yusuke1997/dblp-id-title huggingface dataset:
No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation
Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities
Kernel Based Progressive Distillation for Adder Neural Networks
UP-DP: Unsupervised Prompt Learning for Data Pre-Selection with Vision-Language Models
UniGAN: Reducing Mode Collapse in GANs using a Uniform Generator
Constructing Hidden Units Using Examples and Queries
Egocentric Video-Language Pretraining
PanoGen: Text-Conditioned Panoramic Environment Generation for Vision-and-Language Navigation
Optimal Information Decoding from Neuronal Populations with Specific Stimulus Selectivity
Polynomial time algorithms for dual volume sampling
Generalized Belief Propagation
Active Learning with Safety Constraints
Minimizing UCB: a Better Local Search Strategy in Local Bayesian Optimization
Is RLHF More Difficult than Standard RL? A Theoretical Perspective
SimPO: Simple Preference Optimization with a Reference-Free Reward
Mutual Boosting for Contextual Inference
Inferring Networks From Random Walk-Based Node Similarities
Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach
How Powerful are Performance Predictors in Neural Architecture Search?
A Method for Learning From Hints
UrbanDataLayer: A Unified Data Pipeline for Urban Science
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers
Learning to Merge Tokens via Decoupled Embedding for Efficient Vision Transformers
Diffusion-Based Probabilistic Uncertainty Estimation for Active Domain Adaptation
On the Optimality of Classifier Chain for Multi-label Classification
Offline Model-based Adaptable Policy Learning
Submodular Hamming Metrics
Learning Trajectory and Force Control of an Artificial Muscle Arm
Bayesian Partitioning of Large-Scale Distance Data
Imagine That! Abstract-to-Intricate Text-to-Image Synthesis with Scene Graph Hallucination Diffusion
Rethinking the Role of Token Retrieval in Multi-Vector Retrieval
Variational Bayesian Reinforcement Learning with Regret Bounds
NAOMI: Non-Autoregressive Multiresolution Sequence Imputation
Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning
BiMLP: Compact Binary Architectures for Vision Multi-Layer Perceptrons
Beyond Maximum Likelihood and Density Estimation: A Sample-Based Criterion for Unsupervised Learning of Complex Models
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
Exploration via Planning for Information about the Optimal Trajectory
A computational model of hippocampal function in trace conditioning
Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks
Towards Interpretable Natural Language Understanding with Explanations as Latent Variables
Slow, Decorrelated Features for Pretraining Complex Cell-like Networks
Subsequence Kernels for Relation Extraction
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
Steady State Analysis of Episodic Reinforcement Learning
Analysis and Improvement of Policy Gradient Estimation
HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning
Clustered Multi-Task Learning: A Convex Formulation
CogLTX: Applying BERT to Long Texts
Exploring Functional Connectivities of the Human Brain using Multivariate Information Analysis
Diffused Redundancy in Pre-trained Representations
On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts
Rethinking the Reverse-engineering of Trojan Triggers
Analogy - Watershed or Waterloo? Structural Alignment and the Development of Connectionist Models of Cognition
Information Constraints on Auto-Encoding Variational Bayes
Learning from Label Proportions with Generative Adversarial Networks
Revisiting Area Convexity: Faster Box-Simplex Games and Spectrahedral Generalizations
Graph-Structured Gaussian Processes for Transferable Graph Learning
Maximum Mean Discrepancy Gradient Flow
Parabolic Approximation Line Search for DNNs
Integrating Topics and Syntax
Gated Softmax Classification
The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation?
Visual Dialog for Radiology: Data Curation and FirstSteps
Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering
Fair Wasserstein Coresets
Instance-based Generalization in Reinforcement Learning
Ocular Dominance and Patterned Lateral Connections in a Self-Organizing Model of the Primary Visual Cortex
Distilled Wasserstein Learning for Word Embedding and Topic Modeling
Improving Self-supervised Molecular Representation Learning using Persistent Homology
Deep Recurrent Neural Network-Based Identification of Precursor microRNAs
Few-Round Learning for Federated Learning
Neural Distance Embeddings for Biological Sequences
Efficient coding of natural images with a population of noisy Linear-Nonlinear neurons
Self-Labeling the Job Shop Scheduling Problem
Privacy-Preserving Belief Propagation and Sampling
Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation
Spectrum Random Masking for Generalization in Image-based Reinforcement Learning
A Unified View of cGANs with and without Classifiers
Lisa: Lazy Safety Alignment for Large Language Models against Harmful Fine-tuning Attack
FreGAN: Exploiting Frequency Components for Training GANs under Limited Data
Faster Query Times for Fully Dynamic k-Center Clustering with Outliers
Frequency-aware Generative Models for Multivariate Time Series Imputation
Multivariate tests of association based on univariate tests
Boosting Black Box Variational Inference
Bayesian Active Learning with Fully Bayesian Gaussian Processes
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans
Constrained Hidden Markov Models
Neuro-Symbolic EDA-Based Optimization Using ILP-Enhanced DBNs
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization
Deep Imitation Learning for Bimanual Robotic Manipulation
EV-Eye: Rethinking High-frequency Eye Tracking through the Lenses of Event Cameras
Efficient and Effective Optimal Transport-Based Biclustering
Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle
A Closer Look at Accuracy vs. Robustness
Explanations that reveal all through the definition of encoding
Non-linear Prediction of Acoustic Vectors Using Hierarchical Mixtures of Experts
Using a neural net to instantiate a deformable model
A Machine Learning Approach to Predict Chemical Reactions
Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks
TSDS: Data Selection for Task-Specific Model Finetuning
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation
Near-Optimal Reinforcement Learning with Self-Play
Learning Curves: Asymptotic Values and Rate of Convergence
ReXTime: A Benchmark Suite for Reasoning-Across-Time in Videos
Universality in Transfer Learning for Linear Models
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
Synchronization in Neural Nets
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees
Pairwise Neural Network Classifiers with Probabilistic Outputs
On Distributed Averaging for Stochastic k-PCA
Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks
Exploiting the Replay Memory Before Exploring the Environment: Enhancing Reinforcement Learning Through Empirical MDP Iteration
Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation
Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics Mixture
Inverse M-Kernels for Linear Universal Approximators of Non-Negative Functions
Mutagenetic tree Fisher kernel improves prediction of HIV drug resistance from viral genotype
Dealing with Synthetic Data Contamination in Online Continual Learning
TVLT: Textless Vision-Language Transformer
Empowering and Assessing the Utility of Large Language Models in Crop Science
Asymptotic normality and confidence intervals for derivatives of 2-layers neural network in the random features model
Handwritten Word Recognition using Contextual Hybrid Radial Basis Function Network/Hidden Markov Models
Implicit Regularization and Convergence for Weight Normalization
Reconstruct and Match: Out-of-Distribution Robustness via Topological Homogeneity
Weight Space Probability Densities in Stochastic Learning: II. Transients and Basin Hopping Times
What Makes Partial-Label Learning Algorithms Effective?
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP
Smooth Bilevel Programming for Sparse Regularization
Visual Encoding with Jittering Eyes
Multi-times Monte Carlo Rendering for Inter-reflection Reconstruction
An Infinity-sample Theory for Multi-category Large Margin Classification
Contour-Map Encoding of Shape for Early Vision
Generalized Sliced Wasserstein Distances
A Local Algorithm to Learn Trajectories with Stochastic Neural Networks
The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models
Training Fully Connected Neural Networks is ∃R-Complete
Linear-Sample Learning of Low-Rank Distributions
Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning
Divisive and Subtractive Mask Effects: Linking Psychophysics and Biophysics
FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents
Statistical Inference for Cluster Trees
Unveiling Encoder-Free Vision-Language Models
Human and Machine 'Quick Modeling'
Effects of Stress and Genotype on Meta-parameter Dynamics in Reinforcement Learning
Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization
Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction
When to Intervene: Learning Optimal Intervention Policies for Critical Events
SNIPER: Efficient Multi-Scale Training
Tactical Optimism and Pessimism for Deep Reinforcement Learning
Neural Tangent Kernel Maximum Mean Discrepancy
PAC-learning in the presence of adversaries
Gaussian process regression with Student-t likelihood
Augmenting Feature-driven fMRI Analyses: Semi-supervised learning and resting state activity
WizardArena: Post-training Large Language Models via Simulated Offline Chatbot Arena
FRAGE: Frequency-Agnostic Word Representation
Why neural networks find simple solutions: The many regularizers of geometric complexity
Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex
Optimal visual search based on a model of target detectability in natural images
Sparse PCA via Covariance Thresholding
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
Text to Blind Motion
Identifying General Mechanism Shifts in Linear Causal Representations
Predicting the Performance of Foundation Models via Agreement-on-the-Line
A Bayesian LDA-based model for semi-supervised part-of-speech tagging
Benchmarking Foundation Models with Language-Model-as-an-Examiner
Identifiability of deep generative models without auxiliary information
Unravelling in Collaborative Learning
UDC: Unified DNAS for Compressible TinyML Models for Neural Processing Units
Conditional Structure Generation through Graph Variational Generative Adversarial Nets
Learning Latent Space Energy-Based Prior Model
Diffusion Representation for Asymmetric Kernels via Magnetic Transform
Dimensionality Reduction for Data in Multiple Feature Representations
The Marginal Value of Adaptive Gradient Methods in Machine Learning
Exploiting LLM Quantization
Monotone operator equilibrium networks
Toward the Fundamental Limits of Imitation Learning
A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems
Improving Alignment and Robustness with Circuit Breakers
Field-Programmable Learning Arrays
Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning
Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets
UQE: A Query Engine for Unstructured Databases
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
Normalizing Kalman Filters for Multivariate Time Series Analysis
Speaker Recognition Using Neural Tree Networks
Exploring Example Influence in Continual Learning
Adaptive Development of Connectionist Decoders for Complex Error-Correcting Codes
Molecule Generation by Principal Subgraph Mining and Assembling
Aligning Pretraining for Detection via Object-Level Contrastive Learning
LuSh-NeRF: Lighting up and Sharpening NeRFs for Low-light Scenes
An Integer Projected Fixed Point Method for Graph Matching and MAP Inference
Cross-Image Context for Single Image Inpainting
Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy
Pre-RMSNorm and Pre-CRMSNorm Transformers: Equivalent and Efficient Pre-LN Transformers
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
Recurrent Temporal Revision Graph Networks
GLoMo: Unsupervised Learning of Transferable Relational Graphs
Safe Reinforcement Learning by Imagining the Near Future
Monotonic Networks
Speeding up Magnetic Resonance Image Acquisition by Bayesian Multi-Slice Adaptive Compressed Sensing
Globally Gated Deep Linear Networks
Evaluating Self-Supervised Learning for Molecular Graph Embeddings
Making the Cut: A Bandit-based Approach to Tiered Interviewing
Learning Time-Intensity Profiles of Human Activity using Non-Parametric Bayesian Models
When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment
Alignment-guided Temporal Attention for Video Action Recognition
PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization
Phase retrieval in high dimensions: Statistical and computational phase transitions
Non-Local Manifold Tangent Learning
Silicon Models for Auditory Scene Analysis
High resolution neural connectivity from incomplete tracing data using nonnegative spline regression
Weight Diffusion for Future: Learn to Generalize in Non-Stationary Environments
Tree-Structured Composition in Neural Networks without Tree-Structured Architectures
S-Map: A Network with a Simple Self-Organization Algorithm for Generative Topographic Mappings
Rest-Katyusha: Exploiting the Solution's Structure via Scheduled Restart Schemes
Sparse Quadratic Optimisation over the Stiefel Manifold with Application to Permutation Synchronisation
Training an Open-Vocabulary Monocular 3D Detection Model without 3D Data
Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement
Statistical Inference with M-Estimators on Adaptively Collected Data
Oscillatory Model of Short Term Memory
Have it your way: Individualized Privacy Assignment for DP-SGD
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs
SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process
Falsification before Extrapolation in Causal Effect Estimation
Self Supervised Boosting
Balancing Context Length and Mixing Times for Reinforcement Learning at Scale
Optimal Sizes of Dendritic and Axonal Arbors
Unsupervised Pixel-prediction
Diffusion Maps for Textual Network Embedding
Selecting Landmark Points for Sparse Manifold Learning
Evaluating the inverse decision-making approach to preference learning
Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning
Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion
Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer
Twin-Merging: Dynamic Integration of Modular Expertise in Model Merging
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data
RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem
Learning Shared Latent Structure for Image Synthesis and Robotic Imitation
Active Reasoning in an Open-World Environment
Synchronization of neural networks by mutual learning and its application to cryptography
Binarized Spectral Compressive Imaging
NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
Meta-Neighborhoods
MagR: Weight Magnitude Reduction for Enhancing Post-Training Quantization
"Name That Song!" A Probabilistic Approach to Querying on Music and Text
Logic and MRF Circuitry for Labeling Occluding and Thinline Visual Contours
Bayesian Self-Organization
Communication Efficient Parallel Algorithms for Optimization on Manifolds
Inverse Problems Leveraging Pre-trained Contrastive Representations
Recurrent Bayesian Classifier Chains for Exact Multi-Label Classification
RL-ViGen: A Reinforcement Learning Benchmark for Visual Generalization
Asymptotics of ℓ 2 Regularized Network Embeddings
Understanding Stepwise Generalization of Support Vector Machines: a Toy Model
Node Embeddings and Exact Low-Rank Representations of Complex Networks
Rapid Plug-in Defenders
One-step differentiation of iterative algorithms
Estimating weighted areas under the ROC curve
Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data
Recursive Algorithms for Approximating Probabilities in Graphical Models
Even your Teacher Needs Guidance: Ground-Truth Targets Dampen Regularization Imposed by Self-Distillation
You Never Stop Dancing: Non-freezing Dance Generation via Bank-constrained Manifold Projection
NeurIPS 2019 Competition and Demonstration Track Revised selected papers
Decision Tree for Locally Private Estimation with Public Data
Incremental Scene Synthesis
STAR: A Benchmark for Situated Reasoning in Real-World Videos
Stochastic $L^\natural$-convex Function Minimization
A Spectral Regularization Framework for Multi-Task Structure Learning
OSLO: One-Shot Label-Only Membership Inference Attacks
A Low-Power CMOS Circuit Which Emulates Temporal Electrical Properties of Neurons
Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization
Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need
Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning
SIRI: Spatial Relation Induced Network For Spatial Description Resolution
Periodic Component Analysis: An Eigenvalue Method for Representing Periodic Structure in Speech
Discriminability-Based Transfer between Neural Networks
Adaptive Manifold Learning
Autoregressive Image Generation without Vector Quantization
Advancing Cross-domain Discriminability in Continual Learning of Vision-Language Models
CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion
Learning convex bounds for linear quadratic control policy synthesis
Equivariant Neural Operator Learning with Graphon Convolution
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features
KuaiSim: A Comprehensive Simulator for Recommender Systems
Discrete-Valued Neural Communication
Monte Carlo Tree Descent for Black-Box Optimization
Universal Semi-Supervised Learning
Concentration inequalities under sub-Gaussian and sub-exponential conditions
Robust Compressed Sensing MRI with Deep Generative Priors
TextDiffuser: Diffusion Models as Text Painters
Teaching Inverse Reinforcement Learners via Features and Demonstrations
Active Learning with a Drifting Distribution
What Is Missing For Graph Homophily? Disentangling Graph Homophily For Graph Neural Networks
Biologically Inspired Learning Model for Instructed Vision
FairJob: A Real-World Dataset for Fairness in Online Systems
Learning Distilled Collaboration Graph for Multi-Agent Perception
Convergent Fitted Value Iteration with Linear Function Approximation
Gradient Guidance for Diffusion Models: An Optimization Perspective
On Robust Optimal Transport: Computational Complexity and Barycenter Computation
Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network
Learnable Tree Filter for Structure-preserving Feature Transform
AdaNovo: Towards Robust \emph{De Novo} Peptide Sequencing in Proteomics against Data Biases
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits
Visualizing Group Structure
Private Learning Implies Online Learning: An Efficient Reduction
MCMC Variational Inference via Uncorrected Hamiltonian Annealing
EfficientFormer: Vision Transformers at MobileNet Speed
Reinforcement Learning with Soft State Aggregation
Deep Patch Visual Odometry
Structured Prediction via the Extragradient Method
Sparse Fourier Backpropagation in Cryo-EM Reconstruction
MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction
VidChapters-7M: Video Chapters at Scale
Non-local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation
Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models
Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain
ChatTracker: Enhancing Visual Tracking Performance via Chatting with Multimodal Large Language Model
HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detection
Riemannian Laplace approximations for Bayesian neural networks
Wasserstein Learning of Deep Generative Point Process Models
Geometry-aware training of factorized layers in tensor Tucker format
Probabilistic Conformal Distillation for Enhancing Missing Modality Robustness
Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data
Minimax Estimation of Conditional Moment Models
A Novel Channel Selection System in Cochlear Implants Using Artificial Neural Network
Positional Normalization
Counting Solution Clusters in Graph Coloring Problems Using Belief Propagation
No-Regret M ♮ -Concave Function Maximization: Stochastic Bandit Algorithms and NP-Hardness of Adversarial Full-Information Setting
Multi-model Ensemble Conformal Prediction in Dynamic Environments
Imbalanced Mixed Linear Regression
Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization
Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning
Unfolding Taylor's Approximations for Image Restoration
CleanDiffuser: An Easy-to-use Modularized Library for Diffusion Models in Decision Making
A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation
Non-convex Robust PCA
GraphGT: Machine Learning Datasets for Graph Generation and Transformation
Efficiently Learning Fourier Sparse Set Functions
Fine-grained Control of Generative Data Augmentation in IoT Sensing
Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction
Langevin Autoencoders for Learning Deep Latent Variable Models
Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations
Deep learning with Elastic Averaging SGD
SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud
On the Effectiveness of Persistent Homology
A Biologically Plausible Neural Network for Slow Feature Analysis
Factored Policy Gradients: Leveraging Structure for Efficient Learning in MOMDPs
Optimal Learning from Verified Training Data
C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models
A Decentralized Parallel Algorithm for Training Generative Adversarial Nets
Probabilistic Linear Multistep Methods
Optimal Private and Communication Constraint Distributed Goodness-of-Fit Testing for Discrete Distributions in the Large Sample Regime
Learning from Limited Demonstrations
Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch