Thompson

I'll wear it to bed, says Emma Thompson after award win at Hay Festival

Emma Thompson, a renowned actress and writer, shared her thoughts on the novels she loves and the books that inspired her at the Hay Festival. She expressed her enthusiasm for the literary works that have shaped her perspective and creativity, highlighting the significance of storytelling in her life. Thompson's passion for literature is evident in her words, showcasing her appreciation for the art of writing and its impact on her career.

I'll wear it to bed, says Emma Thompson after award win at Hay Festival

Emma Thompson, a renowned actress and writer, shared her thoughts on the novels she loves and the books that inspired her at the Hay Festival. She expressed her enthusiasm for the literary works that have shaped her perspective, highlighting the significance of storytelling in her life. Thompson's passion for literature is evident in her words, as she reflects on the impact of books on her creative journey.

I'll wear it to bed, says Emma Thompson after award win at Hay Festival

Emma Thompson, a renowned actress and writer, shared her thoughts on the novels she loves and the books that inspired her at the Hay Festival. She expressed her enthusiasm for the literary works that have shaped her perspective, highlighting the significance of storytelling in her life. Thompson's comments provide insight into her creative process and the impact of literature on her artistic endeavors.

Asymptotic Optimality of Thompson Sampling for Risk-Averse Bandits with Sub-Gaussian Rewards

arXiv:2606.09191v1 Announce Type: new Abstract: We prove that $\rho\text{-}\mathrm{NPTS}_{\mathrm{SG}}$, an anchor-free nonparametric Thompson Sampling algorithm for risk-averse bandits, achieves regret matching the instance-dependent lower bound to leading order in $\log n$, establishing it as asymptotically optimal for any continuous risk functional $\rho$ (CVaR, mean-variance, Sharpe ratio, distortion risk measures, and more) on the class of distributions with bounded density and...

Khalif Tahir Thompson explores identity in 'Beautiful Land'

Khalif Tahir Thompson explores identity in 'Beautiful Land' Culture To display this content from YouTube, you must enable advertisement tracking and audience measurement. One of your browser extensions seems to be blocking the video player from loading. To watch this content, you may need to disable it on this site.

Adaptive Prior Selection in Gaussian Process Bandits with Thompson Sampling

arXiv:2502.01226v4 Announce Type: replace Abstract: Gaussian process (GP) bandits provide a powerful framework for performing blackbox optimization of unknown functions. The characteristics of the unknown function depend heavily on the assumed GP prior. Most work in the literature assume that this prior is known but in practice this seldom holds.

Contextual Scalarisation Thompson Sampling for multi-objective decisions in public media

arXiv:2605.31291v1 Announce Type: new Abstract: Recommender systems may operate under multiple, competing objectives. For example, audience reach, cultural values, public service mandate, and operational constraints must be balanced in editorial decisions of public service media. Existing approaches relying on fixed combinations of objectives or Pareto-based optimisation do not adapt to changing priorities across situations.

MINTS: Minimalist Thompson Sampling

Announce Type: cross Abstract: The Bayesian paradigm offers principled tools for sequential decision-making under uncertainty, but its reliance on a probabilistic model for all parameters can hinder the incorporation of complex structural constraints. We introduce a minimalist Bayesian framework that places a prior only on the location of the optimum, while eliminating nuisance parameters through profile likelihood. This yields a generalized posterior that naturally accommodates structural...

I'll wear it to bed, says Emma Thompson after award win at Hay Festival

I'll wear it to bed, says Emma Thompson after award win at Hay Festival

The actress and writer speaks about novels she loves and the books that inspired her.