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Umut Simsekli
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2020 – today
- 2024
- [c71]Benjamin Dupuis, Umut Simsekli:
Generalization Bounds for Heavy-Tailed SDEs through the Fractional Fokker-Planck Equation. ICML 2024 - [c70]Yijun Wan, Melih Barsbey, Abdellatif Zaidi, Umut Simsekli:
Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD. ICML 2024 - [i56]Paul Viallard, Maxime Haddouche, Umut Simsekli, Benjamin Guedj:
Tighter Generalisation Bounds via Interpolation. CoRR abs/2402.05101 (2024) - [i55]Benjamin Dupuis, Umut Simsekli:
Generalization Bounds for Heavy-Tailed SDEs through the Fractional Fokker-Planck Equation. CoRR abs/2402.07723 (2024) - [i54]Maxime Haddouche, Paul Viallard, Umut Simsekli, Benjamin Guedj:
A PAC-Bayesian Link Between Generalisation and Flat Minima. CoRR abs/2402.08508 (2024) - [i53]Fabian Schaipp, Guillaume Garrigos, Umut Simsekli, Robert M. Gower:
SGD with Clipping is Secretly Estimating the Median Gradient. CoRR abs/2402.12828 (2024) - [i52]Umut Simsekli, Mert Gürbüzbalaban, Sinan Yildirim, Lingjiong Zhu:
Differential Privacy of Noisy (S)GD under Heavy-Tailed Perturbations. CoRR abs/2403.02051 (2024) - [i51]Benjamin Dupuis, Paul Viallard, George Deligiannidis, Umut Simsekli:
Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets. CoRR abs/2404.17442 (2024) - [i50]Rayna Andreeva, Benjamin Dupuis, Rik Sarkar, Tolga Birdal, Umut Simsekli:
Topological Generalization Bounds for Discrete-Time Stochastic Optimization Algorithms. CoRR abs/2407.08723 (2024) - [i49]Dario Shariatian, Umut Simsekli, Alain Durmus:
Denoising Lévy Probabilistic Models. CoRR abs/2407.18609 (2024) - [i48]Andrea Bertazzi, Alain Oliviero Durmus, Dario Shariatian, Umut Simsekli, Eric Moulines:
Piecewise deterministic generative models. CoRR abs/2407.19448 (2024) - 2023
- [j11]Mert Gürbüzbalaban, Yuanhan Hu, Umut Simsekli, Lingjiong Zhu:
Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD than Constant Stepsize. Trans. Mach. Learn. Res. 2023 (2023) - [c69]Anant Raj, Melih Barsbey, Mert Gürbüzbalaban, Lingjiong Zhu, Umut Simsekli:
Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares. ALT 2023: 1292-1342 - [c68]Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli:
Generalization Guarantees via Algorithm-dependent Rademacher Complexity. COLT 2023: 4863-4880 - [c67]Benjamin Dupuis, George Deligiannidis, Umut Simsekli:
Generalization Bounds using Data-Dependent Fractal Dimensions. ICML 2023: 8922-8968 - [c66]Anant Raj, Lingjiong Zhu, Mert Gürbüzbalaban, Umut Simsekli:
Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions. ICML 2023: 28578-28597 - [c65]Kruno Lehman, Alain Durmus, Umut Simsekli:
Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent. NeurIPS 2023 - [c64]Anant Raj, Umut Simsekli, Alessandro Rudi:
Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models. NeurIPS 2023 - [c63]Paul Viallard, Maxime Haddouche, Umut Simsekli, Benjamin Guedj:
Learning via Wasserstein-Based High Probability Generalisation Bounds. NeurIPS 2023 - [c62]Lingjiong Zhu, Mert Gürbüzbalaban, Anant Raj, Umut Simsekli:
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent. NeurIPS 2023 - [i47]Anant Raj
, Lingjiong Zhu, Mert Gürbüzbalaban, Umut Simsekli:
Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions. CoRR abs/2301.11885 (2023) - [i46]Benjamin Dupuis, George Deligiannidis, Umut Simsekli:
Generalization Bounds with Data-dependent Fractal Dimensions. CoRR abs/2302.02766 (2023) - [i45]Mert Gürbüzbalaban, Yuanhan Hu, Umut Simsekli, Lingjiong Zhu:
Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD. CoRR abs/2302.05516 (2023) - [i44]Anant Raj, Umut Simsekli, Alessandro Rudi:
Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models. CoRR abs/2303.17109 (2023) - [i43]Lingjiong Zhu, Mert Gürbüzbalaban, Anant Raj
, Umut Simsekli:
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent. CoRR abs/2305.12056 (2023) - [i42]Paul Viallard, Maxime Haddouche, Umut Simsekli, Benjamin Guedj:
Learning via Wasserstein-Based High Probability Generalisation Bounds. CoRR abs/2306.04375 (2023) - [i41]Yijun Wan, Abdellatif Zaidi, Umut Simsekli:
Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD. CoRR abs/2306.08125 (2023) - [i40]Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli:
Generalization Guarantees via Algorithm-dependent Rademacher Complexity. CoRR abs/2307.02501 (2023) - [i39]Bertille Follain, Umut Simsekli, Francis Bach:
Nonparametric Linear Feature Learning in Regression Through Regularisation. CoRR abs/2307.12754 (2023) - [i38]Krunoslav Lehman Pavasovic, Alain Durmus, Umut Simsekli:
Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent. CoRR abs/2310.18455 (2023) - 2022
- [j10]Alireza Fallah, Mert Gürbüzbalaban, Asuman E. Ozdaglar, Umut Simsekli, Lingjiong Zhu:
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks. J. Mach. Learn. Res. 23: 220:1-220:96 (2022) - [c61]Milad Sefidgaran, Amin Gohari, Gaël Richard, Umut Simsekli:
Rate-Distortion Theoretic Generalization Bounds for Stochastic Learning Algorithms. COLT 2022: 4416-4463 - [c60]Soheil Kolouri, Kimia Nadjahi, Shahin Shahrampour, Umut Simsekli:
Generalized Sliced Probability Metrics. ICASSP 2022: 4513-4517 - [c59]Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney:
Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers. ICML 2022: 8774-8795 - [c58]Soon Hoe Lim, Yijun Wan, Umut Simsekli:
Chaotic Regularization and Heavy-Tailed Limits for Deterministic Gradient Descent. NeurIPS 2022 - [c57]Sejun Park, Umut Simsekli, Murat A. Erdogdu:
Generalization Bounds for Stochastic Gradient Descent via Localized $\varepsilon$-Covers. NeurIPS 2022 - [i37]Milad Sefidgaran, Amin Gohari, Gaël Richard, Umut Simsekli:
Rate-Distortion Theoretic Generalization Bounds for Stochastic Learning Algorithms. CoRR abs/2203.02474 (2022) - [i36]Mert Gürbüzbalaban, Yuanhan Hu, Umut Simsekli, Kun Yuan, Lingjiong Zhu:
Heavy-Tail Phenomenon in Decentralized SGD. CoRR abs/2205.06689 (2022) - [i35]Soon Hoe Lim, Yijun Wan, Umut Simsekli:
Chaotic Regularization and Heavy-Tailed Limits for Deterministic Gradient Descent. CoRR abs/2205.11361 (2022) - [i34]Anant Raj
, Melih Barsbey, Mert Gürbüzbalaban, Lingjiong Zhu, Umut Simsekli:
Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares. CoRR abs/2206.01274 (2022) - [i33]Sejun Park, Umut Simsekli, Murat A. Erdogdu:
Generalization Bounds for Stochastic Gradient Descent via Localized ε-Covers. CoRR abs/2209.08951 (2022) - 2021
- [j9]Sinan Yildirim
, M. Burak Kurutmaz
, Melih Barsbey, Umut Simsekli, A. Taylan Cemgil
:
Bayesian Allocation Model: Marginal Likelihood-Based Model Selection for Count Tensors. IEEE J. Sel. Top. Signal Process. 15(3): 560-573 (2021) - [c56]Ondrej Cífka
, Alexey Ozerov, Umut Simsekli, Gaël Richard:
Self-Supervised VQ-VAE for One-Shot Music Style Transfer. ICASSP 2021: 96-100 - [c55]Alexander Camuto, Xiaoyu Wang, Lingjiong Zhu, Chris C. Holmes, Mert Gürbüzbalaban, Umut Simsekli:
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections. ICML 2021: 1249-1260 - [c54]Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu:
The Heavy-Tail Phenomenon in SGD. ICML 2021: 3964-3975 - [c53]Antoine Liutkus, Ondrej Cífka, Shih-Lun Wu, Umut Simsekli, Yi-Hsuan Yang, Gaël Richard:
Relative Positional Encoding for Transformers with Linear Complexity. ICML 2021: 7067-7079 - [c52]Tolga Birdal, Aaron Lou, Leonidas J. Guibas, Umut Simsekli:
Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks. NeurIPS 2021: 6776-6789 - [c51]Kimia Nadjahi, Alain Durmus, Pierre E. Jacob, Roland Badeau, Umut Simsekli:
Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections. NeurIPS 2021: 12411-12424 - [c50]Alexander Camuto, George Deligiannidis, Murat A. Erdogdu, Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu:
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms. NeurIPS 2021: 18774-18788 - [c49]Hongjian Wang, Mert Gürbüzbalaban, Lingjiong Zhu, Umut Simsekli, Murat A. Erdogdu:
Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance. NeurIPS 2021: 18866-18877 - [c48]Melih Barsbey, Milad Sefidgaran, Murat A. Erdogdu, Gaël Richard, Umut Simsekli:
Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks. NeurIPS 2021: 29364-29378 - [i32]Ondrej Cífka
, Alexey Ozerov, Umut Simsekli, Gaël Richard:
Self-Supervised VQ-VAE For One-Shot Music Style Transfer. CoRR abs/2102.05749 (2021) - [i31]Alexander Camuto, Xiaoyu Wang, Lingjiong Zhu, Chris C. Holmes, Mert Gürbüzbalaban, Umut Simsekli:
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections. CoRR abs/2102.07006 (2021) - [i30]Antoine Liutkus, Ondrej Cífka
, Shih-Lun Wu, Umut Simsekli, Yi-Hsuan Yang, Gaël Richard:
Relative Positional Encoding for Transformers with Linear Complexity. CoRR abs/2105.08399 (2021) - [i29]Melih Barsbey, Milad Sefidgaran, Murat A. Erdogdu, Gaël Richard, Umut Simsekli:
Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks. CoRR abs/2106.03795 (2021) - [i28]Alexander Camuto, George Deligiannidis, Murat A. Erdogdu, Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu:
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms. CoRR abs/2106.04881 (2021) - [i27]Kimia Nadjahi, Alain Durmus, Pierre E. Jacob, Roland Badeau, Umut Simsekli:
Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections. CoRR abs/2106.15427 (2021) - [i26]Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney:
Generalization Properties of Stochastic Optimizers via Trajectory Analysis. CoRR abs/2108.00781 (2021) - [i25]Tolga Birdal, Aaron Lou, Leonidas J. Guibas, Umut Simsekli:
Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks. CoRR abs/2111.13171 (2021) - 2020
- [j8]Ondrej Cífka
, Umut Simsekli, Gaël Richard
:
Groove2Groove: One-Shot Music Style Transfer With Supervision From Synthetic Data. IEEE ACM Trans. Audio Speech Lang. Process. 28: 2638-2650 (2020) - [c47]Tolga Birdal
, Michael Arbel, Umut Simsekli, Leonidas J. Guibas:
Synchronizing Probability Measures on Rotations via Optimal Transport. CVPR 2020: 1566-1576 - [c46]Kimia Nadjahi, Valentin De Bortoli, Alain Durmus, Roland Badeau, Umut Simsekli:
Approximate Bayesian Computation with the Sliced-Wasserstein Distance. ICASSP 2020: 5470-5474 - [c45]Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Gürbüzbalaban:
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise. ICML 2020: 8970-8980 - [c44]Valentin De Bortoli, Alain Durmus, Xavier Fontaine, Umut Simsekli:
Quantitative Propagation of Chaos for SGD in Wide Neural Networks. NeurIPS 2020 - [c43]Alexander Camuto, Matthew Willetts, Umut Simsekli, Stephen J. Roberts, Chris C. Holmes:
Explicit Regularisation in Gaussian Noise Injections. NeurIPS 2020 - [c42]Kimia Nadjahi, Alain Durmus, Lénaïc Chizat, Soheil Kolouri, Shahin Shahrampour, Umut Simsekli:
Statistical and Topological Properties of Sliced Probability Divergences. NeurIPS 2020 - [c41]Umut Simsekli, Ozan Sener, George Deligiannidis, Murat A. Erdogdu:
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks. NeurIPS 2020 - [c40]Simon Henriet, Benoit Fuentes, Umut Simsekli, Gaël Richard:
Matrix Factorization for High Frequency Non Intrusive Load Monitoring: Definitions and Algorithms. NILM@SenSys 2020: 20-24 - [i24]Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Gürbüzbalaban:
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise. CoRR abs/2002.05685 (2020) - [i23]Soheil Kolouri, Kimia Nadjahi, Umut Simsekli, Shahin Shahrampour:
Generalized Sliced Distances for Probability Distributions. CoRR abs/2002.12537 (2020) - [i22]Kimia Nadjahi, Alain Durmus, Lénaïc Chizat, Soheil Kolouri, Shahin Shahrampour, Umut Simsekli:
Statistical and Topological Properties of Sliced Probability Divergences. CoRR abs/2003.05783 (2020) - [i21]Tolga Birdal, Michael Arbel, Umut Simsekli, Leonidas J. Guibas:
Synchronizing Probability Measures on Rotations via Optimal Transport. CoRR abs/2004.00663 (2020) - [i20]Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu:
The Heavy-Tail Phenomenon in SGD. CoRR abs/2006.04740 (2020) - [i19]Umut Simsekli, Ozan Sener, George Deligiannidis, Murat A. Erdogdu:
Hausdorff Dimension, Stochastic Differential Equations, and Generalization in Neural Networks. CoRR abs/2006.09313 (2020) - [i18]Valentin De Bortoli, Alain Durmus, Xavier Fontaine, Umut Simsekli:
Quantitative Propagation of Chaos for SGD in Wide Neural Networks. CoRR abs/2007.06352 (2020) - [i17]Alexander Camuto, Matthew Willetts, Umut Simsekli, Stephen J. Roberts, Chris C. Holmes:
Explicit Regularisation in Gaussian Noise Injections. CoRR abs/2007.07368 (2020)
2010 – 2019
- 2019
- [j7]Kamer Kaya, Figen Öztoprak, S. Ilker Birbil
, A. Taylan Cemgil, Umut Simsekli, Nurdan Kuru, Hazal Koptagel, M. Kaan Öztürk:
A framework for parallel second order incremental optimization algorithms for solving partially separable problems. Comput. Optim. Appl. 72(3): 675-705 (2019) - [j6]Simon Henriet
, Umut Simsekli, Sergio Dos Santos
, Benoit Fuentes, Gaël Richard
:
Independent-Variation Matrix Factorization With Application to Energy Disaggregation. IEEE Signal Process. Lett. 26(11): 1643-1647 (2019) - [c39]Tolga Birdal
, Umut Simsekli:
Probabilistic Permutation Synchronization Using the Riemannian Structure of the Birkhoff Polytope. CVPR 2019: 11105-11116 - [c38]Simon Leglaive
, Umut Simsekli, Antoine Liutkus, Laurent Girin, Radu Horaud:
Speech Enhancement with Variational Autoencoders and Alpha-stable Distributions. ICASSP 2019: 541-545 - [c37]Antoine Liutkus, Umut Simsekli, Szymon Majewski, Alain Durmus, Fabian-Robert Stöter:
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions. ICML 2019: 4104-4113 - [c36]Thanh Huy Nguyen, Umut Simsekli, Gaël Richard:
Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization. ICML 2019: 4810-4819 - [c35]Umut Simsekli, Levent Sagun, Mert Gürbüzbalaban:
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks. ICML 2019: 5827-5837 - [c34]Ondrej Cífka, Umut Simsekli, Gaël Richard:
Supervised Symbolic Music Style Translation Using Synthetic Data. ISMIR 2019: 588-595 - [c33]Kimia Nadjahi, Alain Durmus, Umut Simsekli, Roland Badeau:
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance. NeurIPS 2019: 250-260 - [c32]Soheil Kolouri, Kimia Nadjahi, Umut Simsekli, Roland Badeau, Gustavo K. Rohde:
Generalized Sliced Wasserstein Distances. NeurIPS 2019: 261-272 - [c31]Thanh Huy Nguyen, Umut Simsekli, Mert Gürbüzbalaban, Gaël Richard:
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise. NeurIPS 2019: 273-283 - [i16]Umut Simsekli, Levent Sagun, Mert Gürbüzbalaban:
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks. CoRR abs/1901.06053 (2019) - [i15]Thanh Huy Nguyen, Umut Simsekli, Gaël Richard:
Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization. CoRR abs/1901.07487 (2019) - [i14]Soheil Kolouri, Kimia Nadjahi, Umut Simsekli, Roland Badeau, Gustavo K. Rohde:
Generalized Sliced Wasserstein Distances. CoRR abs/1902.00434 (2019) - [i13]Simon Leglaive, Umut Simsekli, Antoine Liutkus, Laurent Girin, Radu Horaud:
Speech enhancement with variational autoencoders and alpha-stable distributions. CoRR abs/1902.03926 (2019) - [i12]Ali Taylan Cemgil, Mehmet Burak Kurutmaz, Sinan Yildirim
, Melih Barsbey, Umut Simsekli:
Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya Urns. CoRR abs/1903.04478 (2019) - [i11]Tolga Birdal, Umut Simsekli:
Probabilistic Permutation Synchronization using the Riemannian Structure of the Birkhoff Polytope. CoRR abs/1904.05814 (2019) - [i10]Kimia Nadjahi, Alain Durmus, Umut Simsekli, Roland Badeau:
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance. CoRR abs/1906.04516 (2019) - [i9]Thanh Huy Nguyen, Umut Simsekli, Mert Gürbüzbalaban, Gaël Richard:
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise. CoRR abs/1906.09069 (2019) - [i8]Ondrej Cífka, Umut Simsekli, Gaël Richard:
Supervised Symbolic Music Style Translation Using Synthetic Data. CoRR abs/1907.02265 (2019) - [i7]Alireza Fallah, Mert Gürbüzbalaban, Asuman E. Ozdaglar, Umut Simsekli, Lingjiong Zhu:
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks. CoRR abs/1910.08701 (2019) - [i6]Umut Simsekli, Mert Gürbüzbalaban, Thanh Huy Nguyen, Gaël Richard, Levent Sagun:
On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks. CoRR abs/1912.00018 (2019) - 2018
- [j5]Thanh Huy Nguyen
, Umut Simsekli, Gaël Richard, Ali Taylan Cemgil:
Efficient Bayesian Model Selection in PARAFAC via Stochastic Thermodynamic Integration. IEEE Signal Process. Lett. 25(5): 725-729 (2018) - [c30]Mathieu Fontaine
, Fabian-Robert Stöter, Antoine Liutkus, Umut Simsekli, Romain Serizel, Roland Badeau:
Multichannel Audio Modeling with Elliptically Stable Tensor Decomposition. LVA/ICA 2018: 13-23 - [c29]Umut Simsekli, Halil Erdogan, Simon Leglaive
, Antoine Liutkus, Roland Badeau, Gaël Richard:
Alpha-Stable Low-Rank Plus Residual Decomposition for Speech Enhancement. ICASSP 2018: 651-655 - [c28]Umut Simsekli, Çagatay Yildiz, Thanh Huy Nguyen, A. Taylan Cemgil, Gaël Richard:
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization. ICML 2018: 4681-4690 - [c27]Tolga Birdal, Umut Simsekli, Mustafa Onur Eken, Slobodan Ilic:
Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC. NeurIPS 2018: 306-317 - [i5]Simon Henriet, Umut Simsekli, Benoit Fuentes, Gaël Richard:
A Generative Model for Non-Intrusive Load Monitoring in Commercial Buildings. CoRR abs/1803.00515 (2018) - [i4]Tolga Birdal, Umut Simsekli, M. Onur Eken, Slobodan Ilic:
Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC. CoRR abs/1805.12279 (2018) - [i3]Umut Simsekli, Çagatay Yildiz, Thanh Huy Nguyen, Gaël Richard, A. Taylan Cemgil:
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization. CoRR abs/1806.02617 (2018) - [i2]Umut Simsekli, Antoine Liutkus, Szymon Majewski, Alain Durmus:
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions. CoRR abs/1806.08141 (2018) - 2017
- [c26]Simon Leglaive
, Umut Simsekli, Antoine Liutkus, Roland Badeau, Gaël Richard:
Alpha-stable multichannel audio source separation. ICASSP 2017: 576-580 - [c25]Umut Simsekli, Alain Durmus, Roland Badeau, Gaël Richard, Eric Moulines, A. Taylan Cemgil:
Parallelized Stochastic Gradient Markov Chain Monte Carlo algorithms for non-negative matrix factorization. ICASSP 2017: 2242-2246 - [c24]Umut Simsekli:
Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for Markov Chain Monte Carlo. ICML 2017: 3200-3209 - [c23]Mainak Jas, Tom Dupré la Tour, Umut Simsekli, Alexandre Gramfort:
Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding. NIPS 2017: 1099-1108 - [c22]Simon Henriet, Umut Simsekli, Gaël Richard, Benoit Fuentes:
Synthetic dataset generation for non-intrusive load monitoring in commercial buildings. BuildSys@SenSys 2017: 39:1-39:2 - 2016
- [c21]Umut Simsekli, Roland Badeau, Gaël Richard, Ali Taylan Cemgil:
Stochastic thermodynamic integration: Efficient Bayesian model selection via stochastic gradient MCMC. ICASSP 2016: 2574-2578 - [c20]Umut Simsekli, Roland Badeau, A. Taylan Cemgil, Gaël Richard:
Stochastic Quasi-Newton Langevin Monte Carlo. ICML 2016: 642-651 - [c19]Alain Durmus, Umut Simsekli, Eric Moulines, Roland Badeau, Gaël Richard:
Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo. NIPS 2016: 2047-2055 - 2015
- [b1]Umut Simsekli:
Tensor fusion: Learning in heterogeneous and distributed data (Tensör tümleştirme: Ayrı cinsten ve dağıtık verilerde öğrenme). Boğaziçi University, Turkey, 2015 - [j4]Umut Simsekli, Tuomas Virtanen
, Ali Taylan Cemgil
:
Non-negative tensor factorization models for Bayesian audio processing. Digit. Signal Process. 47: 178-191 (2015) - [j3]Umut Simsekli, Antoine Liutkus, Ali Taylan Cemgil:
Alpha-Stable Matrix Factorization. IEEE Signal Process. Lett. 22(12): 2289-2293 (2015) - [c18]Antoine Liutkus, Umut Simsekli, A. Taylan Cemgil
:
Extraction of Temporal Patterns in Multi-rate and Multi-modal Datasets. LVA/ICA 2015: 135-142 - [c17]Andre Holzapfel, Umut Simsekli, Sertan Sentürk
, Ali Taylan Cemgil
:
Section-level modeling of musical audio for linking performances to scores in Turkish makam music. ICASSP 2015: 141-145 - [c16]Umut Simsekli, Tolga Birdal
:
A unified probabilistic framework for robust decoding of linear barcodes. ICASSP 2015: 1946-1950 - [c15]Umut Simsekli, Ali Taylan Cemgil
, Beyza Ermis:
Learning mixed divergences in coupled matrix and tensor factorization models. ICASSP 2015: 2120-2124 - [i1]Umut Simsekli, Hazal Koptagel, Figen Öztoprak, S. Ilker Birbil, Ali Taylan Cemgil:
HAMSI: Distributed Incremental Optimization Algorithm Using Quadratic Approximations for Partially Separable Problems. CoRR abs/1509.01698 (2015) - 2014
- [c14]Umut Simsekli, Jonathan Le Roux, John R. Hershey:
Non-negative source-filter dynamical system for speech enhancement. ICASSP 2014: 6206-6210 - 2013
- [c13]Umut Simsekli, Beyza Ermis, A. Taylan Cemgil, Evrim Acar:
Optimal weight learning for Coupled Tensor Factorization with mixed divergences. EUSIPCO 2013: 1-5 - [c12]Umut Simsekli, Ali Taylan Cemgil, Yusuf Kenan Yilmaz:
Learning the beta-Divergence in Tweedie Compound Poisson Matrix Factorization Models. ICML (3) 2013: 1409-1417 - [c11]Umut Simsekli, Tolga Birdal
, Emre Koc, Ali Taylan Cemgil
:
A factorization based recommender system for online services. SIU 2013: 1-4 - [c10]Umut Simsekli, Jonathan Le Roux, John R. Hershey:
Hierarchical and coupled non-negative dynamical systems with application to audio modeling. WASPAA 2013: 1-4 - 2012
- [j2]Umut Simsekli, Orhan Sonmez, Baris Kurt, Ali Taylan Cemgil
:
Combined perception and control for timing in robotic music performances. EURASIP J. Audio Speech Music. Process. 2012: 8 (2012) - [c9]Ismail An, Umut Simsekli, Ali Taylan Cemgil, Lale Akarun:
Large scale polyphonic music transcription using randomized matrix decompositions. EUSIPCO 2012: 2020-2024 - [c8]Umut Simsekli, A. Taylan Cemgil:
Score guided musical source separation using Generalized Coupled Tensor Factorization. EUSIPCO 2012: 2639-2643 - [c7]Umut Simsekli, Yusuf Kenan Yilmaz, Ali Taylan Cemgil
:
Score guided audio restoration via generalised coupled tensor factorisation. ICASSP 2012: 5369-5372 - [c6]Umut Simsekli, A. Taylan Cemgil
:
Markov Chain Monte Carlo inference for probabilistic latent tensor factorization. MLSP 2012: 1-6 - [c5]Ismail Ari, Umut Simsekli, Ali Taylan Cemgil
, Lale Akarun
:
SVD-based polyphonic music transcription. SIU 2012: 1-4 - [c4]Umut Simsekli, Yusuf Kenan Yilmaz, Ali Taylan Cemgil
:
Coupled tensor factorization models for polyphonic music transcription. SIU 2012: 1-4 - 2011
- [j1]Umut Simsekli, Antti Jylhä
, Cumhur Erkut
, Ali Taylan Cemgil
:
Real-Time Recognition of Percussive Sounds by a Model-Based Method. EURASIP J. Adv. Signal Process. 2011 (2011) - [c3]Yusuf Kenan Yilmaz, Ali Taylan Cemgil, Umut Simsekli:
Generalised Coupled Tensor Factorisation. NIPS 2011: 2151-2159 - [c2]Ali Taylan Cemgil
, Umut Simsekli, Yusuf Cem Sübakan:
Probabilistic latent tensor factorization framework for audio modeling. WASPAA 2011: 137-140 - 2010
- [c1]Umut Simsekli:
Automatic Music Genre Classification Using Bass Lines. ICPR 2010: 4137-4140
Coauthor Index
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