no code implementations • 18 Mar 2024 • Zohar Barak, Anupam Gupta, Inbal Talgam-Cohen
We study the $k$-facility location mechanism design problem, where the $n$ agents are strategic and might misreport their location.
no code implementations • 8 Feb 2024 • Anupam Gupta, Ashok Krishnamurthy, Lisa Singh
The results demonstrate the effectiveness of our proposed model in achieving high segmentation accuracy, indicating its potential for various applications in image analysis.
no code implementations • 1 Oct 2023 • Pranjal Awasthi, Anupam Gupta
For sorting we show that it is possible to train models on data consisting of sequences having length at most $20$, and improve the test accuracy on sequences of length $100$ from less than 1% (for standard training) to more than 92% (via task hinting).
no code implementations • 17 Mar 2019 • Rohan Ghosh, Anupam Gupta, Andrei Nakagawa, Alcimar Soares, Nitish Thakor
In this work we introduce spatiotemporal filtering in the spike-event domain, as an alternative way of channeling spatiotemporal information through to a convolutional neural network.
no code implementations • 16 Mar 2019 • Rohan Ghosh, Anupam Gupta, Siyi Tang, Alcimar Soares, Nitish Thakor
Unlike conventional frame-based sensors, event-based visual sensors output information through spikes at a high temporal resolution.
no code implementations • 22 Feb 2019 • Anupam Gupta, Tomer Koren, Kunal Talwar
We study the stochastic multi-armed bandits problem in the presence of adversarial corruption.
no code implementations • 13 Dec 2017 • Nikhil Bansal, Anupam Gupta
This note discusses proofs for convergence of first-order methods based on simple potential-function arguments.
no code implementations • 4 Jun 2017 • Lijie Chen, Anupam Gupta, Jian Li, Mingda Qiao, Ruosong Wang
We provide a novel instance-wise lower bound for the sample complexity of the problem, as well as a nontrivial sampling algorithm, matching the lower bound up to a factor of $\ln|\mathcal{F}|$.
no code implementations • 23 May 2016 • Lijie Chen, Anupam Gupta, Jian Li
In a Best-Basis instance, we are given $n$ stochastic arms with unknown reward distributions, as well as a matroid $\mathcal{M}$ over the arms.
no code implementations • 26 Mar 2009 • Anupam Gupta, Katrina Ligett, Frank McSherry, Aaron Roth, Kunal Talwar
Is it even possible to design good algorithms for this problem that preserve the privacy of the clients?
Data Structures and Algorithms Cryptography and Security Computer Science and Game Theory