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publications
Lorenz gauge quantization in conformally flat spacetimes
Published in Physical Review D, 2015
In this paper we use Dirac’s method to impose the Lorenz gauge condition in a general four-dimensional conformally flat spacetime and find that there is no particle production. We show that in cosmological spacetimes with dimension D≠4 there will be particle production when the scale factor changes, and we calculate the particle production due to a sudden change.
Recommended citation: Jesse C. Cresswell and Dan N. Vollick. Lorenz gauge quantization in conformally flat spacetimes. Phys. Rev. D 91, 084008, 2015 https://journals.aps.org/prd/abstract/10.1103/PhysRevD.91.084008
Kinematic space for conical defects
Published in Journal of High Energy Physics, 2017
Kinematic space can be used as an intermediate step in the AdS/CFT dictionary and lends itself naturally to the description of diffeomorphism invariant quantities. In this work conical defect spacetimes are considered and a duality is established between partial OPE blocks and bulk fields integrated over individual geodesics, minimal or non-minimal.
Recommended citation: Jesse C. Cresswell, Amanda W. Peet. Kinematic space for conical defects. JHEP 11 (2017) 155 https://link.springer.com/article/10.1007/JHEP11(2017)155
Universal entanglement timescale for Rényi entropies
Published in Physical Review A, 2018
The growth of entanglement in an initially separable state, as measured by the purity of subsystems, can be characterized by a timescale that takes a universal form for any Hamiltonian. We show that the same timescale governs the growth of entanglement for all Rényi entropies. Since the family of Rényi entropies completely characterizes the entanglement of a pure bipartite state, our timescale is a universal feature of bipartite entanglement, depending only on the interaction Hamiltonian and the initial state.
Recommended citation: Jesse C. Cresswell. Universal entanglement timescale for Rényi entropies. Phys. Rev. A 97 022317, 2018 https://journals.aps.org/pra/abstract/10.1103/PhysRevA.97.022317
Perturbative expansion of entanglement negativity using patterned matrix calculus
Published in Physical Review A, 2019
Negativity is an entanglement monotone frequently used to quantify entanglement in bipartite states. We develop techniques in the calculus of complex, patterned matrices and use them to conduct a perturbative analysis of negativity in terms of arbitrary variations of the density operator. Our methods are well suited to study the growth and decay of entanglement in a wide range of physical systems, including the generic linear growth of entanglement in many-body systems, and have broad relevance to many functions of quantum states and observables.
Recommended citation: Jesse C. Cresswell, Ilan Tzitrin, and Aaron Z. Goldberg. Perturbative expansion of entanglement negativity using patterned matrix calculus. Phys. Rev. A 99 012322, 2019 https://journals.aps.org/pra/abstract/10.1103/PhysRevA.99.012322
Holographic relations for OPE blocks in excited states
Published in Journal of High Energy Physics, 2019
We study the holographic duality between boundary OPE blocks and geodesic integrated bulk fields in quotients of AdS3 dual to excited CFT states. The quotient geometries exhibit non-minimal geodesics between pairs of spacelike separated boundary points which modify the OPE block duality. We decompose OPE blocks into quotient invariant operators and propose a duality with bulk fields integrated over individual geodesics, minimal or non-minimal.
Recommended citation: Jesse C. Cresswell, Ian T. Jardine, and Amanda W. Peet. Holographic relations for OPE blocks in excited states. JHEP 2019 3, 58 https://link.springer.com/article/10.1007/JHEP03(2019)058
Quantum Information Approaches to Quantum Gravity
Published in University of Toronto Doctoral Thesis, 2019
In this thesis we apply techniques from quantum information theory to study quantum gravity within the framework of the anti-de Sitter / conformal field theory correspondence (AdS/CFT). We present refinements of a duality between operator product expansion (OPE) blocks in the CFT, and geodesic integrated fields in AdS. Working with excited states within AdS3/CFT2, we show how the OPE block decomposes into more fine-grained CFT observables that are dual to AdS fields integrated over non-minimal geodesics. Additionally, this thesis contains results on the dynamics of entanglement measures for general quantum systems. Results are presented for the family of quantum Renyi entropies and entanglement negativity.
Recommended citation: Jesse C. Cresswell, Quantum Information Approaches to Quantum Gravity. University of Toronto Doctoral Thesis https://tspace.library.utoronto.ca/handle/1807/97354
Operational symmetries of entangled states
Published in Journal of Physics A: Mathematical and Theoretical, 2020
There exist quantum entangled states for which all local actions on one subsystem can be equivalently realized by actions on another, thereby possessing operational symmetry. We characterize the states for which this fundamental property of entanglement does and does not hold, including multipartite and mixed states, and draw connections to quantum steering, envariance, the Reeh–Schlieder theorem, and classical entanglement.
Recommended citation: Ilan Tzitrin, Aaron Z. Goldberg, and Jesse C. Cresswell, Operational symmetries of entangled states. J. Phys. A: Math. Theor. 53 095304, 2021 https://iopscience.iop.org/article/10.1088/1751-8121/ab6fc9
C-Learning: Horizon-Aware Cumulative Accessibility Estimation
Published in International Conference on Learning Representations 2021, 2021
We introduce cumulative accessibility functions, which measure the reachability of a goal from a given state within a specified horizon. We show that optimal cumulative accessibility functions are monotonic and can trade off speed and reliability in goal-reaching by suggesting multiple paths to a single goal depending on the provided horizon. We show that our method outperforms state-of-the-art goal-reaching algorithms in success rate, sample complexity, and path optimality.
Recommended citation: Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li, Animesh Garg. C-Learning: Horizon-Aware Cumulative Accessibility Estimation. International Conference on Learning Representations https://openreview.net/forum?id=W3Wf_wKmqm9
Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows
Published in Advances in Neural Information Processing Systems, 2021
Generative modelling allows us to learn patterns in data and generate novel examples that are similar to real ones. Normalizing flows are one technique in machine learning for accomplishing this, however, they cannot directly model the space where realistic data lives. We show that composing a normalizing flow with a conformal embedding can model the data space, and demonstrate the effectiveness of this approach on real-world data sets.
Recommended citation: Brendan Leigh Ross and Jesse C. Cresswell. Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows. In Advances in Neural Information Processing Systems, volume 34, 2021 https://proceedings.neurips.cc/paper/2021/hash/dfd786998e082758be12670d856df755-Abstract.html
Federated Learning and Differential Privacy for Medical Image Analysis
Published in Nature Scientific Reports, 2022
We conduct a case study of applying a differentially private federated learning framework for the analysis of histopathology images, the largest and perhaps most complex medical images. Our work indicates that differentially private federated learning is a viable and reliable framework for the collaborative development of machine learning models in medical image analysis.
Recommended citation: Mohammed Adnan, Shivam Kalra, Jesse C. Cresswell, Graham W. Taylor, and Hamid R. Tizhoosh. Federated Learning and Differential Privacy for Medical Image Analysis. Nature Scientific Reports, 12, 1953, 2022 https://www.nature.com/articles/s41598-022-05539-7
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models
Published in Transactions on Machine Learning Research, 2022
The manifold hypothesis states that observed data lies on a low-dimensional manifold embedded in high-dimensional ambient space. We investigate the pathologies of maximum-likelihood training in the presence of this dimensionality mismatch. We formally prove that degenerate optima are achieved wherein the manifold itself is learned but not the distribution on it, a phenomenon we call manifold overfitting. We propose a class of two-step procedures consisting of a dimensionality reduction step followed by maximum-likelihood density estimation, and prove that they recover the data-generating distribution in the nonparametric regime, thus avoiding manifold overfitting.
Recommended citation: Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell, and Anthony L. Caterini. Diagnosing and Fixing Manifold Overfitting in Deep Generative Models. TMLR 2022 https://openreview.net/forum?id=0nEZCVshxS
Find Your Friends: Personalized Federated Learning with the Right Collaborators
Published in NeurIPS 2022 Workshop on Federated Learning: Recent Advances and New Challanges, 2022
In the traditional federated learning setting, a central server coordinates a network of clients to train one global model, but may serve many clients poorly due to data heterogeneity. We present a decentralized framework, FedeRiCo, where each client can learn as much or as little from other clients as is optimal for its local data distribution. Based on expectation-maximization, FedeRiCo estimates the utilities of other participants’ models on each client’s data so that everyone can select the right collaborators for learning.
Recommended citation: Yi Sui, Junfeng Wen, Yenson Lau, Brendan Leigh Ross, and Jesse C. Cresswell. Find Your Friends: Personalized Federated Learning with the Right Collaborators. NeurIPS 2022 Workshop on Federated Learning: Recent Advances and New Challanges https://arxiv.org/abs/2210.06597
CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
Published in NeurIPS 2022 Workshop on Machine Learning and the Physical Sciences, 2022
Precision measurements and new physics searches at the Large Hadron Collider require efficient simulations of particle propagation and interactions within the detectors including calorimeter showers. However, the high-dimensional representation of showers belies the relative simplicity and structure of the underlying physical laws. We propose modelling calorimeter showers first by learning their manifold structure, and then estimating the density of data across this manifold. Learning manifold structure reduces the dimensionality of the data, which enables fast training and generation when compared with competing methods.
Recommended citation: Jesse C. Cresswell, Brendan Leigh Ross, Gabriel Loaiza-Ganem, Humberto Reyes-Gonzalez, Marco Letizia, and Anthony L. Caterini. CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds. NeurIPS 2022 Workshop on Machine Learning and the Physical Sciences. https://arxiv.org/abs/2211.15380
Denoising Deep Generative Models
Published in NeurIPS 2022 Workshop on Understanding Deep Learning Through Empirical Falsification, 2022
Likelihood-based deep generative models exhibit pathological behaviour as a consequence of using high-dimensional densities to model data with low-dimensional structure. In this paper we propose two methodologies to remove the dimensionality mismatch during training. Our first approach is based on Tweedie’s formula, and the second on models which take the variance of added noise as a conditional input. We show that surprisingly, while well motivated, these approaches only sporadically improve performance over not adding noise, and that other methods of addressing the dimensionality mismatch are more empirically adequate.
Recommended citation: Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, and Anthony L. Caterini. Denoising Deep Generative Models. NeurIPS 2022 Workshop on Understanding Deep Learning Through Empirical Falsification. https://arxiv.org/abs/2212.01265
Disparate Impact in Differential Privacy from Gradient Misalignment
Published in International Conference on Learning Representations 2023, 2023
One of the most widely used techniques for private model training, differentially private stochastic gradient descent (DPSGD), frequently intensifies disparate impact on groups within data. In this work we study the fine-grained causes of unfairness in DPSGD and identify gradient misalignment due to inequitable gradient clipping as the most significant source.
Recommended citation: Maria S. Esipova, Atiyeh Ashari Ghomi, Yaqiao Luo, and Jesse C. Cresswell. Disparate Impact in Differential Privacy from Gradient Misalignment. International Conference on Learning Representations 2023 https://openreview.net/forum?id=qLOaeRvteqbx
Verifying the Union of Manifolds Hypothesis for Image Data
Published in International Conference on Learning Representations 2023, 2023
The manifold hypothesis states that data lies on an unknown manifold of low intrinsic dimension. We argue that this hypothesis does not properly capture the low-dimensional structure typically present in data, and we put forth the union of manifolds hypothesis, which accommodates the existence of non-constant intrinsic dimensions. We empirically verify this hypothesis on commonly-used image datasets, and show that classes with higher intrinsic dimensions are harder to classify.
Recommended citation: Bradley C.A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, and Gabriel Loaiza-Ganem. Verifying the Union of Manifolds Hypothesis for Image Data. International Conference on Learning Representations 2023. https://openreview.net/forum?id=Rvee9CAX4fi
Decentralized Federated Learning through Proxy Model Sharing
Published in Nature Communications, 2023
We propose a communication-efficient scheme for decentralized federated learning called ProxyFL, or proxy-based federated learning. Each participant in ProxyFL maintains two models, a private model, and a publicly shared proxy model designed to protect the participant’s privacy.
Recommended citation: Shivam Kalra, Junfeng Wen, Jesse C. Cresswell, Maksims Volkovs, and Hamid R. Tizhoosh. Decentralized federated learning through proxy model sharing. Nature Communications 14, 2899, 2023. https://www.nature.com/articles/s41467-023-38569-4
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness
Published in ArXiv Preprint, 2023
Differential privacy and randomized smoothing respectively provide certifiable guarantees against privacy and adversarial attacks on machine learning models, however, it is not well understood how implementing either defense impacts the other. We argue that it is possible to achieve both privacy guarantees and certified robustness simultaneously, and provide a framework for integrating certified robustness through randomized smoothing into differentially private model training.
Recommended citation: Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, and Nicholas Papernot. Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. ArXiv Preprint 2306.08656, 2023 https://arxiv.org/abs/2306.08656
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
Published in Advances in Neural Information Processing Systems, 2023
We study image-based generative models spanning semantically-diverse datasets to understand and improve the feature extractors and metrics used to evaluate them. We conduct the largest human experiment evaluating generative models to date, and find that no existing metric strongly correlates with human evaluations, and that diffusion models are unfairly punished by common metrics based on Inception. We show that DINOv2-ViT-L/14 is the best alternative to Inception.
Recommended citation: George Stein, Jesse C. Cresswell, Rasa Hosseingzadeh, Yi Sui, Brendan Leigh Ross, Valentin Villecroze, Anthony L. Caterini, J. Eric T. Taylor, Gabriel Loaiza-Ganem. Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models. In Advances in Neural Information Processing Systems, volume 36, 2023 https://proceedings.neurips.cc/paper_files/paper/2023/hash/0bc795afae289ed465a65a3b4b1f4eb7-Abstract-Conference.html
Neural Implicit Manifold Learning for Topology-Aware Generative Modelling
Published in Transactions on Machine Learning Research, 2024
Natural data is often constrained to a low dimensional manifold. We propose to model the data manifold implicitly as the set of zeros of a neural network. To learn the data distribution on the manifold, we introduce the constrained energy-based model, which uses a constrained variant of Langevin dynamics to train and sample within the learned manifold. The resulting model can be manipulated with an arithmetic of manifolds which allows practitioners to take unions and intersections of model manifolds.
Recommended citation: Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, and Jesse C. Cresswell. Neural Implicit Manifold Learning for Topology-Aware Generative Modelling. TMLR 2024 https://openreview.net/forum?id=lTOku838Zv
Conformal Prediction Sets Improve Human Decision Making
Published in ArXiv Preprint, 2024
In response to everyday queries, humans explicitly signal uncertainty and offer alternative answers when they are unsure. Machine learning models that output calibrated prediction sets through conformal prediction mimic this human behaviour; larger sets signal greater uncertainty while providing alternatives. In this work, we study the usefulness of conformal prediction sets as an aid for human decision making by conducting a pre-registered randomized controlled trial with conformal prediction sets provided to human subjects. With statistical significance, we find that when humans are given conformal prediction sets their accuracy on tasks improves compared to fixed-size prediction sets with the same coverage guarantee.
Recommended citation: Jesse C. Cresswell, Yi Sui, Bhargava Kumar, and Noël Vouitsis. Conformal Prediction Sets Improve Human Decision Making. ArXiv Preprint 2401.13744, 2024 https://arxiv.org/abs/2401.13744
A Geometric Explanation of the Likelihood OOD Detection Paradox
Published in ArXiv Preprint, 2024
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-distribution (OOD) data from simpler sources. Adding to the mystery, OOD samples are never generated by these DGMs despite having higher likelihoods. This two-pronged paradox has yet to be conclusively explained, making likelihood-based OOD detection unreliable. Our primary observation is that high-likelihood regions will not be generated if they contain minimal probability mass. We demonstrate how this seeming contradiction of large densities yet low probability mass can occur around data confined to low-dimensional manifolds. We also show that this scenario can be identified through local intrinsic dimension (LID) estimation, and propose a method for OOD detection which pairs the likelihoods and LID estimates obtained from a pre-trained DGM.
Recommended citation: Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini, Rahul G. Krishnan, Gabriel Loaiza-Ganem. A Geometric Explanation of the Likelihood OOD Detection Paradox. ArXiv Preprint 2403.18910, 2024 https://arxiv.org/abs/2403.18910
Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections
Published in ArXiv Preprint, 2024
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 understanding the reasons why commonly-used DGMs succeed or fail at learning distributions supported on unknown low-dimensional manifolds, as well as developing new models explicitly designed to account for manifold-supported data. This manifold lens provides both clarity as to why some DGMs (e.g. diffusion models and some generative adversarial networks) empirically surpass others (e.g. likelihood-based models such as variational autoencoders, normalizing flows, or energy-based models) at sample generation, and guidance for devising more performant DGMs. We carry out the first survey of DGMs viewed through this lens, making two novel contributions along the way.
Recommended citation: Gabriel Loaiza-Ganem, Brendan Leigh Ross, Rasa Hosseinzadeh, Anthony L. Caterini, esse C. Cresswell. Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections. ArXiv Preprint 2404.02954, 2024 https://arxiv.org/abs/2404.02954
Self-supervised Representation Learning from Random Data Projectors
Published in International Conference on Learning Representations 2024, 2024
Self-supervised representation learning (SSRL) has advanced considerably by exploiting the transformation invariance assumption under artificially designed data augmentations. This paper presents an SSRL approach that can be applied to any data modality and network architecture because it does not rely on augmentations or masking. Specifically, we show that high-quality data representations can be learned by reconstructing random data projections. We evaluate the proposed approach on a wide range of representation learning tasks that span diverse modalities and real-world applications. We show that it outperforms multiple state-of-the-art SSRL baselines.
Recommended citation: Yi Sui, Tongzi Wu, Jesse C. Cresswell, Ga Wu, George Stein, Xiao Shi Huang, Xiaochen Zhang, Maksims Volkovs. Self-supervised Representation Learning from Random Data Projectors. International Conference on Learning Representations 2024 https://openreview.net/forum?id=EpYnZpDpsQ
Data-Efficient Multimodal Fusion on a Single GPU
Published in Computer Vision and Pattern Recognition Conference 2024, 2024
The goal of multimodal alignment is to learn a single latent space that is shared between multimodal inputs. We surmise that existing unimodal encoders pre-trained on large amounts of unimodal data should provide an effective bootstrap to create multimodal models from unimodal ones at much lower costs. We therefore propose FuseMix, a multimodal augmentation scheme that operates on the latent spaces of arbitrary pre-trained unimodal encoders. Using FuseMix for multimodal alignment, we achieve competitive performance in both image-text and audio-text retrieval, with orders of magnitude less compute and data: for example, we outperform CLIP on the Flickr30K text-to-image retrieval task with ∼600× fewer GPU days and ∼80× fewer image-text pairs.
Recommended citation: Noël Vouitsis, Zhaoyan Liu, Satya Krishna Gorti, Valentin Villecroze, Jesse C. Cresswell, Guangwei Yu, Gabriel Loaiza-Ganem, and Maksims Volkovs. Data-Efficient Multimodal Fusion on a Single GPU. Computer Vision and Pattern Recognition Conference 2024 https://arxiv.org/abs/2312.10144
talks
Kinematic Space for Conical Defects
Published:
Privacy Enhancing Technologies for Gigapixel Medical Image Analysis
Published:
Due to the sensitive nature of medical data, hospitals are unable to merge their datasets to develop models. Our work indicates that differentially private federated learning is a viable and reliable framework for the collaborative development of machine learning models in medical image analysis.
CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
Published:
Quantifying Uncertainty in Model Predictions
Published:
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.