# Accepted Paper

Our Frontier4LCD workshop has received 134 outstanding submissions, and we are thrilled to announce that 100 high-quality papers have been accepted for presentation. Below is the list of accepted papers.

Number | Title |
---|---|

1 | AbODE: Ab initio antibody design using conjoined ODEs |

2 | Distributional Distance Classifiers for Goal-Conditioned Reinforcement Learning |

3 | LEAD: Min-Max Optimization from a Physical Perspective |

4 | Balancing exploration and exploitation in Partially Observed Linear Contextual Bandits via Thompson Sampling |

5 | Stochastic Linear Bandits with Unknown Safety Constraints and Local Feedback |

6 | Visual Dexterity: In-hand Dexterous Manipulation from Depth |

7 | Regret Bounds for Risk-sensitive Reinforcement Learning with Lipschitz Dynamic Risk Measures |

8 | On learning history-based policies for controlling Markov decision processes |

9 | Improved sampling via learned diffusions |

10 | Fast Approximation of the Generalized Sliced-Wasserstein Distance |

11 | Synthetic Experience Replay |

12 | A neural RDE approach for continuous-time non-Markovian stochastic control problems |

13 | Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning |

14 | Toward Understanding Latent Model Learning in MuZero: A Case Study in Linear Quadratic Gaussian Control |

15 | Gradient-free training of neural ODEs for system identification and control using ensemble Kalman inversion |

16 | Preventing Reward Hacking with Occupancy Measure Regularization |

17 | Exponential weight averaging as damped harmonic motion |

18 | Bridging RL Theory and Practice with the Effective Horizon |

19 | Accelerated Policy Gradient: On the Nesterov Momentum for Reinforcement Learning |

20 | Neural Optimal Transport with Lagrangian Costs |

21 | Taylor TD-learning |

22 | Coupled Gradient Flows for Strategic Non-Local Distribution Shift |

23 | Kernel Mirror Prox and RKHS Gradient Flow for Mixed Functional Nash Equilibrium |

24 | What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning? |

25 | On the Generalization Capacities of Neural Controlled Differential Equations |

26 | A Best Arm Identification Approach for Stochastic Rising Bandits |

27 | Maximum State Entropy Exploration using Predecessor and Successor Representations |

28 | Guide Your Agent with Adaptive Multimodal Rewards |

29 | Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation |

30 | Unbalanced Optimal Transport meets Sliced-Wasserstein |

31 | Randomly Coupled Oscillators for Time Series Processing |

32 | Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware |

33 | Boosting Off-policy RL with Policy Representation and Policy-extended Value Function Approximator |

34 | Statistics estimation in neural network training: a recursive identification approach |

35 | Embedding Surfaces by Optimizing Neural Networks with Prescribed Riemannian Metric and Beyond |

36 | Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding |

37 | A Flexible Diffusion Model |

38 | Simulation-Free Schrödinger Bridges via Score and Flow Matching |

39 | On the Imitation of Non-Markovian Demonstrations: From Low-Level Stability to High-Level Planning |

40 | Fixed-Budget Hypothesis Best Arm Identification: On the Information Loss in Experimental Design |

41 | Variational Principle and Variational Integrators for Neural Symplectic Forms |

42 | A Policy-Decoupled Method for High-Quality Data Augmentation in Offline Reinforcement Learning |

43 | When is Agnostic Reinforcement Learning Statistically Tractable? |

44 | Algorithms for Optimal Adaptation ofDiffusion Models to Reward Functions |

45 | On Convergence of Approximate Schr\"{o}dinger Bridge with Bounded Cost |

46 | In-Context Decision-Making from Supervised Pretraining |

47 | Unbalanced Diffusion Schrödinger Bridge |

48 | Learning from Sparse Offline Datasets via Conservative Density Estimation |

49 | Parameterized projected Bellman operator |

50 | Randomized methods for computing optimal transport without regularization and their convergence analysis |

51 | Bridging Physics-Informed Neural Networks with Reinforcement Learning: Hamilton-Jacobi-Bellman Proximal Policy Optimization (HJBPPO) |

52 | On a Connection between Differential Games, Optimal Control, and Energy-based Models for Multi-Agent Interactions |

53 | Dynamic Feature-based Newsvendor |

54 | Game Theoretic Neural ODE Optimizer |

55 | Diffusion Model-Augmented Behavioral Cloning |

56 | Vector Quantile Regression on Manifolds |

57 | PAC-Bayesian Bounds for Learning LTI-ss systems with Input from Empirical Loss |

58 | Learning to Optimize with Recurrent Hierarchical Transformers |

59 | Sample Complexity of Hierarchical Decompositions in Markov Decision Processes |

60 | Fairness In a Non-Stationary Environment From an Optimal Control Perspective |

61 | Modular Hierarchical Reinforcement Learning for Robotics: Improving Scalability and Generalizability |

62 | Taylorformer: Probabalistic Modelling for Random Processes including Time Series |

63 | Stability of Multi-Agent Learning: Convergence in Network Games with Many Players |

64 | Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport |

65 | Deep Equilibrium Based Neural Operators for Steady-State PDEs |

66 | Informed POMDP: Leveraging Additional Information in Model-Based RL |

67 | IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control |

68 | Modeling Accurate Long Rollouts with Temporal Neural PDE Solvers |

69 | Sub-linear Regret in Adaptive Model Predictive Control |

70 | Analyzing the Sample Complexity of Model-Free Opponent Shaping |

71 | Continuous Vector Quantile Regression |

72 | Trajectory Generation, Control, and Safety with Denoising Diffusion Probabilistic Models |

73 | Structured State Space Models for In-Context Reinforcement Learning |

74 | Latent Space Editing in Transformer-Based Flow Matching |

75 | Transport, VI, and Diffusions |

76 | Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Markov Chains |

77 | Policy Gradient Algorithms Implicitly Optimize by Continuation |

78 | Improving Offline-to-Online Reinforcement Learning with Q-Ensembles |

79 | Offline Goal-Conditioned RL with Latent States as Actions |

80 | On the effectiveness of neural priors in modeling dynamical systems |

81 | Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning |

82 | Physics-informed Localized Learning for Advection-Diffusion-Reaction Systems |

83 | Factor Learning Portfolio Optimization Informed by Continuous-Time Finance Models |

84 | Equivalence Class Learning for GENERIC Systems |

85 | Model-based Policy Optimization under Approximate Bayesian Inference |

86 | Nonlinear Wasserstein Distributionally Robust Optimal Control |

87 | Online Control with Adversarial Disturbance for Continuous-time Linear Systems |

88 | Leveraging Factored Action Spaces for Off-Policy Evaluation |

89 | Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations |

90 | Limited Information Opponent Modeling |

91 | Tendiffpure: Tensorizing Diffusion Models for Purification |

92 | Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL |

93 | Optimization or Architecture: What Matters in Non-Linear Filtering? |

94 | Variational quantum dynamics of two-dimensional rotor models |

95 | Parallel Sampling of Diffusion Models |

96 | Actor-Critic Methods using Physics-Informed Neural Networks: Control of a 1D PDE Model for Fluid-Cooled Battery Packs |

97 | Undo Maps: A Tool for Adapting Policies to Perceptual Distortions |

98 | Learning with Learning Awareness using Meta-Values |

99 | Aligned Diffusion Schrödinger Bridges |

100 | On First-Order Meta-Reinforcement Learning with Moreau Envelopes |