arXiv research

New algorithms reduce regret in online MDPs by adapting to data and variance.

Mutation improves FTRL convergence in zero-sum games.

New learning dynamics achieve fast convergence in games without needing to know utility scales.

New algorithm achieves data-dependent regret bounds in MDPs with unknown transitions.

Improved regret bounds for online convex optimization under stochastic and adversarial settings.

New bounds for online portfolio selection without smoothness assumptions.

New algorithm optimizes multi-armed bandits with low computational cost.

Recently, much work has been done on extending the scope of online learning and incremental stochastic optimization algorithms. In this paper we contribute to this effort in two ways: First, based on a new regret decomposition and a generalization of Bregman divergences, we provide a self-contained, modular analysis of…

Bayesian classifier improves robustness with optimistic score ratio.

We show that the optimistic limits of the colored Jones polynomials of the hyperbolic knots coincide with the optimistic limits of the Kashaev invariants modulo $4π^2$.

Proposes Optimistic Pessimistically Initialised Q-Learning (OPIQ) for better exploration in RL.

We discuss the relative merits of optimistic and randomized approaches to exploration in reinforcement learning. Optimistic approaches presented in the literature apply an optimistic boost to the value estimate at each state-action pair and select actions that are greedy with respect to the resulting optimistic value f…

The optimistic limit is the mathematical formulation of the classical limit which is a physical method to expect the actual limit by using saddle point method of certain potential function. The original optimistic limit of the Kashaev invariant was formulated by Yokota, and a modified formulation was suggested by the a…

Optimistic estimate predicts best fitting performance of nonlinear models.

New algorithm reduces regret in sequential decision-making problems.

New aggregation strategy handles unbounded losses with regret bounds.

Optimistic NPG improves policy optimization in online RL with efficient sample complexity.

Optimistic likelihoods improve classification accuracy by considering nearby distributions.

Characterizes concept classes for optimistic online learning.

The paper explores how regularization can lead to convergence in imperfect information games.

We show how to take any two parameter-free online learning algorithms with different regret guarantees and obtain a single algorithm whose regret is the minimum of the two base algorithms. Our method is embarrassingly simple: just add the iterates. This trick can generate efficient algorithms that adapt to many norms s…

New algorithms reduce forecasting errors by leveraging optimistic learning and hinting.

Optimal bounds on regret and constraint violation in adversarial COCO.

Omega method mitigates noise in stochastic game optimization.

New method minimizes regret in AMDP with high probability.

Unified framework for model- and value-optimistic reinforcement learning.

New algorithm expands FTRL framework with improved worst-case regret bounds.

It is well known that in stochastic multi-armed bandits (MAB), the sample mean of an arm is typically not an unbiased estimator of its true mean. In this paper, we decouple three different sources of this selection bias: adaptive \emph{sampling} of arms, adaptive \emph{stopping} of the experiment, and adaptively \emph{…

Algorithm optimizes functions without parameters, converging to global minima.

Efficiently solves exploration-exploitation in LQR using Lagrangian relaxation.

Proposes H-UCRL for efficient model-based RL with sublinear regret.

New method for optimistic planning in MDPs using regularization.

Optimistic Hedge achieves optimal regret bounds in two-player zero-sum games.

Optimistic method adapted for faster convex-concave min-max problems.

This paper develops a model of reference-dependent assessment of subjective beliefs in which loss-averse people optimally choose the expectation as the reference point to balance the current felicity from the optimistic anticipation and the future disappointment from the realisation. The choice of over-optimism or over…

Optimistic PPO variant solves linear MDPs with improved regret bound.

Study optimizes linear regression analysis for high-dimensional settings.

Paper proposes an optimistic likelihood approximation for nonparametric likelihoods.

Novel approach to universal online learning for bounded losses, closing open problems.

We address the issue of limit cycling behavior in training Generative Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for training Wasserstein GANs. Recent theoretical results have shown that optimistic mirror decent (OMD) can enjoy faster regret rates in the context of zero-sum games. WGANs …

Solves open problem on universally consistent online learning with unbounded losses.

Optimistic bounds for multi-output learning using self-bounding Lipschitz condition.

In this paper, we prove some convergence results of a special case of optimistic policy iteration algorithm for stochastic shortest path problem. We consider both Monte Carlo and $TD(λ)$ methods for the policy evaluation step under the condition that the termination state will eventually be reached almost surely.

Develops a Best-of-Both-Worlds algorithm for linear contextual bandits with Tsallis entropy.

Accelerates policy optimization in RL with optimistic and adaptive updates.

Unified framework for model-based RL with sample complexity guarantees.

Optimistic pricing algorithm handles online dynamic pricing with censored demand.

Yokota suggested an optimistic limit method of the Kashaev invariants of hyperbolic knots and showed it determines the complex volumes of the knots. His method is very effective and gives almost combinatorial method of calculating the complex volumes. However, to describe the triangulation of the knot complement, he re…