Roman Pogodin - RankMe

17 papers A* 5A 1Journal 10Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	On the Hardness of Conditional Independence Testing In Practice. CoRR Zheng He, Roman Pogodin, Yazhe Li, Namrata Deka, Arthur Gretton, Danica J. Sutherland
2024	J	`jnl`	Practical Kernel Tests of Conditional Independence. CoRR Roman Pogodin, Antonin Schrab, Yazhe Li, Danica J. Sutherland, Arthur Gretton
2024	A*	`conf`	Synaptic Weight Distributions Depend on the Geometry of Plasticity. ICLR Roman Pogodin, Jonathan Cornford, Arna Ghosh, Gauthier Gidel, Guillaume Lajoie, Blake Aaron Richards
2023	A*	`conf`	Efficient Conditionally Invariant Representation Learning. ICLR Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton
2023	J	`jnl`	Synaptic Weight Distributions Depend on the Geometry of Plasticity. CoRR Roman Pogodin, Jonathan Cornford, Arna Ghosh, Gauthier Gidel, Guillaume Lajoie, Blake A. Richards
2022	J	`jnl`	Efficient Conditionally Invariant Representation Learning. CoRR Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton
2021	A*	`conf`	Self-Supervised Learning with Kernel Dependence Maximization. NeurIPS Yazhe Li, Roman Pogodin, Danica J. Sutherland, Arthur Gretton
2021	J	`jnl`	Self-Supervised Learning with Kernel Dependence Maximization. CoRR Yazhe Li, Roman Pogodin, Danica J. Sutherland, Arthur Gretton
2021	A*	`conf`	Towards Biologically Plausible Convolutional Networks. NeurIPS Roman Pogodin, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham
2021	J	`jnl`	Towards Biologically Plausible Convolutional Networks. CoRR Roman Pogodin, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham
2020	A*	`conf`	Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks. NeurIPS Roman Pogodin, Peter E. Latham
2020	J	`jnl`	Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks. CoRR Roman Pogodin, Peter E. Latham
2019	J	`jnl`	Adaptivity, Variance and Separation for Adversarial Bandits. CoRR Roman Pogodin, Tor Lattimore
2019	A	`conf`	On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits. UAI Roman Pogodin, Tor Lattimore
2019	J	`jnl`	Working memory facilitates reward-modulated Hebbian learning in recurrent neural networks. CoRR Roman Pogodin, Dane S. Corneil, Alexander Seeholzer, Joseph Heng, Wulfram Gerstner
2017	J	`jnl`	Efficient Rank Minimization to Tighten Semidefinite Programming for Unconstrained Binary Quadratic Optimization. CoRR Roman Pogodin, Mikhail Krechetov, Yury Maximov
2017	—	`conf`	Efficient rank minimization to tighten semidefinite programming for unconstrained binary quadratic optimization. Allerton Roman Pogodin, Mikhail Krechetov, Yury Maximov