yusuke1997/dblp-id-title huggingface dataset:
TimeInf: Time Series Data Contribution via Influence Functions
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models
Moral Alignment for LLM Agents
Learned Step Size quantization
Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond
The Shape of Data: Intrinsic Distance for Data Distributions
A Periodic Bayesian Flow for Material Generation
DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images
Achieving Near-Optimal Individual Regret & Low Communications in Multi-Agent Bandits
Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF
Learning to Query, Reason, and Answer Questions On Ambiguous Texts
Locality-aware Gaussian Compression for Fast and High-quality Rendering
Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths
CURIE: Evaluating LLMs on Multitask Scientific Long-Context Understanding and Reasoning
Tree-Planner: Efficient Close-loop Task Planning with Large Language Models
Better Fine-Tuning by Reducing Representational Collapse
Towards Identifiable Unsupervised Domain Translation: A Diversified Distribution Matching Approach
Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF
Diffusion Policy Policy Optimization
Understanding and Preventing Capacity Loss in Reinforcement Learning
Hybrid RL: Using both offline and online data can make RL efficient
Bayesian Oracle for bounding information gain in neural encoding models
Coreset Selection via Reducible Loss in Continual Learning
An Intuitive Multi-Frequency Feature Representation for SO(3)-Equivariant Networks
HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments
Continuous Ensemble Weather Forecasting with Diffusion models
Non-Autoregressive Neural Machine Translation
Graph Convolution with Low-rank Learnable Local Filters
Exploring Model-based Planning with Policy Networks
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers
ContraDiff: Planning Towards High Return States via Contrastive Learning
Dual Space Learning with variational Autoencoders
Energy-guided Entropic Neural Optimal Transport
Finetuned Language Models are Zero-Shot Learners
Layered insights into Pyramid feature fusion architecture for SSL
U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields
Second Order Bounds for Contextual Bandits with Function Approximation
HypeR: Multitask Hyper-Prompted Training Enables Large-Scale Retrieval Generalization
On the Optimization Landscape of Low Rank Adaptation Methods for Large Language Models
Contrastive Syn-to-Real Generalization
ProxSGD: Training Structured Neural Networks under Regularization and Constraints
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization
Implicit Causal Models for Genome-wide Association Studies
Poisoning and Backdooring Contrastive Learning
FSPool: Learning Set Representations with Featurewise Sort Pooling
Visual Imitation Learning with Patch Rewards
How Attentive are Graph Attention Networks?
Toward Efficient Low-Precision Training: Data Format Optimization and Hysteresis Quantization
Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations
Revisiting Self-Training for Neural Sequence Generation
Towards Universality: Studying Mechanistic Similarity Across Language Model Architectures
Active Image Indexing
Is Homophily a Necessity for Graph Neural Networks?
PEARL: Towards Permutation-Resilient LLMs
Representation Balancing Offline Model-based Reinforcement Learning
MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion
Sound Randomized Smoothing in Floating-Point Arithmetic
Inference Optimal VLMs Need Fewer Visual Tokens and More Parameters
Safe RLHF: Safe Reinforcement Learning from Human Feedback
ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA
Neural Machine Translation with Universal Visual Representation
Char2Wav: End-to-End Speech Synthesis
On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning
Can Watermarked LLMs be Identified by Users via Crafted Prompts?
Multi-Robot Motion Planning with Diffusion Models
A Simple but Tough-to-Beat Baseline for Sentence Embeddings
MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete Diffusion
GDA-AM: On the Effectiveness of Solving Min-Imax Optimization via Anderson Mixing
Understanding Intrinsic Robustness Using Label Uncertainty
DreamLLM: Synergistic Multimodal Comprehension and Creation
Zero-shot Model-based Reinforcement Learning using Large Language Models
From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency
Toward Generalizing Visual Brain Decoding to Unseen Subjects
DARE the Extreme: Revisiting Delta-Parameter Pruning For Fine-Tuned Models
Optimizing Neural Networks with Gradient Lexicase Selection
Efficient Dynamics Modeling in Interactive Environments with Koopman Theory
Language models scale reliably with over-training and on downstream tasks
Hadamard Product for Low-rank Bilinear Pooling
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
Storybooth: Training-Free Multi-Subject Consistency for Improved Visual Storytelling
Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively
SEPARATE: A Simple Low-rank Projection for Gradient Compression in Modern Large-scale Model Training Process
Retrieval is Accurate Generation
Optimal Non-Asymptotic Rates of Value Iteration for Average-Reward Markov Decision Processes
Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing
VAE Approximation Error: ELBO and Exponential Families
Theoretical Analysis of Robust Overfitting for Wide DNNs: An NTK Approach
GLoRa: A Benchmark to Evaluate the Ability to Learn Long-Range Dependencies in Graphs
ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids
Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data
Closed-form Sample Probing for Learning Generative Models in Zero-shot Learning
Stable Segment Anything Model
Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning
Expressive power of recurrent neural networks
Learning Nearly Decomposable Value Functions Via Communication Minimization
Context-Alignment: Activating and Enhancing LLMs Capabilities in Time Series
In-context Time Series Predictor
Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selection
Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards
Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning
ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond
Learning to Dequantise with Truncated Flows
The LLM Surgeon
Dynamic Sparse Graph for Efficient Deep Learning
Time-Efficient Reinforcement Learning with Stochastic Stateful Policies
Efficient-3Dim: Learning a Generalizable Single-image Novel-view Synthesizer in One Day
Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation
Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior
Can a Large Language Model be a Gaslighter?
Is This the Subspace You Are Looking for? An Interpretability Illusion for Subspace Activation Patching
BigVGAN: A Universal Neural Vocoder with Large-Scale Training
Conflict-Averse Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning
Liquid Structural State-Space Models
IDEAL: Query-Efficient Data-Free Learning from Black-Box Models
Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting
Risk-Averse Offline Reinforcement Learning
Two-Timescale Networks for Nonlinear Value Function Approximation
Language models are multilingual chain-of-thought reasoners
Nearly d-Linear Convergence Bounds for Diffusion Models via Stochastic Localization
Learning Scenario Representation for Solving Two-stage Stochastic Integer Programs
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
Predicting Targets with Data from Non-Conforming Sources
Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation
A generalization of the randomized singular value decomposition
Continual learning in recurrent neural networks
Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information
HalentNet: Multimodal Trajectory Forecasting with Hallucinative Intents
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Optimal Control Via Neural Networks: A Convex Approach
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Cross-Attention Head Position Patterns Can Align with Human Visual Concepts in Text-to-Image Generative Models
Replay Memory as An Empirical MDP: Combining Conservative Estimation with Experience Replay
Dobi-SVD: Differentiable SVD for LLM Compression and Some New Perspectives
KAA: Kolmogorov-Arnold Attention for Enhancing Attentive Graph Neural Networks
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Graph Transformers Dream of Electric Flow
Pure and Spurious Critical Points: a Geometric Study of Linear Networks
Modular Continual Learning in a Unified Visual Environment
Neur2RO: Neural Two-Stage Robust Optimization
Learning from Sparse Offline Datasets via Conservative Density Estimation
Compute-Constrained Data Selection
Four Things Everyone Should Know to Improve Batch Normalization
DRL: Decomposed Representation Learning for Tabular Anomaly Detection
ToolDial: Multi-turn Dialogue Generation Method for Tool-Augmented Language Models
Robust Root Cause Diagnosis using In-Distribution Interventions
Masked Vision and Language Modeling for Multi-modal Representation Learning
Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries
Sound Classification in Indian Cities Using Multi-Label Data and Transfer Learning
Jelly Bean World: A Testbed for Never-Ending Learning
Contrastive Training with more data
Deep Neural Maps
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic
TorchRL: A data-driven decision-making library for PyTorch
SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression
A Unified Approach to Interpreting and Boosting Adversarial Transferability
Fast Equilibrium of SGD in Generic Situations
Self-supervised Representation Learning from Random Data Projectors
Bayesian Low-rank Adaptation for Large Language Models
Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators
The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"
(Certified!!) Adversarial Robustness for Free!
Deep Network Classification by Scattering and Homotopy Dictionary Learning
Probing the Latent Hierarchical Structure of Data via Diffusion Models
Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better
How efficient is LLM-generated code? A rigorous & high-standard benchmark
Neural Controlled Differential Equations with Quantum Hidden Evolutions
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima
Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations
Boosting of Thoughts: Trial-and-Error Problem Solving with Large Language Models
Large Language Models Meet Symbolic Provers for Logical Reasoning Evaluation
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
Off-Policy Primal-Dual Safe Reinforcement Learning
Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks
Data Filtering Networks
Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting
CausalStructCodec: Causally-aware observational and interventional data generator
DreamClean: Restoring Clean Image Using Deep Diffusion Prior
Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Control
Adaptive-saturated RNN: Remember more with less instability
Don't Trust: Verify - Grounding LLM Quantitative Reasoning with Autoformalization
Minimax Optimal Kernel Operator Learning via Multilevel Training
The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings
A Block Minifloat Representation for Training Deep Neural Networks
Activation Gradient based Poisoned Sample Detection Against Backdoor Attacks
Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization
LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection
A Topological Perspective on Demystifying GNN-Based Link Prediction Performance
ChroKnowledge: Unveiling Chronological Knowledge of Language Models in Multiple Domains
Training-Free Activation Sparsity in Large Language Models
Source-Free and Image-Only Unsupervised Domain Adaptation for Category Level Object Pose Estimation
ARTIFICIAL PSYCHOLOGY
Let Your Features Tell The Differences: Understanding Graph Convolution By Feature Splitting
Correlated Proxies: A New Definition and Improved Mitigation for Reward Hacking
On the Hardness of Online Nonconvex Optimization with Single Oracle Feedback
GamePad: A Learning Environment for Theorem Proving
StyleAlign: Analysis and Applications of Aligned StyleGAN Models
Efficient Online Pruning and Abstraction for Imperfect Information Extensive-Form Games
Multi-Draft Speculative Sampling: Canonical Decomposition and Theoretical Limits
CameraCtrl: Enabling Camera Control for Video Diffusion Models
Long-Tailed Learning Requires Feature Learning
Principled Architecture-aware Scaling of Hyperparameters
ChordMixer: A Scalable Neural Attention Model for Sequences with Different Length
ARM: Refining Multivariate Forecasting with Adaptive Temporal-Contextual Learning
Implicit Gaussian process representation of vector fields over arbitrary latent manifolds
A Hypergradient Approach to Robust Regression without Correspondence
Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised Learning
Tractable Multi-Agent Reinforcement Learning through Behavioral Economics
Relational Learning with Variational Bayes
Vision-by-Language for Training-Free Compositional Image Retrieval
Multimodal Analogical Reasoning over Knowledge Graphs
Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability
Progress or Regress? Self-Improvement Reversal in Post-training
Synthesizing Audio with GANs
GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks
Physics of Language Models: Part 3.2, Knowledge Manipulation
Stable Recurrent Models
Solving High Frequency and Multi-Scale PDEs with Gaussian Processes
Rethinking the symmetry-preserving circuits for constrained variational quantum algorithms
Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
Evolutionary Federated Learning Using Particle Swarm Optimization
One After Another: Learning Incremental Skills for a Changing World
PAC-FNO: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images
ProMP: Proximal Meta-Policy Search
Meta-Imitation Learning by Watching Video Demonstrations
COME: Test-time Adaption by Conservatively Minimizing Entropy
Discrete Predictor-Corrector Diffusion Models for Image Synthesis
Make Haste Slowly: A Theory of Emergent Structured Mixed Selectivity in Feature Learning ReLU Networks
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions
GLM-130B: An Open Bilingual Pre-trained Model
InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences
The Ingredients of Real World Robotic Reinforcement Learning
Discrepancy-Based Active Learning for Domain Adaptation
EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation
Learn-by-interact: A Data-Centric Framework For Self-Adaptive Agents in Realistic Environments
Flow Distillation Sampling: Regularizing 3D Gaussians with Pre-trained Matching Priors
Contingency-Aware Exploration in Reinforcement Learning
3D StreetUnveiler with Semantic-aware 2DGS - a simple baseline
The Augmented Image Prior: Distilling 1000 Classes by Extrapolating from a Single Image
Predicting Infectiousness for Proactive Contact Tracing
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation
Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for Classification of Common Mental Illnesses on Social Media Posts
Variational image compression with a scale hyperprior
MMRole: A Comprehensive Framework for Developing and Evaluating Multimodal Role-Playing Agents
Federated Recommendation with Additive Personalization
Trust-PCL: An Off-Policy Trust Region Method for Continuous Control
Measuring Non-Adversarial Reproduction of Training Data in Large Language Models
PPP-Net: Platform-aware Progressive Search for Pareto-optimal Neural Architectures
Generalizing Skills with Semi-Supervised Reinforcement Learning
On the Variance of Neural Network Training with respect to Test Sets and Distributions
LCOT: Linear Circular Optimal Transport
FastFill: Efficient Compatible Model Update
PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts
A Unified Theory of Quantum Neural Network Loss Landscapes
WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild
UTILITY: Utilizing Explainable Reinforcement Learning to Improve Reinforcement Learning
Responsible Disclosure of Generative Models Using Scalable Fingerprinting
Countering Adversarial Images using Input Transformations
CFD: Learning Generalized Molecular Representation via Concept-Enhanced Feedback Disentanglement
Geometric Inductive Biases of Deep Networks: The Role of Data and Architecture
Impact of Representation Learning in Linear Bandits
Constructing a Good Behavior Basis for Transfer using Generalized Policy Updates
CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild
3DMolFormer: A Dual-channel Framework for Structure-based Drug Discovery
Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs
Any-step Dynamics Model Improves Future Predictions for Online and Offline Reinforcement Learning
Efficient Exploration and Discriminative World Model Learning with an Object-Centric Abstraction
Anti-Exposure Bias in Diffusion Models
NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search
Effectiveness of Debiasing Techniques: An Indigenous Qualitative Analysis
Towards Non-Asymptotic Convergence for Diffusion-Based Generative Models
PI3NN: Out-of-distribution-aware Prediction Intervals from Three Neural Networks
BatteryML: An Open-source Platform for Machine Learning on Battery Degradation
UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph
GALA: Geometry-Aware Local Adaptive Grids for Detailed 3D Generation
Navigation-Guided Sparse Scene Representation for End-to-End Autonomous Driving
Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design
On the robustness of Chatgpt under input perturbations for Named Entity Recognition Task
Alice Benchmarks: Connecting Real World Re-Identification with the Synthetic
Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization
An Exact Poly-Time Membership-Queries Algorithm for Extracting a Three-Layer ReLU Network
Cross-Domain Offline Policy Adaptation with Optimal Transport and Dataset Constraint
DeepV2D: Video to Depth with Differentiable Structure from Motion
Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity
Composing Unbalanced Flows for Flexible Docking and Relaxation
Matérn Kernels for Tunable Implicit Surface Reconstruction
DiffAR: Denoising Diffusion Autoregressive Model for Raw Speech Waveform Generation
Deep MMD Gradient Flow without adversarial training
Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language
ETA: Evaluating Then Aligning Safety of Vision Language Models at Inference Time
Deep Learning meets Projective Clustering
Iterative Substructure Extraction for Molecular Relational Learning with Interactive Graph Information Bottleneck
LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5
Evaluation of Similarity-based Explanations
EMMA: Empowering Multi-modal Mamba with Structural and Hierarchical Alignment
Particle Value Functions
HG-Adapter: Improving Pre-Trained Heterogeneous Graph Neural Networks with Dual Adapters
MLLM as Retriever: Interactively Learning Multimodal Retrieval for Embodied Agents
Bridging Vision and Language Spaces with Assignment Prediction
Feature Intertwiner for Object Detection
Provable Sim-to-real Transfer in Continuous Domain with Partial Observations
PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs
Reflexive Guidance: Improving OoDD in Vision-Language Models via Self-Guided Image-Adaptive Concept Generation
On the Discrimination-Generalization Tradeoff in GANs
ZiCo: Zero-shot NAS via inverse Coefficient of Variation on Gradients
Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning
Zeroth-Order Policy Gradient for Reinforcement Learning from Human Feedback without Reward Inference
LQ-LoRA: Low-rank plus Quantized Matrix Decomposition for Efficient Language Model Finetuning
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
Fast Model Editing at Scale
Multi-skill Mobile Manipulation for Object Rearrangement
Flow Matching on General Geometries
Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization
GAN "Steerability" without optimization
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks
A Truncated Newton Method for Optimal Transport
What's Wrong with Deep Learning in Tree Search for Combinatorial Optimization
An Information Criterion for Controlled Disentanglement of Multimodal Data
Distributed Prioritized Experience Replay
Weighted Multi-Prompt Learning with Description-free Large Language Model Distillation
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks
Zero-Mean Regularized Spectral Contrastive Learning: Implicitly Mitigating Wrong Connections in Positive-Pair Graphs
Fast Hyperboloid Decision Tree Algorithms
Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL Policies
Revisiting Graph Adversarial Attack and Defense From a Data Distribution Perspective
A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias
Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models
NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning
Task Ambiguity in Humans and Language Models
Approximability of Discriminators Implies Diversity in GANs
Cycle-consistent Masked AutoEncoder for Unsupervised Domain Generalization
Fidelity-Weighted Learning
MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC
UGMathBench: A Diverse and Dynamic Benchmark for Undergraduate-Level Mathematical Reasoning with Large Language Models
Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling
Spherical Message Passing for 3D Molecular Graphs
Towards Understanding the Robustness of Diffusion-Based Purification: A Stochastic Perspective
Rethinking Style and Content Disentanglement in Variational Autoencoders
Learning a Neural Solver for Parametric PDEs to Enhance Physics-Informed Methods
Interpretations of Domain Adaptations via Layer Variational Analysis
SCAN: Learning Hierarchical Compositional Visual Concepts
On the Expressive Power of Sparse Geometric MPNNs