yusuke1997/dblp-id-title huggingface dataset:
Counterfactual Transportability: A Formal Approach
Size-constrained Submodular Minimization through Minimum Norm Base
Aggregation Buffer: Revisiting DropEdge with a New Parameter Block
Strategic Classification in the Dark
TopoTune: A Framework for Generalized Combinatorial Complex Neural Networks
Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization
ELF: Encoding Speaker-Specific Latent Speech Feature for Speech Synthesis
Adversarially Robust PAC Learnability of Real-Valued Functions
Improving Soft Unification with Knowledge Graph Embedding Methods
Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network
Sever: A Robust Meta-Algorithm for Stochastic Optimization
Iterative Hard Thresholding with Adaptive Regularization: Sparser Solutions Without Sacrificing Runtime
Set-membership Belief State-based Reinforcement Learning for POMDPs
Sample-Optimal Agnostic Boosting with Unlabeled Data
CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations
RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
Stochastic Optimization with Importance Sampling for Regularized Loss Minimization
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning
Converting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryption
Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling
Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration
On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework
Efficient Learning Through Evolution: Neural Programming and Internal Reinforcement
ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Dataset
Learning Compiler Pass Orders using Coreset and Normalized Value Prediction
Deterministic annealing for semi-supervised kernel machines
Recovery guarantee of weighted low-rank approximation via alternating minimization
Out-of-Domain Generalization in Dynamical Systems Reconstruction
Detecting Strategic Deception with Linear Probes
Optimal Coresets for Low-Dimensional Geometric Median
Learning Multiple Models for Reward Maximization
TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference
Improving Predictors via Combination Across Diverse Task Categories
Nesterov Accelerated Shuffling Gradient Method for Convex Optimization
Position: Near to Mid-term Risks and Opportunities of Open-Source Generative AI
The Logical Implication Steering Method for Conditional Interventions on Transformer Generation
Learning Stochastic Behaviour from Aggregate Data
A Manifold Perspective on the Statistical Generalization of Graph Neural Networks
Sample Complexity Bounds for Estimating Probability Divergences under Invariances
Accelerating Transformer Pre-training with 2: 4 Sparsity
Autoregressive Energy Machines
Few-Shot Conformal Prediction with Auxiliary Tasks
Make-A-Shape: a Ten-Million-scale 3D Shape Model
Improving Regression Performance with Distributional Losses
Priv'IT: Private and Sample Efficient Identity Testing
Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization
Directly Forecasting Belief for Reinforcement Learning with Delays
To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning
Oracle Efficient Private Non-Convex Optimization
Gibbsian Polar Slice Sampling
Learning Bayesian Nash Equilibrium in Auction Games via Approximate Best Response
Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances
Learning from Counterfactual Links for Link Prediction
Completeness for Inductive Procedures
On Linear Convergence in Smooth Convex-Concave Bilinearly-Coupled Saddle-Point Optimization: Lower Bounds and Optimal Algorithms
Curvature-aware Graph Attention for PDEs on Manifolds
Revisit the Essence of Distilling Knowledge through Calibration
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification
Policy Optimization as Wasserstein Gradient Flows
Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization
Competitively Consistent Clustering
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
Monte Carlo Bayesian Reinforcement Learning
Proximal Methods for Sparse Hierarchical Dictionary Learning
Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings
Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons
Generalization Analysis for Controllable Learning
LieRE: Lie Rotational Positional Encodings
Reshape and Adapt for Output Quantization (RAOQ): Quantization-aware Training for In-memory Computing Systems
Active + Semi-supervised Learning = Robust Multi-View Learning
Parallel Algorithm for Non-Monotone DR-Submodular Maximization
Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems
Neural Tangent Kernel Empowered Federated Learning
Position: Key Claims in LLM Research Have a Long Tail of Footnotes
No penalty no tears: Least squares in high-dimensional linear models
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation
Dynamical Models and tracking regret in online convex programming
Analytical Guarantees on Numerical Precision of Deep Neural Networks
On Interpolating Experts and Multi-Armed Bandits
A Latent Space Model for Rank Data
MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation
Multiply Robust Off-policy Evaluation and Learning under Truncation by Death
Dynamic Spectral Clustering with Provable Approximation Guarantee
Robust Multi-Task Learning with Excess Risks
On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions
Orthogonality-Enforced Latent Space in Autoencoders: An Approach to Learning Disentangled Representations
Probabilistic Linear Tree
Sequential Counterfactual Risk Minimization
A Simple Model of Inference Scaling Laws
Semi-Parametric Contextual Pricing Algorithm using Cox Proportional Hazards Model
Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models
Performative Recommendation: Diversifying Content via Strategic Incentives
WAVE: Weighted Autoregressive Varying Gate for Time Series Forecasting
Learning and Planning in Average-Reward Markov Decision Processes
On the Impact of Performative Risk Minimization for Binary Random Variables
Learning Belief Representations for Partially Observable Deep RL
Large Continual Instruction Assistant
Learning with Good Feature Representations in Bandits and in RL with a Generative Model
Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach
Robust Influence Maximization for Hyperparametric Models
An Efficient Approach to Sparse Linear Discriminant Analysis
AssistanceZero: Scalably Solving Assistance Games
ELF OpenGo: an analysis and open reimplementation of AlphaZero
Metadata Dependent Mondrian Processes
PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-Performer
A Statistical Theory of Regularization-Based Continual Learning
A General Analysis of the Convergence of ADMM
Enhancing Visual Localization with Cross-Domain Image Generation
From Theory to Practice: Rethinking Green and Martin Kernels for Unleashing Graph Transformers
Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics
Contrastive Representation for Data Filtering in Cross-Domain Offline Reinforcement Learning
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs
How Do Large Language Monkeys Get Their Power (Laws)?
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach
A Framework for Bayesian Optimization in Embedded Subspaces
The Problem with Noise and Small Disjuncts
Boolean Matrix Factorization and Noisy Completion via Message Passing
Explore Visual Concept Formation for Image Classification
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies
Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models
Statistical Models for Networks: A Brief Review of Some Recent Research
On the Statistical Benefits of Curriculum Learning
Cascading Bandits: Learning to Rank in the Cascade Model
Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training
Uniform Convergence, Adversarial Spheres and a Simple Remedy
Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm
Strong NP-Hardness for Sparse Optimization with Concave Penalty Functions
Learning from others' mistakes: Finetuning machine translation models with span-level error annotations
Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling
Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics
Anti-differentiating approximation algorithms: A case study with min-cuts, spectral, and flow
Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?
LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models
On the Inherent Regularization Effects of Noise Injection During Training
Telling cause from effect based on high-dimensional observations
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning
Dynamic analysis of multiagent Q -learning with ε-greedy exploration
Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale
Neural Collapse for Cross-entropy Class-Imbalanced Learning with Unconstrained ReLU Features Model
Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation
StochasticRank: Global Optimization of Scale-Free Discrete Functions
Knowledge Base Refinement and Theory Revision
Data Augmentation for Meta-Learning
Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems
Reinforcement Learning and Regret Bounds for Admission Control
ULTRAFEEDBACK: Boosting Language Models with Scaled AI Feedback
TypyBench: Evaluating LLM Type Inference for Untyped Python Repositories
Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing
Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation
Language Models over Canonical Byte-Pair Encodings
A Model-Based Method for Minimizing CVaR and Beyond
Learning Longer-term Dependencies in RNNs with Auxiliary Losses
Improving Consistency Models with Generator-Augmented Flows
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization
A Market for Accuracy: Classification Under Competition
Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations
CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee
Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks
Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics
Ranking from Stochastic Pairwise Preferences: Recovering Condorcet Winners and Tournament Solution Sets at the Top
To Each (Textual Sequence) Its Own: Improving Memorized-Data Unlearning in Large Language Models
Local Learning for Iterated Time-Series Prediction
SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics
Discovering Environments with XRM
Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement Learning
Trust Region Policy Optimization
XAttnMark: Learning Robust Audio Watermarking with Cross-Attention
Natasha: Faster Non-Convex Stochastic Optimization via Strongly Non-Convex Parameter
Dirichlet Diffusion Score Model for Biological Sequence Generation
Fast Stochastic AUC Maximization with O(1/n)-Convergence Rate
A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data
Opportunistic Constructive Induction
Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
Language Instructed Reinforcement Learning for Human-AI Coordination
Enhancing Class-Imbalanced Learning with Pre-Trained Guidance through Class-Conditional Knowledge Distillation
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message
Adapting While Learning: Grounding LLMs for Scientific Problems with Tool Usage Adaptation
POLITEX: Regret Bounds for Policy Iteration using Expert Prediction
Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting
Fully Dynamic Embedding into ℓp Spaces
A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks
Variational Label Enhancement
Simplicity Bias via Global Convergence of Sharpness Minimization
Off-Policy Actor-Critic with Shared Experience Replay
LAuReL: Learned Augmented Residual Layer
Conceptual Clustering of Mean-Ends Plans
Linear Transformers as VAR Models: Aligning Autoregressive Attention Mechanisms with Autoregressive Forecasting
Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters
Generalization Analysis for Multi-Label Learning
CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning
FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing
Random Shuffling Beats SGD after Finite Epochs
An Optimal Policy for Target Localization with Application to Electron Microscopy
A Chance-Constrained Generative Framework for Sequence Optimization
Neural Collaborative Subspace Clustering
Nonparanormal Information Estimation
Constructive Induction in Knowledge-Based Neural Networks
Devil is in the Details: Density Guidance for Detail-Aware Generation with Flow Models
Decomposing and Editing Predictions by Modeling Model Computation
Decoupled Greedy Learning of CNNs
Relevant Walk Search for Explaining Graph Neural Networks
Diagnosing the Compositional Knowledge of Vision Language Models from a Game-Theoretic View
Shifting Time: Time-series Forecasting with Khatri-Rao Neural Operators
Exemplar-SVMs for Visual Ob ject Detection, Label Transfer and Image Retrieval
Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
Combining online and offline knowledge in UCT
Learning Mixtures of Linear Dynamical Systems
Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection
unMORE: Unsupervised Multi-Object Segmentation via Center-Boundary Reasoning
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs
Exploiting Worker Correlation for Label Aggregation in Crowdsourcing
HGAP: Boosting Permutation Invariant and Permutation Equivariant in Multi-Agent Reinforcement Learning via Graph Attention Network
Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing
Improving Accuracy and Cost of Two-class and Multi-class Probabilistic Classifiers Using ROC Curves
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks
Probabilistic Path Hamiltonian Monte Carlo
Towards Trustworthy Explanation: On Causal Rationalization
On Approximate Thompson Sampling with Langevin Algorithms
Reachability Constrained Reinforcement Learning
Online Choice of Active Learning Algorithms
Trajectory World Models for Heterogeneous Environments
Decision Theoretic Subsampling for Induction on Large Databases
Active Slices for Sliced Stein Discrepancy
Adversarial Combinatorial Semi-bandits with Graph Feedback
PAC-Bayesian Bounds on Rate-Efficient Classifiers
Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts
Oscillation-free Quantization for Low-bit Vision Transformers
Interaction-Aware Gaussian Weighting for Clustered Federated Learning
Active Testing: An Efficient and Robust Framework for Estimating Accuracy
The Relationship Between No-Regret Learning and Online Conformal Prediction
Improving Mini-batch Optimal Transport via Partial Transportation
Sample Complexity of Probability Divergences under Group Symmetry
Disentangling Disentanglement in Variational Autoencoders
Actor-Critic Alignment for Offline-to-Online Reinforcement Learning
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Semantic-Aware Human Object Interaction Image Generation
Exploration Conscious Reinforcement Learning Revisited
Muesli: Combining Improvements in Policy Optimization
Interventional Causal Representation Learning
Learning Online Algorithms with Distributional Advice
A Lexical Based Semantic Bias for Theory Revision
Using a Permutation Test for Attribute Selection in Decision Trees
PDE-Net: Learning PDEs from Data
Learning-based Optimisation of Particle Accelerators Under Partial Observability Without Real-World Training
Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness
Sharp Generalization Error Bounds for Randomly-projected Classifiers
Hierarchical Regularization Cascade for Joint Learning
Generative Pretraining From Pixels
Delving into Noisy Label Detection with Clean Data
Learning Gaussian DAG Models without Condition Number Bounds
Non-isometric manifold learning: analysis and an algorithm
Optimizing estimated loss reduction for active sampling in rank learning
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
A Unified Generative Adversarial Network Training via Self-Labeling and Self-Attention
CRISP: Curriculum based Sequential neural decoders for Polar code family
Gradient descent with identity initialization efficiently learns positive definite linear transformations
Best Subset Selection: Optimal Pursuit for Feature Selection and Elimination
Latent Space Symmetry Discovery
Flow-field inference from neural data using deep recurrent networks
LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation
Airvlc: An Application for Real-Time Forecasting Urban Air Pollution
Multiple instance learning for sparse positive bags
Rethinking Attention-Model Explainability through Faithfulness Violation Test
Double-Filter: Efficient Fine-tuning of Pre-trained Vision-Language Models via Patch&Layer Filtering
Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective
Domain Adaptive Imitation Learning
Estimation of (near) low-rank matrices with noise and high-dimensional scaling
Implicit Geometric Regularization for Learning Shapes
Behavior-agnostic Task Inference for Robust Offline In-context Reinforcement Learning
Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning
Sequential Predictive Conformal Inference for Time Series
ChoiceRank: Identifying Preferences from Node Traffic in Networks
Edge-Colored Clustering in Hypergraphs: Beyond Minimizing Unsatisfied Edges
A Conditional-Gradient-Based Augmented Lagrangian Framework
G-Mixup: Graph Data Augmentation for Graph Classification
Surrogate Functions for Maximizing Precision at the Top
Understanding Nonlinear Implicit Bias via Region Counts in Input Space
Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation
Empirical Bayes for Learning to Learn
Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy
Reinforcement Learning with History Dependent Dynamic Contexts
Obtaining Simplified Rule Bases by Hybrid Learning
Highly Compressed Tokenizer Can Generate Without Training
Hyperbolic Geometric Latent Diffusion Model for Graph Generation
A Differentiable Partially Observable Generalized Linear Model with Forward-Backward Message Passing
Automated Hypothesis Validation with Agentic Sequential Falsifications
Text-To-4D Dynamic Scene Generation
Principled Preferential Bayesian Optimization
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning
Category-Aware Active Domain Adaptation
ExCP: Extreme LLM Checkpoint Compression via Weight-Momentum Joint Shrinking
Single Point-Based Distributed Zeroth-Order Optimization with a Non-Convex Stochastic Objective Function
Modeling Structure with Undirected Neural Networks
Fast condensed nearest neighbor rule
Linguistic Calibration of Long-Form Generations
Constrained Phi-Equilibria
Handling Imbalanced Pseudolabels for Vision-Language Models with Concept Alignment and Confusion-Aware Calibrated Margin
Scalable Safe Policy Improvement for Factored Multi-Agent MDPs
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile
RATE: Causal Explainability of Reward Models with Imperfect Counterfactuals
Simple Ingredients for Offline Reinforcement Learning
Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations
New approaches to support vector ordinal regression
HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections
Experiments in Robot Learning
Data preprocessing to mitigate bias: A maximum entropy based approach
Sequential skewing: an improved skewing algorithm
Extractive Structures Learned in Pretraining Enable Generalization on Finetuned Facts
Boosting for Regression Transfer
Brier Curves: a New Cost-Based Visualisation of Classifier Performance
Consistent estimation of dynamic and multi-layer block models
Position: Relational Deep Learning - Graph Representation Learning on Relational Databases
A Coupled Flow Approach to Imitation Learning
Offline Reinforcement Learning with Pseudometric Learning
Incremental Learning in a Probalistic Information Retrieval System
Learning Despite Concept Variation by Finding Structure in Attribute-based Data
SPEX: Scaling Feature Interaction Explanations for LLMs
Right Time to Learn: Promoting Generalization via Bio-inspired Spacing Effect in Knowledge Distillation
On the Learnability of Distribution Classes with Adaptive Adversaries
Building and Using Mental Models in a Sensory-Motor Domain
Position: On the Possibilities of AI-Generated Text Detection
Efficient List-Decodable Regression using Batches
Active Preference Learning for Large Language Models
TIC-TAC: A Framework For Improved Covariance Estimation In Deep Heteroscedastic Regression
Variational Counterfactual Intervention Planning to Achieve Target Outcomes
Behavior-Regularized Diffusion Policy Optimization for Offline Reinforcement Learning
Learning Equations for Extrapolation and Control
Extending the Valiant Learning Model
SparseTSF: Modeling Long-term Time Series Forecasting with *1k* Parameters
Listwise approach to learning to rank: theory and algorithm
OT-CLIP: Understanding and Generalizing CLIP via Optimal Transport
Statistical Network Analysis: Models, Issues, and New Directions - ICML 2006 Workshop on Statistical Network Analysis, Pittsburgh, PA, USA, June 29, 2006, Revised Selected Papers
Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection
Learning to Learn Kernels with Variational Random Features
A New Concentration Inequality for Sampling Without Replacement and Its Application for Transductive Learning
Conformal Anomaly Detection in Event Sequences
Policy and Value Transfer in Lifelong Reinforcement Learning
SafetyAnalyst: Interpretable, Transparent, and Steerable Safety Moderation for AI Behavior
PARCv2: Physics-aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics Modeling
PISA Experiments: Exploring Physics Post-Training for Video Diffusion Models by Watching Stuff Drop
Efficient Online Bandit Multiclass Learning with Õ(√T) Regret
Causal Discovery with Fewer Conditional Independence Tests
RUN: Reversible Unfolding Network for Concealed Object Segmentation
Interpreting Adversarially Trained Convolutional Neural Networks
Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval
Machine Learning meets Algebraic Combinatorics: A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics