yusuke1997/dblp-id-title huggingface dataset:
Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens
Random Policy Evaluation Uncovers Policies of Generative Flow Networks
On the Convergence of SARSA with Linear Function Approximation
Conservative Offline Goal-Conditioned Implicit V-Learning
Decomposable Submodular Function Minimization via Maximum Flow
ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories
Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation
Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization
Supercharging Graph Transformers with Advective Diffusion
Locally adaptive classification piloted by uncertainty
Single-Trajectory Distributionally Robust Reinforcement Learning
Understanding Policy Gradient Algorithms: A Sensitivity-Based Approach
Tight Data Access Bounds for Private Top-k Selection
Bias-Robust Bayesian Optimization via Dueling Bandits
MetaFun: Meta-Learning with Iterative Functional Updates
Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm
Revisiting Pseudo-Label for Single-Positive Multi-Label Learning
Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees
Kill a Bird with Two Stones: Closing the Convergence Gaps in Non-Strongly Convex Optimization by Directly Accelerated SVRG with Double Compensation and Snapshots
Partial Trace Regression and Low-Rank Kraus Decomposition
A Bayesian Framework for Online Classifier Ensemble
When Representations Align: Universality in Representation Learning Dynamics
A Bayesian Analysis of Algorithms for Learning Finite Functions
Do Topological Characteristics Help in Knowledge Distillation?
On Measuring Causal Contributions via do-interventions
Nonparametric Modern Hopfield Models
Analysis of Kernel Mean Matching under Covariate Shift
RepLoRA: Reparameterizing Low-rank Adaptation via the Perspective of Mixture of Experts
Leveraging Sparse Linear Layers for Debuggable Deep Networks
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization
Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment
DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL
Crystallization Learning with the Delaunay Triangulation
Transfer Learning via Learning to Transfer
The Convexity and Design of Composite Multiclass Losses
Non-Vacuous Generalization Bounds for Large Language Models
Optimal Continual Learning has Perfect Memory and is NP-hard
DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models
Automatic Reparameterisation of Probabilistic Programs
Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees
SHIELD: Multi-task Multi-distribution Vehicle Routing Solver with Sparsity and Hierarchy
Explainable Concept Generation through Vision-Language Preference Learning for Understanding Neural Networks' Internal Representations
MDL and Categorical Theories (Continued)
Too Relaxed to Be Fair
Improved Regret Bounds of Bilinear Bandits using Action Space Analysis
Dealing With Unbounded Gradients in Stochastic Saddle-point Optimization
MASS: Mathematical Data Selection via Skill Graphs for Pretraining Large Language Models
Outlier Path: A Homotopy Algorithm for Robust SVM
A Distributional Analogue to the Successor Representation
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise
Uncertainty Based Selection of Learning Experiences
FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction
PARM: Multi-Objective Test-Time Alignment via Preference-Aware Autoregressive Reward Model
Consensus Based Stochastic Optimal Control
Learning Steady-States of Iterative Algorithms over Graphs
Contour Integration Underlies Human-Like Vision
On Learning Decision Committees
Contrastive Learning Inverts the Data Generating Process
Harmonic Decompositions of Convolutional Networks
Upper bounds for Model-Free Row-Sparse Principal Component Analysis
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
Stronger and Faster Wasserstein Adversarial Attacks
Concrete Autoencoders: Differentiable Feature Selection and Reconstruction
Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity
A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs
Gradient Descent in Neural Networks as Sequential Learning in Reproducing Kernel Banach Space
On learning parametric-output HMMs
Hyperbolic Representation Learning: Revisiting and Advancing
How Contaminated Is Your Benchmark? Measuring Dataset Leakage in Large Language Models with Kernel Divergence
Monte-Carlo simulation balancing
On Stronger Computational Separations Between Multimodal and Unimodal Machine Learning
Deep Generative Stochastic Networks Trainable by Backprop
Policy Regularization with Dataset Constraint for Offline Reinforcement Learning
Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
Deduction in Top-Down Inductive Learning
Fast Bellman Updates for Robust MDPs
InstaHide: Instance-hiding Schemes for Private Distributed Learning
Accelerated Algorithms for Smooth Convex-Concave Minimax Problems with O(1/k^2) Rate on Squared Gradient Norm
ConText: Driving In-context Learning for Text Removal and Segmentation
Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning
OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization
Improved Local Coordinate Coding using Local Tangents
E-LDA: Toward Interpretable LDA Topic Models with Strong Guarantees in Logarithmic Parallel Time
Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks
On the Occupancy Measure of Non-Markovian Policies in Continuous MDPs
FEAT-KD: Learning Concise Representations for Single and Multi-Target Regression via TabNet Knowledge Distillation
Position: Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?
Sparsest Models Elude Pruning: An Exposé of Pruning's Current Capabilities
Nonstationary kernel combination
Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models
Learning Distance Functions using Equivalence Relations
Workshop summary: Abstraction in reinforcement learning
Critical feature learning in deep neural networks
Adversarially Regularized Autoencoders
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation
Linearly Constrained Bilevel Optimization: A Smoothed Implicit Gradient Approach
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders
ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning
Provable Privacy with Non-Private Pre-Processing
Regularizing towards Causal Invariance: Linear Models with Proxies
Ensembles of nested dichotomies for multi-class problems
MGD3 : Mode-Guided Dataset Distillation using Diffusion Models
Provable Domain Generalization via Invariant-Feature Subspace Recovery
Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding
MedLDA: maximum margin supervised topic models for regression and classification
SE(3)-Equivariant Diffusion Policy in Spherical Fourier Space
Solving cluster ensemble problems by bipartite graph partitioning
Deep Electromagnetic Structure Design Under Limited Evaluation Budgets
The Implicit Regularization of Dynamical Stability in Stochastic Gradient Descent
Function-Space Regularization in Neural Networks: A Probabilistic Perspective
The Buckley-Osthus model and the block preferential attachment model: statistical analysis and application
LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens
Toward Optimal Feature Selection
Neural Network Pruning Denoises the Features and Makes Local Connectivity Emerge in Visual Tasks
Generalized-Smooth Nonconvex Optimization is As Efficient As Smooth Nonconvex Optimization
TabFSBench: Tabular Benchmark for Feature Shifts in Open Environments
High Dimensional Semiparametric Gaussian Copula Graphical Models
Identification of Latent Confounders via Investigating the Tensor Ranks of the Nonlinear Observations
Improving Model Selection by Employing the Test Data
Utilizing Expert Features for Contrastive Learning of Time-Series Representations
DANCE: Enhancing saliency maps using decoys
Enhancing Trajectory Prediction through Self-Supervised Waypoint Distortion Prediction
Neural Tangent Kernel Analysis of Deep Narrow Neural Networks
Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time
Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding
Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging
Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation
Simple, robust, scalable semi-supervised learning via expectation regularization
Hyperbolic Disk Embeddings for Directed Acyclic Graphs
Provably Efficient Algorithms for Multi-Objective Competitive RL
Finite-Sample Analysis of Lasso-TD
Margin Distribution and Learning
Learning-Augmented Algorithms for MTS with Bandit Access to Multiple Predictors
Learning to Satisfy Conjunctive Goals
Tighter Information-Theoretic Generalization Bounds from Supersamples
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization
A Local Algorithm for Finding Well-Connected Clusters
Augmenting Domain Theory for Explanation-Based Generalization
How To Grade a Test Without Knowing the Answers - A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing
Ambiguity-Aware Abductive Learning
Latent Space Policies for Hierarchical Reinforcement Learning
Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond
Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
Bayes-optimal Learning of Deep Random Networks of Extensive-width
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis
Smooth Tchebycheff Scalarization for Multi-Objective Optimization
A Bayesian Model Selection Criterion for Selecting Pretraining Checkpoints
SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation Models
Variational On-the-Fly Personalization
Factorial Multi-Task Learning : A Bayesian Nonparametric Approach
Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms
A Bayesian Approach to Approximate Joint Diagonalization of Square Matrices
Exact Upper and Lower Bounds for the Output Distribution of Neural Networks with Random Inputs
Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation
Toward Better Generalization Bounds with Locally Elastic Stability
Conceptual Clumping of Binary Vectors with Occam's Razor
Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment
Beam sampling for the infinite hidden Markov model
Neural Separation of Observed and Unobserved Distributions
Optimal Task Order for Continual Learning of Multiple Tasks
Fairness-Aware Learning for Continuous Attributes and Treatments
Catching Two Birds with One Stone: Reward Shaping with Dual Random Networks for Balancing Exploration and Exploitation
Sequential and Parallel Constrained Max-value Entropy Search via Information Lower Bound
DAMA: Data- and Model-aware Alignment of Multi-modal LLMs
Hedged learning: regret-minimization with learning experts
On the Tension between Byzantine Robustness and No-Attack Accuracy in Distributed Learning
Online Decision Transformer
Generative Trees: Adversarial and Copycat
Fast Algorithms for Hypergraph PageRank with Applications to Semi-Supervised Learning
Distribution Augmentation for Generative Modeling
Efficient Fine-Grained Guidance for Diffusion Model Based Symbolic Music Generation
Information-theoretic metric learning
Accelerated Spectral Ranking
Iterative RELIEF for feature weighting
Objective drives the consistency of representational similarity across datasets
Estimating Possible Causal Effects with Latent Variables via Adjustment
Blind Pareto Fairness and Subgroup Robustness
OW-VAP: Visual Attribute Parsing for Open World Object Detection
Learning Parameterized Skills
Going Deeper into Locally Differentially Private Graph Neural Networks
Unsupervised prediction of citation influences
Predictive Multiplicity in Classification
Prompting Large Language Model for Machine Translation: A Case Study
Community-Invariant Graph Contrastive Learning
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
Federated Linear Contextual Bandits with User-level Differential Privacy
PAC Lower Bounds and Efficient Algorithms for The Max \(K\)-Armed Bandit Problem
Using Eligibility Traces to Find the Best Memoryless Policy in Partially Observable Markov Decision Processes
A Category-theoretical Meta-analysis of Definitions of Disentanglement
Convex Formulation for Learning from Positive and Unlabeled Data
Block-wise construction of acyclic relational features with monotone irreducibility and relevancy properties
Safe Reinforcement Learning using Finite-Horizon Gradient-based Estimation
Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition
Learning with the Set Covering Machine
Using relative novelty to identify useful temporal abstractions in reinforcement learning
Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention
Sparse Autoencoders for Hypothesis Generation
Nonlinear Filtering with Brenier Optimal Transport Maps
An Improved Predictive Accuracy Bound for Averaging Classifiers
Putting MRFs on a Tensor Train
Latent Logic Tree Extraction for Event Sequence Explanation from LLMs
Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering
Interactive Inverse Reinforcement Learning for Cooperative Games
Why Target Networks Stabilise Temporal Difference Methods
Making Old Things New: A Unified Algorithm for Differentially Private Clustering
Robust mixtures in the presence of measurement errors
Learning Disjunctive Concepts by Means of Genetic Algorithms
Ensemble Pruning for Out-of-distribution Generalization
Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming
Efficient Continuous-Time Markov Chain Estimation
Self-supervised learning with random-projection quantizer for speech recognition
Revealing the Structure of Deep Neural Networks via Convex Duality
Residual TPP: A Unified Lightweight Approach for Event Stream Data Analysis
Fixed Points of Approximate Value Iteration and Temporal-Difference Learning
High-Dimensional Gaussian Process Inference with Derivatives
Minimax estimation of discontinuous optimal transport maps: The semi-discrete case
Efficient Continuous Pareto Exploration in Multi-Task Learning
WildChat-50M: A Deep Dive Into the Role of Synthetic Data in Post-Training
Graph Structure of Neural Networks
BoltzRank: learning to maximize expected ranking gain
Interactive learning of mappings from visual percepts to actions
Offline Transition Modeling via Contrastive Energy Learning
Highway Value Iteration Networks
Best of Both Worlds: Advantages of Hybrid Graph Sequence Models
Query Learning Strategies Using Boosting and Bagging
On tracking portfolios with certainty equivalents on a generalization of Markowitz model: the Fool, the Wise and the Adaptive
Dynamic mixed membership blockmodel for evolving networks
Speculative Prefill: Turbocharging TTFT with Lightweight and Training-Free Token Importance Estimation
Deep Networks Always Grok and Here is Why
Principal Component Analysis on non-Gaussian Dependent Data
Unsupervised rank aggregation with distance-based models
Modelling Microbial Communities with Graph Neural Networks
Conformal Prediction for Deep Classifier via Label Ranking
SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process
Condensed Filter Tree for Cost-Sensitive Multi-Label Classification
Neural Joint Source-Channel Coding
Iterative Learning and Denoising in Convolutional Neural Associative Memories
What do CNNs Learn in the First Layer and Why? A Linear Systems Perspective
Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning
Semantics-aware Test-time Adaptation for 3D Human Pose Estimation
On Contrastive Learning for Likelihood-free Inference
Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning
Uprooting and Rerooting Graphical Models
High-dimensional Location Estimation via Norm Concentration for Subgamma Vectors
StealthInk: A Multi-bit and Stealthy Watermark for Large Language Models
Randomized Variable Elimination
Triadic-OCD: Asynchronous Online Change Detection with Provable Robustness, Optimality, and Convergence
A Classification-Based Study of Covariate Shift in GAN Distributions
Inducing Causal Structure for Interpretable Neural Networks
diff History for Neural Language Agents
Learning Autoencoders with Relational Regularization
CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration
Feature selection for high-dimensional genomic microarray data
Concept Drift Detection Through Resampling
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
Structural Entropy Guided Graph Hierarchical Pooling
Adaptive Sampling for Best Policy Identification in Markov Decision Processes
Maximum margin clustering made practical
Spectral Approximate Inference
Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability
Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification
Efficient Training of LDA on a GPU by Mean-for-Mode Estimation
Large-Margin Softmax Loss for Convolutional Neural Networks
A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization
Learning To Stop While Learning To Predict
Transformer Quality in Linear Time
Efficient co-regularised least squares regression
AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs
Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders
BAMDT: Bayesian Additive Semi-Multivariate Decision Trees for Nonparametric Regression
Instabilities of Offline RL with Pre-Trained Neural Representation
InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation
A Unified Linear Programming Framework for Offline Reward Learning from Human Demonstrations and Feedback
Toward Efficient Gradient-Based Value Estimation
Robust Inference for High-Dimensional Linear Models via Residual Randomization
Learning from Human-Generated Lists
BalancEdit: Dynamically Balancing the Generality-Locality Trade-off in Multi-modal Model Editing
The Persistent Laplacian for Data Science: Evaluating Higher-Order Persistent Spectral Representations of Data
Learning Analytical Knowledge About VLSI-Design from Observation
Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead
ToMA: Token Merge with Attention for Diffusion Models
Auditing Private Prediction
Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations
An Integrated Framework of Inducing Rules from Examples
Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach
Near Optimal Non-asymptotic Sample Complexity of 1-Identification
Variational Phylogenetic Inference with Products over Bipartitions
Discrete Distribution Estimation under Local Privacy
Guided Zeroth-Order Methods for Stochastic Non-convex Problems with Decision-Dependent Distributions
Provably efficient RL with Rich Observations via Latent State Decoding
"Why Is There a Tumor?": Tell Me the Reason, Show Me the Evidence
TD Models: Modeling the World at a Mixture of Time Scales
LPQP for MAP: Putting LP Solvers to Better Use
Auxiliary Learning with Joint Task and Data Scheduling
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels
Test-time Correlation Alignment
Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations
LQER: Low-Rank Quantization Error Reconstruction for LLMs
LADA: Scalable Label-Specific CLIP Adapter for Continual Learning
Gradient Based Clustering
RLVF: Learning from Verbal Feedback without Overgeneralization
Explicit Exploration for High-Welfare Equilibria in Game-Theoretic Multiagent Reinforcement Learning
Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity
Multiple Identifications in Multi-Armed Bandits
SaVeR: Optimal Data Collection Strategy for Safe Policy Evaluation in Tabular MDP
Interaction-based Retrieval-augmented Diffusion Models for Protein-specific 3D Molecule Generation
Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks
New metrics and search algorithms for weighted causal DAGs
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks
The Hidden Dimensions of LLM Alignment: A Multi-Dimensional Analysis of Orthogonal Safety Directions
Offline Actor-Critic Reinforcement Learning Scales to Large Models
Shortest path distance in random k-nearest neighbor graphs
Sparsing Law: Towards Large Language Models with Greater Activation Sparsity
Learning Categorical Decision Criteria in Biomedical Domains
Few-Sample Feature Selection via Feature Manifold Learning
Environment Design for Inverse Reinforcement Learning
Composing Partial Differential Equations with Physics-Aware Neural Networks
Automatically marginalized MCMC in probabilistic programming
High-Fidelity Image Generation With Fewer Labels
Minimum-Norm Interpolation Under Covariate Shift
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More
Joker: Joint Optimization Framework for Lightweight Kernel Machines
R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning
Query Formulation Through Knowledge Acquisition
On the Sampling Problem for Kernel Quadrature
Toward Adaptive Reasoning in Large Language Models with Thought Rollback
Privacy for Free: How does Dataset Condensation Help Privacy?
On the Private Estimation of Smooth Transport Maps
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator
(Locally) Differentially Private Combinatorial Semi-Bandits
Invariant Deep Uplift Modeling for Incentive Assignment in Online Marketing via Probability of Necessity and Sufficiency
Reasoning Limitations of Multimodal Large Language Models. A case study of Bongard Problems
Selective Inference for Sparse High-Order Interaction Models
Unsupervised Domain Adaptation for Anatomical Structure Detection in Ultrasound Images
Connecting the Dots: Collaborative Fine-tuning for Black-Box Vision-Language Models
Deep Probability Estimation
Provable Zero-Shot Generalization in Offline Reinforcement Learning
Variable Selection in Model-Based Clustering: To Do or To Facilitate
An Intrinsic Vector Heat Network
Statistical Estimation from Dependent Data
The Pre-Image Problem in Kernel Methods
Leveraging Non-uniformity in First-order Non-convex Optimization
Gradual Divergence for Seamless Adaptation: A Novel Domain Incremental Learning Method
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search