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ORCIDGuido Montúfar
Guido F. Montúfar – Guido Francisco Montúfar Cuartas
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2020 – today
- 2024
[j24]Mareike Dressler, Marina Garrote-López
, Guido Montúfar, Johannes Müller, Kemal Rose:
Algebraic optimization of sequential decision problems. J. Symb. Comput. 121: 102241 (2024)- [j23]Kathlén Kohn, Guido Montúfar, Vahid Shahverdi, Matthew Trager:
Function Space and Critical Points of Linear Convolutional Networks. SIAM J. Appl. Algebra Geom. 8(2): 333-362 (2024) - [j22]Kedar Karhadkar, Michael Murray, Hanna Tseran, Guido Montúfar:
Mildly Overparameterized ReLU Networks Have a Favorable Loss Landscape. Trans. Mach. Learn. Res. 2024 (2024) - [j21]Shuang Liang, Renata Turkes, Jiayi Li, Nina Otter, Guido Montúfar:
Pull-back Geometry of Persistent Homology Encodings. Trans. Mach. Learn. Res. 2024 (2024) - [i62]Kedar Karhadkar, Erin George, Michael Murray, Guido Montúfar, Deanna Needell:
Benign overfitting in leaky ReLU networks with moderate input dimension. CoRR abs/2403.06903 (2024) - [i61]Marie-Charlotte Brandenburg, Georg Loho, Guido Montúfar:
The Real Tropical Geometry of Neural Networks. CoRR abs/2403.11871 (2024) - [i60]Johannes Müller, Semih Cayci, Guido Montúfar:
Fisher-Rao Gradient Flows of Linear Programs and State-Action Natural Policy Gradients. CoRR abs/2403.19448 (2024) - [i59]Kedar Karhadkar, Michael Murray, Guido Montúfar:
Bounds for the smallest eigenvalue of the NTK for arbitrary spherical data of arbitrary dimension. CoRR abs/2405.14630 (2024) - [i58]Shuang Liang, Guido Montúfar:
Implicit Bias of Mirror Descent for Shallow Neural Networks in Univariate Regression. CoRR abs/2410.03988 (2024) - [i57]Niket Patel, Guido Montúfar:
On the Local Complexity of Linear Regions in Deep ReLU Networks. CoRR abs/2412.18283 (2024) - 2023
- [j20]Johannes Rauh, Pradeep Kr. Banerjee, Eckehard Olbrich, Guido Montúfar, Jürgen Jost:
Continuity and additivity properties of information decompositions. Int. J. Approx. Reason. 161: 108979 (2023) - [j19]Hui Jin, Guido Montúfar:
Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks. J. Mach. Learn. Res. 24: 137:1-137:97 (2023) - [c35]Kedar Karhadkar, Pradeep Kr. Banerjee, Guido Montúfar:
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs. ICLR 2023 - [c34]Michael Murray, Hui Jin, Benjamin Bowman, Guido Montúfar:
Characterizing the spectrum of the NTK via a power series expansion. ICLR 2023 - [c33]Pierre Bréchet, Katerina Papagiannouli, Jing An, Guido Montúfar:
Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss. ICML 2023: 3106-3147 - [c32]Hanna Tseran, Guido Montúfar:
Expected Gradients of Maxout Networks and Consequences to Parameter Initialization. ICML 2023: 34491-34532 - [i56]Hanna Tseran, Guido Montúfar:
Expected Gradients of Maxout Networks and Consequences to Parameter Initialization. CoRR abs/2301.06956 (2023) - [i55]Pierre Bréchet, Katerina Papagiannouli, Jing An, Guido Montúfar:
Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss. CoRR abs/2303.03027 (2023) - [i54]Kathlén Kohn, Guido Montúfar, Vahid Shahverdi, Matthew Trager:
Function Space and Critical Points of Linear Convolutional Networks. CoRR abs/2304.05752 (2023) - [i53]Rishi Sonthalia, Anna Seigal, Guido Montúfar:
Supermodular Rank: Set Function Decomposition and Optimization. CoRR abs/2305.14632 (2023) - [i52]Kedar Karhadkar, Michael Murray, Hanna Tseran, Guido Montúfar:
Mildly Overparameterized ReLU Networks Have a Favorable Loss Landscape. CoRR abs/2305.19510 (2023) - 2022
- [j18]Guido Montúfar, Yu Guang Wang:
Distributed Learning via Filtered Hyperinterpolation on Manifolds. Found. Comput. Math. 22(4): 1219-1271 (2022) - [j17]Guido Montúfar, Yue Ren, Leon Zhang:
Sharp Bounds for the Number of Regions of Maxout Networks and Vertices of Minkowski Sums. SIAM J. Appl. Algebra Geom. 6(1): 618-649 (2022) - [j16]Kathlén Kohn, Thomas Merkh, Guido Montúfar, Matthew Trager:
Geometry of Linear Convolutional Networks. SIAM J. Appl. Algebra Geom. 6(3): 368-406 (2022) - [c31]Pradeep Kr. Banerjee, Kedar Karhadkar, Yuguang Wang, Uri Alon, Guido Montúfar:
Oversquashing in GNNs through the lens of information contraction and graph expansion. Allerton 2022: 1-8 - [c30]Benjamin Bowman, Guido Montúfar:
Implicit Bias of MSE Gradient Optimization in Underparameterized Neural Networks. ICLR 2022 - [c29]Hui Jin, Pradeep Kr. Banerjee, Guido Montúfar:
Learning Curves for Gaussian Process Regression with Power-Law Priors and Targets. ICLR 2022 - [c28]Johannes Müller, Guido Montúfar:
The Geometry of Memoryless Stochastic Policy Optimization in Infinite-Horizon POMDPs. ICLR 2022 - [c27]Benjamin Bowman, Guido F. Montúfar:
Spectral Bias Outside the Training Set for Deep Networks in the Kernel Regime. NeurIPS 2022 - [c26]Renata Turkes, Guido F. Montúfar, Nina Otter:
On the Effectiveness of Persistent Homology. NeurIPS 2022 - [i51]Benjamin Bowman, Guido Montúfar:
Implicit Bias of MSE Gradient Optimization in Underparameterized Neural Networks. CoRR abs/2201.04738 (2022) - [i50]Johannes Rauh, Pradeep Kr. Banerjee, Eckehard Olbrich, Guido Montúfar, Jürgen Jost:
Continuity and Additivity Properties of Information Decompositions. CoRR abs/2204.10982 (2022) - [i49]Johannes Müller, Guido Montúfar:
Solving infinite-horizon POMDPs with memoryless stochastic policies in state-action space. CoRR abs/2205.14098 (2022) - [i48]Benjamin Bowman, Guido Montúfar:
Spectral Bias Outside the Training Set for Deep Networks in the Kernel Regime. CoRR abs/2206.02927 (2022) - [i47]Renata Turkes, Guido Montúfar, Nina Otter:
On the effectiveness of persistent homology. CoRR abs/2206.10551 (2022) - [i46]Pradeep Kr. Banerjee, Kedar Karhadkar, Yu Guang Wang, Uri Alon, Guido Montúfar:
Oversquashing in GNNs through the lens of information contraction and graph expansion. CoRR abs/2208.03471 (2022) - [i45]Laura Escobar, Patricio Gallardo, Javier González-Anaya, José L. González, Guido Montúfar, Alejandro H. Morales:
Enumeration of max-pooling responses with generalized permutohedra. CoRR abs/2209.14978 (2022) - [i44]Kedar Karhadkar, Pradeep Kr. Banerjee, Guido Montúfar:
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs. CoRR abs/2210.11790 (2022) - [i43]Johannes Müller, Guido Montúfar:
Geometry and convergence of natural policy gradient methods. CoRR abs/2211.02105 (2022) - [i42]Michael Murray, Hui Jin, Benjamin Bowman, Guido Montúfar:
Characterizing the Spectrum of the NTK via a Power Series Expansion. CoRR abs/2211.07844 (2022) - [i41]Mareike Dressler, Marina Garrote-López, Guido Montúfar, Johannes Müller, Kemal Rose:
Algebraic optimization of sequential decision problems. CoRR abs/2211.09439 (2022) - 2021
- [j15]Türkü Özlüm Çelik, Asgar Jamneshan, Guido Montúfar, Bernd Sturmfels, Lorenzo Venturello:
Wasserstein distance to independence models. J. Symb. Comput. 104: 855-873 (2021) - [c25]Alex Tong Lin, Wuchen Li, Stanley J. Osher, Guido Montúfar:
Wasserstein Proximal of GANs. GSI 2021: 524-533 - [c24]Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F. Montúfar, Pietro Lió, Michael M. Bronstein:
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. ICML 2021: 1026-1037 - [c23]Quynh Nguyen, Marco Mondelli, Guido F. Montúfar:
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks. ICML 2021: 8119-8129 - [c22]Xuebin Zheng, Bingxin Zhou, Junbin Gao, Yuguang Wang, Pietro Lió, Ming Li, Guido Montúfar:
How Framelets Enhance Graph Neural Networks. ICML 2021: 12761-12771 - [c21]Pradeep Kr. Banerjee, Guido Montúfar:
Information Complexity and Generalization Bounds. ISIT 2021: 676-681 - [c20]Alex Tong Lin, Mark J. Debord, Katia Estabridis, Gary A. Hewer, Guido Montúfar, Stanley J. Osher:
Decentralized Multi-Agents by Imitation of a Centralized Controller. MSML 2021: 619-651 - [c19]Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yuguang Wang, Pietro Liò, Guido F. Montúfar, Michael M. Bronstein:
Weisfeiler and Lehman Go Cellular: CW Networks. NeurIPS 2021: 2625-2640 - [c18]Hanna Tseran, Guido Montúfar:
On the Expected Complexity of Maxout Networks. NeurIPS 2021: 28995-29008 - [i40]Alex Tong Lin, Wuchen Li, Stanley J. Osher, Guido Montúfar:
Wasserstein Proximal of GANs. CoRR abs/2102.06862 (2021) - [i39]Xuebin Zheng, Bingxin Zhou, Junbin Gao, Yu Guang Wang, Pietro Liò, Ming Li, Guido Montúfar:
How Framelets Enhance Graph Neural Networks. CoRR abs/2102.06986 (2021) - [i38]Cristian Bodnar, Fabrizio Frasca, Yu Guang Wang, Nina Otter, Guido Montúfar, Pietro Liò, Michael M. Bronstein:
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. CoRR abs/2103.03212 (2021) - [i37]Guido Montúfar, Yue Ren, Leon Zhang:
Sharp bounds for the number of regions of maxout networks and vertices of Minkowski sums. CoRR abs/2104.08135 (2021) - [i36]Pradeep Kr. Banerjee, Guido Montúfar:
Information Complexity and Generalization Bounds. CoRR abs/2105.01747 (2021) - [i35]Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yu Guang Wang, Pietro Liò, Guido Montúfar, Michael M. Bronstein:
Weisfeiler and Lehman Go Cellular: CW Networks. CoRR abs/2106.12575 (2021) - [i34]Hanna Tseran, Guido Montúfar:
On the Expected Complexity of Maxout Networks. CoRR abs/2107.00379 (2021) - [i33]Kathlén Kohn, Thomas Merkh, Guido Montúfar, Matthew Trager:
Geometry of Linear Convolutional Networks. CoRR abs/2108.01538 (2021) - [i32]Johannes Müller, Guido Montúfar:
The Geometry of Memoryless Stochastic Policy Optimization in Infinite-Horizon POMDPs. CoRR abs/2110.07409 (2021) - [i31]Hui Jin, Pradeep Kr. Banerjee, Guido Montúfar:
Learning curves for Gaussian process regression with power-law priors and targets. CoRR abs/2110.12231 (2021) - [i30]Dohyun Kwon, Yeoneung Kim, Guido Montúfar, Insoon Yang:
Training Wasserstein GANs without gradient penalties. CoRR abs/2110.14150 (2021) - 2020
- [j14]Thomas Merkh, Guido Montúfar:
Factorized mutual information maximization. Kybernetika 56(5): 948-978 (2020) - [c17]Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montúfar:
Kernelized Wasserstein Natural Gradient. ICLR 2020 - [c16]Yonatan Dukler, Quanquan Gu, Guido Montúfar:
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers. ICML 2020: 2751-2760 - [c15]Yuguang Wang, Ming Li, Zheng Ma, Guido Montúfar, Xiaosheng Zhuang, Yanan Fan:
Haar Graph Pooling. ICML 2020: 9952-9962 - [c14]Pradeep Kr. Banerjee, Guido Montúfar:
The Variational Deficiency Bottleneck. IJCNN 2020: 1-8 - [i29]Yonatan Dukler, Quanquan Gu, Guido Montúfar:
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers. CoRR abs/2006.06878 (2020) - [i28]Hui Jin, Guido Montúfar:
Implicit bias of gradient descent for mean squared error regression with wide neural networks. CoRR abs/2006.07356 (2020) - [i27]Guido Montúfar, Yu Guang Wang:
Distributed Learning via Filtered Hyperinterpolation on Manifolds. CoRR abs/2007.09392 (2020) - [i26]Guido Montúfar, Nina Otter, Yuguang Wang:
Can neural networks learn persistent homology features? CoRR abs/2011.14688 (2020) - [i25]Quynh Nguyen, Marco Mondelli, Guido Montúfar:
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks. CoRR abs/2012.11654 (2020)
2010 – 2019
- 2019
- [c13]Wuchen Li, Alex Tong Lin, Guido Montúfar:
Affine Natural Proximal Learning. GSI 2019: 705-714 - [c12]Yonatan Dukler, Wuchen Li, Alex Tong Lin, Guido Montúfar:
Wasserstein of Wasserstein Loss for Learning Generative Models. ICML 2019: 1716-1725 - [c11]Türkü Özlüm Çelik, Asgar Jamneshan, Guido Montúfar, Bernd Sturmfels, Lorenzo Venturello:
Optimal Transport to a Variety. MACIS 2019: 364-381 - [i24]Thomas Merkh, Guido Montúfar:
Factorized Mutual Information Maximization. CoRR abs/1906.05460 (2019) - [i23]Alex Tong Lin, Yonatan Dukler, Wuchen Li, Guido Montúfar:
Wasserstein Diffusion Tikhonov Regularization. CoRR abs/1909.06860 (2019) - [i22]Yu Guang Wang, Ming Li, Zheng Ma, Guido Montúfar, Xiaosheng Zhuang, Yanan Fan:
HaarPooling: Graph Pooling with Compressive Haar Basis. CoRR abs/1909.11580 (2019) - [i21]Anton Mallasto, Guido Montúfar, Augusto Gerolin:
How Well Do WGANs Estimate the Wasserstein Metric? CoRR abs/1910.03875 (2019) - [i20]Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montúfar:
Kernelized Wasserstein Natural Gradient. CoRR abs/1910.09652 (2019) - [i19]Thomas Merkh, Guido Montúfar:
Stochastic Feedforward Neural Networks: Universal Approximation. CoRR abs/1910.09763 (2019) - 2018
- [c10]Pradeep Kr. Banerjee, Johannes Rauh, Guido Montúfar:
Computing the Unique Information. ISIT 2018: 141-145 - [i18]Wuchen Li, Guido Montúfar:
Natural gradient via optimal transport I. CoRR abs/1803.07033 (2018) - [i17]Guido Montúfar:
Restricted Boltzmann Machines: Introduction and Review. CoRR abs/1806.07066 (2018) - [i16]Wuchen Li, Guido Montúfar:
Ricci curvature for parametric statistics via optimal transport. CoRR abs/1807.07095 (2018) - [i15]Pradeep Kr. Banerjee, Guido Montúfar:
The Variational Deficiency Bottleneck. CoRR abs/1810.11677 (2018) - 2017
- [j13]Guido Montúfar, Johannes Rauh:
Hierarchical models as marginals of hierarchical models. Int. J. Approx. Reason. 88: 531-546 (2017) - [j12]Guido Montúfar, Jason Morton:
Dimension of Marginals of Kronecker Product Models. SIAM J. Appl. Algebra Geom. 1(1): 126-151 (2017) - [c9]Guido Montúfar, Johannes Rauh:
Geometry of Policy Improvement. GSI 2017: 282-290 - [c8]Keyan Ghazi-Zahedi, Raphael Deimel, Guido Montúfar, Vincent Wall, Oliver Brock:
Morphological computation: The good, the bad, and the ugly. IROS 2017: 464-469 - [i14]Guido Montúfar, Johannes Rauh:
Geometry of Policy Improvement. CoRR abs/1704.01785 (2017) - [i13]Pradeep Kr. Banerjee, Johannes Rauh, Guido Montúfar:
Computing the Unique Information. CoRR abs/1709.07487 (2017) - 2016
- [j11]Keyan Ghazi-Zahedi, Daniel F. B. Haeufle, Guido Montúfar, Syn Schmitt, Nihat Ay:
Evaluating Morphological Computation in Muscle and DC-Motor Driven Models of Hopping Movements. Frontiers Robotics AI 3: 42 (2016) - [i12]Guido Montúfar, Keyan Ghazi-Zahedi, Nihat Ay:
Information Theoretically Aided Reinforcement Learning for Embodied Agents. CoRR abs/1605.09735 (2016) - 2015
- [j10]Guido Montúfar, Jason Morton:
Discrete restricted Boltzmann machines. J. Mach. Learn. Res. 16: 653-672 (2015) - [j9]Guido Montúfar, Nihat Ay, Keyan Ghazi-Zahedi:
Geometry and expressive power of conditional restricted Boltzmann machines. J. Mach. Learn. Res. 16: 2405-2436 (2015) - [j8]Guido Montúfar, Keyan Ghazi-Zahedi, Nihat Ay:
A Theory of Cheap Control in Embodied Systems. PLoS Comput. Biol. 11(9) (2015) - [j7]Guido F. Montúfar, Jason Morton:
When Does a Mixture of Products Contain a Product of Mixtures? SIAM J. Discret. Math. 29(1): 321-347 (2015) - [c7]Guido Montúfar:
Deep Narrow Boltzmann Machines are Universal Approximators. ICLR (Poster) 2015 - [i11]Guido Montúfar, Keyan Ghazi-Zahedi, Nihat Ay:
Geometry and Determinism of Optimal Stationary Control in Partially Observable Markov Decision Processes. CoRR abs/1503.07206 (2015) - [i10]Guido Montúfar:
Universal Approximation of Markov Kernels by Shallow Stochastic Feedforward Networks. CoRR abs/1503.07211 (2015) - [i9]Guido Montúfar, Johannes Rauh:
Hierarchical Models as Marginals of Hierarchical Models. CoRR abs/1508.03606 (2015) - [i8]Guido Montúfar, Jason Morton:
Dimension of Marginals of Kronecker Product Models. CoRR abs/1511.03570 (2015) - [i7]Keyan Ghazi-Zahedi, Daniel F. B. Haeufle, Guido Montúfar, Syn Schmitt, Nihat Ay:
Evaluating Morphological Computation in Muscle and DC-motor Driven Models of Human Hopping. CoRR abs/1512.00250 (2015) - 2014
- [j6]Guido Montúfar, Johannes Rauh, Nihat Ay:
On the Fisher Metric of Conditional Probability Polytopes. Entropy 16(6): 3207-3233 (2014) - [j5]Guido F. Montúfar, Johannes Rauh:
Scaling of model approximation errors and expected entropy distances. Kybernetika 50(2): 234-245 (2014) - [j4]Guido F. Montúfar:
Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units. Neural Comput. 26(7): 1386-1407 (2014) - [c6]Guido Montúfar, Razvan Pascanu, KyungHyun Cho, Yoshua Bengio:
On the Number of Linear Regions of Deep Neural Networks. NIPS 2014: 2924-2932 - [c5]Razvan Pascanu, Guido Montúfar, Yoshua Bengio:
On the number of inference regions of deep feed forward networks with piece-wise linear activations. ICLR (Poster) 2014 - [i6]Guido Montúfar, Razvan Pascanu, Kyunghyun Cho, Yoshua Bengio:
On the Number of Linear Regions of Deep Neural Networks. CoRR abs/1402.1869 (2014) - [i5]Guido Montúfar, Nihat Ay, Keyan Zahedi:
Expressive Power of Conditional Restricted Boltzmann Machines for Sensorimotor Control. CoRR abs/1402.3346 (2014) - [i4]Guido Montúfar, Nihat Ay, Keyan Ghazi-Zahedi:
A Framework for Cheap Universal Approximation in Embodied Systems. CoRR abs/1407.6836 (2014) - [i3]Tyll Krueger, Guido Montúfar, Ruedi Seiler, Rainer Siegmund-Schultze:
Sequential Recurrence-Based Multidimensional Universal Source Coding of Lempel-Ziv Type. CoRR abs/1408.4433 (2014) - 2013
- [j3]Guido Montúfar:
Mixture decompositions of exponential families - using a decomposition of their sample spaces. Kybernetika 49(1): 23-39 (2013) - [j2]Tyll Krüger, Guido Montúfar, Ruedi Seiler, Rainer Siegmund-Schultze:
Universally typical sets for ergodic sources of multidimensional data. Kybernetika 49(6): 868-882 (2013) - [c4]Guido Montúfar, Johannes Rauh, Nihat Ay:
Maximal Information Divergence from Statistical Models Defined by Neural Networks. GSI 2013: 759-766 - [c3]Guido F. Montúfar, Jason Morton:
When Does a Mixture of Products Contain a Product of Mixtures? ICLR (Workshop Poster) 2013 - [c2]Guido Montúfar, Jason Morton:
Discrete Restricted Boltzmann Machines. ICLR 2013 - [i2]Guido F. Montúfar:
Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units. CoRR abs/1303.7461 (2013) - 2012
- [b1]Guido Francisco Montúfar Cuartas:
On the expressive power of discrete mixture models, restricted Boltzmann machines, and deep belief networks: a unified mathematical treatment. University of Leipzig, 2012, pp. 1-148 - 2011
- [j1]Guido Montúfar, Nihat Ay:
Refinements of Universal Approximation Results for Deep Belief Networks and Restricted Boltzmann Machines. Neural Comput. 23(5): 1306-1319 (2011) - [c1]Guido Montúfar, Johannes Rauh, Nihat Ay:
Expressive Power and Approximation Errors of Restricted Boltzmann Machines. NIPS 2011: 415-423 - [i1]Tyll Krueger, Guido Montúfar, Ruedi Seiler, Rainer Siegmund-Schultze:
Universally Typical Sets for Ergodic Sources of Multidimensional Data. CoRR abs/1105.0393 (2011)
Coauthor Index
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