arXiv research

Optimistic search speeds up change point detection in large datasets.

Bayesian classifier improves robustness with optimistic score ratio.

New method minimizes regret in AMDP with high probability.

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

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

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

Accelerates policy optimization in RL with optimistic and adaptive updates.

We consider a covariate shift problem where one has access to several different training datasets for the same learning problem and a small validation set which possibly differs from all the individual training distributions. This covariate shift is caused, in part, due to unobserved features in the datasets. The objec…

PCTS optimizes noisy, delayed, multi-fidelity feedbacks in black-box optimization.

SIREN protocol corrects optimistic winner's scores in LLM evaluation.

Bayesian optimisation (BO) is a well-known efficient algorithm for finding the global optimum of expensive, black-box functions. The current practical BO algorithms have regret bounds ranging from $\mathcal{O}(\frac{logN}{\sqrt{N}})$ to $\mathcal O(e^{-\sqrt{N}})$, where $N$ is the number of evaluations. This paper exp…

We adopt a multi-view approach for analyzing two knowledge transfer settings---learning using privileged information (LUPI) and distillation---in a common framework. Under reasonable assumptions about the complexities of hypothesis spaces, and being optimistic about the expected loss achievable by the student (in disti…

We study adaptive data-dependent dimensionality reduction in the context of supervised learning in general metric spaces. Our main statistical contribution is a generalization bound for Lipschitz functions in metric spaces that are doubling, or nearly doubling. On the algorithmic front, we describe an analogue of PCA f…

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.

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.

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

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.

A fundamental problem arising in many areas of machine learning is the evaluation of the likelihood of a given observation under different nominal distributions. Frequently, these nominal distributions are themselves estimated from data, which makes them susceptible to estimation errors. We thus propose to replace each…

New method for optimistic planning in MDPs using regularization.

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

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.

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.

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

SOO uses bandit theory to optimize functions with limited evaluations.

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…

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.

The likelihood function is a fundamental component in Bayesian statistics. However, evaluating the likelihood of an observation is computationally intractable in many applications. In this paper, we propose a non-parametric approximation of the likelihood that identifies a probability measure which lies in the neighbor…

Kernel-based function approximation improves reinforcement learning performance.

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…