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Found 18 papers, 3 papers with code
Efficient Model-Free Exploration in Low-Rank MDPs
A major challenge in reinforcement learning is to develop practical, sample-efficient algorithms for exploration in high-dimensional domains where generalization and function approximation is required.
Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL
We address these issues by providing the first computationally efficient algorithm that attains rate-optimal sample complexity with respect to the desired accuracy level, with minimal statistical assumptions.
Quasi-Newton Steps for Efficient Online Exp-Concave Optimization
Typical algorithms for these settings, such as the Online Newton Step (ONS), can guarantee a $O(d\ln T)$ bound on their regret after $T$ rounds, where $d$ is the dimension of the feasible set.
Model Predictive Control via On-Policy Imitation Learning
Recent approaches to data-driven MPC have used the simplest form of imitation learning known as behavior cloning to learn controllers that mimic the performance of MPC by online sampling of the trajectories of the closed-loop MPC system.
Exploiting the Curvature of Feasible Sets for Faster Projection-Free Online Learning
In this paper, we leverage recent results in parameter-free Online Learning, and develop an OCO algorithm that makes two calls to an LO Oracle per round and achieves the near-optimal $\widetilde{O}(\sqrt{T})$ regret whenever the feasible set is strongly convex.
Damped Online Newton Step for Portfolio Selection
In this paper, we build on the recent work by Haipeng et al. 2018 and present the first practical online portfolio selection algorithm with a logarithmic regret and whose per-round time and space complexities depend only logarithmically on the horizon.
Risk Monotonicity in Statistical Learning
Acquisition of data is a difficult task in many applications of machine learning, and it is only natural that one hopes and expects the population risk to decrease (better performance) monotonically with increasing data points.
Efficient Projection-Free Online Convex Optimization with Membership Oracle
However, the Frank-Wolfe algorithm and its variants do not achieve the optimal performance, in terms of regret or rate, for general convex sets.
Risk-Monotonicity in Statistical Learning
Acquisition of data is a difficult task in many applications of machine learning, and it is only natural that one hopes and expects the population risk to decrease (better performance) monotonically with increasing data points.
Learning the Linear Quadratic Regulator from Nonlinear Observations
We introduce a new algorithm, RichID, which learns a near-optimal policy for the RichLQR with sample complexity scaling only with the dimension of the latent state space and the capacity of the decoder function class.
PAC-Bayesian Bound for the Conditional Value at Risk
Conditional Value at Risk (CVaR) is a family of "coherent risk measures" which generalize the traditional mathematical expectation.
Lipschitz and Comparator-Norm Adaptivity in Online Learning
We study Online Convex Optimization in the unbounded setting where neither predictions nor gradient are constrained.
Lipschitz Adaptivity with Multiple Learning Rates in Online Learning
For MetaGrad, we further improve the computational efficiency of handling constraints on the domain of prediction, and we remove the need to specify the number of rounds in advance.
Geometry Aware Constrained Optimization Techniques for Deep Learning
In this paper, we extend some popular optimization algorithm to the Riemannian (constrained) setting.
Constant Regret, Generalized Mixability, and Mirror Descent
For a given entropy $\Phi$, losses for which a constant regret is possible using the \textsc{GAA} are called $\Phi$-mixable.
Adversarial Generation of Real-time Feedback with Neural Networks for Simulation-based Training
It is the aim of this paper to develop an efficient and effective feedback generation method for the provision of real-time feedback in SBT.
Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections
Our contributions are as follows; we first show that constraining the transition matrix to be unitary is a special case of an orthogonal constraint.
