Umut Simsekli

According to our database1, Umut Simsekli authored at least 107 papers between 2010 and 2024.

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Bibliography

2024
Piecewise deterministic generative models.
CoRR, 2024

Denoising Lévy Probabilistic Models.
CoRR, 2024

Topological Generalization Bounds for Discrete-Time Stochastic Optimization Algorithms.
CoRR, 2024

Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets.
CoRR, 2024

Differential Privacy of Noisy (S)GD under Heavy-Tailed Perturbations.
CoRR, 2024

SGD with Clipping is Secretly Estimating the Median Gradient.
CoRR, 2024

A PAC-Bayesian Link Between Generalisation and Flat Minima.
CoRR, 2024

Tighter Generalisation Bounds via Interpolation.
CoRR, 2024

Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Generalization Bounds for Heavy-Tailed SDEs through the Fractional Fokker-Planck Equation.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD than Constant Stepsize.
Trans. Mach. Learn. Res., 2023

Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent.
CoRR, 2023

Nonparametric Linear Feature Learning in Regression Through Regularisation.
CoRR, 2023

Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD.
CoRR, 2023

Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD.
CoRR, 2023

Generalization Bounds with Data-dependent Fractal Dimensions.
CoRR, 2023

Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning via Wasserstein-Based High Probability Generalisation Bounds.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions.
Proceedings of the International Conference on Machine Learning, 2023

Generalization Bounds using Data-Dependent Fractal Dimensions.
Proceedings of the International Conference on Machine Learning, 2023

Generalization Guarantees via Algorithm-dependent Rademacher Complexity.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares.
Proceedings of the International Conference on Algorithmic Learning Theory, 2023

2022
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks.
J. Mach. Learn. Res., 2022

Generalization Bounds for Stochastic Gradient Descent via Localized ε-Covers.
CoRR, 2022

Heavy-Tail Phenomenon in Decentralized SGD.
CoRR, 2022

Generalization Bounds for Stochastic Gradient Descent via Localized $\varepsilon$-Covers.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Chaotic Regularization and Heavy-Tailed Limits for Deterministic Gradient Descent.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers.
Proceedings of the International Conference on Machine Learning, 2022

Generalized Sliced Probability Metrics.
Proceedings of the IEEE International Conference on Acoustics, 2022

Rate-Distortion Theoretic Generalization Bounds for Stochastic Learning Algorithms.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021
Bayesian Allocation Model: Marginal Likelihood-Based Model Selection for Count Tensors.
IEEE J. Sel. Top. Signal Process., 2021

Generalization Properties of Stochastic Optimizers via Trajectory Analysis.
CoRR, 2021

Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Relative Positional Encoding for Transformers with Linear Complexity.
Proceedings of the 38th International Conference on Machine Learning, 2021

The Heavy-Tail Phenomenon in SGD.
Proceedings of the 38th International Conference on Machine Learning, 2021

Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections.
Proceedings of the 38th International Conference on Machine Learning, 2021

Self-Supervised VQ-VAE for One-Shot Music Style Transfer.
Proceedings of the IEEE International Conference on Acoustics, 2021

2020
Groove2Groove MIDI Dataset: synthetic accompaniments in 3k styles.
Dataset, August, 2020

Groove2Groove: One-Shot Music Style Transfer With Supervision From Synthetic Data.
IEEE ACM Trans. Audio Speech Lang. Process., 2020

Hausdorff Dimension, Stochastic Differential Equations, and Generalization in Neural Networks.
CoRR, 2020

Generalized Sliced Distances for Probability Distributions.
CoRR, 2020

Matrix Factorization for High Frequency Non Intrusive Load Monitoring: Definitions and Algorithms.
Proceedings of the NILM '20, 2020

Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Statistical and Topological Properties of Sliced Probability Divergences.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Explicit Regularisation in Gaussian Noise Injections.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Quantitative Propagation of Chaos for SGD in Wide Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise.
Proceedings of the 37th International Conference on Machine Learning, 2020

Approximate Bayesian Computation with the Sliced-Wasserstein Distance.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Synchronizing Probability Measures on Rotations via Optimal Transport.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019
Independent-Variation Matrix Factorization With Application to Energy Disaggregation.
IEEE Signal Process. Lett., 2019

On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks.
CoRR, 2019

Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya Urns.
CoRR, 2019

A framework for parallel second order incremental optimization algorithms for solving partially separable problems.
Comput. Optim. Appl., 2019

First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Generalized Sliced Wasserstein Distances.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Supervised Symbolic Music Style Translation Using Synthetic Data.
Proceedings of the 20th International Society for Music Information Retrieval Conference, 2019

A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks.
Proceedings of the 36th International Conference on Machine Learning, 2019

Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization.
Proceedings of the 36th International Conference on Machine Learning, 2019

Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions.
Proceedings of the 36th International Conference on Machine Learning, 2019

Speech Enhancement with Variational Autoencoders and Alpha-stable Distributions.
Proceedings of the IEEE International Conference on Acoustics, 2019

Probabilistic Permutation Synchronization Using the Riemannian Structure of the Birkhoff Polytope.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018
Efficient Bayesian Model Selection in PARAFAC via Stochastic Thermodynamic Integration.
IEEE Signal Process. Lett., 2018

Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions.
CoRR, 2018

A Generative Model for Non-Intrusive Load Monitoring in Commercial Buildings.
CoRR, 2018

Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization.
Proceedings of the 35th International Conference on Machine Learning, 2018

Alpha-Stable Low-Rank Plus Residual Decomposition for Speech Enhancement.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

Multichannel Audio Modeling with Elliptically Stable Tensor Decomposition.
Proceedings of the Latent Variable Analysis and Signal Separation, 2018

2017
Synthetic dataset generation for non-intrusive load monitoring in commercial buildings.
Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments, 2017

Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for Markov Chain Monte Carlo.
Proceedings of the 34th International Conference on Machine Learning, 2017

Parallelized Stochastic Gradient Markov Chain Monte Carlo algorithms for non-negative matrix factorization.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

Alpha-stable multichannel audio source separation.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

2016
Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Stochastic Quasi-Newton Langevin Monte Carlo.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Stochastic thermodynamic integration: Efficient Bayesian model selection via stochastic gradient MCMC.
Proceedings of the 2016 IEEE International Conference on Acoustics, 2016

2015
Tensor fusion: Learning in heterogeneous and distributed data (Tensör tümleştirme: Ayrı cinsten ve dağıtık verilerde öğrenme)
PhD thesis, 2015

Alpha-Stable Matrix Factorization.
IEEE Signal Process. Lett., 2015

Non-negative tensor factorization models for Bayesian audio processing.
Digit. Signal Process., 2015

HAMSI: Distributed Incremental Optimization Algorithm Using Quadratic Approximations for Partially Separable Problems.
CoRR, 2015

Learning mixed divergences in coupled matrix and tensor factorization models.
Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

A unified probabilistic framework for robust decoding of linear barcodes.
Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

Section-level modeling of musical audio for linking performances to scores in Turkish makam music.
Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

Extraction of Temporal Patterns in Multi-rate and Multi-modal Datasets.
Proceedings of the Latent Variable Analysis and Signal Separation, 2015

2014
Non-negative source-filter dynamical system for speech enhancement.
Proceedings of the IEEE International Conference on Acoustics, 2014

2013
Hierarchical and coupled non-negative dynamical systems with application to audio modeling.
Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2013

A factorization based recommender system for online services.
Proceedings of the 21st Signal Processing and Communications Applications Conference, 2013

Learning the beta-Divergence in Tweedie Compound Poisson Matrix Factorization Models.
Proceedings of the 30th International Conference on Machine Learning, 2013

Optimal weight learning for Coupled Tensor Factorization with mixed divergences.
Proceedings of the 21st European Signal Processing Conference, 2013

2012
Combined perception and control for timing in robotic music performances.
EURASIP J. Audio Speech Music. Process., 2012

Coupled tensor factorization models for polyphonic music transcription.
Proceedings of the 20th Signal Processing and Communications Applications Conference, 2012

SVD-based polyphonic music transcription.
Proceedings of the 20th Signal Processing and Communications Applications Conference, 2012

Markov Chain Monte Carlo inference for probabilistic latent tensor factorization.
Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2012

Score guided audio restoration via generalised coupled tensor factorisation.
Proceedings of the 2012 IEEE International Conference on Acoustics, 2012

Score guided musical source separation using Generalized Coupled Tensor Factorization.
Proceedings of the 20th European Signal Processing Conference, 2012

Large scale polyphonic music transcription using randomized matrix decompositions.
Proceedings of the 20th European Signal Processing Conference, 2012

2011
Real-Time Recognition of Percussive Sounds by a Model-Based Method.
EURASIP J. Adv. Signal Process., 2011

Probabilistic latent tensor factorization framework for audio modeling.
Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2011

Generalised Coupled Tensor Factorisation.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010
Automatic Music Genre Classification Using Bass Lines.
Proceedings of the 20th International Conference on Pattern Recognition, 2010


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