2 citations · 2 across the 9 of their papers we have counts for
10 papers
Fenchel Tilting: Weighted Correction for Efficient Finetuning of Generative Models
Maksim Bobrin, Maksim Zhdanov, Dmitry Dylov
Adapting a pretrained generative model to an arbitrary preference expressed as a utility function underlies reward alignment, guided design, and constraint satisfaction, enabling d…
Plan, Don't Pose: Long Composite Motion Generation with Text-Aligned BFM
Nikolay Shvetsov, Maksim Bobrin, Nazar Buzun +2
Text-to-motion (T2M) generation has broad applications in character animation, virtual avatars, and human-robot interaction. Existing methods typically generate pose trajectories o…
Midpoint Generative Models
Daniil Shlenskii, Nikita Gushchin, Lev Novitskiy +2
We introduce Midpoint Generative Models (MGM), a principled framework for training one-step generative models. MGM is based on a simple symmetry of Flow Matching with linear interp…
Dual Advantage Fields
Alexey Zemtsov, Maxim Bobrin, Alexander Nikulin +5
Offline goal-conditioned reinforcement learning requires both long-horizon reachability estimates and local action comparisons. Dual goal representations provide value fields that…
Zero-Shot Off-Policy Learning
Arip Asadulaev, Maksim Bobrin, Salem Lahlou +3
Off-policy learning methods seek to derive an optimal policy directly from a fixed dataset of prior interactions. This objective presents significant challenges, primarily due to t…
HOTA: Hamiltonian framework for Optimal Transport Advection
Nazar Buzun, Daniil Shlenskii, Maxim Bobrin +1
Optimal transport (OT) has become a natural framework for guiding the probability flows. Yet, the majority of recent generative models assume trivial geometry (e.g., Euclidean) and…
Zero-Shot Adaptation of Behavioral Foundation Models to Unseen Dynamics
Maksim Bobrin, Ilya Zisman, Alexander Nikulin +2
Behavioral Foundation Models (BFMs) proved successful in producing policies for arbitrary tasks in a zero-shot manner, requiring no test-time training or task-specific fine-tuning.…
Suppressing Modulation Instability with Reinforcement Learning
Nikolay Kalmykov, Rishat Zagidullin, Oleg Rogov +2
Modulation instability is a phenomenon of spontaneous pattern formation in nonlinear media, oftentimes leading to an unpredictable behaviour and a degradation of a signal of intere…
ENOT: Expectile Regularization for Fast and Accurate Training of Neural Optimal Transport
Nazar Buzun, Maksim Bobrin, Dmitry V. Dylov
We present a new approach for Neural Optimal Transport (NOT) training procedure, capable of accurately and efficiently estimating optimal transportation plan via specific regulariz…
Align Your Intents: Offline Imitation Learning via Optimal Transport
Maksim Bobrin, Nazar Buzun, Dmitrii Krylov +1
Offline Reinforcement Learning (RL) addresses the problem of sequential decision-making by learning optimal policy through pre-collected data, without interacting with the environm…