yusuke1997/dblp-id-title huggingface dataset:
Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation
FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations
Near-Optimal k-Clustering in the Sliding Window Model
IntraMix: Intra-Class Mixup Generation for Accurate Labels and Neighbors
Students Parrot Their Teachers: Membership Inference on Model Distillation
Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield
Precise expressions for random projections: Low-rank approximation and randomized Newton
Learning shape correspondence with anisotropic convolutional neural networks
MCBoost: Multiple Classifier Boosting for Perceptual Co-clustering of Images and Visual Features
Approximate Allocation Matching for Structural Causal Bandits with Unobserved Confounders
Practical Differentially Private Hyperparameter Tuning with Subsampling
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?
Estimating Noise Correlations Across Continuous Conditions With Wishart Processes
Network-to-Network Translation with Conditional Invertible Neural Networks
Localizing Bugs in Program Executions with Graphical Models
FACMAC: Factored Multi-Agent Centralised Policy Gradients
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians
Finite Sample Analysis of Average-Reward TD Learning and $Q$-Learning
PDSketch: Integrated Domain Programming, Learning, and Planning
Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems
Universal guarantees for decision tree induction via a higher-order splitting criterion
Learning Generalizable Agents via Saliency-guided Features Decorrelation
Compression-aware Training of Deep Networks
Top Rank Optimization in Linear Time
Rethinking Exploration in Reinforcement Learning with Effective Metric-Based Exploration Bonus
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions
Disentangling factors of variation in deep representation using adversarial training
Efficient Nonlinear Control with Actor-Tutor Architecture
Active Portfolio-Management based on Error Correction Neural Networks
Learning multiple visual domains with residual adapters
Instructor-inspired Machine Learning for Robust Molecular Property Prediction
Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation
Interpolation and Regularization for Causal Learning
A Neural Expert System with Automated Extraction of Fuzzy If-Then Rules
Spike Feature Extraction Using Informative Samples
HDR-GS: Efficient High Dynamic Range Novel View Synthesis at 1000x Speed via Gaussian Splatting
VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting
Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates
Performance of a Stochastic Learning Microchip
Discovered Policy Optimisation
Hierarchical Dirichlet Processes with Random Effects
Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding
Supervised Contrastive Learning
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise
Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic
Online multiclass boosting
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization
Towards a Theoretical Framework of Out-of-Distribution Generalization
Semi-Supervised Cell Instance Segmentation for Multi-Modality Microscope Images
Online false discovery rate control for anomaly detection in time series
Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation
ETAB: A Benchmark Suite for Visual Representation Learning in Echocardiography
A Sober Look at the Robustness of CLIPs to Spurious Features
Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps
An Alternating Optimization Method for Bilevel Problems under the Polyak-Łojasiewicz Condition
Implicit Generation and Modeling with Energy Based Models
Tree-structured Gaussian Process Approximations
TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models
Learning Plannable Representations with Causal InfoGAN
Physics-Informed Bayesian Optimization of Variational Quantum Circuits
Characterization of Overfitting in Robust Multiclass Classification
LuminAIRe: Illumination-Aware Conditional Image Repainting for Lighting-Realistic Generation
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity
SMEM Algorithm for Mixture Models
Worst-case bounds on the quality of max-product fixed-points
Convergence analysis of ODE models for accelerated first-order methods via positive semidefinite kernels
Empowering Convolutional Neural Nets with MetaSin Activation
A Universal Law of Robustness via Isoperimetry
Sequential Bayesian Kernel Regression
An Adaptive Network That Learns Sequences of Transitions
Probabilistic Logic Neural Networks for Reasoning
Planning with General Objective Functions: Going Beyond Total Rewards
Attractor Neural Networks with Local Inhibition: From Statistical Physics to a Digitial Programmable Integrated Circuit
Multi-criteria Anomaly Detection using Pareto Depth Analysis
Large language model validity via enhanced conformal prediction methods
Neural Implementation of Bayesian Inference in Population Codes
Generalizing CNNs to graphs with learnable neighborhood quantization
Exploiting Generative Models in Discriminative Classifiers
CP-SLAM: Collaborative Neural Point-based SLAM System
Comparing the prediction accuracy of artifical neural networks and other statistical models for breast cancer survival
The Infinite Partially Observable Markov Decision Process
Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction
Are Self-Attentions Effective for Time Series Forecasting?
MeMo: Meaningful, Modular Controllers via Noise Injection
Harmonising Chorales by Probabilistic Inference
Graph Policy Network for Transferable Active Learning on Graphs
DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification
Robust Clustering as Ensembles of Affinity Relations
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation
ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Models
Semi-supervised Regression via Parallel Field Regularization
Implicit Transfer Operator Learning: Multiple Time-Resolution Models for Molecular Dynamics
A Heavy-Tailed Algebra for Probabilistic Programming
Variational Inference for the Nested Chinese Restaurant Process
Natural Policy Gradient Methods with Parameter-based Exploration for Control Tasks
Learning Near-Pareto-Optimal Conventions in Polynomial Time
Reservoir Boosting : Between Online and Offline Ensemble Learning
Offline Reinforcement Learning with Reverse Model-based Imagination
Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task
Constrained Cross-Entropy Method for Safe Reinforcement Learning
Bumptrees for Efficient Function, Constraint and Classification Learning
Unsmearing Visual Motion: Development of Long-Range Horizontal Intrinsic Connections
Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence
OpenXAI: Towards a Transparent Evaluation of Model Explanations
Aligning Silhouette Topology for Self-Adaptive 3D Human Pose Recovery
Data-driven Optimal Filtering for Linear Systems with Unknown Noise Covariances
Bayesian Attention Modules
Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data
Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering
Learning invariant representations and applications to face verification
Meek Separators and Their Applications in Targeted Causal Discovery
Unified Covariate Adjustment for Causal Inference
Practical Large-Scale Optimization for Max-norm Regularization
Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors
Maximizing utility in multi-agent environments by anticipating the behavior of other learners
Evaluating multi-class learning strategies in a generative hierarchical framework for object detection
ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction
Experimental design for MRI by greedy policy search
Inverse Design for Fluid-Structure Interactions using Graph Network Simulators
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning
Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning
Continual Deep Learning by Functional Regularisation of Memorable Past
On Locality of Local Explanation Models
Learning Equilibria in Adversarial Team Markov Games: A Nonconvex-Hidden-Concave Min-Max Optimization Problem
Improved off-policy training of diffusion samplers
Rapid Visual Processing using Spike Asynchrony
Implicit Regularization in Matrix Factorization
OrganITE: Optimal transplant donor organ offering using an individual treatment effect
Information-theoretic Task Selection for Meta-Reinforcement Learning
SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integration
Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality
Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification
Penalty Decomposition Methods for Rank Minimization
CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset
Efficient Mirror Descent Ascent Methods for Nonsmooth Minimax Problems
Efficient Potential-based Exploration in Reinforcement Learning using Inverse Dynamic Bisimulation Metric
Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective
Private Attribute Inference from Images with Vision-Language Models
Model Uncertainty in Classical Conditioning
On Properties of Networks of Neuron-Like Elements
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
DreamShard: Generalizable Embedding Table Placement for Recommender Systems
GraphQNTK: Quantum Neural Tangent Kernel for Graph Data
Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL
Confident Approximate Policy Iteration for Efficient Local Planning in $q^\pi$-realizable MDPs
Optimizing Energy Production Using Policy Search and Predictive State Representations
Detecting Hands and Recognizing Physical Contact in the Wild
Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Games
Data-Driven Discovery of Dynamical Systems in Pharmacology using Large Language Models
Construction of Dependent Dirichlet Processes based on Poisson Processes
StressID: a Multimodal Dataset for Stress Identification
Multi-Task Feature Learning
Provable Representation Learning for Imitation with Contrastive Fourier Features
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
Decision Heuristics for Comparison: How Good Are They?
Margin-Based Generalization Lower Bounds for Boosted Classifiers
SubgDiff: A Subgraph Diffusion Model to Improve Molecular Representation Learning
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
Rethinking Kernel Methods for Node Representation Learning on Graphs
Learning to Follow Instructions in Text-Based Games
Toward a Characterization of Loss Functions for Distribution Learning
Behavior Alignment via Reward Function Optimization
Unsupervised Classification with Non-Gaussian Mixture Models Using ICA
A New View of Automatic Relevance Determination
(FL) 2 : Overcoming Few Labels in Federated Semi-Supervised Learning
Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random
Object-Oriented Dynamics Predictor
Theory-Based Causal Inference
FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning
Neural Temporal Point Processes For Modelling Electronic Health Records
Physics-Informed Variational State-Space Gaussian Processes
How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers
Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model
Anytime Induction of Cost-sensitive Trees
Data Analysis Using G/Splines
Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes
Convex Two-Layer Modeling
Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization
A Unified, Scalable Framework for Neural Population Decoding
Independent Component Analysis of Intracellular Calcium Spike Data
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning
Provable Posterior Sampling with Denoising Oracles via Tilted Transport
New Algorithms for Efficient High Dimensional Non-parametric Classification
Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking
(RF)^2 - Random Forest Random Field
Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning
Transfer Learning via Minimizing the Performance Gap Between Domains
Many-shot Jailbreaking
Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks
Multiclass Boosting and the Cost of Weak Learning
Differentially Private Multi-Armed Bandits in the Shuffle Model
Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting
Do Transformers Really Perform Badly for Graph Representation?
Theory of localized synfire chain: characteristic propagation speed of stable spike pattern
The Cambridge Law Corpus: A Corpus for Legal AI Research
A Label is Worth A Thousand Images in Dataset Distillation
Sm: enhanced localization in Multiple Instance Learning for medical imaging classification
Reconstructing MEG Sources with Unknown Correlations
Leveraged Vector Machines
CBP: backpropagation with constraint on weight precision using a pseudo-Lagrange multiplier method
Adversarial Attacks on Deep Graph Matching
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders
Hyperbolic Embeddings of Supervised Models
Add and Thin: Diffusion for Temporal Point Processes
Bayesian Adaptive Calibration and Optimal Design
Occupancy-based Policy Gradient: Estimation, Convergence, and Optimality
Revisit last-iterate convergence of mSGD under milder requirement on step size
Knowledge Circuits in Pretrained Transformers
Learning in Computer Vision and Image Understanding
Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure
A Communication Efficient Stochastic Multi-Block Alternating Direction Method of Multipliers
Toward a Stable, Fair, and Comprehensive Evaluation of Object Hallucination in Large Vision-Language Models
3-in-1: 2D Rotary Adaptation for Efficient Finetuning, Efficient Batching and Composability
Mixtures of Controllers for Jump Linear and Non-Linear Plants
Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent
Practical Sharpness-Aware Minimization Cannot Converge All the Way to Optima
Squared Neural Families: A New Class of Tractable Density Models
Instance-Dependent Bounds for Zeroth-order Lipschitz Optimization with Error Certificates
QUEEN: QUantized Efficient ENcoding of Dynamic Gaussians for Streaming Free-viewpoint Videos
Calibrating Deep Neural Networks using Focal Loss
Newton-Cotes Graph Neural Networks: On the Time Evolution of Dynamic Systems
Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport
Unsupervised Behavior Extraction via Random Intent Priors
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions
Estimating the Hallucination Rate of Generative AI
Streaming Linear System Identification with Reverse Experience Replay
The Forgetron: A Kernel-Based Perceptron on a Fixed Budget
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs
Differentiable Modal Synthesis for Physical Modeling of Planar String Sound and Motion Simulation
Structure learning of antiferromagnetic Ising models
TL-Lite: Temporal Visualization and Learning for Clinical Forecasting
FERERO: A Flexible Framework for Preference-Guided Multi-Objective Learning
DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning
Likelihood-Based Diffusion Language Models
Conditioning non-linear and infinite-dimensional diffusion processes
Leveraging the Exact Likelihood of Deep Latent Variable Models
Blessing of Depth in Linear Regression: Deeper Models Have Flatter Landscape Around the True Solution
Query-Based Adversarial Prompt Generation
Beyond Normal: On the Evaluation of Mutual Information Estimators
FUG: Feature-Universal Graph Contrastive Pre-training for Graphs with Diverse Node Features
On the Identifiability of Poisson Branching Structural Causal Model Using Probability Generating Function
Diagnostic Classifiers Revealing how Neural Networks Process Hierarchical Structure
Antipodes of Label Differential Privacy: PATE and ALIBI
Improving Calibration through the Relationship with Adversarial Robustness
FIFO-Diffusion: Generating Infinite Videos from Text without Training
Causality-driven Hierarchical Structure Discovery for Reinforcement Learning
CAT-Walk: Inductive Hypergraph Learning via Set Walks
LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning
Average Sensitivity of Euclidean k-Clustering
Constrained Independent Component Analysis
Reinforcement learning for optimization of variational quantum circuit architectures
An Uncertainty Principle is a Price of Privacy-Preserving Microdata
Deep Equilibrium Based Neural Operators for Steady-State PDEs
MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views
Learning with Symmetric Label Noise: The Importance of Being Unhinged
Benchmarking and Analyzing 3D Human Pose and Shape Estimation Beyond Algorithms
Variational Inference with Tail-adaptive f-Divergence
Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise
TOA: Task-oriented Active VQA
DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models
Regularized Winnow Methods
Learning Wake-Sleep Recurrent Attention Models
Pre-Trained Language Models for Interactive Decision-Making
Dynamic Conditional Optimal Transport through Simulation-Free Flows
Differentiable Quantum Computing for Large-scale Linear Control
Compression with Flows via Local Bits-Back Coding
A Holistic Approach to Compositional Semantics: A Connectionist Model and Robot Experiments
Reward-rational (implicit) choice: A unifying formalism for reward learning
MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy
Are AlphaZero-like Agents Robust to Adversarial Perturbations?
When Does Differentially Private Learning Not Suffer in High Dimensions?
Generalizable Person Re-identification via Balancing Alignment and Uniformity
Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving
Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations
A Theoretical Analysis of Robust Coding over Noisy Overcomplete Channels
Truly Deterministic Policy Optimization
Arbitrarily Scalable Environment Generators via Neural Cellular Automata
A solvable connectionist model of immediate recall of ordered lists
Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs
Variance-Reduced Stochastic Gradient Descent on Streaming Data
Proximal Mapping for Deep Regularization
Large-Scale Price Optimization via Network Flow
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty
Learning about an exponential amount of conditional distributions
JPMAX: Learning to Recognize Moving Objects as a Model-fitting Problem
Effective Meta-Regularization by Kernelized Proximal Regularization
Hierarchical Mixtures of Experts Methodology Applied to Continuous Speech Recognition
Finding the Homology of Decision Boundaries with Active Learning
Extracting Relevant Structures with Side Information
Simple Spin Models for the Development of Ocular Dominance Columns and Iso-Orientation Patches
Speech Recognition Using Connectionist Approaches
Riemannian Neural SDE: Learning Stochastic Representations on Manifolds
FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection
Uniform Convergence with Square-Root Lipschitz Loss
Algebraic Analysis for Non-regular Learning Machines
Computationally and statistically efficient learning of causal Bayes nets using path queries
Lower Bounds on Adversarial Robustness from Optimal Transport
Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
Policy Search for Motor Primitives in Robotics
ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design
Harnessing small projectors and multiple views for efficient vision pretraining
Stochastic Gradient Geodesic MCMC Methods
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM
Demixing odors - fast inference in olfaction
DESPOT: Online POMDP Planning with Regularization
Representing Spatial Trajectories as Distributions
RClicks: Realistic Click Simulation for Benchmarking Interactive Segmentation
Explaining Away in Weight Space
Recognizing Hand-written Digits Using Hierarchical Products of Experts
Visual Concepts Tokenization
Communication-efficient Distributed SGD with Sketching
Latent Learning Progress Drives Autonomous Goal Selection in Human Reinforcement Learning
Connectionist Learning of Expert Preferences by Comparison Training
Learning to parse images of articulated bodies
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
Exploring Generalization in Deep Learning
Learning to Predict Trustworthiness with Steep Slope Loss
4+3 Phases of Compute-Optimal Neural Scaling Laws
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training
Efficient Training of Energy-Based Models Using Jarzynski Equality
On Hardware-Aware Probabilistic Frameworks for Resource Constrained Embedded Applications
A Conditional Randomization Test for Sparse Logistic Regression in High-Dimension
Learning to Influence Human Behavior with Offline Reinforcement Learning
End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking
UniAR: A Unified model for predicting human Attention and Responses on visual content
Neural Control of Sensory Acquisition: The Vestibulo-Ocular Reflex
Learning Segmentation from Point Trajectories
SF-V: Single Forward Video Generation Model
Bandit Learning with Positive Externalities
RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency
OPUS: Occupancy Prediction Using a Sparse Set
Zero-shot Generalizable Incremental Learning for Vision-Language Object Detection
An Electronic Photoreceptor Sensitive to Small Changes in Intensity
A Unified Framework for Alternating Offline Model Training and Policy Learning
Competitive Gradient Descent
Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions
A message-passing algorithm for multi-agent trajectory planning
PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning
Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods
Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation
Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound
Natural Image Denoising with Convolutional Networks
Blind Super-Resolution Kernel Estimation using an Internal-GAN
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification
Cross-channel Communication Networks
Simplifying Latent Dynamics with Softly State-Invariant World Models
Statistical Mechanics of Learning in a Large Committee Machine
Leveraging Inter-Layer Dependency for Post -Training Quantization