1 code implementation • 22 Aug 2023 • Kate Donahue, Sreenivas Gollapudi, Kostas Kollias
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.
no code implementations • 23 Jan 2023 • Pranjal Awasthi, Kush Bhatia, Sreenivas Gollapudi, Kostas Kollias
For the linear contextual bandit setup, our algorithm, based on an iterative least squares planner, achieves policy regret $\tilde{O}(\sqrt{dT} + \Delta)$.
no code implementations • 4 Nov 2022 • Aditya Bhaskara, Sreenivas Gollapudi, Sungjin Im, Kostas Kollias, Kamesh Munagala
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, $O(n^2)$.
no code implementations • NeurIPS 2021 • Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi
Graph Neural Networks~(GNNs) are a powerful class of architectures for solving learning problems on graphs.
no code implementations • NeurIPS 2021 • Sreenivas Gollapudi, Guru Guruganesh, Kostas Kollias, Pasin Manurangsi, Renato Paes Leme, Jon Schneider
We design algorithms for this problem which achieve regret $O(d\log T)$ and $\exp(O(d \log d))$.
no code implementations • 1 Jan 2021 • Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi
Finally, we empirically demonstrate the effectiveness of our proposed architecture for a variety of graph problems.
no code implementations • 1 Jan 2021 • Pranjal Awasthi, Sreenivas Gollapudi, Kostas Kollias, Apaar Sadhwani
We study the design of efficient online learning algorithms tolerant to adversarially corrupted rewards.
no code implementations • 1 Jan 2021 • Sreenivas Gollapudi, Kostas Kollias, Benjamin Plaut, Ameya Velingker
We consider the problem of routing users through a network with unknown congestion functions over an infinite time horizon.
no code implementations • NeurIPS 2020 • Aditya Bhaskara, Sreenivas Gollapudi, Kostas Kollias, Kamesh Munagala
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.
no code implementations • 8 Apr 2019 • Abhimanyu Das, Sreenivas Gollapudi, Ravi Kumar, Rina Panigrahy
In this paper we study the learnability of deep random networks from both theoretical and practical points of view.
no code implementations • ICML 2017 • Flavio Chierichetti, Sreenivas Gollapudi, Ravi Kumar, Silvio Lattanzi, Rina Panigrahy, David P. Woodruff
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.