4 citations · 4 across the 12 of their papers we have counts for
12 papers
Topology Obstructs Pure Foundation Neural Quantum States
Timothy Heightman, Elena Orlova, Philip Mantrov +1
Foundation models for ground states in spin-1/2 systems are a promising method for problems ranging from quantum chemistry to identifying new phase diagrams. Nearly all such models…
Hamilton-Zero: A Neural Tensor-Network Foundation Model for Ground States of Arbitrary Quadratic Qubit Hamiltonians
Timothy Heightman, Elena Orlova, Philip Mantrov +1
A central promise of useful quantum advantage is the ability to compute ground states of Hamiltonian systems beyond the reach of classical simulation methods. Here we demonstrate t…
Variance Reduction for Heavy-Tailed Monetization Metrics in Ranking Experiments via Post-Stratification
Neeti Pokharna, Olivier Jeunen, Yatharth Saraf +1
Online evaluation of ranking and retrieval systems often relies on downstream monetization metrics such as app revenue or creator earnings. These metrics are typically heavy-tailed…
Benchmarking Simulacra AI's Quantum Accurate Synthetic Data Generation for Chemical Sciences
Fabio Falcioni, Elena Orlova, Timothy Heightman +2
In this work, we benchmark \simulacra's synthetic data generation pipeline against a state-of-the-art Microsoft pipeline on a dataset of small to large systems. By analyzing the en…
Powerful A/B-Testing Metrics and Where to Find Them
Olivier Jeunen, Shubham Baweja, Neeti Pokharna +1
Online controlled experiments, colloquially known as A/B-tests, are the bread and butter of real-world recommender system evaluation. Typically, end-users are randomly assigned som…
: Off-Policy Estimation with Pairs of Policies
Olivier Jeunen, Aleksei Ustimenko
The off-policy paradigm casts recommendation as a counterfactual decision-making task, allowing practitioners to unbiasedly estimate online metrics using offline data. This leads t…
Multi-Objective Recommendation via Multivariate Policy Learning
Olivier Jeunen, Jatin Mandav, Ivan Potapov +4
Real-world recommender systems often need to balance multiple objectives when deciding which recommendations to present to users. These include behavioural signals (e.g. clicks, sh…
Learning Metrics that Maximise Power for Accelerated A/B-Tests
Olivier Jeunen, Aleksei Ustimenko
Online controlled experiments are a crucial tool to allow for confident decision-making in technology companies. A North Star metric is defined (such as long-term revenue or user r…
Variance Reduction in Ratio Metrics for Efficient Online Experiments
Shubham Baweja, Neeti Pokharna, Aleksei Ustimenko +1
Online controlled experiments, such as A/B-tests, are commonly used by modern tech companies to enable continuous system improvements. Despite their paramount importance, A/B-tests…
Learning-to-Rank with Nested Feedback
Hitesh Sagtani, Olivier Jeunen, Aleksei Ustimenko
Many platforms on the web present ranked lists of content to users, typically optimized for engagement-, satisfaction- or retention- driven metrics. Advances in the Learning-to-Ran…
On Gradient Boosted Decision Trees and Neural Rankers: A Case-Study on Short-Video Recommendations at ShareChat
Olivier Jeunen, Hitesh Sagtani, Himanshu Doi +7
Practitioners who wish to build real-world applications that rely on ranking models, need to decide which modelling paradigm to follow. This is not an easy choice to make, as the r…
Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting
Aleksei Ustimenko, Aleksandr Beznosikov
In this work, we consider rather general and broad class of Markov chains, Ito chains, that look like Euler-Maryama discretization of some Stochastic Differential Equation. The cha…