Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Tiapkin, Daniil"'
Autor:
Perrault, Pierre, Belomestny, Denis, Ménard, Pierre, Moulines, Éric, Naumov, Alexey, Tiapkin, Daniil, Valko, Michal
In this paper, we introduce a novel approach for bounding the cumulant generating function (CGF) of a Dirichlet process (DP) $X \sim \text{DP}(\alpha \nu_0)$, using superadditivity. In particular, our key technical contribution is the demonstration o
Externí odkaz:
http://arxiv.org/abs/2409.18621
In this paper, we consider the problem of learning in adversarial Markov decision processes [MDPs] with an oblivious adversary in a full-information setting. The agent interacts with an environment during $T$ episodes, each of which consists of $H$ s
Externí odkaz:
http://arxiv.org/abs/2407.05704
Generative Flow Networks (GFlowNets) treat sampling from distributions over compositional discrete spaces as a sequential decision-making problem, training a stochastic policy to construct objects step by step. Recent studies have revealed strong con
Externí odkaz:
http://arxiv.org/abs/2406.13655
Autor:
Scheid, Antoine, Tiapkin, Daniil, Boursier, Etienne, Capitaine, Aymeric, Mhamdi, El Mahdi El, Moulines, Eric, Jordan, Michael I., Durmus, Alain
This work considers a repeated principal-agent bandit game, where the principal can only interact with her environment through the agent. The principal and the agent have misaligned objectives and the choice of action is only left to the agent. Howev
Externí odkaz:
http://arxiv.org/abs/2403.03811
Autor:
Tiapkin, Daniil, Belomestny, Denis, Calandriello, Daniele, Moulines, Eric, Munos, Remi, Naumov, Alexey, Perrault, Pierre, Valko, Michal, Menard, Pierre
In this paper, we introduce Randomized Q-learning (RandQL), a novel randomized model-free algorithm for regret minimization in episodic Markov Decision Processes (MDPs). To the best of our knowledge, RandQL is the first tractable model-free posterior
Externí odkaz:
http://arxiv.org/abs/2310.18186
Autor:
Tiapkin, Daniil, Belomestny, Denis, Calandriello, Daniele, Moulines, Eric, Naumov, Alexey, Perrault, Pierre, Valko, Michal, Menard, Pierre
Incorporating expert demonstrations has empirically helped to improve the sample efficiency of reinforcement learning (RL). This paper quantifies theoretically to what extent this extra information reduces RL's sample complexity. In particular, we st
Externí odkaz:
http://arxiv.org/abs/2310.17303
In this paper we consider the problem of obtaining sharp bounds for the performance of temporal difference (TD) methods with linear function approximation for policy evaluation in discounted Markov decision processes. We show that a simple algorithm
Externí odkaz:
http://arxiv.org/abs/2310.14286
The recently proposed generative flow networks (GFlowNets) are a method of training a policy to sample compositional discrete objects with probabilities proportional to a given reward via a sequence of actions. GFlowNets exploit the sequential nature
Externí odkaz:
http://arxiv.org/abs/2310.12934
In this work, we derive sharp non-asymptotic deviation bounds for weighted sums of Dirichlet random variables. These bounds are based on a novel integral representation of the density of a weighted Dirichlet sum. This representation allows us to obta
Externí odkaz:
http://arxiv.org/abs/2304.03056
We consider the problem of minimizing a non-convex function over a smooth manifold $\mathcal{M}$. We propose a novel algorithm, the Orthogonal Directions Constrained Gradient Method (ODCGM) which only requires computing a projection onto a vector spa
Externí odkaz:
http://arxiv.org/abs/2303.09261