160 citations · 317 across the 55 of their papers we have counts for
12 papers · 1 filter
Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples
Noël Vouitsis, Rasa Hosseinzadeh, Brendan Leigh Ross +4
Although diffusion models can generate remarkably high-quality samples, they are intrinsically bottlenecked by their expensive iterative sampling procedure. Consistency models (CMs…
A Geometric Framework for Understanding Memorization in Generative Models
Brendan Leigh Ross, Hamidreza Kamkari, Tongzi Wu +5
As deep generative models have progressed, recent work has shown them to be capable of memorizing and reproducing training datapoints when deployed. These findings call into questi…
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka +66
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of i…
TabDPT: Scaling Tabular Foundation Models on Real Data
Junwei Ma, Valentin Thomas, Rasa Hosseinzadeh +7
Tabular data is one of the most ubiquitous sources of information worldwide, spanning a wide variety of domains. This inherent heterogeneity has slowed the development of Tabular F…
MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation
Satya Krishna Gorti, Ilan Gofman, Zhaoyan Liu +5
Text-to-SQL generation enables non-experts to interact with databases via natural language. Recent advances rely on large closed-source models like GPT-4 that present challenges in…
Conformal Prediction Sets Can Cause Disparate Impact
Jesse C. Cresswell, Bhargava Kumar, Yi Sui +1
Conformal prediction is a statistically rigorous method for quantifying uncertainty in models by having them output sets of predictions, with larger sets indicating more uncertaint…
Scaling Up Diffusion and Flow-based XGBoost Models
Jesse C. Cresswell, Taewoo Kim
Novel machine learning methods for tabular data generation are often developed on small datasets which do not match the scale required for scientific applications. We investigate a…
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks
Antoni Kowalczuk, Jan Dubiński, Atiyeh Ashari Ghomi +6
Large-scale vision models have become integral in many applications due to their unprecedented performance and versatility across downstream tasks. However, the robustness of these…
A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models
Hamidreza Kamkari, Brendan Leigh Ross, Rasa Hosseinzadeh +2
High-dimensional data commonly lies on low-dimensional submanifolds, and estimating the local intrinsic dimension (LID) of a datum -- i.e. the dimension of the submanifold it belon…
Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Rasa Hosseinzadeh +2
In recent years there has been increased interest in understanding the interplay between deep generative models (DGMs) and the manifold hypothesis. Research in this area focuses on…
A Geometric Explanation of the Likelihood OOD Detection Paradox
Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell +3
Likelihood-based deep generative models (DGMs) commonly exhibit a puzzling behaviour: when trained on a relatively complex dataset, they assign higher likelihood values to out-of-d…
Conformal Prediction Sets Improve Human Decision Making
Jesse C. Cresswell, Yi Sui, Bhargava Kumar +1
In response to everyday queries, humans explicitly signal uncertainty and offer alternative answers when they are unsure. Machine learning models that output calibrated prediction…