is a contextual bandit algorithm that pairs an exploitation neural net with a separate exploration neural net and proves an instance-dependent Õ(√T) regret bound while beating linear and neural baselines on real data.
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Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization
Proposes projected quantum kernels with misspecified GP bandit algorithms and regret bounds to trade off expressivity against learnability in quantum kernel optimization.
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Variational Proximal Policy Optimization
VP2O maps PPO to SVGD in a MoE architecture using functional kernels and expert orthogonalization, claiming +179 ELO on Codeforces and 32% token reduction on AIME for a 33B/4B model.