A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
A new algorithm for social network recommendations using side-observations.
Two algorithms minimize regret in adversarial bandit problems with side-observation losses.
Study invariant Lipschitz bandits, improving regret bounds.
This paper considers stochastic bandits with side observations, a model that accounts for both the exploration/exploitation dilemma and relationships between arms. In this setting, after pulling an arm i, the decision maker also observes the rewards for some other actions related to i. We will see that this model is su…
We consider an adversarial online learning setting where a decision maker can choose an action in every stage of the game. In addition to observing the reward of the chosen action, the decision maker gets side observations on the reward he would have obtained had he chosen some of the other actions. The observation str…
Investigates sequential problems on graph structures and large action spaces.
New algorithm for online learning with noisy side observations.
Contextual linear optimization shows naive plug-in methods can outperform direct optimization.
We study the stochastic multi-armed bandit (MAB) problem in the presence of side-observations across actions that occur as a result of an underlying network structure. In our model, a bipartite graph captures the relationship between actions and a common set of unknowns such that choosing an action reveals observations…
We consider the classical stochastic multi-armed bandit but where, from time to time and roughly with frequency , an extra observation is gathered by the agent for free. We prove that, no matter how small is the agent can ensure a regret uniformly bounded in time. More precisely, we construct an algorithm with a…
We consider a sequential learning problem with Gaussian payoffs and side information: after selecting an action , the learner receives information about the payoff of every action in the form of Gaussian observations whose mean is the same as the mean payoff, but the variance depends on the pair (and may…
A network of agents attempt to learn some unknown state of the world drawn by nature from a finite set. Agents observe private signals conditioned on the true state, and form beliefs about the unknown state accordingly. Each agent may face an identification problem in the sense that she cannot distinguish the truth in …
Optimizes arm selection with side information in Gaussian bandits.
We consider the best-arm identification problem in multi-armed bandits, which focuses purely on exploration. A player is given a fixed budget to explore a finite set of arms, and the rewards of each arm are drawn independently from a fixed, unknown distribution. The player aims to identify the arm with the largest expe…
The holographic duality can be extended to include quantum theories with broken coordinate invariance leading to the appearance of the gravitational anomalies. On the gravity side one adds the gravitational Chern-Simons term to the bulk action which gauge invariance is only up to the boundary terms. We analyze in detai…
New algorithms for efficient learning with partial information, reducing regret.