arXiv research

Optimistic likelihoods improve classification accuracy by considering nearby distributions.

Paper proposes an optimistic likelihood approximation for nonparametric likelihoods.

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

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.

Unified CS for GLMs improves bandit regret bounds.

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…

This study tackles adversarial corruption in model-based reinforcement learning.

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.

Study optimal policy regret in partially observable Markov games with adaptive opponents.

New algorithm reduces regret in sequential decision-making problems.

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

Characterizes concept classes for optimistic online learning.

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.

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 achieves nearly optimal regret with one-pass updates for GLB problems.

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{…

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.

This study compares MC and QMC methods for likelihood functions.

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.

Accelerates policy optimization in RL with optimistic and adaptive updates.

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…

Study values and optimizes forestry leases under risk and uncertainty.

New algorithms solve linear bandits in high dimensions efficiently.

Safe linear bandits over unknown polytopes avoid safety violations and suboptimal actions.

Optimistic RL algorithms are simplified for deep RL with competitive performance.

A generalized optimistic method for saddle point problems with improved complexity.

Kernel-based function approximation improves reinforcement learning performance.

Optimistic actor-critic tackles linear MDPs with parametric policies.

I calculate optimistically asymptotic behaviors of the WRT SU(2) invariants for the three-manifolds obtained from the figure-eight knot by p-surgeries with p=0,1,2,...,10, from which one can extract volumes and the Chern-Simons invariants of these closed manifolds. I conjecture that this also holds for general closed t…

This paper tackles sample-efficient reinforcement learning for partially observable Markov games.