yusuke1997/dblp-id-title huggingface dataset:
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback
Multiple-Instance Learning of Real-Valued Data
On the Iteration Complexity of Hypergradient Computation
Scalable Bilinear Learning Using State and Action Features
ZeroFlow: Overcoming Catastrophic Forgetting is Easier than You Think
NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion
Efficient softmax approximation for GPUs
Prefer to Classify: Improving Text Classifiers via Auxiliary Preference Learning
Discovering Mixtures of Structural Causal Models from Time Series Data
Doubly-Competitive Distribution Estimation
Tree-Sliced Wasserstein Distance with Nonlinear Projection
Fast direct policy evaluation using multiscale analysis of Markov diffusion processes
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models
Multi-objective Linear Reinforcement Learning with Lexicographic Rewards
Utilizing Domain Knowledge in Neuroevolution
Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations
A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs
Near-optimal sensor placements in Gaussian processes
ConvexVST: A Convex Optimization Approach to Variance-stabilizing Transformation
AMPO: Active Multi Preference Optimization for Self-play Preference Selection
Efficient Neural Audio Synthesis
Self-Driven Entropy Aggregation for Byzantine-Robust Heterogeneous Federated Learning
Variational Feature Pyramid Networks
Active Learning with Disagreement Graphs
Differentially Private Database Release via Kernel Mean Embeddings
Towards Causal Algorithmic Recourse
Designing Decision Support Systems using Counterfactual Prediction Sets
Asymmetric Tri-training for Unsupervised Domain Adaptation
Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective
Augmenting and Efficiently Utilizing Domain Theory in Explanation-Based Natural Language Acquisition
1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects
Tensor Variable Elimination for Plated Factor Graphs
A regularization framework for multiple-instance learning
Efficient Algorithms for Empirical Group Distributionally Robust Optimization and Beyond
Bayesian Models of Data Streams with Hierarchical Power Priors
On Coresets for Clustering in Small Dimensional Euclidean spaces
Accurate Uncertainties for Deep Learning Using Calibrated Regression
Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage
PRIME: Deep Imbalanced Regression with Proxies
Differentially Private Learning with Kernels
Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning
Disentanglement Learning via Topology
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
Functional Alignment Can Mislead: Examining Model Stitching
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
Coalition calculation in a dynamic agent environment
Neuro-Visualizer: A Novel Auto-Encoder-Based Loss Landscape Visualization Method With an Application in Knowledge-Guided Machine Learning
Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration
Align, then memorise: the dynamics of learning with feedback alignment
An Information Geometry of Statistical Manifold Learning
No Free Lunch from Random Feature Ensembles: Scaling Laws and Near-Optimality Conditions
Approximate inference for planning in stochastic relational worlds
Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees
OSN: Infinite Representations of Dynamic 3D Scenes from Monocular Videos
Simple and Critical Iterative Denoising: A Recasting of Discrete Diffusion in Graph Generation
Mimicking Better by Matching the Approximate Action Distribution
Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization
Schwarz-Schur Involution: Lightspeed Differentiable Sparse Linear Solvers
Delving into Deep Imbalanced Regression
Efficient bandit algorithms for online multiclass prediction
Does Continual Learning Equally Forget All Parameters?
Which Attention Heads Matter for In-Context Learning?
GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model
Internal World Models and Supervised Learning
An Efficient Posterior Regularized Latent Variable Model for Interactive Sound Source Separation
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Learning to Learn from APIs: Black-Box Data-Free Meta-Learning
Efficient multiclass maximum margin clustering
One Leaf Reveals the Season: Occlusion-Based Contrastive Learning with Semantic-Aware Views for Efficient Visual Representation
CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks
Learning to learn implicit queries from gaze patterns
Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection
HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption
Learning with Incrutable Theories
Collaborative Filtering Using Weighted Majority Prediction Algorithms
Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization
Bootstrapped Representations in Reinforcement Learning
A Supra-Classifier Architecture for Scalable Knowledge Reuse
Active Sampling for Min-Max Fairness
Friend-or-Foe Q-learning in General-Sum Games
A Statistical Manifold Framework for Point Cloud Data
Learning predictive state representations using non-blind policies
SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering?
Self-Imitation Learning
Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions
Elementary Estimators for High-Dimensional Linear Regression
DoCoFL: Downlink Compression for Cross-Device Federated Learning
Marginalized Denoising Auto-encoders for Nonlinear Representations
Approximate Inference in Collective Graphical Models
Opponent-Limited Online Search for Imperfect Information Games
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation
Kepler codebook
Convergence of Policy Mirror Descent Beyond Compatible Function Approximation
LaVAN: Localized and Visible Adversarial Noise
Taming Knowledge Conflicts in Language Models
Deep Models of Interactions Across Sets
Parsing Natural Scenes and Natural Language with Recursive Neural Networks
Graphical-model based estimation and inference for differential privacy
Recurrent Predictive State Policy Networks
Replica Conditional Sequential Monte Carlo
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing
Sensitivity Analysis of Linear Structural Causal Models
A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel
The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability
Automatic Selection of Split Criterion during Tree Growing Based on Node Location
Neural Autoregressive Flows
Learning by Turning: Neural Architecture Aware Optimisation
Hilbert Space Embeddings of Hidden Markov Models
Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification
Density Constrained Reinforcement Learning
Fast (1+ε)-Approximation Algorithms for Binary Matrix Factorization
Learning Augmented Binary Search Trees
Robust Subtask Learning for Compositional Generalization
Retrospective Motion Correction of Magnitude-Input MR Images
Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilities
Dirichlet Flow Matching with Applications to DNA Sequence Design
Variational Inference with Locally Enhanced Bounds for Hierarchical Models
Parameter Estimation in DAGs from Incomplete Data via Optimal Transport
Accelerating Natural Gradient with Higher-Order Invariance
End-to-End Learning Framework for Solving Non-Markovian Optimal Control
Finite-Time Analysis of Discrete-Time Stochastic Interpolants
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent
Learning Goal-Decomposition Rules using Exercises
Simple Disentanglement of Style and Content in Visual Representations
Collaborative Learning with Different Labeling Functions
Improved Operator Learning by Orthogonal Attention
Sparse Additive Generative Models of Text
Efficient Learning of Logic Programs with Non-determinant, Non-discriminating Literals
Understanding the Representation and Computation of Multilayer Perceptrons: A Case Study in Speech Recognition
Streaming Principal Component Analysis in Noisy Settings
Multi-fidelity Bayesian Optimisation with Continuous Approximations
Graph Neural Network Explanations are Fragile
What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?
Popular decision tree algorithms are provably noise tolerant
Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications
Implicit Jacobian regularization weighted with impurity of probability output
A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts
Causal Inference out of Control: Estimating Performativity without Treatment Randomization
Robust Budget Allocation via Continuous Submodular Functions
Model-Based Reinforcement Learning with Value-Targeted Regression
Differentially Private Sharpness-Aware Training
Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization
LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework
Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization
Does the Data Induce Capacity Control in Deep Learning?
LCA-on-the-Line: Benchmarking Out of Distribution Generalization with Class Taxonomies
The Saddle-Point Method in Differential Privacy
Implicit differentiation of Lasso-type models for hyperparameter optimization
Anytime Exploration for Multi-armed Bandits using Confidence Information
Why Experimentation can be better than "Perfect Guidance"
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning
Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformer
Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks
Revisiting Peng's Q(λ) for Modern Reinforcement Learning
Discriminative cluster analysis
Run-Sort-ReRun: Escaping Batch Size Limitations in Sliced Wasserstein Generative Models
Continuous Deep Q-Learning with Model-based Acceleration
Simplicity Bias of Two-Layer Networks beyond Linearly Separable Data
Combining discriminative features to infer complex trajectories
Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections
Learning Diverse-Structured Networks for Adversarial Robustness
Online Adaptive Anomaly Thresholding with Confidence Sequences
Narrowing the Gap: Random Forests In Theory and In Practice
On the Convergence of Gradient Flow on Multi-layer Linear Models
A Stick-Breaking Construction of the Beta Process
Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron
Scaling Exponents Across Parameterizations and Optimizers
An Expressive and Self-Adaptive Dynamical System for Efficient Function Learning
A Random Matrix Approach to Echo-State Neural Networks
Locally linear metric adaptation for semi-supervised clustering
Training SVM with indefinite kernels
Primal-Dual Rates and Certificates
Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts
Sparsity-Based Generalization Bounds for Predictive Sparse Coding
Toward Availability Attacks in 3D Point Clouds
Exemplar-Based Theory Rejection: An Approach to the Experience Consistency Problem
Local learning projections
Coordinated Multi-Agent Imitation Learning
Temporally Correlated Task Scheduling for Sequence Learning
Experiments on the Costs and Benefits of Windowing in ID3
A Persistent Weisfeiler-Lehman Procedure for Graph Classification
Attack-free Evaluating and Enhancing Adversarial Robustness on Categorical Data
Communicating Activations Between Language Model Agents
Reinforcement Learning Control of a Physical Robot Device for Assisted Human Walking without a Simulator
NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction
Disparate Conditional Prediction in Multiclass Classifiers
Sparse-pivot: Dynamic correlation clustering for node insertions
Scalable Model Merging with Progressive Layer-wise Distillation
Improving Generalization in Offline Reinforcement Learning via Adversarial Data Splitting
Bias Plus Variance Decomposition for Zero-One Loss Functions
Large Scale Variational Bayesian Inference for Structured Scale Mixture Models
RBench: Graduate-level Multi-disciplinary Benchmarks for LLM & MLLM Complex Reasoning Evaluation
EDISON: Enhanced Dictionary-Induced Tensorized Incomplete Multi-View Clustering with Gaussian Error Rank Minimization
Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers
Induction Over the Unexplained: Integrated Learning of Concepts with Both Explainable and Conventional Aspects
Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach
Controlling Neural Collapse Enhances Out-of-Distribution Detection and Transfer Learning
Towards Learning Geometric Eigen-Lengths Crucial for Fitting Tasks
Interpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based approach
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback
Improving Neural Additive Models with Bayesian Principles
Improved StyleGAN-v2 based Inversion for Out-of-Distribution Images
Large-Scale Convex Minimization with a Low-Rank Constraint
SurProGenes: Survival Risk-Ordered Representation of Cancer Patients and Genes for the Identification of Prognostic Genes
Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning
A Powerful Heuristic for the Discovery of Complex Patterned Behaviour
Better Diffusion Models Further Improve Adversarial Training
Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML 2007), Corvallis, Oregon, USA, June 20-24, 2007
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape
Learning to Optimize Multigrid PDE Solvers
Does Label Smoothing Help Deep Partial Label Learning?
Generalised Policy Improvement with Geometric Policy Composition
Sequential Bayesian prediction in the presence of changepoints
Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation
MTSTRec: Multimodal Time-Aligned Shared Token Recommender
Scaling Multidimensional Gaussian Processes using Projected Additive Approximations
Online Matching with Stochastic Rewards: Provable Better Bound via Adversarial Reinforcement Learning
Symbolic Network: Generalized Neural Policies for Relational MDPs
Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models
Neural Inverse Transform Sampler
Flow-Guided Sparse Transformer for Video Deblurring
Haar Graph Pooling
Efficient Domain-Independent Experimentation
Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark
Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk
DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning
SpikF: Spiking Fourier Network for Efficient Long-term Prediction
SGA: A Robust Algorithm for Partial Recovery of Tree-Structured Graphical Models with Noisy Samples
Weakly-Supervised Disentanglement Without Compromises
Provably Better Explanations with Optimized Aggregation of Feature Attributions
Training structural SVMs when exact inference is intractable
VCT: Training Consistency Models with Variational Noise Coupling
Alternating Randomized Block Coordinate Descent
Fast Approximate Spectral Clustering for Dynamic Networks
Incorporating Diversity in Active Learning with Support Vector Machines
ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Models
Average-Reward Off-Policy Policy Evaluation with Function Approximation
Concept Sharing: A Means to Improve Multi-Concept Learning
Eliciting Language Model Behaviors with Investigator Agents
Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation
Thresholded Lasso Bandit
Prototypical Transformer As Unified Motion Learners
Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior
Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001
Topological Neural Networks go Persistent, Equivariant, and Continuous
Gradient LASSO for feature selection
Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural Networks
Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants
Missing Data Imputation using Optimal Transport
Learning fair representation with a parametric integral probability metric
Machine Unlearning for Random Forests
A Unified Framework for Generalization Error Analysis of Learning with Arbitrary Discrete Weak Features
Probing Visual Language Priors in VLMs
DBSCAN++: Towards fast and scalable density clustering
Consistent Multiclass Algorithms for Complex Performance Measures
Conformal Prediction Sets with Limited False Positives
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
Orthogonal Machine Learning: Power and Limitations
ELLA: An Efficient Lifelong Learning Algorithm
Convergence rates for persistence diagram estimation in Topological Data Analysis
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
Acquisition of Dynamic Control Knowledge for a Robotic Manipulator
SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning
Faster Approximation Algorithms for k-Center via Data Reduction
Rapid Learning without Catastrophic Forgetting in the Morris Water Maze
SMaRt: Improving GANs with Score Matching Regularity
Synthesizing Software Engineering Data in a Test-Driven Manner
Harmonizing Geometry and Uncertainty: Diffusion with Hyperspheres
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
Adversarial Robustness Guarantees for Random Deep Neural Networks
Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games
No-regret Algorithms for Capturing Events in Poisson Point Processes
Online Continual Learning through Mutual Information Maximization
Bipartite Matching in Massive Graphs: A Tight Analysis of EDCS
Constructive Induction in Theory Refinement
Fluctuations of the largest eigenvalues of transformed spiked Wigner matrices
Scalable and Robust Bayesian Inference via the Median Posterior
Improving Transformers with Dynamically Composable Multi-Head Attention
Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!
Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo
Dual Supervised Learning
Convex Regularization in Monte-Carlo Tree Search
Provably Efficient Offline Reinforcement Learning for Partially Observable Markov Decision Processes
Dead-ends and Secure Exploration in Reinforcement Learning
Coactive Learning for Large Language Models using Implicit User Feedback
Approval Voting and Incentives in Crowdsourcing
HAWQ-V3: Dyadic Neural Network Quantization
Testing the Feasibility of Linear Programs with Bandit Feedback
NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscation
Federated Learning under Arbitrary Communication Patterns
Differentially-Private Clustering of Easy Instances
Consistent k-Clustering
An Analysis of Linear Time Series Forecasting Models
A Graphbased Framework for Multi-Task Multi-View Learning
Retrofitting Decision Tree Classifiers Using Kernel Density Estimation
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift
Soft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration
Trading-Off Payments and Accuracy in Online Classification with Paid Stochastic Experts
Budgeted Experiment Design for Causal Structure Learning
Local Bayesian Optimization of Motor Skills
Group lasso with overlap and graph lasso
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning
Low Rank Approximation using Error Correcting Coding Matrices
Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems
A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery
Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations
Ad-Hoc Human-AI Coordination Challenge
OTTER: A Vision-Language-Action Model with Text-Aware Visual Feature Extraction
Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis
Combinatorial Pure Exploration for Dueling Bandit
Fairness for Image Generation with Uncertain Sensitive Attributes
Adjustment Criteria for Generalizing Experimental Findings
To be Robust or to be Fair: Towards Fairness in Adversarial Training
Redundancy Undermines the Trustworthiness of Self-Interpretable GNNs
Mixture Proportion Estimation Beyond Irreducibility
On Preemption and Learning in Stochastic Scheduling
Differentially Private Ordinary Least Squares
Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling
Mitigating Neural Network Overconfidence with Logit Normalization
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders
Proactive DP: A Multiple Target Optimization Framework for DP-SGD
Diagnosing Bottlenecks in Deep Q-learning Algorithms
Unsupervised Representation Learning via Neural Activation Coding
Graph Networks as Learnable Physics Engines for Inference and Control
Shampoo: Preconditioned Stochastic Tensor Optimization
eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data
Consistency of Causal Inference under the Additive Noise Model
Scalable Differential Privacy with Certified Robustness in Adversarial Learning
The dynamics of representation learning in shallow, non-linear autoencoders
Imitating Latent Policies from Observation
High Confidence Generalization for Reinforcement Learning
Feature selection using e-values
Small Data, Big Decisions: Model Selection in the Small-Data Regime
Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions
Local Linear Convergence of Douglas-Rachford for Linear Programming: a Probabilistic Analysis
Dash: Semi-Supervised Learning with Dynamic Thresholding
Kernel Sufficient Dimension Reduction and Variable Selection for Compositional Data via Amalgamation
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions
DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents
Smart Initial Basis Selection for Linear Programs
Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D
Incorporating Grouping Information into Bayesian Decision Tree Ensembles
Goal-directed Learning to Fly
Inverse Reinforcement Learning with Switching Rewards and History Dependency for Characterizing Animal Behaviors
Exploiting Cyclic Symmetry in Convolutional Neural Networks
Alleviating Privacy Attacks via Causal Learning
An Architecture Search Framework for Inference-Time Techniques
Improved Information Gain Estimates for Decision Tree Induction
Expensive Function Optimization with Stochastic Binary Outcomes