Accept-Oral
Accept-Spotlight
[1]. Generalized Policy Iteration using Tensor Approximation for Hybrid Control
[2]. A Theoretical Explanation of Deep RL Performance in Stochastic Environments
[3]. A Benchmark on Robust Semi-Supervised Learning in Open Environments
[4]. Generative Adversarial Inverse Multiagent Learning
[5]. AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents
[6]. Confronting Reward Model Overoptimization with Constrained RLHF
[7]. Improved Efficiency Based on Learned Saccade and Continuous Scene Reconstruction From Foveated Visual Sampling
[8]. Harnessing Density Ratios for Online Reinforcement Learning
[9]. Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning
[10]. Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community
[11]. Improving Offline RL by Blending Heuristics
[12]. Tool-Augmented Reward Modeling
[13]. Reward-Consistent Dynamics Models are Strongly Generalizable for Offline Reinforcement Learning
[14]. Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback
[15]. Dual RL: Unification and New Methods for Reinforcement and Imitation Learning
[16]. Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data
[17]. Safe RLHF: Safe Reinforcement Learning from Human Feedback
[18]. Cross$Q$: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity
[19]. Blending Imitation and Reinforcement Learning for Robust Policy Improvement
[20]. On the Role of General Function Approximation in Offline Reinforcement Learning
[21]. Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies
[22]. Massively Scalable Inverse Reinforcement Learning for Route Optimization
[23]. Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation
[24]. Towards Principled Representation Learning from Videos for Reinforcement Learning
[25]. TorchRL: A data-driven decision-making library for PyTorch
[26]. Towards Robust Offline Reinforcement Learning under Diverse Data Corruption
[27]. DyST: Towards Dynamic Neural Scene Representations on Real-World Videos
[28]. Impact of Computation in Integral Reinforcement Learning for Continuous-Time Control
[29]. Maximum Entropy Heterogeneous-Agent Reinforcement Learning
[30]. Learning Hierarchical World Models with Adaptive Temporal Abstractions from Discrete Latent Dynamics
[31]. Text2Reward: Dense Reward Generation with Language Models for Reinforcement Learning
[32]. Submodular Reinforcement Learning
[33]. Query-Policy Misalignment in Preference-Based Reinforcement Learning
[34]. Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL Policies
[35]. Provable Offline Preference-Based Reinforcement Learning
[36]. Provable Reward-Agnostic Preference-Based Reinforcement Learning
[37]. Entity-Centric Reinforcement Learning for Object Manipulation from Pixels
[38]. Constrained Bi-Level Optimization: Proximal Lagrangian Value function Approach and Hessian-free Algorithm
[39]. Addressing Signal Delay in Deep Reinforcement Learning
[40]. DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization
[41]. RealChat-1M: A Large-Scale Real-World LLM Conversation Dataset
[42]. EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models
[43]. SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series
[44]. Quasi-Monte Carlo for 3D Sliced Wasserstein
[45]. Cascading Reinforcement Learning
[46]. Task Adaptation from Skills: Information Geometry, Disentanglement, and New Objectives for Unsupervised Reinforcement Learning
[47]. Efficient Distributed Training with Full Communication-Computation Overlap
[48]. PTaRL: Prototype-based Tabular Representation Learning via Space Calibration
[49]. $\mathcal{B}$-Coder: On Value-Based Deep Reinforcement Learning for Program Synthesis
[50]. Physics-Regulated Deep Reinforcement Learning: Invariant Embeddings
[51]. Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization
[52]. Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy
[53]. ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
[54]. SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution
[55]. BarLeRIa: An Efficient Tuning Framework for Referring Image Segmentation
[56]. Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision
[57]. TD-MPC2: Scalable, Robust World Models for Continuous Control
[58]. Adaptive Rational Activations to Boost Deep Reinforcement Learning
[59]. Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula
Accept-poster
[1]. Locality Sensitive Sparse Encoding for Learning World Models Online
[2]. Demonstration-Regularized RL
[3]. KoLA: Carefully Benchmarking World Knowledge of Large Language Models
[4]. On Representation Complexity of Model-based and Model-free Reinforcement Learning
[5]. RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems
[6]. Policy Rehearsing: Training Generalizable Policies for Reinforcement Learning
[7]. NP-GL: Extending Power of Nature from Binary Problems to Real-World Graph Learning
[8]. Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning
[9]. Improving Language Models with Advantage-based Offline Policy Gradients
[10]. Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking
[11]. PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization
[12]. Large Language Models as Automated Aligners for benchmarking Vision-Language Models
[13]. Reverse Diffusion Monte Carlo
[14]. PlaSma: Procedural Knowledge Models for Language-based Planning and Re-Planning
[15]. Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
[16]. Training Diffusion Models with Reinforcement Learning
[17]. Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning
[18]. Federated Q-Learning: Linear Regret Speedup with Low Communication Cost
[19]. The Trickle-down Impact of Reward Inconsistency on RLHF
[20]. Maximum Entropy Model Correction in Reinforcement Learning
[21]. Simple Hierarchical Planning with Diffusion
[22]. Regularized Robust MDPs and Risk-Sensitive MDPs: Equivalence, Policy Gradient, and Sample Complexity
[23]. Curriculum reinforcement learning for quantum architecture search under hardware errors
[24]. Variance-aware Regret Bounds for Stochastic Contextual Dueling Bandits
[25]. Directly Fine-Tuning Diffusion Models on Differentiable Rewards
[26]. Tree Search-Based Policy Optimization under Stochastic Execution Delay
[27]. Offline RL with Observation Histories: Analyzing and Improving Sample Complexity
[28]. Understanding Hidden Context in Preference Learning: Consequences for RLHF
[29]. Eureka: Human-Level Reward Design via Coding Large Language Models
[30]. Active Retrosynthetic Planning Aware of Route Quality
[31]. Fiber Monte Carlo
[32]. Retrieval-Guided Reinforcement Learning for Boolean Circuit Minimization
[33]. Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes
[34]. Follow-the-Perturbed-Leader for Adversarial Bandits: Heavy Tails, Robustness, and Privacy
[35]. ViLMA: A Zero-Shot Benchmark for Linguistic and Temporal Grounding in Video-Language Models
[36]. Score Models for Offline Goal-Conditioned Reinforcement Learning
[37]. A Policy Gradient Method for Confounded POMDPs
[38]. Achieving Fairness in Multi-Agent MDP Using Reinforcement Learning
[39]. Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning
[40]. Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning
[41]. Hindsight PRIORs for Reward Learning from Human Preferences
[42]. Reward Model Ensembles Help Mitigate Overoptimization
[43]. Feasibility-Guided Safe Offline Reinforcement Learning
[44]. Compositional Conservatism: A Transductive Approach in Offline Reinforcement Learning
[45]. Flow to Better: Offline Preference-based Reinforcement Learning via Preferred Trajectory Generation
[46]. PAE: Reinforcement Learning from External Knowledge for Efficient Exploration
[47]. Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML
[48]. Identifying Policy Gradient Subspaces
[49]. Contextual Bandits with Online Neural Regression
[50]. PARL: A Unified Framework for Policy Alignment in Reinforcement Learning
[51]. SafeDreamer: Safe Reinforcement Learning with World Models
[52]. MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlation
[53]. GnnX-Bench: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking
[54]. Confidence-aware Reward Optimization for Fine-tuning Text-to-Image Models
[55]. Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback
[56]. Goodhart's Law in Reinforcement Learning
[57]. Score Regularized Policy Optimization through Diffusion Behavior
[58]. Making RL with Preference-based Feedback Efficient via Randomization
[59]. Adaptive Regret for Bandits Made Possible: Two Queries Suffice
[60]. Negatively Correlated Ensemble Reinforcement Learning for Online Diverse Game Level Generation
[61]. Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Grouping
[62]. Demystifying Linear MDPs and Novel Dynamics Aggregation Framework
[63]. PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
[64]. Steve-Eye: Equipping LLM-based Embodied Agents with Visual Perception in Open Worlds
[65]. Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning
[66]. Contrastive Preference Learning: Learning from Human Feedback without Reinforcement Learning
[67]. Privileged Sensing Scaffolds Reinforcement Learning
[68]. Learning Planning Abstractions from Language
[69]. Tailoring Self-Rationalizers with Multi-Reward Distillation
[70]. Building Cooperative Embodied Agents Modularly with Large Language Models
[71]. A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning
[72]. CrossLoco: Human Motion Driven Control of Legged Robots via Guided Unsupervised Reinforcement Learning
[73]. Let Models Speak Ciphers: Multiagent Debate through Embeddings
[74]. Learning interpretable control inputs and dynamics underlying animal locomotion
[75]. Does Progress On Object Recognition Benchmarks Improve Generalization on Crowdsourced, Global Data?
[76]. Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX
[77]. Searching for High-Value Molecules Using Reinforcement Learning and Transformers
[78]. Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning
[79]. Towards Diverse Behaviors: A Benchmark for Imitation Learning with Human Demonstrations
[80]. Privately Aligning Language Models with Reinforcement Learning
[81]. On the Expressivity of Objective-Specification Formalisms in Reinforcement Learning
[82]. S$2$AC: Energy-Based Reinforcement Learning with Stein Soft Actor Critic
[83]. Robust Model-Based Optimization for Challenging Fitness Landscapes
[84]. Replay across Experiments: A Natural Extension of Off-Policy RL
[85]. BEND: Benchmarking DNA Language Models on Biologically Meaningful Tasks
[86]. Piecewise Linear Parametrization of Policies: Towards Interpretable Deep Reinforcement Learning
[87]. Time-Efficient Reinforcement Learning with Stochastic Stateful Policies
[88]. Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning
[89]. Incentivized Truthful Communication for Federated Bandits
[90]. Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization
[91]. On Trajectory Augmentations for Off-Policy Evaluation
[92]. Understanding the Effects of RLHF on LLM Generalisation and Diversity
[93]. Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods
[94]. Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding
[95]. Prioritized Soft Q-Decomposition for Lexicographic Reinforcement Learning
[96]. GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks
[97]. Incentive-Aware Federated Learning with Training-Time Model Rewards
[98]. Early Neuron Alignment in Two-layer ReLU Networks with Small Initialization
[99]. Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity
[100]. Off-Policy Primal-Dual Safe Reinforcement Learning
[101]. STARC: A General Framework For Quantifying Differences Between Reward Functions
[102]. GAIA: a benchmark for General AI Assistants
[103]. Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning
[104]. Discovering Temporally-Aware Reinforcement Learning Algorithms
[105]. Revisiting Data Augmentation in Deep Reinforcement Learning
[106]. Reward-Free Curricula for Training Robust World Models
[107]. Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
[108]. CPPO: Continual Learning for Reinforcement Learning with Human Feedback
[109]. Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations
[110]. Bandits with Replenishable Knapsacks: the Best of both Worlds
[111]. A Study of Generalization in Offline Reinforcement Learning
[112]. Diverse Projection Ensembles for Distributional Reinforcement Learning
[113]. MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
[114]. RLIF: Interactive Imitation Learning as Reinforcement Learning
[115]. Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World
[116]. Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization
[117]. FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods
[118]. EasyTPP: Towards Open Benchmarking Temporal Point Processes
[119]. Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization
[120]. FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods
[121]. EasyTPP: Towards Open Benchmarking Temporal Point Processes
[122]. Combinatorial Bandits for Maximum Value Reward Function under Value-Index Feedback
[123]. Alice Benchmarks: Connecting Real World Object Re-Identification with the Synthetic
[124]. Video Language Planning
[125]. Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining
[126]. Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks
[127]. Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised Learning
[128]. Diffusion Models for Multi-Task Generative Modeling
[129]. Neural Active Learning Beyond Bandits
[130]. Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages
[131]. Sample-efficient Learning of Infinite-horizon Average-reward MDPs with General Function Approximation
[132]. Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees
[133]. SALMON: Self-Alignment with Principle-Following Reward Models
[134]. Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models
[135]. SemiReward: A General Reward Model for Semi-supervised Learning
[136]. Horizon-Free Regret for Linear Markov Decision Processes
[137]. On Differentially Private Federated Linear Contextual Bandits
[138]. Neural Neighborhood Search for Multi-agent Path Finding
[139]. Understanding when Dynamics-Invariant Data Augmentations Benefit Model-free Reinforcement Learning Updates
[140]. Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks
[141]. The Update Equivalence Framework for Decision-Time Planning
[142]. Learning Reusable Dense Rewards for Multi-Stage Tasks
[143]. Time Fairness in Online Knapsack Problems
[144]. On the Hardness of Constrained Cooperative Multi-Agent Reinforcement Learning
[145]. RLCD: Reinforcement Learning from Contrastive Distillation for LM Alignment
[146]. Reasoning with Latent Diffusion in Offline Reinforcement Learning
[147]. Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time
[148]. Belief-Enriched Pessimistic Q-Learning against Adversarial State Perturbations
[149]. SmartPlay : A Benchmark for LLMs as Intelligent Agents
[150]. SOHES: Self-supervised Open-world Hierarchical Entity Segmentation
[151]. Robust NAS benchmark under adversarial training: assessment, theory, and beyond
[152]. SCHEMA: State CHangEs MAtter for Procedure Planning in Instructional Videos
[153]. DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genomes
[154]. Reward Design for Justifiable Sequential Decision-Making
[155]. Fast Value Tracking for Deep Reinforcement Learning
[156]. MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
[157]. Tree-Planner: Efficient Close-loop Task Planning with Large Language Models
[158]. LOQA: Learning with Opponent Q-Learning Awareness
[159]. Intelligent Switching for Reset-Free RL
[160]. On the Limitations of Temperature Scaling for Distributions with Overlaps
[161]. True Knowledge Comes from Practice: Aligning Large Language Models with Embodied Environments via Reinforcement Learning
[162]. Skill Machines: Temporal Logic Skill Composition in Reinforcement Learning
[163]. Who to imitate: Imitating desired behavior from divserse multi-agent datasets
[164]. SweetDreamer: Aligning Geometric Priors in 2D diffusion for Consistent Text-to-3D
[165]. Uni-RLHF: Universal Platform and Benchmark Suite for Reinforcement Learning with Diverse Human Feedback
[166]. Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning
[167]. Learning Multi-Agent Communication from Graph Modeling Perspective
[168]. Efficient Multi-agent Reinforcement Learning by Planning
[169]. Sample-Efficient Multi-Agent RL: An Optimization Perspective
[170]. CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
[171]. SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores
[172]. Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks
[173]. Robust Model Based Reinforcement Learning Using $\mathcal{L}_1$ Adaptive Control
[174]. Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion
[175]. Parameter-Efficient Multi-Task Model Fusion with Partial Linearizeation
[176]. Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs
[177]. Multi-task Learning with 3D-Aware Regularization
[178]. DMBP: Diffusion model based predictor for robust offline reinforcement learning against state observation perturbations
[179]. Alignment as Reward-Guided Search
[180]. Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts
[181]. Retro-fallback: retrosynthetic planning in an uncertain world
[182]. Duolando: Follower GPT with Off-Policy Reinforcement Learning for Dance Accompaniment
[183]. AdaMerging: Adaptive Model Merging for Multi-Task Learning
[184]. MetaTool Benchmark: Deciding Whether to Use Tools and Which to Use
[185]. AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models
[186]. Integrating Planning and Deep Reinforcement Learning via Automatic Induction of Task Substructures
[187]. LoTa-Bench: Benchmarking Language-oriented Task Planners for Embodied Agents
[188]. Outliers with Opposing Signals Have an Outsized Effect on Neural Network Optimization
[189]. Threshold-Consistent Margin Loss for Open-World Deep Metric Learning
[190]. Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in RL
[191]. Learning Multi-Agent Communication with Contrastive Learning
[192]. Closing the Gap between TD Learning and Supervised Learning - A Generalisation Point of View.
[193]. On Stationary Point Convergence of PPO-Clip
[194]. Provably Efficient CVaR RL in Low-rank MDPs
[195]. COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL
[196]. Transport meets Variational Inference: Controlled Monte Carlo Diffusions
[197]. In-context Exploration-Exploitation for Reinforcement Learning
[198]. The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World
[199]. TASK PLANNING FOR VISUAL ROOM REARRANGEMENT UNDER PARTIAL OBSERVABILITY
[200]. Optimal Sample Complexity for Average Reward Markov Decision Processes
[201]. DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing
[202]. Meta Inverse Constrained Reinforcement Learning: Convergence Guarantee and Generalization Analysis
[203]. Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform
[204]. Combining Spatial and Temporal Abstraction in Planning for Better Generalization
[205]. Decision Transformer is a Robust Contender for Offline Reinforcement Learning
[206]. ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
[207]. Bridging State and History Representations: Understanding Self-Predictive RL
[208]. InstructDET: Diversifying Referring Object Detection with Generalized Instructions
[209]. Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
[210]. GRAPH-CONSTRAINED DIFFUSION FOR END-TO-END PATH PLANNING
[211]. Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios
[212]. VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks
[213]. Grounding Multimodal Large Language Models to the World
[214]. VFLAIR: A Research Library and Benchmark for Vertical Federated Learning
[215]. Stylized Offline Reinforcement Learning: Extracting Diverse High-Quality Behaviors from Heterogeneous Datasets
[216]. Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight
[217]. Pre-training with Synthetic Data Helps Offline Reinforcement Learning
[218]. AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
[219]. Efficient Planning with Latent Diffusion
[220]. A Benchmark Study on Calibration
[221]. Attention-Guided Contrastive Role Representations for Multi-agent Reinforcement Learning
[222]. Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL
[223]. Quantifying the Sensitivity of Inverse Reinforcement Learning to Misspecification
[224]. Byzantine Robust Cooperative Multi-Agent Reinforcement Learning as a Bayesian Game
[225]. AutoVP: An Automated Visual Prompting Framework and Benchmark
[226]. AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval
[227]. REValueD: Regularised Ensemble Value-Decomposition for Factorisable Markov Decision Processes
[228]. Language Model Self-improvement by Reinforcement Learning Contemplation
[229]. Towards Offline Opponent Modeling with In-context Learning
[230]. Early Stopping Against Label Noise Without Validation Data
[231]. Langevin Monte Carlo for strongly log-concave distributions: Randomized midpoint revisited
欢迎加入交流讨论群
