Guido Montúfar

125 papers A* 17A 1B 3Journal 93Unranked 11

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Algebraic Robustness Verification of Neural Networks. CoRR Yulia Alexandr, Hao Duan, Guido Montúfar
2026	J	`jnl`	Harmful Overfitting in Sobolev Spaces. CoRR Kedar Karhadkar, Alexander Sietsema, Deanna Needell, Guido Montúfar
2025	J	`jnl`	Constraining the outputs of ReLU neural networks. CoRR Yulia Alexandr, Guido Montúfar
2025	A*	`conf`	Demystifying Topological Message-Passing with Relational Structures: A Case Study on Oversquashing in Simplicial Message-Passing. ICLR Diaaeldin Taha, James Chapman, Marzieh Eidi, Karel Devriendt, Guido Montúfar
2025	J	`jnl`	Demystifying Topological Message-Passing with Relational Structures: A Case Study on Oversquashing in Simplicial Message-Passing. CoRR Diaaeldin Taha, James Chapman, Marzieh Eidi, Karel Devriendt, Guido Montúfar
2025	J	`jnl`	Fisher-Rao Gradient Flows of Linear Programs and State-Action Natural Policy Gradients. SIAM J. Optim. Johannes Müller, Semih Cayci, Guido Montúfar
2025	J	`jnl`	Gradient Descent with Large Step Sizes: Chaos and Fractal Convergence Region. CoRR Shuang Liang, Guido Montúfar
2025	A*	`conf`	Implicit Bias of Mirror Flow for Shallow Neural Networks in Univariate Regression. ICLR Shuang Liang, Guido Montúfar
2025	J	`jnl`	Low Rank Gradients and Where to Find Them. CoRR Rishi Sonthalia, Michael Murray, Guido Montúfar
2025	A*	`conf`	On the Local Complexity of Linear Regions in Deep ReLU Networks. ICML Niket Patel, Guido Montúfar
2025	J	`jnl`	Supermodular Rank: Set Function Decomposition and Optimization. SIAM J. Math. Data Sci. Rishi Sonthalia, Anna Seigal, Guido Montúfar
2025	J	`jnl`	Understanding Learning Invariance in Deep Linear Networks. CoRR Hao Duan, Guido Montúfar
2025	J	`jnl`	Zero-Shot Context Generalization in Reinforcement Learning from Few Training Contexts. CoRR James Chapman, Kedar Karhadkar, Guido Montúfar
2024	J	`jnl`	Algebraic optimization of sequential decision problems. J. Symb. Comput. Mareike Dressler, Marina Garrote-López, Guido Montúfar, Johannes Müller, Kemal Rose
2024	J	`jnl`	Benign overfitting in leaky ReLU networks with moderate input dimension. CoRR Kedar Karhadkar, Erin George, Michael Murray, Guido Montúfar, Deanna Needell
2024	J	`jnl`	Bounds for the smallest eigenvalue of the NTK for arbitrary spherical data of arbitrary dimension. CoRR Kedar Karhadkar, Michael Murray, Guido Montúfar
2024	J	`jnl`	Fisher-Rao Gradient Flows of Linear Programs and State-Action Natural Policy Gradients. CoRR Johannes Müller, Semih Cayci, Guido Montúfar
2024	J	`jnl`	Function Space and Critical Points of Linear Convolutional Networks. SIAM J. Appl. Algebra Geom. Kathlén Kohn, Guido Montúfar, Vahid Shahverdi, Matthew Trager
2024	J	`jnl`	Implicit Bias of Mirror Descent for Shallow Neural Networks in Univariate Regression. CoRR Shuang Liang, Guido Montúfar
2024	J	`jnl`	Mildly Overparameterized ReLU Networks Have a Favorable Loss Landscape. Trans. Mach. Learn. Res. Kedar Karhadkar, Michael Murray, Hanna Tseran, Guido Montúfar
2024	J	`jnl`	On the Local Complexity of Linear Regions in Deep ReLU Networks. CoRR Niket Patel, Guido Montúfar
2024	J	`jnl`	Pull-back Geometry of Persistent Homology Encodings. Trans. Mach. Learn. Res. Shuang Liang, Renata Turkes, Jiayi Li, Nina Otter, Guido Montúfar
2024	J	`jnl`	The Real Tropical Geometry of Neural Networks for Binary Classification. Trans. Mach. Learn. Res. Marie-Charlotte Brandenburg, Georg Loho, Guido Montúfar
2024	J	`jnl`	The Real Tropical Geometry of Neural Networks. CoRR Marie-Charlotte Brandenburg, Georg Loho, Guido Montúfar
2023	A*	`conf`	Characterizing the spectrum of the NTK via a power series expansion. ICLR Michael Murray, Hui Jin, Benjamin Bowman, Guido Montúfar
2023	J	`jnl`	Continuity and additivity properties of information decompositions. Int. J. Approx. Reason. Johannes Rauh, Pradeep Kr. Banerjee, Eckehard Olbrich, Guido Montúfar, Jürgen Jost
2023	A*	`conf`	Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss. ICML Pierre Bréchet, Katerina Papagiannouli, Jing An, Guido Montúfar
2023	J	`jnl`	Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss. CoRR Pierre Bréchet, Katerina Papagiannouli, Jing An, Guido Montúfar
2023	A*	`conf`	Expected Gradients of Maxout Networks and Consequences to Parameter Initialization. ICML Hanna Tseran, Guido Montúfar
2023	J	`jnl`	Expected Gradients of Maxout Networks and Consequences to Parameter Initialization. CoRR Hanna Tseran, Guido Montúfar
2023	A*	`conf`	FoSR: First-order spectral rewiring for addressing oversquashing in GNNs. ICLR Kedar Karhadkar, Pradeep Kr. Banerjee, Guido Montúfar
2023	J	`jnl`	Function Space and Critical Points of Linear Convolutional Networks. CoRR Kathlén Kohn, Guido Montúfar, Vahid Shahverdi, Matthew Trager
2023	J	`jnl`	Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks. J. Mach. Learn. Res. Hui Jin, Guido Montúfar
2023	J	`jnl`	Mildly Overparameterized ReLU Networks Have a Favorable Loss Landscape. CoRR Kedar Karhadkar, Michael Murray, Hanna Tseran, Guido Montúfar
2023	J	`jnl`	Supermodular Rank: Set Function Decomposition and Optimization. CoRR Rishi Sonthalia, Anna Seigal, Guido Montúfar
2022	J	`jnl`	Algebraic optimization of sequential decision problems. CoRR Mareike Dressler, Marina Garrote-López, Guido Montúfar, Johannes Müller, Kemal Rose
2022	J	`jnl`	Characterizing the Spectrum of the NTK via a Power Series Expansion. CoRR Michael Murray, Hui Jin, Benjamin Bowman, Guido Montúfar
2022	J	`jnl`	Continuity and Additivity Properties of Information Decompositions. CoRR Johannes Rauh, Pradeep Kr. Banerjee, Eckehard Olbrich, Guido Montúfar, Jürgen Jost
2022	J	`jnl`	Distributed Learning via Filtered Hyperinterpolation on Manifolds. Found. Comput. Math. Guido Montúfar, Yu Guang Wang
2022	J	`jnl`	Enumeration of max-pooling responses with generalized permutohedra. CoRR Laura Escobar, Patricio Gallardo, Javier González-Anaya, José L. González, Guido Montúfar, Alejandro H. Morales
2022	J	`jnl`	FoSR: First-order spectral rewiring for addressing oversquashing in GNNs. CoRR Kedar Karhadkar, Pradeep Kr. Banerjee, Guido Montúfar
2022	J	`jnl`	Geometry and convergence of natural policy gradient methods. CoRR Johannes Müller, Guido Montúfar
2022	J	`jnl`	Geometry of Linear Convolutional Networks. SIAM J. Appl. Algebra Geom. Kathlén Kohn, Thomas Merkh, Guido Montúfar, Matthew Trager
2022	A*	`conf`	Implicit Bias of MSE Gradient Optimization in Underparameterized Neural Networks. ICLR Benjamin Bowman, Guido Montúfar
2022	J	`jnl`	Implicit Bias of MSE Gradient Optimization in Underparameterized Neural Networks. CoRR Benjamin Bowman, Guido Montúfar
2022	A*	`conf`	Learning Curves for Gaussian Process Regression with Power-Law Priors and Targets. ICLR Hui Jin, Pradeep Kr. Banerjee, Guido Montúfar
2022	J	`jnl`	On the effectiveness of persistent homology. CoRR Renata Turkes, Guido Montúfar, Nina Otter
2022	—	`conf`	Oversquashing in GNNs through the lens of information contraction and graph expansion. Allerton Pradeep Kr. Banerjee, Kedar Karhadkar, Yuguang Wang, Uri Alon, Guido Montúfar
2022	J	`jnl`	Oversquashing in GNNs through the lens of information contraction and graph expansion. CoRR Pradeep Kr. Banerjee, Kedar Karhadkar, Yu Guang Wang, Uri Alon, Guido Montúfar
2022	J	`jnl`	Sharp Bounds for the Number of Regions of Maxout Networks and Vertices of Minkowski Sums. SIAM J. Appl. Algebra Geom. Guido Montúfar, Yue Ren, Leon Zhang
2022	J	`jnl`	Solving infinite-horizon POMDPs with memoryless stochastic policies in state-action space. CoRR Johannes Müller, Guido Montúfar
2022	J	`jnl`	Spectral Bias Outside the Training Set for Deep Networks in the Kernel Regime. CoRR Benjamin Bowman, Guido Montúfar
2022	A*	`conf`	The Geometry of Memoryless Stochastic Policy Optimization in Infinite-Horizon POMDPs. ICLR Johannes Müller, Guido Montúfar
2021	—	`conf`	Decentralized Multi-Agents by Imitation of a Centralized Controller. MSML Alex Tong Lin, Mark J. Debord, Katia Estabridis, Gary A. Hewer, Guido Montúfar, Stanley J. Osher
2021	J	`jnl`	Geometry of Linear Convolutional Networks. CoRR Kathlén Kohn, Thomas Merkh, Guido Montúfar, Matthew Trager
2021	A*	`conf`	How Framelets Enhance Graph Neural Networks. ICML Xuebin Zheng, Bingxin Zhou, Junbin Gao, Yuguang Wang, Pietro Lió, Ming Li, Guido Montúfar
2021	J	`jnl`	How Framelets Enhance Graph Neural Networks. CoRR Xuebin Zheng, Bingxin Zhou, Junbin Gao, Yu Guang Wang, Pietro Liò, Ming Li, Guido Montúfar
2021	B	`conf`	Information Complexity and Generalization Bounds. ISIT Pradeep Kr. Banerjee, Guido Montúfar
2021	J	`jnl`	Information Complexity and Generalization Bounds. CoRR Pradeep Kr. Banerjee, Guido Montúfar
2021	J	`jnl`	Learning curves for Gaussian process regression with power-law priors and targets. CoRR Hui Jin, Pradeep Kr. Banerjee, Guido Montúfar
2021	A*	`conf`	On the Expected Complexity of Maxout Networks. NeurIPS Hanna Tseran, Guido Montúfar
2021	J	`jnl`	On the Expected Complexity of Maxout Networks. CoRR Hanna Tseran, Guido Montúfar
2021	J	`jnl`	Sharp bounds for the number of regions of maxout networks and vertices of Minkowski sums. CoRR Guido Montúfar, Yue Ren, Leon Zhang
2021	J	`jnl`	The Geometry of Memoryless Stochastic Policy Optimization in Infinite-Horizon POMDPs. CoRR Johannes Müller, Guido Montúfar
2021	J	`jnl`	Training Wasserstein GANs without gradient penalties. CoRR Dohyun Kwon, Yeoneung Kim, Guido Montúfar, Insoon Yang
2021	—	`conf`	Wasserstein Proximal of GANs. GSI Alex Tong Lin, Wuchen Li, Stanley J. Osher, Guido Montúfar
2021	J	`jnl`	Wasserstein Proximal of GANs. CoRR Alex Tong Lin, Wuchen Li, Stanley J. Osher, Guido Montúfar
2021	J	`jnl`	Wasserstein distance to independence models. J. Symb. Comput. Türkü Özlüm Çelik, Asgar Jamneshan, Guido Montúfar, Bernd Sturmfels, Lorenzo Venturello
2021	J	`jnl`	Weisfeiler and Lehman Go Cellular: CW Networks. CoRR Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yu Guang Wang, Pietro Liò, Guido Montúfar, Michael M. Bronstein
2021	J	`jnl`	Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. CoRR Cristian Bodnar, Fabrizio Frasca, Yu Guang Wang, Nina Otter, Guido Montúfar, Pietro Liò, Michael M. Bronstein
2020	J	`jnl`	Can neural networks learn persistent homology features? CoRR Guido Montúfar, Nina Otter, Yuguang Wang
2020	J	`jnl`	Distributed Learning via Filtered Hyperinterpolation on Manifolds. CoRR Guido Montúfar, Yu Guang Wang
2020	J	`jnl`	Factorized mutual information maximization. Kybernetika Thomas Merkh, Guido Montúfar
2020	A*	`conf`	Haar Graph Pooling. ICML Yuguang Wang, Ming Li, Zheng Ma, Guido Montúfar, Xiaosheng Zhuang, Yanan Fan
2020	J	`jnl`	Implicit bias of gradient descent for mean squared error regression with wide neural networks. CoRR Hui Jin, Guido Montúfar
2020	A*	`conf`	Kernelized Wasserstein Natural Gradient. ICLR Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montúfar
2020	A*	`conf`	Optimization Theory for ReLU Neural Networks Trained with Normalization Layers. ICML Yonatan Dukler, Quanquan Gu, Guido Montúfar
2020	J	`jnl`	Optimization Theory for ReLU Neural Networks Trained with Normalization Layers. CoRR Yonatan Dukler, Quanquan Gu, Guido Montúfar
2020	B	`conf`	The Variational Deficiency Bottleneck. IJCNN Pradeep Kr. Banerjee, Guido Montúfar
2020	J	`jnl`	Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks. CoRR Quynh Nguyen, Marco Mondelli, Guido Montúfar
2019	—	`conf`	Affine Natural Proximal Learning. GSI Wuchen Li, Alex Tong Lin, Guido Montúfar
2019	J	`jnl`	Factorized Mutual Information Maximization. CoRR Thomas Merkh, Guido Montúfar
2019	J	`jnl`	HaarPooling: Graph Pooling with Compressive Haar Basis. CoRR Yu Guang Wang, Ming Li, Zheng Ma, Guido Montúfar, Xiaosheng Zhuang, Yanan Fan
2019	J	`jnl`	How Well Do WGANs Estimate the Wasserstein Metric? CoRR Anton Mallasto, Guido Montúfar, Augusto Gerolin
2019	J	`jnl`	Kernelized Wasserstein Natural Gradient. CoRR Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montúfar
2019	—	`conf`	Optimal Transport to a Variety. MACIS Türkü Özlüm Çelik, Asgar Jamneshan, Guido Montúfar, Bernd Sturmfels, Lorenzo Venturello
2019	J	`jnl`	Stochastic Feedforward Neural Networks: Universal Approximation. CoRR Thomas Merkh, Guido Montúfar
2019	J	`jnl`	Wasserstein Diffusion Tikhonov Regularization. CoRR Alex Tong Lin, Yonatan Dukler, Wuchen Li, Guido Montúfar
2019	A*	`conf`	Wasserstein of Wasserstein Loss for Learning Generative Models. ICML Yonatan Dukler, Wuchen Li, Alex Tong Lin, Guido Montúfar
2018	B	`conf`	Computing the Unique Information. ISIT Pradeep Kr. Banerjee, Johannes Rauh, Guido Montúfar
2018	J	`jnl`	Natural gradient via optimal transport I. CoRR Wuchen Li, Guido Montúfar
2018	J	`jnl`	Restricted Boltzmann Machines: Introduction and Review. CoRR Guido Montúfar
2018	J	`jnl`	Ricci curvature for parametric statistics via optimal transport. CoRR Wuchen Li, Guido Montúfar
2018	J	`jnl`	The Variational Deficiency Bottleneck. CoRR Pradeep Kr. Banerjee, Guido Montúfar
2017	J	`jnl`	Computing the Unique Information. CoRR Pradeep Kr. Banerjee, Johannes Rauh, Guido Montúfar
2017	J	`jnl`	Dimension of Marginals of Kronecker Product Models. SIAM J. Appl. Algebra Geom. Guido Montúfar, Jason Morton
2017	—	`conf`	Geometry of Policy Improvement. GSI Guido Montúfar, Johannes Rauh
2017	J	`jnl`	Geometry of Policy Improvement. CoRR Guido Montúfar, Johannes Rauh
2017	J	`jnl`	Hierarchical models as marginals of hierarchical models. Int. J. Approx. Reason. Guido Montúfar, Johannes Rauh
2017	A	`conf`	Morphological computation: The good, the bad, and the ugly. IROS Keyan Ghazi-Zahedi, Raphael Deimel, Guido Montúfar, Vincent Wall, Oliver Brock
2016	J	`jnl`	Evaluating Morphological Computation in Muscle and DC-Motor Driven Models of Hopping Movements. Frontiers Robotics AI Keyan Ghazi-Zahedi, Daniel F. B. Haeufle, Guido Montúfar, Syn Schmitt, Nihat Ay
2016	J	`jnl`	Information Theoretically Aided Reinforcement Learning for Embodied Agents. CoRR Guido Montúfar, Keyan Ghazi-Zahedi, Nihat Ay
2015	J	`jnl`	A Theory of Cheap Control in Embodied Systems. PLoS Comput. Biol. Guido Montúfar, Keyan Ghazi-Zahedi, Nihat Ay
2015	—	`conf`	Deep Narrow Boltzmann Machines are Universal Approximators. ICLR (Poster) Guido Montúfar
2015	J	`jnl`	Dimension of Marginals of Kronecker Product Models. CoRR Guido Montúfar, Jason Morton
2015	J	`jnl`	Discrete restricted Boltzmann machines. J. Mach. Learn. Res. Guido Montúfar, Jason Morton
2015	J	`jnl`	Evaluating Morphological Computation in Muscle and DC-motor Driven Models of Human Hopping. CoRR Keyan Ghazi-Zahedi, Daniel F. B. Haeufle, Guido Montúfar, Syn Schmitt, Nihat Ay
2015	J	`jnl`	Geometry and Determinism of Optimal Stationary Control in Partially Observable Markov Decision Processes. CoRR Guido Montúfar, Keyan Ghazi-Zahedi, Nihat Ay
2015	J	`jnl`	Geometry and expressive power of conditional restricted Boltzmann machines. J. Mach. Learn. Res. Guido Montúfar, Nihat Ay, Keyan Ghazi-Zahedi
2015	J	`jnl`	Hierarchical Models as Marginals of Hierarchical Models. CoRR Guido Montúfar, Johannes Rauh
2015	J	`jnl`	Universal Approximation of Markov Kernels by Shallow Stochastic Feedforward Networks. CoRR Guido Montúfar
2014	J	`jnl`	A Framework for Cheap Universal Approximation in Embodied Systems. CoRR Guido Montúfar, Nihat Ay, Keyan Ghazi-Zahedi
2014	J	`jnl`	Expressive Power of Conditional Restricted Boltzmann Machines for Sensorimotor Control. CoRR Guido Montúfar, Nihat Ay, Keyan Zahedi
2014	J	`jnl`	On the Fisher Metric of Conditional Probability Polytopes. Entropy Guido Montúfar, Johannes Rauh, Nihat Ay
2014	—	`conf`	On the Number of Linear Regions of Deep Neural Networks. NIPS Guido Montúfar, Razvan Pascanu, Kyunghyun Cho, Yoshua Bengio
2014	J	`jnl`	On the Number of Linear Regions of Deep Neural Networks. CoRR Guido Montúfar, Razvan Pascanu, Kyunghyun Cho, Yoshua Bengio
2014	—	`conf`	On the number of inference regions of deep feed forward networks with piece-wise linear activations. ICLR (Poster) Razvan Pascanu, Guido Montúfar, Yoshua Bengio
2014	J	`jnl`	Sequential Recurrence-Based Multidimensional Universal Source Coding of Lempel-Ziv Type. CoRR Tyll Krueger, Guido Montúfar, Ruedi Seiler, Rainer Siegmund-Schultze
2013	A*	`conf`	Discrete Restricted Boltzmann Machines ICLR Guido Montúfar, Jason Morton
2013	—	`conf`	Maximal Information Divergence from Statistical Models Defined by Neural Networks. GSI Guido Montúfar, Johannes Rauh, Nihat Ay
2013	J	`jnl`	Mixture decompositions of exponential families - using a decomposition of their sample spaces. Kybernetika Guido Montúfar
2013	J	`jnl`	Universally typical sets for ergodic sources of multidimensional data. Kybernetika Tyll Krüger, Guido Montúfar, Ruedi Seiler, Rainer Siegmund-Schultze
2011	—	`conf`	Expressive Power and Approximation Errors of Restricted Boltzmann Machines. NIPS Guido Montúfar, Johannes Rauh, Nihat Ay
2011	J	`jnl`	Refinements of Universal Approximation Results for Deep Belief Networks and Restricted Boltzmann Machines. Neural Comput. Guido Montúfar, Nihat Ay
2011	J	`jnl`	Universally Typical Sets for Ergodic Sources of Multidimensional Data CoRR Tyll Krueger, Guido Montúfar, Ruedi Seiler, Rainer Siegmund-Schultze