Accepted Paper

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