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Found 11 papers, 1 papers with code
Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search
This work studies a central extremal graph theory problem inspired by a 1975 conjecture of Erd\H{o}s, which aims to find graphs with a given size (number of nodes) that maximize the number of edges without having 3- or 4-cycles.
Old Dog Learns New Tricks: Randomized UCB for Bandit Problems
We propose $\tt RandUCB$, a bandit strategy that builds on theoretically derived confidence intervals similar to upper confidence bound (UCB) algorithms, but akin to Thompson sampling (TS), it uses randomization to trade off exploration and exploitation.
Regret Bounds for Batched Bandits
We present simple and efficient algorithms for the batched stochastic multi-armed bandit and batched stochastic linear bandit problems.
A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players
We study a multiplayer stochastic multi-armed bandit problem in which players cannot communicate, and if two or more players pull the same arm, a collision occurs and the involved players receive zero reward.
Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes
We prove that ϴ(k d^2 / ε^2) samples are necessary and sufficient for learning a mixture of k Gaussians in R^d, up to error ε in total variation distance.
Multiplayer bandits without observing collision information
We give the first theoretical guarantees for the second model: an algorithm with a logarithmic regret, and an algorithm with a square-root regret type that does not depend on the gaps between the means.
The Minimax Learning Rates of Normal and Ising Undirected Graphical Models
Let $G$ be an undirected graph with $m$ edges and $d$ vertices.
Some techniques in density estimation
Density estimation is an interdisciplinary topic at the intersection of statistics, theoretical computer science and machine learning.
Near-optimal Sample Complexity Bounds for Robust Learning of Gaussians Mixtures via Compression Schemes
We prove that $\tilde{\Theta}(k d^2 / \varepsilon^2)$ samples are necessary and sufficient for learning a mixture of $k$ Gaussians in $\mathbb{R}^d$, up to error $\varepsilon$ in total variation distance.
Sample-Efficient Learning of Mixtures
Let $\mathcal F$ be an arbitrary class of probability distributions, and let $\mathcal{F}^k$ denote the class of $k$-mixtures of elements of $\mathcal F$.
Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks
We prove new upper and lower bounds on the VC-dimension of deep neural networks with the ReLU activation function.
