yusuke1997/dblp-id-title huggingface dataset:
Learning and evaluating classifiers under sample selection bias
Ensemble-Based Tracking: Aggregating Crowdsourced Structured Time Series Data
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries
Boximator: Generating Rich and Controllable Motions for Video Synthesis
S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning
Text Bundling: Statistics Based Data-Reduction
Decoupled Neural Interfaces using Synthetic Gradients
Unsupervised and Transfer Learning - Workshop held at ICML 2011, Bellevue, Washington, USA, July 2, 2011
Asymptotic Analysis of Generative Semi-Supervised Learning
Towards Lifelong Model Editing via Simulating Ideal Editor
Graph clustering with network structure indices
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play
Topologically Densified Distributions
Automatic Differentiation of Optimization Algorithms with Time-Varying Updates
A Mixture-Based Framework for Guiding Diffusion Models
Compositional Exemplars for In-context Learning
Noise Conditional Variational Score Distillation
Determinant Estimation under Memory Constraints and Neural Scaling Laws
Provably and Practically Efficient Neural Contextual Bandits
Understanding Chain-of-Thought in LLMs through Information Theory
Improved Algorithms for White-Box Adversarial Streams
Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling
Complexity of Block Coordinate Descent with Proximal Regularization and Applications to Wasserstein CP-dictionary Learning
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation
SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks
Multiclass reduced-set support vector machines
Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network
Provably Robust DPO: Aligning Language Models with Noisy Feedback
Directed Graph Embeddings in Pseudo-Riemannian Manifolds
Modified K-means Algorithm with Local Optimality Guarantees
Parseval Networks: Improving Robustness to Adversarial Examples
Particle Flow Bayes' Rule
Aligned Multi Objective Optimization
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables
Unsupervised Episode Generation for Graph Meta-learning
Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics
Subgroups Matter for Robust Bias Mitigation
PDE-Controller: LLMs for Autoformalization and Reasoning of PDEs
A Theoretical Analysis of the Learning Dynamics under Class Imbalance
Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach
Neural Network Accelerated Implicit Filtering: Integrating Neural Network Surrogates With Provably Convergent Derivative Free Optimization Methods
Submodular framework for structured-sparse optimal transport
An Approach Based on Integrated Learning to Generating Stories
Concentration in unbounded metric spaces and algorithmic stability
An Iterative Min-Min Optimization Method for Sparse Bayesian Learning
Implementing regularization implicitly via approximate eigenvector computation
FedClean: A General Robust Label Noise Correction for Federated Learning
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
Pursuit-Evasion Without Regret, with an Application to Trading
Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions
ExpProof : Operationalizing Explanations for Confidential Models with ZKPs
Autonomy-of-Experts Models
ROMA: Multi-Agent Reinforcement Learning with Emergent Roles
Finite Smoothing Algorithm for High-Dimensional Support Vector Machines and Quantile Regression
Bridging Environments and Language with Rendering Functions and Vision-Language Models
A Statistical Approach to Decision Tree Modeling
Demystifying Singular Defects in Large Language Models
KV-Runahead: Scalable Causal LLM Inference by Parallel Key-Value Cache Generation
A Theoretical Analysis of Metric Hypothesis Transfer Learning
Mask-Enhanced Autoregressive Prediction: Pay Less Attention to Learn More
Online Stochastic Optimization under Correlated Bandit Feedback
Relevance Feedback using Support Vector Machines
Rate-Distortion Analysis of Minimum Excess Risk in Bayesian Learning
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
Pruning in ordered bagging ensembles
Online Learning for Group Lasso
Nonparametric Density Estimation under Distribution Drift
Tilt your Head: Activating the Hidden Spatial-Invariance of Classifiers
A Theory of Generative ConvNet
Autoregressive Quantile Networks for Generative Modeling
Preventing "Overfitting" of Cross-Validation Data
Approximately Optimal Core Shapes for Tensor Decompositions
Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization
Diffuse, Sample, Project: Plug-And-Play Controllable Graph Generation
A Lower Bound for the Optimization of Finite Sums
A Recurrent Neural Cascade-based Model for Continuous-Time Diffusion
Celer: a Fast Solver for the Lasso with Dual Extrapolation
Learning Task Grouping and Overlap in Multi-task Learning
Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction
LightningDrag: Lightning Fast and Accurate Drag-based Image Editing Emerging from Videos
Generating Immune-aware SARS-CoV-2 Spike Proteins for Universal Vaccine Design
Learning Embedded Maps of Markov Processes
SSL4Q: Semi-Supervised Learning of Quantum Data with Application to Quantum State Classification
Partitioned Tensor Factorizations for Learning Mixed Membership Models
Improving Optimization in Models With Continuous Symmetry Breaking
Contextual Online Decision Making with Infinite-Dimensional Functional Regression
Testing Exchangeability On-Line
Provable Acceleration of Heavy Ball beyond Quadratics for a Class of Polyak-Lojasiewicz Functions when the Non-Convexity is Averaged-Out
Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning
Multi-Environment Pretraining Enables Transfer to Action Limited Datasets
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process
Improved Decision Trees: A Generalized Version of ID3
Topologically-constrained latent variable models
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts
DreamDPO: Aligning Text-to-3D Generation with Human Preferences via Direct Preference Optimization
On the Global Convergence Rates of Softmax Policy Gradient Methods
Representation-Driven Reinforcement Learning
Statistical analysis of stochastic gradient methods for generalized linear models
Navigating Conflicting Views: Harnessing Trust for Learning
Sparse-IFT: Sparse Iso-FLOP Transformations for Maximizing Training Efficiency
Spotlight: Optimizing Device Placement for Training Deep Neural Networks
Bayesian Games for Adversarial Regression Problems
Simple DFA are Polynomially Probably Exactly Learnable from Simple Examples
Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement
Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm
Position: Intent-aligned AI Systems Must Optimize for Agency Preservation
Computationally Efficient Nyström Approximation using Fast Transforms
Adaptive Second Order Coresets for Data-efficient Machine Learning
Pfeife: Automatic Pipeline Parallelism for PyTorch
Bounded Off-Policy Evaluation with Missing Data for Course Recommendation and Curriculum Design
Sounding that Object: Interactive Object-Aware Image to Audio Generation
Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation
Learning from Integral Losses in Physics Informed Neural Networks
Training Discrete Deep Generative Models via Gapped Straight-Through Estimator
Bidirectional Reciprocative Information Communication for Few-Shot Semantic Segmentation
State Abstractions for Lifelong Reinforcement Learning
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation
Transition Matrix Estimation in High Dimensional Time Series
How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model
How to Learn when Data Reacts to Your Model: Performative Gradient Descent
Curvature Enhanced Data Augmentation for Regression
Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning
Large-scale log-determinant computation through stochastic Chebyshev expansions
Improving the Statistical Efficiency of Cross-Conformal Prediction
A Unified Adaptive Testing System Enabled by Hierarchical Structure Search
Composite Kernels for Hypertext Categorisation
Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes
DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection
On the Similarities of Embeddings in Contrastive Learning
A Classification View on Meta Learning Bandits
Approximation Theory Based Methods for RKHS Bandits
Residual Q-Learning Applied to Visual Attention
A convex relaxation for weakly supervised classifiers
Latent Multi-group Membership Graph Model
Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation
Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization
A Machine Learning Approach to Duality in Statistical Physics
Fast maximum margin matrix factorization for collaborative prediction
Understanding Complexity in VideoQA via Visual Program Generation
Causal Dynamics Learning for Task-Independent State Abstraction
Approximation Via Value Unification
I Think, Therefore I Diffuse: Enabling Multimodal In-Context Reasoning in Diffusion Models
Max-value Entropy Search for Efficient Bayesian Optimization
Stochastic Alternating Direction Method of Multipliers
Few-bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction
An End-to-End Model for Logits-Based Large Language Models Watermarking
Reliable Algorithm Selection for Machine Learning-Guided Design
Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation
Efficient World Models with Context-Aware Tokenization
Conjugate Markov Decision Processes
An empirical evaluation of supervised learning in high dimensions
AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Opponents
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach
convSeq: Fast and Scalable Method for Detecting Patterns in Spike Data
Temporal Difference Methods for the Variance of the Reward To Go
Efficient Heterogeneity-Aware Federated Active Data Selection
Enhancing Cross-Modal Fine-Tuning with Gradually Intermediate Modality Generation
Linear Mode Connectivity between Multiple Models modulo Permutation Symmetries
DiTAR: Diffusion Transformer Autoregressive Modeling for Speech Generation
Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing
Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving
Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm and Scalability
A Case Study in the Use of Theory Revision in Requirements Validation
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density
Rectifying Conformity Scores for Better Conditional Coverage
Scaling Sparse Feature Circuits For Studying In-Context Learning
Efficient Parametric Approximations of Neural Network Function Space Distance
On dimensionality of feature vectors in MPNNs
Generalization to New Sequential Decision Making Tasks with In-Context Learning
Online Learning and Information Exponents: The Importance of Batch size & Time/Complexity Tradeoffs
Regret-Free Reinforcement Learning for Temporal Logic Specifications
From Adaptive Query Release to Machine Unlearning
Rethinking Optimization and Architecture for Tiny Language Models
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR
Active Statistical Inference
Batch Stationary Distribution Estimation
Quadratic Upper Bound for Boosting Robustness
A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing
Harnessing Heterogeneous Statistical Strength for Personalized Federated Learning via Hierarchical Bayesian Inference
Regression Error Characteristic Curves
Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM
NeRF-VAE: A Geometry Aware 3D Scene Generative Model
Keypoint-based Progressive Chain-of-Thought Distillation for LLMs
Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling
Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization
Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits
BECoTTA: Input-dependent Online Blending of Experts for Continual Test-time Adaptation
Density Level Set Estimation on Manifolds with DBSCAN
Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms
Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies
Visual Grounding of Learned Physical Models
Eliminating the Invariance on the Loss Landscape of Linear Autoencoders
Synthesizing Images on Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss
A First-order Generative Bilevel Optimization Framework for Diffusion Models
3D Convolutional Neural Networks for Human Action Recognition
Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global
Scalable Fair Clustering
Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning
Tuning Computer Vision Models With Task Rewards
Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization
Neural Jump-Diffusion Temporal Point Processes
Learning Context-dependent Label Permutations for Multi-label Classification
Overfitting in adversarially robust deep learning
Distributionally Robust Q-Learning
Structure-based drug design by denoising voxel grids
Training Deep Learning Models with Norm-Constrained LMOs
Target Concrete Score Matching: A Holistic Framework for Discrete Diffusion
Stochastic Simultaneous Optimistic Optimization
Near Optimal Behavior via Approximate State Abstraction
Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLMs
Towards Omni-generalizable Neural Methods for Vehicle Routing Problems
Counterfactual Identifiability of Bijective Causal Models
Supported Trust Region Optimization for Offline Reinforcement Learning
WorldSimBench: Towards Video Generation Models as World Simulators
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification
Scaling Laws for Fine-Grained Mixture of Experts
Beyond In-Domain Scenarios: Robust Density-Aware Calibration
Transfer of samples in batch reinforcement learning
Reward Modeling with Ordinal Feedback: Wisdom of the Crowd
MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance
Differentially Private Analysis for Binary Response Models: Optimality, Estimation, and Inference
Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit Emergence
Estimating Unknown Population Sizes Using the Hypergeometric Distribution
Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs
Exploiting Independent Instruments: Identification and Distribution Generalization
Model Alignment as Prospect Theoretic Optimization
UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning
Data augmentation for deep learning based accelerated MRI reconstruction with limited data
A Sub-Problem Quantum Alternating Operator Ansatz for Correlation Clustering
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"
FLAM: Frame-Wise Language-Audio Modeling
HOPE: High-order Graph ODE For Modeling Interacting Dynamics
Optimising area under the ROC curve using gradient descent
Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM
A low variance consistent test of relative dependency
Differentially Private Chi-squared Test by Unit Circle Mechanism
Learning to Maximize Mutual Information for Dynamic Feature Selection
Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function
Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty
Kernel-Based Reinforcement Learning in Robust Markov Decision Processes
On the Difficulty of Nearest Neighbor Search
Improved Lower Bounds for First-order Stochastic Non-convex Optimization under Markov Sampling
Dimension-independent Certified Neural Network Watermarks via Mollifier Smoothing
Optimization without Retraction on the Random Generalized Stiefel Manifold
A reversible infinite HMM using normalised random measures
Integrating Partial Model Knowledge in Model Free RL Algorithms
TRUST-VLM: Thorough Red-Teaming for Uncovering Safety Threats in Vision-Language Models
Scalable Online Exploration via Coverability
Adaptive Partitioning Schemes for Optimistic Optimization
Generalization Bounds for Learning Kernels
Probabilistic Categorical Adversarial Attack and Adversarial Training
Making Decisions that Reduce Discriminatory Impacts
Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF
Preconditioned temporal difference learning
Gradient-based Hyperparameter Optimization through Reversible Learning
Overcoming Multi-model Forgetting
Improved Trip Planning by Learning from Travelers' Choices
Learning Optimal Linear Regularizers
Position: Levels of AGI for Operationalizing Progress on the Path to AGI
Target Tracking for Contextual Bandits: Application to Demand Side Management
Neural Dynamic Programming for Musical Self Similarity
Control Regularization for Reduced Variance Reinforcement Learning
Data Poisoning Attacks on Stochastic Bandits
Asymmetric boosting
Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables
Learning Policy Representations in Multiagent Systems
Enhancing Statistical Validity and Power in Hybrid Controlled Trials: A Randomization Inference Approach with Conformal Selective Borrowing
On Sampling with Approximate Transport Maps
Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks
Smoothing Proximal Gradient Methods for Nonsmooth Sparsity Constrained Optimization: Optimality Conditions and Global Convergence
Provable Efficiency of Guidance in Diffusion Models for General Data Distribution
Learning Changes in Graphon Attachment Network Models
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
Online Algorithms with Multiple Predictions
Multi-slots Online Matching with High Entropy
Selfish Sparse RNN Training
Multiclass learning with margin: exponential rates with no bias-variance trade-off
Learning to Explore in POMDPs with Informational Rewards
Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement
Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains
Theoretical Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability
Learning in Integer Latent Variable Models with Nested Automatic Differentiation
Competitive Gradient Optimization
Monarch: Expressive Structured Matrices for Efficient and Accurate Training
Data-Efficient Policy Evaluation Through Behavior Policy Search
Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processes
Structure-informed Language Models Are Protein Designers
Confidence-weighted linear classification
On Asking the Right Questions
Theory-guided Empirical Speedup Learning of Goal Decomposition Rules
Supervised clustering of streaming data for email batch detection
TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning
Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes
Sample and Predict Your Latent: Modality-free Sequential Disentanglement via Contrastive Estimation
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time
Graph Reinforcement Learning for Network Control via Bi-Level Optimization
Continuous-Time Belief Propagation
Growing Action Spaces
On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization
Subsampling Methods for Persistent Homology
Improving Multi-Class Calibration through Normalization-Aware Isotonic Techniques
Fast Rates for Maximum Entropy Exploration
The Generalization Error of Dictionary Learning with Moreau Envelopes
Certification for Differentially Private Prediction in Gradient-Based Training
Efficient First-Order Algorithms for Adaptive Signal Denoising
TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching
Learning to Discover Sparse Graphical Models
Finding Generalization Measures by Contrasting Signal and Noise
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
Learning Dependency Structures for Weak Supervision Models
Fully-Adaptive Composition in Differential Privacy
Towards Global-level Mechanistic Interpretability: A Perspective of Modular Circuits of Large Language Models
Adding vs. Averaging in Distributed Primal-Dual Optimization
OCD: Learning to Overfit with Conditional Diffusion Models
Exploiting Human-AI Dependence for Learning to Defer
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling
Unlocking Slot Attention by Changing Optimal Transport Costs
DDGR: Continual Learning with Deep Diffusion-based Generative Replay
Safe Delta: Consistently Preserving Safety when Fine-Tuning LLMs on Diverse Datasets
Hierarchical Unsupervised Learning
Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance
In-Context Unlearning: Language Models as Few-Shot Unlearners
Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach
Contextual Bandits for Unbounded Context Distributions
Lightspeed Geometric Dataset Distance via Sliced Optimal Transport
iDPA: Instance Decoupled Prompt Attention for Incremental Medical Object Detection
Frustratingly Simple Few-Shot Object Detection
On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games
Incremental Learning of Explanation Patterns and Their Indices
Learning from Demonstration: Provably Efficient Adversarial Policy Imitation with Linear Function Approximation
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks
Saddle Points and Accelerated Perceptron Algorithms
Concurrent Reinforcement Learning from Customer Interactions
Adaptive Self-improvement LLM Agentic System for ML Library Development
Cross-Modal Alignment via Variational Copula Modelling
A Case Study of Explanation-Based Control
FlexControl: Computation-Aware Conditional Control with Differentiable Router for Text-to-Image Generation
Self-Organized Polynomial-Time Coordination Graphs
Sequential Monte Carlo Learning for Time Series Structure Discovery
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements
Robust Graph Matching when Nodes are Corrupt
Averaging n-step Returns Reduces Variance in Reinforcement Learning
Increasing the Performance and Consistency of Classification Trees by Using the Accuracy Criterion at the Leaves
Exploiting Curvature in Online Convex Optimization with Delayed Feedback
Improving Adversarial Robustness via Promoting Ensemble Diversity
Disentangled Graph Spectral Domain Adaptation