yusuke1997/dblp-id-title huggingface dataset:
Pre-training Auto-regressive Robotic Models with 4D Representations
Online Cascade Learning for Efficient Inference over Streams
2D-Shapley: A Framework for Fragmented Data Valuation
Differentiable Distributionally Robust Optimization Layers
Efficient Differentiable Simulation of Articulated Bodies
A-PSRO: A Unified Strategy Learning Method with Advantage Metric for Normal-form Games
Automating Path Analysis for Building Causal Models from Data
Relational temporal difference learning
Inverse Optimal Control with Linearly-Solvable MDPs
Bridging Data Gaps in Diffusion Models with Adversarial Noise-Based Transfer Learning
Bayesian inference for Plackett-Luce ranking models
PAC Prediction Sets for Large Language Models of Code
Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning
StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models
Decentralized Convex Finite-Sum Optimization with Better Dependence on Condition Numbers
Second Tier for Decision Trees
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization
Identifying and Understanding Cross-Class Features in Adversarial Training
Sliced Wasserstein Kernel for Persistence Diagrams
Escaping saddle points in zeroth-order optimization: the power of two-point estimators
Large Language Model-driven Large Neighborhood Search for Large-Scale MILP Problems
Why is Posterior Sampling Better than Optimism for Reinforcement Learning?
Active sampling for detecting irrelevant features
An Interpretable Evaluation of Entropy-based Novelty of Generative Models
Structured World Belief for Reinforcement Learning in POMDP
Functional Subspace Clustering with Application to Time Series
Revisiting Chain-of-Thought in Code Generation: Do Language Models Need to Learn Reasoning before Coding?
Estimating Canopy Height at Scale
Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision
Irrelevant Features and the Subset Selection Problem
GRAND: Graph Neural Diffusion
Representation Topology Divergence: A Method for Comparing Neural Network Representations
Robust Stable Spiking Neural Networks
FairProof : Confidential and Certifiable Fairness for Neural Networks
Holistic Physics Solver: Learning PDEs in a Unified Spectral-Physical Space
Compressing Tabular Data via Latent Variable Estimation
Exploiting Negative Samples: A Catalyst for Cohort Discovery in Healthcare Analytics
Adaptive Exploration for Multi-Reward Multi-Policy Evaluation
Time Limits in Reinforcement Learning
Automatic Extrapolation of Missing Road Network Data in OpenStreetMap
Differential Geometric Regularization for Supervised Learning of Classifiers
Meta-Thompson Sampling
The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning
Unsupervised Label Noise Modeling and Loss Correction
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
R2-T2: Re-Routing in Test-Time for Multimodal Mixture-of-Experts
EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data
Position: Will we run out of data? Limits of LLM scaling based on human-generated data
The Implicit Regularization of Stochastic Gradient Flow for Least Squares
Fairwashing: the risk of rationalization
Consensus Multiplicative Weights Update: Learning to Learn using Projector-based Game Signatures
Inferring Change Points in High-Dimensional Linear Regression via Approximate Message Passing
Incentivized Bandit Learning with Self-Reinforcing User Preferences
Gradient-based Visual Explanation for Transformer-based CLIP
Evaluating and Analyzing Relationship Hallucinations in Large Vision-Language Models
PaLM-E: An Embodied Multimodal Language Model
Binary Classification with Karmic, Threshold-Quasi-Concave Metrics
Simultaneous Graph Signal Clustering and Graph Learning
Differentially Private Quantiles
A Unifying View of Representer Theorems
Beyond the Edge of Stability via Two-step Gradient Updates
Attention is not all you need: pure attention loses rank doubly exponentially with depth
StrWAEs to Invariant Representations
Non-asymptotic Error Bounds in W2-Distance with Sqrt(d) Dimension Dependence and First Order Convergence for Langevin Monte Carlo beyond Log-Concavity
SEAD: Unsupervised Ensemble of Streaming Anomaly Detectors
Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity
Efficient Learning of Selective Bayesian Network Classifiers
Geometric Latent Diffusion Models for 3D Molecule Generation
Convex Multitask Learning with Flexible Task Clusters
Exact Soft Analytical Side-Channel Attacks using Tractable Circuits
A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization
LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in Multimodal Large Language Models
Learning Antidote Data to Individual Unfairness
The Hidden Joules: Evaluating the Energy Consumption of Vision Backbones for Progress Towards More Efficient Model Inference
Online Nonstochastic Control with Adversarial and Static Constraints
TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision
High-Dimensional Covariance Decomposition into Sparse Markov and Independence Domains
AdaNet: Adaptive Structural Learning of Artificial Neural Networks
Level-Set Methods for Finite-Sum Constrained Convex Optimization
ReLU Network with Width d+O(1) Can Achieve Optimal Approximation Rate
Reconstructing Cell Lineage Trees from Phenotypic Features with Metric Learning
When and How Does CLIP Enable Domain and Compositional Generalization?
Curvature-corrected learning dynamics in deep neural networks
Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation
Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment
Neural Representational Consistency Emerges from Probabilistic Neural-Behavioral Representation Alignment
Learning random walks to rank nodes in graphs
Soft Prompt Recovers Compressed LLMs, Transferably
Semi-supervised Clustering by Seeding
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses
Semi-Supervised Learning with Normalizing Flows
Tree Edit Distance Learning via Adaptive Symbol Embeddings
Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning
ProofAug: Efficient Neural Theorem Proving via Fine-grained Proof Structure Analysis
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows
Efficient Time Series Processing for Transformers and State-Space Models through Token Merging
Time Series Clustering: Complex is Simpler!
Understanding Robust Overfitting of Adversarial Training and Beyond
Additive Gaussian Processes Revisited
Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints
Why Are Learned Indexes So Effective?
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
Bayesian Inductive Logic Programming
ACPO: A Policy Optimization Algorithm for Average MDPs with Constraints
On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes
Solving large scale linear prediction problems using stochastic gradient descent algorithms
BlockDialect: Block-wise Fine-grained Mixed Format Quantization for Energy-Efficient LLM Inference
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization
Fast nearest neighbor retrieval for bregman divergences
Semantically-correlated memories in a dense associative model
Invariant Risk Minimization Is A Total Variation Model
A Faster Iterative Scaling Algorithm for Conditional Exponential Model
Learning with Exact Invariances in Polynomial Time
Cross-Graph Learning of Multi-Relational Associations
Blink of an eye: a simple theory for feature localization in generative models
Choosing Answers in Epsilon-Best-Answer Identification for Linear Bandits
From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information?
Parameter Learning and Convergent Inference for Dense Random Fields
EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification
Hierarchical Variational Models
Diving into Self-Evolving Training for Multimodal Reasoning
Model-Free Opponent Shaping
SE(3) diffusion model with application to protein backbone generation
Compositional Fairness Constraints for Graph Embeddings
Weighted Tallying Bandits: Overcoming Intractability via Repeated Exposure Optimality
Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption
AKORN: Adaptive Knots generated Online for RegressioN splines
Robust RegBayes: Selectively Incorporating First-Order Logic Domain Knowledge into Bayesian Models
Learning User Evaluation Functions for Adaptive Scheduling Assistance
RSC: Accelerate Graph Neural Networks Training via Randomized Sparse Computations
SPDY: Accurate Pruning with Speedup Guarantees
Identify the Nash Equilibrium in Static Games with Random Payoffs
Leveraging Procedural Generation to Benchmark Reinforcement Learning
Finding Options that Minimize Planning Time
TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL
Boosting margin based distance functions for clustering
Interactively optimizing information retrieval systems as a dueling bandits problem
Active causal structure learning with advice
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Doubly Decomposing Nonparametric Tensor Regression
Learning-Augmented Hierarchical Clustering
Learning Evaluation Functions for Large Acyclic Domains
CataBEEM: Integrating Latent Interaction Categories in Node-wise Community Detection Models for Network Data
Boosting Offline Optimizers with Surrogate Sensitivity
Decentralised Learning with Random Features and Distributed Gradient Descent
Isoelastic Agents and Wealth Updates in Machine Learning Markets
Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization
A Gromov-Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
Cooperative Exploration for Multi-Agent Deep Reinforcement Learning
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound
Optimization Landscape and Expressivity of Deep CNNs
Addressing Catastrophic Forgetting in Few-Shot Problems
Coevolutionary Learning: A Case Study
Unlocking the Power of Spatial and Temporal Information in Medical Multimodal Pre-training
Efficient Structured Prediction with Latent Variables for General Graphical Models
Adaptively Learning the Crowd Kernel
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
On the Out-of-Distribution Generalization of Self-Supervised Learning
Computing Voting Rules with Improvement Feedback
Isometric Embedding and Continuum ISOMAP
Classification Trees for Information Retrieval
From Patches to Images: A Nonparametric Generative Model
Topology-aware Generalization of Decentralized SGD
Breeding Decision Trees Using Evolutionary Techniques
Data Shapley: Equitable Valuation of Data for Machine Learning
Multi-level Lasso for Sparse Multi-task Regression
Griffin: Towards a Graph-Centric Relational Database Foundation Model
Near-optimal Sketchy Natural Gradients for Physics-Informed Neural Networks
Private Query Release Assisted by Public Data
An Independence-promoting Loss for Music Generation with Language Models
HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation
BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining
Exploiting the Omission of Irrelevant Data
Understanding Doubly Stochastic Clustering
Is Combining Classifiers Better than Selecting the Best One
Detecting Overlapping and Correlated Communities without Pure Nodes: Identifiability and Algorithm
Contextual Memory Trees
FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning
Elastic Graph Neural Networks
Graph Inductive Biases in Transformers without Message Passing
Classification of Alzheimer's Disease Using Discriminant Manifolds of Hippocampus Shapes
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
Partially Observable Reinforcement Learning with Memory Traces
IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion Models
Robust and Discriminative Self-Taught Learning
Neural NeRF Compression
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms
Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels
When can in-context learning generalize out of task distribution?
Demystifying Inductive Biases for (Beta-)VAE Based Architectures
Conformal prediction for multi-dimensional time series by ellipsoidal sets
Agent Instructs Large Language Models to be General Zero-Shot Reasoners
Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features
Asynchronous Distributed ADMM for Consensus Optimization
A Domain-Independent Framework for Effective Experimentation in Planning
Decouple then Classify: A Dynamic Multi-view Labeling Strategy with Shared and Specific Information
Decoupling Value and Policy for Generalization in Reinforcement Learning
Gaussian Covariance and Scalable Variational Inference
Design Integrated Learning Systems for Engineering Design
Modelling Non-stationary and Non-separable Spatio-Temporal Changes in Neurodegeneration via Gaussian Process Convolution
Prompt-based Visual Alignment for Zero-shot Policy Transfer
Metagenomic Binning using Connectivity-constrained Variational Autoencoders
Ai-sampler: Adversarial Learning of Markov kernels with involutive maps
Bootstrap in High Dimension with Low Computation
Learning Selection Strategies in Buchberger's Algorithm
Efficient Robotic Policy Learning via Latent Space Backward Planning
On the Universality of Volume-Preserving and Coupling-Based Normalizing Flows
Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation
Semi-Supervised Collective Classification via Hybrid Label Regularization
Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network
Expected Gradients of Maxout Networks and Consequences to Parameter Initialization
Progressive Tempering Sampler with Diffusion
Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint
Epigraph projections for fast general convex programming
Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier
Leveraging Randomness in Model and Data Partitioning for Privacy Amplification
Knowledge Representation Issues in Control Knowledge Learning
Clustering by Sum of Norms: Stochastic Incremental Algorithm, Convergence and Cluster Recovery
Evaluating Model Bias Requires Characterizing its Mistakes
Optimal Transport for structured data with application on graphs
PANDAS: Improving Many-shot Jailbreaking via Positive Affirmation, Negative Demonstration, and Adaptive Sampling
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis
Position: Scaling Simulation is Neither Necessary Nor Sufficient for In-the-Wild Robot Manipulation
A Divide and Conquer Approach to Learning from Prior Knowledge
Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models
Retraining with Predicted Hard Labels Provably Increases Model Accuracy
An Incremental Learning Approach for Completable Planning
Causal Inference using Gaussian Processes with Structured Latent Confounders
Analyzing Open Data from the City of Montreal
Learning to classify with missing and corrupted features
Latent Semantic Kernels
Developing Bug-Free Machine Learning Systems With Formal Mathematics
Generalized Linear Rule Models
Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning
Moment-Based Variational Inference for Markov Jump Processes
Off-Policy Evaluation under Nonignorable Missing Data
Multiclass multiple kernel learning
Boosting Lazy Decision Trees
Langevin Monte Carlo for Contextual Bandits
LLMScan: Causal Scan for LLM Misbehavior Detection
Leveraging Demonstrations to Improve Online Learning: Quality Matters
On the Iteration Complexity of Support Recovery via Hard Thresholding Pursuit
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation
Semi-supervised learning using label mean
Implicit Bias of the Step Size in Linear Diagonal Neural Networks
Training data-efficient image transformers & distillation through attention
An Algorithmic Framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-Gradient Method
Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions
AbODE: Ab initio antibody design using conjoined ODEs
Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation
A New PHO-rmula for Improved Performance of Semi-Structured Networks
A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1
Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling
Learning to Generate Fast Signal Processing Implementations
Lexico: Extreme KV Cache Compression via Sparse Coding over Universal Dictionaries
Interpreting Robust Optimization via Adversarial Influence Functions
"Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC
Bayesian Optimization of Composite Functions
Tensor-Var: Efficient Four-Dimensional Variational Data Assimilation
Inverse Active Sensing: Modeling and Understanding Timely Decision-Making
RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning
Wrapped Gaussian on the manifold of Symmetric Positive Definite Matrices
Auto-Regressive Next-Token Predictors are Universal Learners
Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks
A Unified Comparative Study with Generalized Conformity Scores for Multi-Output Conformal Regression
WMAdapter: Adding WaterMark Control to Latent Diffusion Models
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification
Tight Regret Bounds for Bayesian Optimization in One Dimension
Graph Diffusion for Robust Multi-Agent Coordination
Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning
Atomic Spatial Processes
Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design
Targeted Unlearning with Single Layer Unlearning Gradient
Efficient Length-Generalizable Attention via Causal Retrieval for Long-Context Language Modeling
From Hypergraph Energy Functions to Hypergraph Neural Networks
Efficient Robust Conformal Prediction via Lipschitz-Bounded Networks
Multiple Non-Redundant Spectral Clustering Views
Computational Doob h-transforms for Online Filtering of Discretely Observed Diffusions
Automatic shaping and decomposition of reward functions
Dropout as a Structured Shrinkage Prior
Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs
What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models
Dataset Dynamics via Gradient Flows in Probability Space
Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning
Path-Level Network Transformation for Efficient Architecture Search
Training Second-Order Recurrent Neural Networks using Hints
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
Understanding and correcting pathologies in the training of learned optimizers
The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention
A Unified Framework for Outlier-Robust PCA-like Algorithms
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems
Nesterov Method for Asynchronous Pipeline Parallel Optimization
Kandinsky Conformal Prediction: Beyond Class- and Covariate-Conditional Coverage
Scaling Laws for Differentially Private Language Models
Diffusion Sampling Correction via Approximately 10 Parameters
Knowledge Graphs Can be Learned with Just Intersection Features
Preserving AUC Fairness in Learning with Noisy Protected Groups
BiMark: Unbiased Multilayer Watermarking for Large Language Models
Learning DNF Via Probabilistic Evidence Combination
Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data
Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization
Robustly Learning a Single Neuron via Sharpness
Quantum Algorithms and Lower Bounds for Finite-Sum Optimization
Homomorphic Sensing: Sparsity and Noise
Performance Bounds for Active Binary Testing with Information Maximization
Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models
Achieving Fairness at No Utility Cost via Data Reweighing with Influence
Accelerated dual decomposition for MAP inference
Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing
A Topic Model for Melodic Sequences
Novel Spectral Algorithms for the Partial Credit Model
On Nesting Monte Carlo Estimators
Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets
Zero-shot AutoML with Pretrained Models
Geometric Resampling in Nearly Linear Time for Follow-the-Perturbed-Leader with Best-of-Both-Worlds Guarantee in Bandit Problems
Robustly Learning Single-Index Models via Alignment Sharpness
A Free-Energy Principle for Representation Learning
HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training
Intractability and clustering with constraints
The Boomerang Sampler
Reduced Space and Faster Convergence in Imperfect-Information Games via Pruning
Vector Grimoire: Codebook-based Shape Generation under Raster Image Supervision
BoA: Attention-aware Post-training Quantization without Backpropagation
Improving Out-of-Distribution Robustness via Selective Augmentation
Beyond Entropy: Region Confidence Proxy for Wild Test-Time Adaptation
Stresscapes: Validating Linkages between Place and Stress Expression on Social Media
Bounding and Counting Linear Regions of Deep Neural Networks
Stochastic Smoothed Primal-Dual Algorithms for Nonconvex Optimization with Linear Inequality Constraints
Scalable Learning in Reproducing Kernel Krein Spaces
Understanding the Learning Dynamics of Alignment with Human Feedback
A Regret Minimization Approach to Iterative Learning Control
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
Subset-Based Instance Optimality in Private Estimation
Grid-Functioned Neural Networks
Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA
Reasoning Over Virtual Knowledge Bases With Open Predicate Relations
Constrained fractional set programs and their application in local clustering and community detection
SkipGPT: Each Token is One of a Kind
MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis
Fast nonparametric clustering with Gaussian blurring mean-shift
Operation-Aware Soft Channel Pruning using Differentiable Masks
Boosting Deep Neural Network Efficiency with Dual-Module Inference
SDCA without Duality, Regularization, and Individual Convexity
Direct Discriminative Optimization: Your Likelihood-Based Visual Generative Model is Secretly a GAN Discriminator
Do Outliers Ruin Collaboration?
Two Heads are Actually Better than One: Towards Better Adversarial Robustness via Transduction and Rejection
Accelerating Greedy Coordinate Descent Methods
The Unreasonable Effectiveness of Few-shot Learning for Machine Translation