Statistical text-to-speech synthesis with improved dynamics. - Stas Tiomkin; David Malah (INTERSPEECH, 2008)

Statistical Text-to-Speech Synthesis Based on Segment-Wise Representation With a Norm Constraint. - Stas Tiomkin; David Malah; Slava Shechtman (IEEE Trans. Speech Audio Process., 2010)

A Hybrid Text-to-Speech System That Combines Concatenative and Statistical Synthesis Units. - Stas Tiomkin; David Malah; Slava Shechtman; Zvi Kons (IEEE Trans. Speech Audio Process., 2011)

Past-future Information Bottleneck for linear feedback systems. - Nadav Amir; Stas Tiomkin; Naftali Tishby (CDC, 2015)

Cognitive workload and vocabulary sparseness: theory and practice. - Ron M. Hecht; Aharon Bar-Hillel; Stas Tiomkin; Hadar Levi; Omer Tsimhoni; Naftali Tishby (INTERSPEECH, 2015)

Control Capacity of Partially Observable Dynamic Systems in Continuous Time. - Stas Tiomkin; Daniel Polani; Naftali Tishby (CoRR, 2017)

A Unified Bellman Equation for Causal Information and Value in Markov Decision Processes. - Stas Tiomkin; Naftali Tishby (CoRR, 2017)

Learning Efficient Representation for Intrinsic Motivation. - Ruihan Zhao 001; Stas Tiomkin; Pieter Abbeel (CoRR, 2019)

Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewards. - Xingyu Lu; Stas Tiomkin; Pieter Abbeel (CoRR, 2019)

AvE: Assistance via Empowerment. - Yuqing Du; Stas Tiomkin; Emre Kiciman; Daniel Polani; Pieter Abbeel; Anca D. Dragan (NeurIPS, 2020)

Preventing Imitation Learning with Adversarial Policy Ensembles. - Albert Zhan; Stas Tiomkin; Pieter Abbeel (CoRR, 2020)

Efficient Online Estimation of Empowerment for Reinforcement Learning. - Ruihan Zhao 001; Pieter Abbeel; Stas Tiomkin (CoRR, 2020)

Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning. - Xingyu Lu; Kimin Lee; Pieter Abbeel; Stas Tiomkin (CoRR, 2020)

Efficient Empowerment Estimation for Unsupervised Stabilization. - Ruihan Zhao 001; Kevin Lu; Pieter Abbeel; Stas Tiomkin (ICLR, 2021)

GEM: Group Enhanced Model for Learning Dynamical Control Systems. - Philippe Hansen-Estruch; Wenling Shang; Lerrel Pinto; Pieter Abbeel; Stas Tiomkin (CoRR, 2021)

Closed-Form Analytical Results for Maximum Entropy Reinforcement Learning. - Argenis Arriojas; Stas Tiomkin; Rahul V. Kulkarni (CoRR, 2021)

Multi-Objective Policy Gradients with Topological Constraints. - Kyle Hollins Wray; Stas Tiomkin; Mykel J. Kochenderfer; Pieter Abbeel (IROS, 2022)

Utilizing Prior Solutions for Reward Shaping and Composition in Entropy-Regularized Reinforcement Learning. - Jacob Adamczyk; Argenis Arriojas; Stas Tiomkin; Rahul V. Kulkarni (AAAI, 2023)

Bounding the optimal value function in compositional reinforcement learning. - Jacob Adamczyk; Volodymyr Makarenko; Argenis Arriojas; Stas Tiomkin; Rahul V. Kulkarni (UAI, 2023)

Bayesian inference approach for entropy regularized reinforcement learning with stochastic dynamics. - Argenis Arriojas; Jacob Adamczyk; Stas Tiomkin; Rahul V. Kulkarni (UAI, 2023)

Intrinsic Motivation in Dynamical Control Systems. - Stas Tiomkin; Ilya Nemenman; Daniel Polani; Naftali Tishby (CoRR, 2023)

Compositionality and Bounds for Optimal Value Functions in Reinforcement Learning. - Jacob Adamczyk; Stas Tiomkin; Rahul V. Kulkarni (CoRR, 2023)

Dimensionality Reduction of Dynamics on Lie Manifolds via Structure-Aware Canonical Correlation Analysis. - Wooyoung Chung; Daniel Polani; Stas Tiomkin (CoRR, 2023)

Taming Waves: A Physically-Interpretable Machine Learning Framework for Realizable Control of Wave Dynamics. - Tristan Shah; Feruza Amirkulova; Stas Tiomkin (CoRR, 2023)

Dimensionality Reduction of Dynamics on Lie Groups via Structure-Aware Canonical Correlation Analysis. - Wooyoung Chung; Daniel Polani; Stas Tiomkin (ACC, 2024)

Controllability-Constrained Deep Network Models for Enhanced Control of Dynamical Systems. - Suruchi Sharma; Volodymyr Makarenko; Gautam Kumar; Stas Tiomkin (ACC, 2024)

Boosting Soft Q-Learning by Bounding. - Jacob Adamczyk; Volodymyr Makarenko; Stas Tiomkin; Rahul V. Kulkarni (RLJ, 2024)

Multi-Resolution Diffusion for Privacy-Sensitive Recommender Systems. - Derek Lilienthal; Paul Mello; Magdalini Eirinaki; Stas Tiomkin (IEEE Access, 2024)

Learning telic-controllable state representations. - Nadav Amir; Stas Tiomkin; Angela Langdon (CoRR, 2024)

SuPLE: Robot Learning with Lyapunov Rewards. - Phu Nguyen; Daniel Polani; Stas Tiomkin (CoRR, 2024)

Bootstrapped Reward Shaping. - Jacob Adamczyk; Volodymyr Makarenko; Stas Tiomkin; Rahul V. Kulkarni (AAAI, 2025)

Average-Reward Reinforcement Learning with Entropy Regularization. - Jacob Adamczyk; Volodymyr Makarenko; Stas Tiomkin; Rahul V. Kulkarni (CoRR, 2025)

EVAL: EigenVector-based Average-reward Learning. - Jacob Adamczyk; Volodymyr Makarenko; Stas Tiomkin; Rahul V. Kulkarni (CoRR, 2025)

Acoustic Wave Manipulation Through Sparse Robotic Actuation. - Tristan Shah; Noam Smilovich; Feruza Amirkulova; Samer Gerges; Stas Tiomkin (CoRR, 2025)

Decentralized Traffic Flow Optimization Through Intrinsic Motivation. - Himaja Papala; Daniel Polani; Stas Tiomkin (CoRR, 2025)