63 citations · 81 across the 42 of their papers we have counts for
13 papers · 1 filter
Density-Ratio Weighted Behavioral Cloning: Learning Control Policies from Corrupted Datasets
Shriram Karpoora Sundara Pandian, Ali Baheri
Offline reinforcement learning (RL) enables policy optimization from fixed datasets, making it suitable for safety-critical applications where online exploration is infeasible. How…
Split Conformal Prediction in the Function Space with Neural Operators
David Millard, Lars Lindemann, Ali Baheri
Uncertainty quantification for neural operators remains an open problem in the infinite-dimensional setting due to the lack of finite-sample coverage guarantees over functional out…
Metriplectic Conditional Flow Matching for Dissipative Dynamics
Ali Baheri, Lars Lindemann
Metriplectic conditional flow matching (MCFM) learns dissipative dynamics without violating first principles. Neural surrogates often inject energy and destabilize long-horizon rol…
Geometry-Aware Decentralized Sinkhorn for Wasserstein Barycenters
Ali Baheri, Alireza Vahid
Distributed systems require fusing heterogeneous local probability distributions into a global summary over sparse and unreliable communication networks. Traditional consensus algo…
Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning
Ali Baheri
Cooperative multi-agent reinforcement learning (MARL) demands principled mechanisms to align heterogeneous policies while preserving the capacity for specialized behavior. We intro…
Implicit Constraint-Aware Off-Policy Correction for Offline Reinforcement Learning
Ali Baheri
Offline reinforcement learning promises policy improvement from logged interaction data alone, yet state-of-the-art algorithms remain vulnerable to value over-estimation and to vio…
DEF: Diffusion-augmented Ensemble Forecasting
David Millard, Arielle Carr, Stéphane Gaudreault +1
We present DEF (\textbf{\ul{D}}iffusion-augmented \textbf{\ul{E}}nsemble \textbf{\ul{F}}orecasting), a novel approach for generating initial condition perturbations. Modern approac…
Wasserstein Barycenter Soft Actor-Critic
Zahra Shahrooei, Ali Baheri
Deep off-policy actor-critic algorithms have emerged as the leading framework for reinforcement learning in continuous control domains. However, most of these algorithms suffer fro…
PEARL: Preconditioner Enhancement through Actor-critic Reinforcement Learning
David Millard, Arielle Carr, Stéphane Gaudreault +1
We present PEARL (Preconditioner Enhancement through Actor-critic Reinforcement Learning), a novel approach to learning matrix preconditioners. Existing preconditioners such as Jac…
Multi-Scale Conformal Prediction: A Theoretical Framework with Coverage Guarantees
Ali Baheri, Marzieh Amiri Shahbazi
We propose a multi-scale extension of conformal prediction, an approach that constructs prediction sets with finite-sample coverage guarantees under minimal statistical assumptions…
Optimal Transport-Guided Safety in Temporal Difference Reinforcement Learning
Zahra Shahrooei, Ali Baheri
The primary goal of reinforcement learning is to develop decision-making policies that prioritize optimal performance, frequently without considering safety. In contrast, safe rein…
SMTL: A Stratified Logic for Expressive Multi-Level Temporal Specifications
Ali Baheri, Peng Wei
We present Stratified Metric Temporal Logic (SMTL), a novel formalism for specifying and verifying properties of complex cyber-physical systems that exhibit behaviors across multip…
Wasserstein Adaptive Value Estimation for Actor-Critic Reinforcement Learning
Ali Baheri, Zahra Shahrooei, Chirayu Salgarkar
We present Wasserstein Adaptive Value Estimation for Actor-Critic (WAVE), an approach to enhance stability in deep reinforcement learning through adaptive Wasserstein regularizatio…