yusuke1997/dblp-id-title huggingface dataset:
GradAug: A New Regularization Method for Deep Neural Networks
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation
What is needed for simple spatial language capabilities in VQA?
Over-parameterized Student Model via Tensor Decomposition Boosted Knowledge Distillation
Multi-LLM Debate: Framework, Principals, and Interventions
Nuclear Fusion Diamond Polishing Dataset
Universal consistency and minimax rates for online Mondrian Forests
Transformer as a hippocampal memory consolidation model based on NMDAR-inspired nonlinearity
Out-of-Distribution Generalization in Kernel Regression
Modality-Agnostic Self-Supervised Learning with Meta-Learned Masked Auto-Encoder
Information through a Spiking Neuron
Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation
No evidence for active sparsification in the visual cortex
Consistent Multilabel Classification
Large Language Models are Visual Reasoning Coordinators
Adversarial Reprogramming Revisited
Catch Me, If You Can! A Mediated Perception Approach Towards Fully Autonomous Drone Racing
Large Language Model Unlearning via Embedding-Corrupted Prompts
Estimation, Optimization, and Parallelism when Data is Sparse
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks
Context-Sensitive Decision Forests for Object Detection
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos
EcoLight: Intersection Control in Developing Regions Under Extreme Budget and Network Constraints
Large Memory Layers with Product Keys
Provably Efficient Interactive-Grounded Learning with Personalized Reward
Unsupervised Part Discovery from Contrastive Reconstruction
Causal discovery with scale-mixture model for spatiotemporal variance dependencies
On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds
Tractability in Structured Probability Spaces
Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework
Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses
Adversarial Attacks on Stochastic Bandits
Passive learning of active causal strategies in agents and language models
Towards optimally abstaining from prediction with OOD test examples
Sensitivity in Translation Averaging
Coarticulation in Markov Decision Processes
Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models
DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models
Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests
Relational Learning with Gaussian Processes
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data
List-Decodable Sparse Mean Estimation
Optimizing Instructional Policies
Greedy Subspace Clustering
Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling
Learning to classify complex patterns using a VLSI network of spiking neurons
Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium
Removing Noise in On-Line Search using Adaptive Batch Sizes
A Hierarchical Compositional System for Rapid Object Detection
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning
Fine-Grained Semantically Aligned Vision-Language Pre-Training
Wasserstein Variational Inference
Long-tailed Object Detection Pretraining: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction
PAC-Bayesian Generic Chaining
3DP3: 3D Scene Perception via Probabilistic Programming
SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL
Multi-Task Learning for Interpretation of Brain Decoding Models
Approximate Planning in Large POMDPs via Reusable Trajectories
ZipLM: Inference-Aware Structured Pruning of Language Models
Stochastic Gradient Descent with Only One Projection
Learning towards Minimum Hyperspherical Energy
Phase-coupling in Two-Dimensional Networks of Interacting Oscillators
Fairness-guided Few-shot Prompting for Large Language Models
Towards Estimating Bounds on the Effect of Policies under Unobserved Confounding
Distributed Parameter Estimation in Probabilistic Graphical Models
An Application of Reinforcement Learning to Aerobatic Helicopter Flight
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting
Online robust non-stationary estimation
Minimizing Uncertainty in Pipelines
Learning to Simulate Self-driven Particles System with Coordinated Policy Optimization
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation
Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization
Compositional Generalization from First Principles
Collaborative Learning for Deep Neural Networks
URL: A Representation Learning Benchmark for Transferable Uncertainty Estimates
DEL: Discrete Element Learner for Learning 3D Particle Dynamics with Neural Rendering
Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators
Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension
Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching
Learning from Dyadic Data
The Role of Top-down and Bottom-up Processes in Guiding Eye Movements during Visual Search
Select-and-Sample for Spike-and-Slab Sparse Coding
Moment Matching Denoising Gibbs Sampling
Adversarial Regression with Doubly Non-negative Weighting Matrices
Learning with convolution and pooling operations in kernel methods
Transfer of Deep Reactive Policies for MDP Planning
Reading Tea Leaves: How Humans Interpret Topic Models
CLEVRER-Humans: Describing Physical and Causal Events the Human Way
Reusing Pretrained Models by Multi-linear Operators for Efficient Training
Efficient Online Portfolio with Logarithmic Regret
Learning a latent manifold of odor representations from neural responses in piriform cortex
Archimedes Meets Privacy: On Privately Estimating Quantiles in High Dimensions Under Minimal Assumptions
Unified Lexical Representation for Interpretable Visual-Language Alignment
MomentumSMoE: Integrating Momentum into Sparse Mixture of Experts
The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data
Thresholding Procedures for High Dimensional Variable Selection and Statistical Estimation
A Model of Early Visual Processing
Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks
Learning-to-learn non-convex piecewise-Lipschitz functions
Interactive Deep Clustering via Value Mining
Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model
Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning
Gaussian Process Preference Elicitation
Correlation Functions in a Large Stochastic Network
Learning Conjoint Attentions for Graph Neural Nets
Adversarial Ranking for Language Generation
Adaptive Concentration Inequalities for Sequential Decision Problems
Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees
Off-Policy Evaluation for Human Feedback
An Improved Analysis of Training Over-parameterized Deep Neural Networks
Dynamic Resolution Network
DECO-Bench: Unified Benchmark for Decoupled Task-Agnostic Synthetic Data Release
Deep Transformers with Latent Depth
Temporal-Difference Networks
RevColV2: Exploring Disentangled Representations in Masked Image Modeling
Neural Network Definition of Highly Predictable Protein Secondary Structure Classes
SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained debugging and analysis
Learning Libraries of Subroutines for Neurally-Guided Bayesian Program Induction
A Reinforcement Learning Variant for Control Scheduling
Policy gradients in linearly-solvable MDPs
Stability and Generalization of Kernel Clustering: from Single Kernel to Multiple Kernel
Improved Imaging by Invex Regularizers with Global Optima Guarantees
Learning Complete Protein Representation by Dynamically Coupling of Sequence and Structure
Stimulus Evoked Independent Factor Analysis of MEG Data with Large Background Activity
Active-Passive SimStereo - Benchmarking the Cross-Generalization Capabilities of Deep Learning-based Stereo Methods
Cormorant: Covariant Molecular Neural Networks
Adversarial Self-Training Improves Robustness and Generalization for Gradual Domain Adaptation
Automatically Learning Compact Quality-aware Surrogates for Optimization Problems
Parametric Embedding for Class Visualization
CLUES: Collaborative Private-domain High-quality Data Selection for LLMs via Training Dynamics
Learning Action and Reasoning-Centric Image Editing from Videos and Simulation
Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data
On the Complexity of Teaching a Family of Linear Behavior Cloning Learners
Learning Nonlinear Overcomplete Representations for Efficient Coding
Learning to Parse Images
LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model
Object Localization based on Structural SVM using Privileged Information
Unsupervised Image-to-Image Translation Networks
Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform
ComGAN: Unsupervised Disentanglement and Segmentation via Image Composition
Compact Proofs of Model Performance via Mechanistic Interpretability
Associative Decorrelation Dynamics: A Theory of Self-Organization and Optimization in Feedback Networks
Sub-optimality of the Naive Mean Field approximation for proportional high-dimensional Linear Regression
Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features
Learning Generalizable Part-based Feature Representation for 3D Point Clouds
Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition
Learning to Solve SMT Formulas
CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing
On the Theory of Reinforcement Learning with Once-per-Episode Feedback
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques
DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA
Fetch and Forge: Efficient Dataset Condensation for Object Detection
Twice regularized MDPs and the equivalence between robustness and regularization
Convergent Policy Optimization for Safe Reinforcement Learning
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy
Multiscale Dictionary Learning for Estimating Conditional Distributions
A Simple and Optimal Approach for Universal Online Learning with Gradient Variations
SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm
On the Parameter Identifiability of Partially Observed Linear Causal Models
HGDL: Heterogeneous Graph Label Distribution Learning
EGonc : Energy-based Open-Set Node Classification with substitute Unknowns
Surprise Minimizing Multi-Agent Learning with Energy-based Models
"Why Not Other Classes?": Towards Class-Contrastive Back-Propagation Explanations
UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation
Restoring Negative Information in Few-Shot Object Detection
Polynomially Over-Parameterized Convolutional Neural Networks Contain Structured Strong Winning Lottery Tickets
Random Path Selection for Continual Learning
Computing a human-like reaction time metric from stable recurrent vision models
NovoBench: Benchmarking Deep Learning-based \emph{De Novo} Sequencing Methods in Proteomics
Graph Matching with Hierarchical Discrete Relaxation
ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text Translation
Sketched Lanczos uncertainty score: a low-memory summary of the Fisher information
Random Noise Defense Against Query-Based Black-Box Attacks
Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation
WildfireDB: An Open-Source Dataset Connecting Wildfire Occurrence with Relevant Determinants
Forster Decomposition and Learning Halfspaces with Noise
Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs
Cal-DETR: Calibrated Detection Transformer
Dynamic Submodular Maximization
Fully Unconstrained Online Learning
Non-conjugate Variational Message Passing for Multinomial and Binary Regression
Performance Comparisons Between Backpropagation Networks and Classification Trees on Three Real-World Applications
Disrupting Deep Uncertainty Estimation Without Harming Accuracy
Dynamic influence maximization
Self-Guided Masked Autoencoder
Comprehensive Knowledge Distillation with Causal Intervention
Para-CFlows: $C^k$-universal diffeomorphism approximators as superior neural surrogates
Towards Global Optimal Visual In-Context Learning Prompt Selection
Advances in Learning Bayesian Networks of Bounded Treewidth
Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data
Identity testing for Mallows model
Rethinking Resolution in the Context of Efficient Video Recognition
Efficient Convex Relaxations for Streaming PCA
MOTE-NAS: Multi-Objective Training-based Estimate for Efficient Neural Architecture Search
TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery
An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks
Shared Autonomy with IDA: Interventional Diffusion Assistance
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration
In-N-Out: Lifting 2D Diffusion Prior for 3D Object Removal via Tuning-Free Latents Alignment
Approximately Efficient Online Mechanism Design
Batched Thompson Sampling
Relevance Topic Model for Unstructured Social Group Activity Recognition
TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models
Multi-marginal Wasserstein GAN
A 3D Generative Model for Structure-Based Drug Design
T2VSafetyBench: Evaluating the Safety of Text-to-Video Generative Models
Cross-lingual Language Model Pretraining
A Riemannian Exponential Augmented Lagrangian Method for Computing the Projection Robust Wasserstein Distance
Exponential Concentration of a Density Functional Estimator
Adversarial Counterfactual Learning and Evaluation for Recommender System
A Neural Network to Detect Homologies in Proteins
Bootstrapping the Error of Oja's Algorithm
Statistical-Computational Tradeoff in Single Index Models
A Structured Prediction Approach for Label Ranking
An Alternative Model for Mixtures of Experts
IDEA: An Invariant Perspective for Efficient Domain Adaptive Image Retrieval
Object Detection with Grammar Models
Dynamic Network Model from Partial Observations
A Solvable High-Dimensional Model of GAN
Latent Hierarchical Causal Structure Discovery with Rank Constraints
Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models
Scalable DP-SGD: Shuffling vs. Poisson Subsampling
Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training
Generalizing Analytic Shrinkage for Arbitrary Covariance Structures
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate
Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb
TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation
LibAMM: Empirical Insights into Approximate Computing for Accelerating Matrix Multiplication
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting
On UMAP's True Loss Function
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
Filtering Variational Objectives
The Robustness-Performance Tradeoff in Markov Decision Processes
BIRD: Generalizable Backdoor Detection and Removal for Deep Reinforcement Learning
Enhancing Adversarial Robustness via Score-Based Optimization
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems
Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL
Estimation of Skill Distribution from a Tournament
Adaptive Active Hypothesis Testing under Limited Information
Convergent Combinations of Reinforcement Learning with Linear Function Approximation
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution
SPO: Sequential Monte Carlo Policy Optimisation
High-dimensional Asymptotics of Denoising Autoencoders
Promoting Fairness Among Dynamic Agents in Online-Matching Markets under Known Stationary Arrival Distributions
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features
Understanding spiking networks through convex optimization
Estimating Riemannian Metric with Noise-Contaminated Intrinsic Distance
Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
Unsupervised Image-to-Image Translation with Density Changing Regularization
Reservoir Computing meets Recurrent Kernels and Structured Transforms
Spherical Motion Dynamics: Learning Dynamics of Normalized Neural Network using SGD and Weight Decay
ESPACE: Dimensionality Reduction of Activations for Model Compression
A Geometric Perspective on Variational Autoencoders
Regularized Gradient Boosting
UPS: Unified Projection Sharing for Lightweight Single-Image Super-resolution and Beyond
Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
Influence Maximization with ε-Almost Submodular Threshold Functions
ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation
Learning to Learn with Compound HD Models
Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations
Benchmarking Feed-Forward Neural Networks: Models and Measures
Generalized Hopfield Networks and Nonlinear Optimization
Noisy Label Learning with Instance-Dependent Outliers: Identifiability via Crowd Wisdom
Certified Adversarial Robustness with Additive Noise
Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs
MonkeySee: Space-time-resolved reconstructions of natural images from macaque multi-unit activity
PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression
Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems
Fast Encoder-Based 3D from Casual Videos via Point Track Processing
DisARM: An Antithetic Gradient Estimator for Binary Latent Variables
Theoretical evidence for adversarial robustness through randomization
A Multi-class Linear Learning Algorithm Related to Winnow
Learning to Play With Intrinsically-Motivated, Self-Aware Agents
Finding the Key to a Synapse
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
Learning the Expected Core of Strictly Convex Stochastic Cooperative Games
Learning Fine Motion by Markov Mixtures of Experts
Graph Neural Networks Do Not Always Oversmooth
Inferring Brain Networks through Graphical Models with Hidden Variables
Representation Learning for Treatment Effect Estimation from Observational Data
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods
HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection
Learning Multiple Models via Regularized Weighting
The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data Only
On Single-Index Models beyond Gaussian Data
Deep Support Vectors
Learning in Silicon: Timing is Everything
Aggregate-and-Adapt Natural Language Prompts for Downstream Generalization of CLIP
A Quantitative Geometric Approach to Neural-Network Smoothness
Mixing Properties of Conditional Markov Chains with Unbounded Feature Functions
Riemannian batch normalization for SPD neural networks
Generalizable Multi-linear Attention Network
Lower Bounds on Rate of Convergence of Cutting Plane Methods
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
Time-uniform confidence bands for the CDF under nonstationarity
Merging Models with Fisher-Weighted Averaging
Strategic Littlestone Dimension: Improved Bounds on Online Strategic Classification
Accumulator Networks: Suitors of Local Probability Propagation
Multi-Grid Methods for Reinforcement Learning in Controlled Diffusion Processes
Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning
Online Agnostic Boosting via Regret Minimization
Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze-like Environments
Deep Dynamical Modeling and Control of Unsteady Fluid Flows
Testing Determinantal Point Processes
A General Protocol to Probe Large Vision Models for 3D Physical Understanding
Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout
Computational Efficiency: A Common Organizing Principle for Parallel Computer Maps and Brain Maps?
Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN)
When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning
Pseudo-Riemannian Graph Convolutional Networks
Accelerated Training via Incrementally Growing Neural Networks using Variance Transfer and Learning Rate Adaptation
A General Boosting Method and its Application to Learning Ranking Functions for Web Search
FouriDown: Factoring Down-Sampling into Shuffling and Superposing
Max-Sliced Mutual Information
An Inverse Scaling Law for CLIP Training
Towards Understanding How Transformers Learn In-context Through a Representation Learning Lens
Stochastic Spectral and Conjugate Descent Methods
Kernel Methods for Deep Learning
Visual Development Aids the Acquisition of Motion Velocity Sensitivities
Synergy and Redundancy among Brain Cells of Behaving Monkeys
Structured Convolutions for Efficient Neural Network Design
Private Graph All-Pairwise-Shortest-Path Distance Release with Improved Error Rate
Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales?
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)
Model-Based Control with Sparse Neural Dynamics
A Benchmark Suite for Evaluating Neural Mutual Information Estimators on Unstructured Datasets
AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment
Replicable Clustering
Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum Games
A Productive, Systematic Framework for the Representation of Visual Structure
The Space Complexity of Approximating Logistic Loss
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
Interpretable Generalized Additive Models for Datasets with Missing Values
An Entropic Estimator for Structure Discovery
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions
Fast Training Method for Stochastic Compositional Optimization Problems
Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models
AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties
VICRegL: Self-Supervised Learning of Local Visual Features
CoMIR: Contrastive Multimodal Image Representation for Registration
Digital Boltzmann VLSI for Constraint Satisfaction and Learning
FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training
A Multilabel Classification Framework for Approximate Nearest Neighbor Search