Solving the Rubik's Cube Without Human Knowledge. - Stephen McAleer; Forest Agostinelli; Alexander Shmakov; Pierre Baldi (CoRR, 2018)

Solving the Rubik's Cube with Approximate Policy Iteration. - Stephen McAleer; Forest Agostinelli; Alexander Shmakov; Pierre Baldi (ICLR (Poster), 2019)

ColosseumRL: A Framework for Multiagent Reinforcement Learning in N-Player Games. - Alexander Shmakov; John B. Lanier; Stephen McAleer; Rohan Achar; Cristina V. Lopes; Pierre Baldi (CoRR, 2019)

Solving the Rubik's cube with deep reinforcement learning and search. - Forest Agostinelli; Stephen McAleer; Alexander Shmakov; Pierre Baldi (Nat. Mach. Intell., 2019)

Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images. - Gregor Urban; Kevin Bache; Duc T. T. Phan; Agua Sobrino; Alexander Shmakov; Stephanie J. Hachey; Christopher C. W. Hughes; Pierre Baldi (IEEE ACM Trans. Comput. Biol. Bioinform., 2019)

Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks. - Michael James Fenton; Alexander Shmakov; Ta-Wei Ho; Shih-Chieh Hsu; Daniel Whiteson; Pierre Baldi (CoRR, 2020)

A* Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks. - Forest Agostinelli; Alexander Shmakov; Stephen McAleer; Roy Fox; Pierre Baldi (CoRR, 2021)

SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention. - Alexander Shmakov; Michael James Fenton; Ta-Wei Ho; Shih-Chieh Hsu; Daniel Whiteson; Pierre Baldi (CoRR, 2021)

Multi-Agent Reinforcement Learning Controller to Maximize Energy Efficiency for Multi-Generator Industrial Wave Energy Converter. - Soumyendu Sarkar; Vineet Gundecha; Alexander Shmakov; Sahand Ghorbanpour; Ashwin Ramesh Babu; Paolo Faraboschi; Mathieu Cocho; Alexandre Pichard; Jonathan Fievez (AAAI, 2022)

Skip Training for Multi-Agent Reinforcement Learning Controller for Industrial Wave Energy Converters. - Soumyendu Sarkar; Vineet Gundecha; Sahand Ghorbanpour; Alexander Shmakov; Ashwin Ramesh Babu; Alexandre Pichard; Mathieu Cocho (CASE, 2022)

Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation. - Mohammadamin Tavakoli; Alexander Shmakov; Francesco Ceccarelli; Pierre Baldi (CoRR, 2022)

Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission. - Alexander Shmakov; Mohammadamin Tavakoli; Pierre Baldi; Christopher M. Karwin; Alex Broughton; Simona Murgia (CoRR, 2022)

Robustness with Black-Box Adversarial Attack using Reinforcement Learning. - Soumyendu Sarkar; Ashwin Ramesh Babu; Sajad Mousavi; Vineet Gundecha; Sahand Ghorbanpour; Alexander Shmakov; Ricardo Luna Gutierrez 001; Antonio Guillen; Avisek Naug (SafeAI@AAAI, 2023)

RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels. - Alexander Shmakov; Avisek Naug; Vineet Gundecha; Sahand Ghorbanpour; Ricardo Luna Gutierrez 001; Ashwin Ramesh Babu; Antonio Guillen; Soumyendu Sarkar (CASE, 2023)

Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves. - Soumyendu Sarkar; Vineet Gundecha; Sahand Ghorbanpour; Alexander Shmakov; Ashwin Ramesh Babu; Avisek Naug; Alexandre Pichard; Mathieu Cocho (IJCAI, 2023)

End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics. - Alexander Shmakov; Kevin Greif; Michael James Fenton; Aishik Ghosh; Pierre Baldi; Daniel Whiteson (NeurIPS, 2023)

AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning. - Mohammadamin Tavakoli; Pierre Baldi; Ann Marie Carlton; Yin Ting T. Chiu; Alexander Shmakov; David Van Vranken (NeurIPS, 2023)

Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers. - Alexander Shmakov; Alejandro Yankelevich; Jianming Bian; Pierre Baldi (CoRR, 2023)

Extended Symmetry Preserving Attention Networks for LHC Analysis. - Michael James Fenton; Alexander Shmakov; Hideki Okawa; Yuji Li; Ko-Yang Hsiao; Shih-Chieh Hsu; Daniel Whiteson; Pierre Baldi (CoRR, 2023)

Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion. - Alexander Shmakov; Kevin Greif; Michael James Fenton; Aishik Ghosh; Pierre Baldi; Daniel Whiteson (CoRR, 2024)

The Landscape of Unfolding with Machine Learning. - Nathan Huetsch; Javier Mariño Villadamigo; Alexander Shmakov; Sascha Diefenbacher; Vinicius Mikuni; Theo Heimel; Michael James Fenton; Kevin Greif; Benjamin Nachman; Daniel Whiteson; Anja Butter; Tilman Plehn (CoRR, 2024)

Reconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networks. - Haoyang Li; Marko Stamenkovic; Alexander Shmakov; Michael Fenton; Darius Shih-Chieh Chao; Kaitlyn Maiya White; Caden Mikkelsen; Jovan Mitic; Cristina Mantilla Suarez; Melissa Quinnan; Greg Landsberg; Harvey B. Newman; et al. (CoRR, 2024)