no code implementations • 16 Mar 2024 • Tingting Tang, Yue Niu, Salman Avestimehr, Murali Annavaram
Eclipse adds noise to the low-rank singular values instead of the entire graph, thereby preserving the graph privacy while still maintaining enough of the graph structure to maintain model utility.
no code implementations • 13 Mar 2024 • Lei Gao, Yue Niu, Tingting Tang, Salman Avestimehr, Murali Annavaram
Evaluations show Ethos is more effective in removing undesired knowledge and maintaining the overall model performance compared to current task arithmetic methods.
no code implementations • 8 Jun 2023 • Tung D. Nguyen, Yixiang Wu, Tingting Tang, Amy Veprauskas, Ying Zhou, Behzad Djafari Rouhani, Zhisheng Shuai
Our earlier work in \cite{nguyen2022population} shows that concentrating the resources on the upstream end tends to maximize the total biomass in a metapopulation model for a stream species.
no code implementations • 27 Jul 2021 • Tingting Tang, Ramy E. Ali, Hanieh Hashemi, Tynan Gangwani, Salman Avestimehr, Murali Annavaram
Much of the overhead in prior schemes comes from the fact that they tightly couple coding for all three problems into a single framework.
no code implementations • 11 Jan 2021 • Hrishikesh Bodas, Benjamin Drabkin, Caleb Fong, Su Jin, Justin Kim, Wenxuan Li, Alexandra Seceleanu, Tingting Tang, Brendan Williams
We study several consequences of the packing problem, a conjecture from combinatorial optimization, using algebraic invariants of square-free monomial ideals.
Combinatorial Optimization Commutative Algebra Combinatorics 13C70, 13F55, 05E40, 05C65, 05C15
no code implementations • 11 Jan 2021 • João Camarneiro, Benjamin Drabkin, Duarte Fragoso, William Frendreiss, Daniel Hoffman, Alexandra Seceleanu, Tingting Tang, Sewon Yang
Continuing a well established tradition of associating convex bodies to monomial ideals, we initiate a program to construct asymptotic Newton polyhedra from decompositions of monomial ideals.
Commutative Algebra Combinatorics 13F55, 13F20, 52B20, 14M25
no code implementations • 24 Jun 2020 • Edgar A. Bernal, Jonathan D. Hauenstein, Dhagash Mehta, Margaret H. Regan, Tingting Tang
This article views locating the real discriminant locus as a supervised classification problem in machine learning where the goal is to determine classification boundaries over the parameter space, with the classes being the number of real solutions.
no code implementations • 17 Oct 2018 • Dhagash Mehta, Tianran Chen, Tingting Tang, Jonathan D. Hauenstein
By using the viewpoint of modern computational algebraic geometry, we explore properties of the optimization landscapes of the deep linear neural network models.