Search Results for author: Ashish Jagmohan

Found 4 papers, 0 papers with code

Automating question generation from educational text

no code implementations26 Sep 2023 Ayan Kumar Bhowmick, Ashish Jagmohan, Aditya Vempaty, Prasenjit Dey, Leigh Hall, Jeremy Hartman, Ravi Kokku, Hema Maheshwari

The use of question-based activities (QBAs) is wide-spread in education, traditionally forming an integral part of the learning and assessment process.

Multiple-choice Question Generation +1

Deep Policy Iteration with Integer Programming for Inventory Management

no code implementations4 Dec 2021 Pavithra Harsha, Ashish Jagmohan, Jayant R. Kalagnanam, Brian Quanz, Divya Singhvi

Finally, to make RL algorithms more accessible for inventory management researchers, we also discuss a modular Python library developed that can be used to test the performance of RL algorithms with various supply chain structures.

Decision Making Management +2

Nonstationary Reinforcement Learning with Linear Function Approximation

no code implementations8 Oct 2020 Huozhi Zhou, Jinglin Chen, Lav R. Varshney, Ashish Jagmohan

We also derive the first minimax dynamic regret lower bound for nonstationary MDPs to show that our proposed algorithms are near-optimal.

reinforcement-learning Reinforcement Learning (RL)

Differentially Private Distributed Data Summarization under Covariate Shift

no code implementations NeurIPS 2019 Kanthi Sarpatwar, Karthikeyan Shanmugam, Venkata Sitaramagiridharganesh Ganapavarapu, Ashish Jagmohan, Roman Vaculin

Our central result is a novel protocol that (a) ensures the curator accesses at most $O(K^{\frac{1}{3}}|D_s| + |D_v|)$ points (b) has formal privacy guarantees on the leakage of information between the data owners and (c) closely matches the best known non-private greedy algorithm.

Data Summarization Prototype Selection

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