40 citations · 40 across the 8 of their papers we have counts for
10 papers
When is a better LLM worth paying for? Stochastic Optimization with Switching Mixed-Fidelity Oracles
Swati Gupta, Tianjiao Li
Large language models (LLMs) are increasingly used upstream of decision-making to clean data, detect label errors, and reconstruct missing values. Yet LLM-assisted data curation cr…
Why Global LLM Leaderboards Are Misleading: Small Portfolios for Heterogeneous Supervised ML
Jai Moondra, Ayela Chughtai, Bhargavi Lanka +1
Ranking LLMs via pairwise human feedback underpins current leaderboards for open-ended tasks, such as creative writing and problem-solving. We analyze ~89K comparisons in 116 langu…
Many Preferences, Few Policies: Compact Portfolios for Multi-Objective LLM Alignment
Cheol Woo Kim, Jai Moondra, Roozbeh Nahavandi +3
Aligning large language models (LLMs) requires balancing competing objectives such as helpfulness, harmlessness, and conciseness. The appropriate balance varies across users and ap…
Block-Norm Geometries for Online Mirror Descent with Sparse Losses
Swati Gupta, Jai Moondra, Mohit Singh
The performance of online mirror descent depends critically on the geometry induced by its mirror map, yet standard algorithms largely rely on two canonical choices: Euclidean and…
Provably Small Portfolios for Multiobjective Optimization with Application to Subsidized Facility Location
Swati Gupta, Jai Moondra, Mohit Singh
Many multiobjective real-world problems, such as facility location and bus routing, become more complex when optimizing the priorities of multiple stakeholders. These are often mod…
Comparison of Hyperplane Rounding for Max-Cut and Quantum Approximate Optimization Algorithm over Certain Regular Graph Families
Reuben Tate, Swati Gupta
There is a strong interest in finding challenging instances of NP-hard problems, from the perspective of showing quantum advantage. Due to the limits of near-term NISQ devices, it…
Promise of Graph Sparsification and Decomposition for Noise Reduction in QAOA: Analysis for Trapped-Ion Compilations
Jai Moondra, Phillip C. Lotshaw, Greg Mohler +1
We develop new approximate compilation schemes that significantly reduce the expense of compiling the Quantum Approximate Optimization Algorithm (QAOA) for solving the Max-Cut prob…
Balancing Notions of Equity: Trade-offs Between Fair Portfolio Sizes and Achievable Guarantees
Swati Gupta, Jai Moondra, Mohit Singh
Motivated by fairness concerns, we study the `portfolio problem': given an optimization problem with set of feasible solutions, a class of fairness objective funct…
Which norm is the fairest? Approximations for fair facility location across all ""
Swati Gupta, Jai Moondra, Mohit Singh
Fair facility location problems try to balance access costs to open facilities borne by different groups of people by minimizing the norm of these group distances. However, t…
Warm-Started QAOA with Custom Mixers Provably Converges and Computationally Beats Goemans-Williamson's Max-Cut at Low Circuit Depths
Reuben Tate, Jai Moondra, Bryan Gard +2
We generalize the Quantum Approximate Optimization Algorithm (QAOA) of Farhi et al. (2014) to allow for arbitrary separable initial states with corresponding mixers such that the s…