Vadim Sokolov

33 papers A* 1Journal 28Unranked 4
YearRankTypeTitle / Venue / Authors
2026 J jnl
Bayesian Methods for the Navier-Stokes Equations.
CoRR
Nicholas Polson, Vadim Sokolov
2026 J jnl
Fast Compute for ML Optimization.
CoRR
Nick Polson, Vadim Sokolov
2026 J jnl
Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates.
CoRR
Nick Polson, Vadim Sokolov
2026 J jnl
Horseshoe Mixtures-of-Experts (HS-MoE).
CoRR
Nick Polson, Vadim Sokolov
2026 J jnl
Photons = Tokens: The Physics of AI and the Economics of Knowledge.
CoRR
Alec Litowitz, Nick Polson, Vadim Sokolov
2025 J jnl
Bayesian Double Descent.
CoRR
Nick Polson, Vadim Sokolov
2025 J jnl
Generative AI for Bayesian Computation.
Entropy
Nick Polson, Vadim Sokolov
2025 J jnl
Generative AI for Validating Physics Laws.
CoRR
Maria Nareklishvili, Nicholas Polson, Vadim Sokolov
2025 J jnl
Generative Modeling: A Review.
CoRR
Nick Polson, Vadim Sokolov
2025 J jnl
Kolmogorov GAM Networks Are All You Need!
Entropy
Sarah Polson, Vadim Sokolov
2025 J jnl
Kolmogorov GAM Networks are all you need!
CoRR
Sarah Polson, Vadim Sokolov
2024 J jnl
Generative Bayesian Computation for Maximum Expected Utility.
Entropy
Nick Polson, Fabrizio Ruggeri, Vadim Sokolov
2023 J jnl
Deep Learning: A Tutorial.
CoRR
Nick Polson, Vadim Sokolov
2023 J jnl
On the Value of Chess Squares.
Entropy
Aditya Gupta, Shiva Maharaj, Nicholas Polson, Vadim Sokolov
2023 J jnl
The Value of Chess Squares.
CoRR
Aditya Gupta, Shiva Maharaj, Nicholas Polson, Vadim Sokolov
2023 J jnl
Virtual Light Sensing Technology for Fast Calculation of Daylight Autonomy Metrics.
Sensors
Sergey V. Ershov, Vadim Sokolov, Vladimir A. Galaktionov, Alexey G. Voloboy
2022 J jnl
Quantum Bayes AI.
CoRR
Nicholas G. Polson, Vadim Sokolov, Jianeng Xu
2021 J jnl
Deep Generative Models for Vehicle Speed Trajectories.
CoRR
Farnaz Behnia, Dominik Karbowski, Vadim Sokolov
2021 J jnl
Merging Two Cultures: Deep and Statistical Learning.
CoRR
Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson, Vadim Sokolov, Jianeng Xu
2019 J jnl
Clusters of Driving Behavior From Observational Smartphone Data.
IEEE Intell. Transp. Syst. Mag.
Josh Warren, Jeff Lipkowitz, Vadim Sokolov
2019 A* conf
FakeTables: Using GANs to Generate Functional Dependency Preserving Tables with Bounded Real Data.
IJCAI
Haipeng Chen, Sushil Jajodia, Jing Liu, Noseong Park, Vadim Sokolov, V. S. Subrahmanian
2019 J jnl
Solving Large-Scale 0-1 Knapsack Problems and its Application to Point Cloud Resampling.
CoRR
Duanshun Li, Jing Liu, Noseong Park, Dongeun Lee, Giridhar Ramachandran, Ali Seyedmazloom, Kookjin Lee, Chen Feng, Vadim Sokolov, Rajesh Ganesan
2018 — conf
Bayesian Optimization for Transportation Simulators.
ANT/SEIT
Laura Schultz, Vadim Sokolov
2018 J jnl
Bayesian Particle Tracking of Traffic Flows.
IEEE Trans. Intell. Transp. Syst.
Nicholas Polson, Vadim Sokolov
2018 J jnl
Deep Learning for Energy Markets.
CoRR
Michael Polson, Vadim Sokolov
2018 J jnl
Deep Learning: Computational Aspects.
CoRR
Nicholas Polson, Vadim Sokolov
2018 J jnl
Deep Reinforcement Learning for Dynamic Urban Transportation Problems.
CoRR
Laura Schultz, Vadim Sokolov
2018 — conf
Effects of Predictive Real-Time Traffic Signal Information.
ITSC
Vadim Sokolov, Muhammad Imran, David W. Etherington, Dominik Karbowski, Aymeric Rousseau
2017 J jnl
Clusters of Driving Behavior from Observational Smartphone Data.
CoRR
Josh Warren, Jeff Lipkowitz, Vadim Sokolov
2017 J jnl
Deep Learning: A Bayesian Perspective.
CoRR
Nicholas Polson, Vadim Sokolov
2017 J jnl
Platoon formation maximization through centralized routing and departure time coordination.
CoRR
Vadim Sokolov, Jeffrey Larson, Todd S. Munson, Joshua Auld, Dominik Karbowski
2016 — conf
Coordinated Platoon Routing in a Metropolitan Network.
CSC
Jeffrey Larson, Todd S. Munson, Vadim Sokolov
2012 — conf
A Flexible Framework for Developing Integrated Models of Transportation Systems Using an Agent-based Approach.
ANT/MobiWIS
Vadim Sokolov, Joshua Auld, Michael Hope