Efficient rank minimization to tighten semidefinite programming for unconstrained binary quadratic optimization. - Roman Pogodin; Mikhail Krechetov; Yury Maximov (Allerton, 2017)

On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits. - Roman Pogodin; Tor Lattimore (UAI, 2019)

Adaptivity, Variance and Separation for Adversarial Bandits. - Roman Pogodin; Tor Lattimore (CoRR, 2019)

Working memory facilitates reward-modulated Hebbian learning in recurrent neural networks. - Roman Pogodin; Dane S. Corneil; Alexander Seeholzer; Joseph Heng; Wulfram Gerstner (CoRR, 2019)

Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks. - Roman Pogodin; Peter E. Latham (NeurIPS, 2020)

Self-Supervised Learning with Kernel Dependence Maximization. - Yazhe Li; Roman Pogodin; Danica J. Sutherland; Arthur Gretton (NeurIPS, 2021)

Towards Biologically Plausible Convolutional Networks. - Roman Pogodin; Yash Mehta; Timothy P. Lillicrap; Peter E. Latham (NeurIPS, 2021)

Efficient Conditionally Invariant Representation Learning. - Roman Pogodin; Namrata Deka; Yazhe Li; Danica J. Sutherland; Victor Veitch; Arthur Gretton (ICLR, 2023)

Synaptic Weight Distributions Depend on the Geometry of Plasticity. - Roman Pogodin; Jonathan Cornford; Arna Ghosh; Gauthier Gidel; Guillaume Lajoie; Blake Aaron Richards (ICLR, 2024)

Practical Kernel Tests of Conditional Independence. - Roman Pogodin; Antonin Schrab; Yazhe Li; Danica J. Sutherland; Arthur Gretton (CoRR, 2024)