Advances on Adaptive Experimentation Workshop

17 posts

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Advances on Adaptive Experimentation Workshop

Advances on Adaptive Experimentation Workshop

@aaeworkshop

Sequential Learning x Adaptive Experimentation x Causal Inference - Workshop Dates: June 18th-19th, 2026 Address: 25 Howland St, London W1T 4JG, United Kingdom

London, UK Katılım Nisan 2026
6 Takip Edilen14 Takipçiler
Advances on Adaptive Experimentation Workshop
Speaker spotlight #5: Koulik Khamaru, Rutgers University. Can regularization make contextual bandits more stable? Koulik will present Stabilizing Bandit Algorithms via Regularization, on adaptive algorithms that combine low regret with reliable statistical inference.
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Advances on Adaptive Experimentation Workshop
Speaker spotlight #4: Gergely Neu, ICREA & UPF. Can stochastic control offer a new route to generative modeling? Gergely will present Value-Driven Transport, a stochastic-control approach to generative modeling via efficient sample transport.
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Advances on Adaptive Experimentation Workshop
Speaker spotlight #3: Kelly Zhang, Imperial College London. Can we build valid confidence intervals for the average reward of adaptive bandit algorithms? Kelly will present Bandit Simulation for Inference, a framework for inference with on-policy or off-policy bandit data.
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Advances on Adaptive Experimentation Workshop
Speakers include Emma Brunskill, Tor Lattimore, Gergely Neu, Koulik Khamaru, Kelly Zhang, Aurélien Bibaut, and Ian Waudby-Smith. Application deadline: May 28.
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Speaker spotlight #2: Aurélien Bibaut, @NetflixResearch. Can policy-based contextual bandits achieve fast regret without realizability? Aurélien will explore this at AAE through variance-aware pessimistic policy learning and self-normalized martingale tools for adaptive data.
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Advances on Adaptive Experimentation Workshop
Speaker spotlight #1: Tor Lattimore (@LattimoreTor), DeepMind. What can continuous-time diffusions teach us about RL, ML, and adaptive experimentation? Tor will explore this at AAE through sequential design and policy gradients for stochastic bandits.
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