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Found 9 papers, 2 papers with code
Parameter-free Stochastic Optimization of Variationally Coherent Functions
We design and analyze an algorithm for first-order stochastic optimization of a large class of functions on $\mathbb{R}^d$.
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case
Under the more challenging weak linear separability condition, we design an efficient algorithm with a mistake bound of $\min (2^{\widetilde{O}(K \log^2 (1/\gamma))}, 2^{\widetilde{O}(\sqrt{1/\gamma} \log K)})$.
The information-theoretic value of unlabeled data in semi-supervised learning
Learning with the knowledge of the distribution (a. k. a.
Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP
Online sparse linear regression is an online problem where an algorithm repeatedly chooses a subset of coordinates to observe in an adversarially chosen feature vector, makes a real-valued prediction, receives the true label, and incurs the squared loss.
Coin Betting and Parameter-Free Online Learning
We present a new intuitive framework to design parameter-free algorithms for \emph{both} online linear optimization over Hilbert spaces and for learning with expert advice, based on reductions to betting on outcomes of adversarial coins.
Scale-Free Online Learning
We design and analyze algorithms for online linear optimization that have optimal regret and at the same time do not need to know any upper or lower bounds on the norm of the loss vectors.
Hardness of Online Sleeping Combinatorial Optimization Problems
We show that several online combinatorial optimization problems that admit efficient no-regret algorithms become computationally hard in the sleeping setting where a subset of actions becomes unavailable in each round.
Improved Algorithms for Linear Stochastic Bandits
We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic multi-armed bandit problem.
Estimation of Rényi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs
We present simple and computationally efficient nonparametric estimators of R\'enyi entropy and mutual information based on an i. i. d.
