yusuke1997/dblp-id-title huggingface dataset:
LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite
Discrete Compositional Representations as an Abstraction for Goal Conditioned Reinforcement Learning
Connectionist Temporal Classification with Maximum Entropy Regularization
Understanding the detrimental class-level effects of data augmentation
Quantized Estimation of Gaussian Sequence Models in Euclidean Balls
A unified variance-reduced accelerated gradient method for convex optimization
AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite Imagery
ReMoDetect: Reward Models Recognize Aligned LLM's Generations
Efficient High-Order Interaction-Aware Feature Selection Based on Conditional Mutual Information
Probability Estimator from a Database Using a Gibbs Energy Model
Large Margin Learning of Upstream Scene Understanding Models
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
Don't Roll the Dice, Ask Twice: The Two-Query Distortion of Matching Problems and Beyond
EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images
Focused Transformer: Contrastive Training for Context Scaling
Parallel Submodular Function Minimization
Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection
A Robust Functional EM Algorithm for Incomplete Panel Count Data
Size-Noise Tradeoffs in Generative Networks
Predicting User Activity Level In Point Processes With Mass Transport Equation
Learning Energy Networks with Generalized Fenchel-Young Losses
ReLU Regression with Massart Noise
Exploring DCN-like architecture for fast image generation with arbitrary resolution
Neural Computing with Small Weights
Task and Spatial Frequency Effects on Face Specialization
When Do Flat Minima Optimizers Work?
Total Least Squares Regression in Input Sparsity Time
Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo
Sketching Method for Large Scale Combinatorial Inference
CPR for CSPs: A Probabilistic Relaxation of Constraint Propagation
Efficient estimation of neural tuning during naturalistic behavior
Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention
A Scalable Hierarchical Distributed Language Model
No-Regret Bandit Exploration based on Soft Tree Ensemble Model
How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval
Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching
Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus
Self-Supervised Visual Representation Learning from Hierarchical Grouping
Multi-Step Generalized Policy Improvement by Leveraging Approximate Models
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks
On the Generalization Power of the Overfitted Three-Layer Neural Tangent Kernel Model
Input Similarity from the Neural Network Perspective
Convergence Guarantees for Adaptive Bayesian Quadrature Methods
DenoiseRep: Denoising Model for Representation Learning
Differentially Private Markov Chain Monte Carlo
Bayes-Adaptive Simulation-based Search with Value Function Approximation
Semi-Markov Conditional Random Fields for Information Extraction
QuantSR: Accurate Low-bit Quantization for Efficient Image Super-Resolution
Coresets for Archetypal Analysis
Association of Objects May Engender Stereotypes: Mitigating Association-Engendered Stereotypes in Text-to-Image Generation
Kalman Filtering Attention for User Behavior Modeling in CTR Prediction
Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models
Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents
Model Selection for Support Vector Machines
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel
Learning a Tree of Metrics with Disjoint Visual Features
Constructive Algorithms for Hierarchical Mixtures of Experts
ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction
Scalable inference of topic evolution via models for latent geometric structures
Optimistic Bandit Convex Optimization
Probabilistic principles in unsupervised learning of visual structure: human data and a model
The Pareto Frontier of model selection for general Contextual Bandits
Consistency of Constrained Spectral Clustering under Graph Induced Fair Planted Partitions
Computing Gaussian Mixture Models with EM Using Equivalence Constraints
A Universal Error Measure for Input Predictions Applied to Online Graph Problems
This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish
A Max-Min Entropy Framework for Reinforcement Learning
Learning Hierarchical Information Flow with Recurrent Neural Modules
Improved Deep Metric Learning with Multi-class N-pair Loss Objective
Covariance Kernels from Bayesian Generative Models
Solvable Models of Artificial Neural Networks
Submodular Maximization in Clean Linear Time
Robustness of Graph Neural Networks at Scale
Anonymous Bandits for Multi-User Systems
Efficient Meta Learning via Minibatch Proximal Update
Inverse Reinforcement Learning with the Average Reward Criterion
Constructing Fast Network through Deconstruction of Convolution
A Multi-Implicit Neural Representation for Fonts
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies
Timing and Partial Observability in the Dopamine System
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning
Learning Structured Representations with Hyperbolic Embeddings
On Empirical Risk Minimization with Dependent and Heavy-Tailed Data
Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRI
Indian Buffet Processes with Power-law Behavior
Analytical Study of the Interplay between Architecture and Predictability
Generalized Beta Mixtures of Gaussians
Multiresolution Gaussian Processes
Combining Classifiers Using Correspondence Analysis
On the Utility of Learning about Humans for Human-AI Coordination
LION: Latent Point Diffusion Models for 3D Shape Generation
Compiler Auto-Vectorization with Imitation Learning
Statistical Insights into HSIC in High Dimensions
Triangulation Residual Loss for Data-efficient 3D Pose Estimation
Translation-equivariant Representation in Recurrent Networks with a Continuous Manifold of Attractors
Local to Global: Learning Dynamics and Effect of Initialization for Transformers
Distributional Preference Alignment of LLMs via Optimal Transport
REBEL: Reinforcement Learning via Regressing Relative Rewards
Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks
A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models
MLLE: Modified Locally Linear Embedding Using Multiple Weights
Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation
Correlation Priors for Reinforcement Learning
B-Pref: Benchmarking Preference-Based Reinforcement Learning
Learning Place Cell Representations and Context-Dependent Remapping
For SALE: State-Action Representation Learning for Deep Reinforcement Learning
Fast Decomposable Submodular Function Minimization using Constrained Total Variation
Color-Oriented Redundancy Reduction in Dataset Distillation
DeiSAM: Segment Anything with Deictic Prompting
Vision Foundation Model Enables Generalizable Object Pose Estimation
Privately Publishable Per-instance Privacy
TriBERT: Human-centric Audio-visual Representation Learning
Value Function in Frequency Domain and the Characteristic Value Iteration Algorithm
What makes some POMDP problems easy to approximate?
The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark
Universal Rates of Empirical Risk Minimization
MACK: Multimodal Aligned Conceptual Knowledge for Unpaired Image-text Matching
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again
Statistical Prediction with Kanerva's Sparse Distributed Memory
Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care
Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations
Debiased Bayesian inference for average treatment effects
Auditing: Active Learning with Outcome-Dependent Query Costs
Predictive-State Decoders: Encoding the Future into Recurrent Networks
New Hardware for Massive Neural Networks
Bayesian Semi-supervised Learning with Graph Gaussian Processes
Causal analysis of Covid-19 Spread in Germany
BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images
StreamNet: Memory-Efficient Streaming Tiny Deep Learning Inference on the Microcontroller
OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset
Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift
Discovery of Useful Questions as Auxiliary Tasks
Causal normalizing flows: from theory to practice
Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space
On the Algorithmics and Applications of a Mixed-norm based Kernel Learning Formulation
Large-Scale Category Structure Aware Image Categorization
Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain
On the Convergence of Leveraging
Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation
Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
Counterfactual Generation with Identifiability Guarantees
Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics
On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm
Ensemble Sampling
InsPro: Propagating Instance Query and Proposal for Online Video Instance Segmentation
The First Optimal Acceleration of High-Order Methods in Smooth Convex Optimization
Differentiable Causal Discovery from Interventional Data
How They Vote: Issue-Adjusted Models of Legislative Behavior
Learning Invariant Representations of Molecules for Atomization Energy Prediction
Kernel Regression and Backpropagation Training With Noise
On Stochastic Complexity and Admissible Models for Neural Network Classifiers
FashionR2R: Texture-preserving Rendered-to-Real Image Translation with Diffusion Models
Contrastive Moments: Unsupervised Halfspace Learning in Polynomial Time
Model LEGO: Creating Models Like Disassembling and Assembling Building Blocks
Tetrahedron Splatting for 3D Generation
A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities
Conditional Mutual Information for Disentangled Representations in Reinforcement Learning
Optimal Algorithms for Learning Partitions with Faulty Oracles
Identifiability in inverse reinforcement learning
CASTLE: Regularization via Auxiliary Causal Graph Discovery
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency
Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex
The Kernel Gibbs Sampler
Positively Weighted Kernel Quadrature via Subsampling
Semiparametric Support Vector and Linear Programming Machines
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
Multi-Agent Imitation Learning: Value is Easy, Regret is Hard
OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents
Sketching Structured Matrices for Faster Nonlinear Regression
Volumetric Correspondence Networks for Optical Flow
A Comparative Framework for Preconditioned Lasso Algorithms
Model-Free Active Exploration in Reinforcement Learning
Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives
Optimality and Stability in Federated Learning: A Game-theoretic Approach
Constrained convex minimization via model-based excessive gap
Federated Ensemble-Directed Offline Reinforcement Learning
A Partially-Supervised Reinforcement Learning Framework for Visual Active Search
Scaling laws for language encoding models in fMRI
Generalization Bounds for Supervised Dimensionality Reduction
Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning
PlasticityNet: Learning to Simulate Metal, Sand, and Snow for Optimization Time Integration
Autonomous Learning of Action Models for Planning
Parallelizing Thompson Sampling
AbDiffuser: full-atom generation of in-vitro functioning antibodies
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error
Laplacian Autoencoders for Learning Stochastic Representations
Sparse maximal update parameterization: A holistic approach to sparse training dynamics
Learning Sparse Codes with a Mixture-of-Gaussians Prior
When Is Unsupervised Disentanglement Possible?
Mask-based Latent Reconstruction for Reinforcement Learning
Rank Diminishing in Deep Neural Networks
Bayesian Image Super-resolution, Continued
H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks
Robust Density Estimation under Besov IPM Losses
Multi-output Polynomial Networks and Factorization Machines
Faster Neighborhood Attention: Reducing the O(n^2) Cost of Self Attention at the Threadblock Level
Principled Weight Initialisation for Input-Convex Neural Networks
Theoretically Guaranteed Bidirectional Data Rectification for Robust Sequential Recommendation
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
A Recurrent Neural Network for Word Identification from Continuous Phoneme Strings
Perceptual Multistability as Markov Chain Monte Carlo Inference
Discriminative Fields for Modeling Spatial Dependencies in Natural Images
TrajCLIP: Pedestrian trajectory prediction method using contrastive learning and idempotent networks
LoCo: Learning 3D Location-Consistent Image Features with a Memory-Efficient Ranking Loss
An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models
VCC: Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens
Multi-turn Reinforcement Learning with Preference Human Feedback
Learning to Take Concurrent Actions
Neural Network Models of Chemotaxis in the Nematode Caenorhabditis Elegans
Boundary thickness and robustness in learning models
Inferring Ground Truth from Subjective Labelling of Venus Images
Listenable Maps for Zero-Shot Audio Classifiers
Associative Memory in a Simple Model of Oscillating Cortex
Basis Selection for Wavelet Regression
3D Self-Supervised Methods for Medical Imaging
Efficient identification of informative features in simulation-based inference
An Incremental Nearest Neighbor Algorithm with Queries
On the Robustness of Mechanism Design under Total Variation Distance
Shift Invariance Can Reduce Adversarial Robustness
Learning Sensor Multiplexing Design through Back-propagation
Learnability of high-dimensional targets by two-parameter models and gradient flow
Robust Reinforcement Learning
Variance estimation in compound decision theory under boundedness
Task-Oriented Feature Distillation
epsilon-Entropy and the Complexity of Feedforward Neural Networks
Predictive Attractor Models
CBD: A Certified Backdoor Detector Based on Local Dominant Probability
Adversarially Robust Change Point Detection
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
Breaking the Span Assumption Yields Fast Finite-Sum Minimization
Batchnorm Allows Unsupervised Radial Attacks
On Neural Networks with Minimal Weights
Pearls from Pebbles: Improved Confidence Functions for Auto-labeling
Cola: A Benchmark for Compositional Text-to-image Retrieval
Brains on Beats
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network
Homomorphic Matrix Completion
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis
Efficient Knowledge Distillation from Model Checkpoints
GumBolt: Extending Gumbel trick to Boltzmann priors
Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States
Multi-Scale Spectral Decomposition of Massive Graphs
Generative vs. Discriminative: Rethinking The Meta-Continual Learning
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Offline Imitation Learning with Variational Counterfactual Reasoning
Scale-invariant Optimal Sampling for Rare-events Data and Sparse Models
SGD on Neural Networks Learns Functions of Increasing Complexity
Efficient Neural Network Training via Forward and Backward Propagation Sparsification
Distributional Reinforcement Learning with Regularized Wasserstein Loss
Provable convergence guarantees for black-box variational inference
Improving Equivariant Model Training via Constraint Relaxation
A Neural Model of Visual Contour Integration
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin
Size Regularized Cut for Data Clustering
Spectral Cues in Human Sound Localization
Probabilistic Visualisation of High-Dimensional Binary Data
Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation
MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Models
Going Beyond Linear Mode Connectivity: The Layerwise Linear Feature Connectivity
Assaying Out-Of-Distribution Generalization in Transfer Learning
Analytic Study of Families of Spurious Minima in Two-Layer ReLU Neural Networks: A Tale of Symmetry II
Neural Network Exploration Using Optimal Experiment Design
DARE: Disentanglement-Augmented Rationale Extraction
Greedy Poisson Rejection Sampling
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning
Two view learning: SVM-2K, Theory and Practice
Optimal Single-Class Classification Strategies
Robust Detection of Adversarial Attacks by Modeling the Intrinsic Properties of Deep Neural Networks
Predictive PAC Learning and Process Decompositions
Structured Learning of Compositional Sequential Interventions
Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation
Adapting to a Market Shock: Optimal Sequential Market-Making
Sparse-view Pose Estimation and Reconstruction via Analysis by Generative Synthesis
Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics
On Masked Pre-training and the Marginal Likelihood
LLM Circuit Analyses Are Consistent Across Training and Scale
Posture-Informed Muscular Force Learning for Robust Hand Pressure Estimation
Input-to-State Stable Coupled Oscillator Networks for Closed-form Model-based Control in Latent Space
LogiGAN: Learning Logical Reasoning via Adversarial Pre-training
Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs
Proppo: a Message Passing Framework for Customizable and Composable Learning Algorithms
CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator
QuIP: 2-Bit Quantization of Large Language Models With Guarantees
Improving Policies without Measuring Merits
kGym: A Platform and Dataset to Benchmark Large Language Models on Linux Kernel Crash Resolution
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization
On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks
Explain Any Concept: Segment Anything Meets Concept-Based Explanation
A New Probabilistic Model for Rank Aggregation
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation
A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent
Measure Based Regularization
Performance Through Consistency: MS-TDNN's for Large Vocabulary Continuous Speech Recognition
Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders
Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack
High Rank Path Development: an approach to learning the filtration of stochastic processes
Autoregressive Score Matching
Understanding How Encoder-Decoder Architectures Attend
Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms
FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event Detection
Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited
DiffusionPDE: Generative PDE-Solving under Partial Observation
When will a Genetic Algorithm Outperform Hill Climbing
Graph Structured Prediction Energy Networks
Towards an Organizing Principle for a Layered Perceptual Network
On the Size and Approximation Error of Distilled Datasets
Cost-efficient Gaussian tensor network embeddings for tensor-structured inputs
Generalization in Reinforcement Learning: Safely Approximating the Value Function
Machine learning detects terminal singularities
Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling
One More Step Towards Reality: Cooperative Bandits with Imperfect Communication
Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals
MIMIC: Finding Optima by Estimating Probability Densities
The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning
On the Value of Interaction and Function Approximation in Imitation Learning
CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders
Adaptive Oracle-Efficient Online Learning
Recognizing Activities by Attribute Dynamics
SCOREQ: Speech Quality Assessment with Contrastive Regression
TopoLogic: An Interpretable Pipeline for Lane Topology Reasoning on Driving Scenes
Data Poisoning Attacks on Factorization-Based Collaborative Filtering
Variational Bayesian Monte Carlo
Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade
LinkerNet: Fragment Poses and Linker Co-Design with 3D Equivariant Diffusion
A Dynamical System View of Langevin-Based Non-Convex Sampling
Prophet Attention: Predicting Attention with Future Attention
One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning
Unsupervised Adversarial Invariance
Efficient Approximation Algorithms for Strings Kernel Based Sequence Classification
Learning to Specialize with Knowledge Distillation for Visual Question Answering
Remember What You Want to Forget: Algorithms for Machine Unlearning
Incentivized Communication for Federated Bandits
Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy
Melting Pot Contest: Charting the Future of Generalized Cooperative Intelligence
FactorSim: Generative Simulation via Factorized Representation
Estimating Generalization Performance Along the Trajectory of Proximal SGD in Robust Regression
TweetNERD - End to End Entity Linking Benchmark for Tweets
Advances in Neural Information Processing Systems 2, [NIPS Conference, Denver, Colorado, USA, November 27-30, 1989]
Are GATs Out of Balance?
Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines
Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net
Selecting the independent coordinates of manifolds with large aspect ratios
Streaming, Memory Limited Algorithms for Community Detection
Scalable Constrained Policy Optimization for Safe Multi-agent Reinforcement Learning
Deliberative Explanations: visualizing network insecurities
Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs
FindingEmo: An Image Dataset for Emotion Recognition in the Wild
Measuring Systematic Generalization in Neural Proof Generation with Transformers
Will Bilevel Optimizers Benefit from Loops