| Self-supervised learning with kernel dependence maximization Y Li*, R Pogodin*, DJ Sutherland, A Gretton Advances in Neural Information Processing Systems 34, 2021 | 49 | 2021 |
| Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks R Pogodin, P Latham Advances in Neural Information Processing Systems 33, 2020 | 32 | 2020 |
| Towards biologically plausible convolutional networks R Pogodin, Y Mehta, T Lillicrap, P Latham Advances in Neural Information Processing Systems 34, 2021 | 23 | 2021 |
| On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits R Pogodin, T Lattimore The Conference on Uncertainty in Artificial Intelligence (UAI) 2019, 2019 | 22* | 2019 |
| Efficient Rank Minimization to Tighten Semidefinite Programming for Unconstrained Binary Quadratic Optimization R Pogodin, M Krechetov, Y Maximov 2017 55th Annual Allerton Conference on Communication, Control, and …, 2017 | 6 | 2017 |
| Efficient conditionally invariant representation learning R Pogodin*, N Deka*, Y Li*, DJ Sutherland, V Veitch, A Gretton ICLR 2023 notable top 5%, 2022 | 4 | 2022 |
| Synaptic Weight Distributions Depend on the Geometry of Plasticity R Pogodin*, J Cornford*, A Ghosh, G Gidel, G Lajoie, B Richards arXiv preprint arXiv:2305.19394, 2023 | 3 | 2023 |
| Locally connected networks as ventral stream models R Pogodin, PE Latham Brain-Score Workshop, 2022 | 1 | 2022 |
| Working memory facilitates reward-modulated Hebbian learning in recurrent neural networks R Pogodin, D Corneil, A Seeholzer, J Heng, W Gerstner NeurIPS 2019 workshop "Real Neurons & Hidden Units: Future directions at the …, 2019 | 1 | 2019 |
| Deep Learning Models of Learning in the Brain R Pogodin UCL (University College London), 2023 | | 2023 |
| Quadratic Programming Approach to Fit Protein Complexes into Electron Density Maps R Pogodin, A Katrutsa, S Grudinin Information Technology and Systems 2016, 2017 | | 2017 |
