yusuke1997/dblp-id-title huggingface dataset:
PDP: Parameter-free Differentiable Pruning is All You Need
Restructuring Sparse High Dimensional Data for Effective Retrieval
Black-box optimization of noisy functions with unknown smoothness
A Sparse Interactive Model for Matrix Completion with Side Information
Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond
MoLE: Enhancing Human-centric Text-to-image Diffusion via Mixture of Low-rank Experts
The All-or-Nothing Phenomenon in Sparse Tensor PCA
MCMAE: Masked Convolution Meets Masked Autoencoders
The Closeness of In-Context Learning and Weight Shifting for Softmax Regression
Characterizing Out-of-Distribution Error via Optimal Transport
How do fair decisions fare in long-term qualification?
Direct Estimation of Differences in Causal Graphs
Evidential Mixture Machines: Deciphering Multi-Label Correlations for Active Learning Sensitivity
Entangled Monte Carlo
A Learning Analog Neural Network Chip with Continuous-Time Recurrent Dynamics
Skeleton-bridged Point Completion: From Global Inference to Local Adjustment
Inferring rankings under constrained sensing
The Value of Information When Deciding What to Learn
Information Geometrical Framework for Analyzing Belief Propagation Decoder
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data
Using Manifold Stucture for Partially Labeled Classification
GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension
Fairness Aware Counterfactuals for Subgroups
Multilingual Diversity Improves Vision-Language Representations
Discovery of Single Independent Latent Variable
From Text to Trajectory: Exploring Complex Constraint Representation and Decomposition in Safe Reinforcement Learning
End-To-End Causal Effect Estimation from Unstructured Natural Language Data
BackdoorAlign: Mitigating Fine-tuning based Jailbreak Attack with Backdoor Enhanced Safety Alignment
Beyond L1: Faster and Better Sparse Models with skglm
Perplexity-aware Correction for Robust Alignment with Noisy Preferences
Balanced Meta-Softmax for Long-Tailed Visual Recognition
Recovering Unbalanced Communities in the Stochastic Block Model with Application to Clustering with a Faulty Oracle
Learning Causal Models under Independent Changes
One-Step Diffusion Distillation through Score Implicit Matching
The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions
Contour Organisation with the EM Algorithm
Context-PIPs: Persistent Independent Particles Demands Context Features
Lipschitz Bandits with Batched Feedback
USCILab3D: A Large-scale, Long-term, Semantically Annotated Outdoor Dataset
Fast recovery from a union of subspaces
Keypoint-Guided Optimal Transport with Applications in Heterogeneous Domain Adaptation
Learning a Concept Hierarchy from Multi-labeled Documents
Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation
Collapsed Inference for Bayesian Deep Learning
Saliency-Aware Neural Architecture Search
Offline Imitation Learning with a Misspecified Simulator
ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
High dimensional, tabular deep learning with an auxiliary knowledge graph
Vocabulary-free Image Classification
Actor-Critic Algorithms for Risk-Sensitive MDPs
Restricting exchangeable nonparametric distributions
An NLP Benchmark Dataset for Assessing Corporate Climate Policy Engagement
Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments
Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Linear Response for Approximate Inference
Noether Embedding: Efficient Learning of Temporal Regularities
Bayesian Methods for Mixtures of Experts
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation
Improved Analysis of Clipping Algorithms for Non-convex Optimization
Kernel Memory Networks: A Unifying Framework for Memory Modeling
Adaptive Gradient-Based Meta-Learning Methods
Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces
Adaptive Machine Unlearning
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise
Neural Power Units
Learning both Weights and Connections for Efficient Neural Network
Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression
Inference and communication in the game of Password
Frequency-Enhanced Data Augmentation for Vision-and-Language Navigation
Estimating Mixture Models via Mixtures of Polynomials
Dual-stream Network for Visual Recognition
Time-independent Generalization Bounds for SGLD in Non-convex Settings
Connectionist Architectures for Multi-Speaker Phoneme Recognition
Online Bayesian Goal Inference for Boundedly Rational Planning Agents
Efficient Projection onto the Perfect Phylogeny Model
Automatic Derivation of Statistical Algorithms: The EM Family and Beyond
Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs
Bayesian Intermittent Demand Forecasting for Large Inventories
KnowGPT: Knowledge Graph based Prompting for Large Language Models
DisCEdit: Model Editing by Identifying Discriminative Components
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
Fooling Neural Network Interpretations via Adversarial Model Manipulation
Hierarchical Image Probability (H1P) Models
Uncovering the Redundancy in Graph Self-supervised Learning Models
MAP Inference in Chains using Column Generation
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs
Dual Parameterization of Sparse Variational Gaussian Processes
Data-driven Estimation of Sinusoid Frequencies
A Benchmark for Systematic Generalization in Grounded Language Understanding
Schedule Your Edit: A Simple yet Effective Diffusion Noise Schedule for Image Editing
Instabilities in Eye Movement Control: A Model of Periodic Alternating Nystagmus
Data-Driven Online to Batch Conversions
<tt>RP-Mod</tt>&<tt>RP-Crowd: </tt> Moderator- and Crowd-Annotated German News Comment Datasets
Learning to Teach with Dynamic Loss Functions
Repeated Games against Budgeted Adversaries
PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier
Improving Performance in Neural Networks Using a Boosting Algorithm
What Distributions are Robust to Indiscriminate Poisoning Attacks for Linear Learners?
Fast Parameter Estimation Using Green's Functions
Near-Optimal Bayesian Active Learning with Noisy Observations
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?
Separating Style and Content
Uncertainty Quantification and Deep Ensembles
Dense Associative Memory Through the Lens of Random Features
Truncation-free Online Variational Inference for Bayesian Nonparametric Models
Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices
ROBIN: Robust and Invisible Watermarks for Diffusion Models with Adversarial Optimization
Undirected Probabilistic Model for Tensor Decomposition
MemVLT: Vision-Language Tracking with Adaptive Memory-based Prompts
Layered image motion with explicit occlusions, temporal consistency, and depth ordering
Faithful Inversion of Generative Models for Effective Amortized Inference
SyncTREE: Fast Timing Analysis for Integrated Circuit Design through a Physics-informed Tree-based Graph Neural Network
Causal-structure Driven Augmentations for Text OOD Generalization
Inferring synaptic conductances from spike trains with a biophysically inspired point process model
On skip connections and normalisation layers in deep optimisation
Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation Models
Learning Spectral Clustering
PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks
Learning to Rank with Nonsmooth Cost Functions
VQ-Map: Bird's-Eye-View Map Layout Estimation in Tokenized Discrete Space via Vector Quantization
Paraphrasing Complex Network: Network Compression via Factor Transfer
Geometric optimisation on positive definite matrices for elliptically contoured distributions
Orchid: Flexible and Data-Dependent Convolution for Sequence Modeling
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation
Generalized Off-Policy Actor-Critic
Learning Truncated Causal History Model for Video Restoration
Group Contextual Encoding for 3D Point Clouds
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020
Fool's Gold: Extracting Finite State Machines from Recurrent Network Dynamics
Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression
Connectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion
Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation
Bayesian Policy Learning with Trans-Dimensional MCMC
Recurrent networks of coupled Winner-Take-All oscillators for solving constraint satisfaction problems
Improving the Performance of Radial Basis Function Networks by Learning Center Locations
Grammar-Aligned Decoding
A novel notion of barycenter for probability distributions based on optimal weak mass transport
Coded Residual Transform for Generalizable Deep Metric Learning
It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals
Can Pre-Trained Text-to-Image Models Generate Visual Goals for Reinforcement Learning?
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models
Proximal Newton-type methods for convex optimization
FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision
Learning filter widths of spectral decompositions with wavelets
SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning
Spatial Latent Dirichlet Allocation
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks
Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum
Content Provider Dynamics and Coordination in Recommendation Ecosystems
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning
MeshXL: Neural Coordinate Field for Generative 3D Foundation Models
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
Efficient Graph Similarity Computation with Alignment Regularization
How many samples are needed to leverage smoothness?
A no-regret generalization of hierarchical softmax to extreme multi-label classification
Thinning for Accelerating the Learning of Point Processes
An Information Maximization Approach to Overcomplete and Recurrent Representations
New Bounds for Hyperparameter Tuning of Regression Problems Across Instances
Policy Shaping: Integrating Human Feedback with Reinforcement Learning
A Mulitscale Attentional Framework for Relaxation Neural Networks
Lifted Weighted Mini-Bucket
UniTS: A Unified Multi-Task Time Series Model
Auditing Black-Box Prediction Models for Data Minimization Compliance
Grounding Representation Similarity Through Statistical Testing
Matching on Balanced Nonlinear Representations for Treatment Effects Estimation
How Receptive Field Parameters Affect Neural Learning
Wasserstein Distributionally Robust Kalman Filtering
Dual Kalman Filtering Methods for Nonlinear Prediction, Smoothing and Estimation
Asynchronous Decentralized Online Learning
TokenLearner: Adaptive Space-Time Tokenization for Videos
Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever
Nonlinear Processing in LGN Neurons
Early Stopping-But When?
Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction
Deep Learning for Computing Convergence Rates of Markov Chains
Near-Optimal Private and Scalable $k$-Clustering
Multiscale Fields of Patterns
Transelliptical Component Analysis
Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients
Semi-Implicit Graph Variational Auto-Encoders
SANFlow: Semantic-Aware Normalizing Flow for Anomaly Detection
Beyond Euclidean: Dual-Space Representation Learning for Weakly Supervised Video Violence Detection
Contextual Bilevel Reinforcement Learning for Incentive Alignment
Learning in Multi-Stage Decentralized Matching Markets
The Star Geometry of Critic-Based Regularizer Learning
Label-Aware Global Consistency for Multi-Label Learning with Single Positive Labels
RedCaps: Web-curated image-text data created by the people, for the people
Unsupervised Learning of Mixtures of Multiple Causes in Binary Data
Agent Modelling under Partial Observability for Deep Reinforcement Learning
Multiresolution Tangent Distance for Affine-invariant Classification
Off-policy estimation with adaptively collected data: the power of online learning
Only Train Once: A One-Shot Neural Network Training And Pruning Framework
Score Distillation via Reparametrized DDIM
Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning
Provably Efficient Q-Learning with Low Switching Cost
Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality
Logarithmic Regret for Online Control
Learning with Noisy Labels
Multi-Layer Perceptrons with B-Spline Receptive Field Functions
Interpolation can hurt robust generalization even when there is no noise
Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction
Spiking Transformer with Experts Mixture
Instance-Based State Identification for Reinforcement Learning
OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology
A Bayesian method for reducing bias in neural representational similarity analysis
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation
Multiscale Random Fields with Application to Contour Grouping
EgoEnv: Human-centric environment representations from egocentric video
Overparameterization Improves Robustness to Covariate Shift in High Dimensions
Learning Group Actions on Latent Representations
EM-DD: An Improved Multiple-Instance Learning Technique
Information Maximization in Noisy Channels : A Variational Approach
ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning
Replica-Exchange Nosé-Hoover Dynamics for Bayesian Learning on Large Datasets
Renovating Names in Open-Vocabulary Segmentation Benchmarks
Recognition of Manipulated Objects by Motor Learning
GAUDI: A Neural Architect for Immersive 3D Scene Generation
Robust Mixture Learning when Outliers Overwhelm Small Groups
GhostNetV2: Enhance Cheap Operation with Long-Range Attention
Generalized Belief Transport
$SE(3)$ Equivariant Ray Embeddings for Implicit Multi-View Depth Estimation
Non-stationary continuous dynamic Bayesian networks
Kernel Measures of Conditional Dependence
Stochastic Optimization Schemes for Performative Prediction with Nonconvex Loss
RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection
Text-Adaptive Multiple Visual Prototype Matching for Video-Text Retrieval
CRAG - Comprehensive RAG Benchmark
Discrete Object Generation with Reversible Inductive Construction
Probabilistic Event Cascades for Alzheimer's disease
Gradient Sparsification for Communication-Efficient Distributed Optimization
Towards the Difficulty for a Deep Neural Network to Learn Concepts of Different Complexities
Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation
Variational Bayes under Model Misspecification
Dynamic Personalized Federated Learning with Adaptive Differential Privacy
VisDA-2021 Competition: Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data
Unsupervised Representation Transfer for Small Networks: I Believe I Can Distill On-the-Fly
Acceleration and Averaging in Stochastic Descent Dynamics
The Effective Number of Parameters: An Analysis of Generalization and Regularization in Nonlinear Learning Systems
Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization
SODA: Robust Training of Test-Time Data Adaptors
Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models
Submodular Attribute Selection for Action Recognition in Video
Toward Efficient Inference for Mixture of Experts
Factorized Latent Spaces with Structured Sparsity
The Neural Covariance SDE: Shaped Infinite Depth-and-Width Networks at Initialization
Enhancing Sharpness-Aware Optimization Through Variance Suppression
PAC-Bayes Analysis Beyond the Usual Bounds
Mapping Estimation for Discrete Optimal Transport
Contextual Similarity Aggregation with Self-attention for Visual Re-ranking
Self-Chained Image-Language Model for Video Localization and Question Answering
Sensory Modality Segregation
Cost-efficient Knowledge-based Question Answering with Large Language Models
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis
Inference, Attention, and Decision in a Bayesian Neural Architecture
Documents as multiple overlapping windows into grids of counts
Visual Prompting via Image Inpainting
Stochastic Approximation for Canonical Correlation Analysis
Active 3D Shape Reconstruction from Vision and Touch
Self-Supervised Learning via Maximum Entropy Coding
Understanding Attention and Generalization in Graph Neural Networks
Bayesian Model Comparison and Backprop Nets
Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization
Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks
Interlaced Greedy Algorithm for Maximization of Submodular Functions in Nearly Linear Time
Finite Continuum-Armed Bandits
Conformal Off-Policy Prediction in Contextual Bandits
Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators
Weight Space Probability Densities in Stochastic Learning: I. Dynamics and Equilibria
Universal Exact Compression of Differentially Private Mechanisms
Depth-Limited Solving for Imperfect-Information Games
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
Stacking Your Transformers: A Closer Look at Model Growth for Efficient LLM Pre-Training
Multiple Instance Learning on Structured Data
DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank Correlation
Random function priors for exchangeable arrays with applications to graphs and relational data
The Early Word Catches the Weights
Analysis of Spectral Kernel Design based Semi-supervised Learning
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration
From Similarity to Superiority: Channel Clustering for Time Series Forecasting
Revisit the Power of Vanilla Knowledge Distillation: from Small Scale to Large Scale
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning
Optimistic Mirror Descent Either Converges to Nash or to Strong Coarse Correlated Equilibria in Bimatrix Games
What You See is What You Read? Improving Text-Image Alignment Evaluation
Pass-efficient unsupervised feature selection
Bayesian optimization explains human active search
An Analytical Study of Utility Functions in Multi-Objective Reinforcement Learning
Score-based 3D molecule generation with neural fields
Rethinking Parity Check Enhanced Symmetry-Preserving Ansatz
Reinforced Molecular Optimization with Neighborhood-Controlled Grammars
Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games
Estimating Internal Variables and Paramters of a Learning Agent by a Particle Filter
ASPiRe: Adaptive Skill Priors for Reinforcement Learning
An Auditory Paradigm for Brain-Computer Interfaces
PAC-Bayesian Analysis of Contextual Bandits
NATURE: Natural Auxiliary Text Utterances for Realistic Spoken Language Evaluation
Cell Segmenter: A General Framework for Multi-modality Cell Segmentation
Linear Learning: Landscapes and Algorithms
Geodesic Multi-Modal Mixup for Robust Fine-Tuning
Generalized Random Utility Models with Multiple Types
Power-law efficient neural codes provide general link between perceptual bias and discriminability
Boosting Transferability and Discriminability for Time Series Domain Adaptation
Factored Bandits
Query Complexity of Derivative-Free Optimization
DppNet: Approximating Determinantal Point Processes with Deep Networks
A Definition of Continual Reinforcement Learning
D4Explainer: In-distribution Explanations of Graph Neural Network via Discrete Denoising Diffusion
Computing and maximizing influence in linear threshold and triggering models
No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
Object DGCNN: 3D Object Detection using Dynamic Graphs
No Free Lunch from Deep Learning in Neuroscience: A Case Study through Models of the Entorhinal-Hippocampal Circuit
Multi-Stage Multi-Task Feature Learning
Autobahn: Automorphism-based Graph Neural Nets
Constructing Non-isotropic Gaussian Diffusion Model Using Isotropic Gaussian Diffusion Model for Image Editing
Learning by Minimizing the Sum of Ranked Range
Sequential Memory with Temporal Predictive Coding
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models
Robust Fisher Discriminant Analysis
Energy Transformer
Non-Stochastic Control with Bandit Feedback
Fair and Efficient Allocations Without Obvious Manipulations
Principles of real-time computing with feedback applied to cortical microcircuit models
Finding significant combinations of features in the presence of categorical covariates
Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization
Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space
Parametrically Retargetable Decision-Makers Tend To Seek Power
Train Hard, Fight Easy: Robust Meta Reinforcement Learning
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits
All your loss are belong to Bayes
Dendritic cortical microcircuits approximate the backpropagation algorithm
Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous Control
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning
The Rectified Gaussian Distribution
Bayesian Model Scoring in Markov Random Fields
Warm-starting Push-Relabel
Reconfigurable Neural Net Chip with 32K Connections
Structure Learning with Adaptive Random Neighborhood Informed MCMC
Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation
Complexity of Decentralized Control: Special Cases
HAWK: Learning to Understand Open-World Video Anomalies
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
Back-Modality: Leveraging Modal Transformation for Data Augmentation
Hilbert Distillation for Cross-Dimensionality Networks
Constrained episodic reinforcement learning in concave-convex and knapsack settings
QUIC-SVD: Fast SVD Using Cosine Trees
Implementable Prescriptive Decision Making
tspDB: Time Series Predict DB
May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations
Outlier Detection and Robust PCA Using a Convex Measure of Innovation
Bessel Equivariant Networks for Inversion of Transmission Effects in Multi-Mode Optical Fibres