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Found 11 papers, 1 papers with code
Algorithms for $\ell_p$ Low-Rank Approximation
We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the entrywise $\ell_p$-approximation error, for any $p \geq 1$; the case $p = 2$ is the classical SVD problem.
On the Learnability of Deep Random Networks
In this paper we study the learnability of deep random networks from both theoretical and practical points of view.
Adaptive Probing Policies for Shortest Path Routing
Inspired by traffic routing applications, we consider the problem of finding the shortest path from a source $s$ to a destination $t$ in a graph, when the lengths of the edges are unknown.
Beyond GNNs: A Sample Efficient Architecture for Graph Problems
Finally, we empirically demonstrate the effectiveness of our proposed architecture for a variety of graph problems.
Online Learning under Adversarial Corruptions
We study the design of efficient online learning algorithms tolerant to adversarially corrupted rewards.
Robust Learning for Congestion-Aware Routing
We consider the problem of routing users through a network with unknown congestion functions over an infinite time horizon.
Contextual Recommendations and Low-Regret Cutting-Plane Algorithms
We design algorithms for this problem which achieve regret $O(d\log T)$ and $\exp(O(d \log d))$.
A Convergence Analysis of Gradient Descent on Graph Neural Networks
Graph Neural Networks~(GNNs) are a powerful class of architectures for solving learning problems on graphs.
Online Learning and Bandits with Queried Hints
For stochastic MAB, we also consider a stronger model where a probe reveals the reward values of the probed arms, and show that in this case, $k=3$ probes suffice to achieve parameter-independent constant regret, $O(n^2)$.
Congested Bandits: Optimal Routing via Short-term Resets
For the linear contextual bandit setup, our algorithm, based on an iterative least squares planner, achieves policy regret $\tilde{O}(\sqrt{dT} + \Delta)$.
When Are Two Lists Better than One?: Benefits and Harms in Joint Decision-making
Surprisingly, we show that for multiple of noise models, it is optimal to set $k \in [2, n-1]$ - that is, there are strict benefits to collaborating, even when the human and algorithm have equal accuracy separately.
