yusuke1997/dblp-id-title huggingface dataset:
MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time
Invertible generative models for inverse problems: mitigating representation error and dataset bias
Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model
An Analysis of Learning to Plan as a Search Problem
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning
Tied-Augment: Controlling Representation Similarity Improves Data Augmentation
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?
Hgformer: Hyperbolic Graph Transformer for Collaborative Filtering
CtrlSynth: Controllable Image Text Synthesis for Data-Efficient Multimodal Learning
Text Categorization and Relational Learning
Target-based Surrogates for Stochastic Optimization
Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization
Algorithmic Stability and Hypothesis Complexity
Total Variation and Euler's Elastica for Supervised Learning
Stabilizing GANs' Training with Brownian Motion Controller
Monte-Carlo Tree Search as Regularized Policy Optimization
FeDXL: Provable Federated Learning for Deep X-Risk Optimization
Simultaneous Similarity-based Self-Distillation for Deep Metric Learning
SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization
The Complexity of Learning Sparse Superposed Features with Feedback
Counterfactual Analysis in Dynamic Latent State Models
Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting
Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss
AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N
Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano
Patch-wise Structural Loss for Time Series Forecasting
Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic
On Last-Iterate Convergence Beyond Zero-Sum Games
Non-negative tensor factorization with applications to statistics and computer vision
Learning Vision and Language Concepts for Controllable Image Generation
Calibrated Learning to Defer with One-vs-All Classifiers
UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction
On the Guidance of Flow Matching
Kernel information embeddings
IDYNO: Learning Nonparametric DAGs from Interventional Dynamic Data
Model Fusion for Personalized Learning
Crowdsourcing with Arbitrary Adversaries
CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints
Automatic basis function construction for approximate dynamic programming and reinforcement learning
Constrained Causal Bayesian Optimization
Tractable structured natural-gradient descent using local parameterizations
Deep Spectral Clustering Learning
Counterfactual Data-Fusion for Online Reinforcement Learners
Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense
Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization
Advancing Dynamic Sparse Training by Exploring Optimization Opportunities
Compiling Learning Vocabulary from a Performance System Description
k-means++: few more steps yield constant approximation
An Efficient Maximal Ancestral Graph Listing Algorithm
On the Identifiability and Estimation of Causal Location-Scale Noise Models
Deep Streaming Label Learning
Optimal Estimation of High Dimensional Smooth Additive Function Based on Noisy Observations
Latent Intention Dialogue Models
LipsNet++: Unifying Filter and Controller into a Policy Network
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning
Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble
Can Transformers Reason Logically? A Study in SAT Solving
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation
Tracking value function dynamics to improve reinforcement learning with piecewise linear function approximation
Understanding Backdoor Attacks through the Adaptability Hypothesis
Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation
Scalable First-order Method for Certifying Optimal k-Sparse GLMs
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets
Quantum Expectation-Maximization for Gaussian mixture models
Which Tricks are Important for Learning to Rank?
Estimating Heterogeneous Treatment Effects: Mutual Information Bounds and Learning Algorithms
Full regularization path for sparse principal component analysis
Concentration Inequalities for General Functions of Heavy-Tailed Random Variables
Distribution Free Domain Generalization
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
GeoReasoner: Geo-localization with Reasoning in Street Views using a Large Vision-Language Model
A connection between Tempering and Entropic Mirror Descent
A Constraint-Motivated Model of Lexical Acquisition
dugMatting: Decomposed-Uncertainty-Guided Matting
Gaussian Processes Multiple Instance Learning
Convergence of Invariant Graph Networks
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption
Incorporating Information into Shapley Values: Reweighting via a Maximum Entropy Approach
On the Quality of the Initial Basin in Overspecified Neural Networks
Paging with Succinct Predictions
Efficient and Effective Time-Series Forecasting with Spiking Neural Networks
Learning Intra-Batch Connections for Deep Metric Learning
Locally Sparse Neural Networks for Tabular Biomedical Data
Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond
Asymptotically consistent estimation of the number of change points in highly dependent time series
Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems?
Weighted distance nearest neighbor condensing
Auto-Encoding Morph-Tokens for Multimodal LLM
Separating Skills from Preference: Using Learning to Program by Reward
Learning a kernel function for classification with small training samples
Generalized Sobolev Transport for Probability Measures on a Graph
Self-Paced Context Evaluation for Contextual Reinforcement Learning
Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes
Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm
Discriminative Gaussian Mixture Models: A Comparison with Kernel Classifiers
Improving Sign Random Projections With Additional Information
NICE Data Selection for Instruction Tuning in LLMs with Non-differentiable Evaluation Metric
Instruction Tuning for Secure Code Generation
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation
Semi-supervised learning of compact document representations with deep networks
Large Language Models Struggle to Learn Long-Tail Knowledge
Optimal PAC Multiple Arm Identification with Applications to Crowdsourcing
Delay-agnostic Asynchronous Coordinate Update Algorithm
Generative Marginalization Models
Incremental Topological Ordering and Cycle Detection with Predictions
Tutorial summary: Machine learning in IR: recent successes and new opportunities
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing-and Back
ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks
Preselection Bandits
Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)
Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees
Deep Reinforcement Learning from Hierarchical Preference Design
Quantile Credit Assignment
On the Power of Decision Lists
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games
Copyright Traps for Large Language Models
EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction
Active Risk Estimation
To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO
STAMP Your Content: Proving Dataset Membership via Watermarked Rephrasings
Rethinking Point Cloud Data Augmentation: Topologically Consistent Deformation
Robust Inverse Covariance Estimation under Noisy Measurements
Robust Principal Component Analysis with Side Information
Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications
Speeding up Policy Simulation in Supply Chain RL
Anderson Acceleration of Proximal Gradient Methods
Learning by Incomplete Explanation-Based Learning
On the Feasibility of Single-Pass Full-Capacity Learning in Linear Threshold Neurons with Binary Input Vectors
Variational Generative Stochastic Networks with Collaborative Shaping
Evaluating alternative Instance Representations
Learning dissimilarities by ranking: from SDP to QP
Transforming and Combining Rewards for Aligning Large Language Models
Making Better Use of Global Discretization
Learning Texture Manifolds with the Periodic Spatial GAN
Sonar-Based Mapping of Large-Scale Mobile Robot Environments using EM
Training conditional random fields via gradient tree boosting
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games
ELoRA: Low-Rank Adaptation for Equivariant GNNs
Optimization over Sparse Support-Preserving Sets: Two-Step Projection with Global Optimality Guarantees
IPBoost - Non-Convex Boosting via Integer Programming
Estimating Model Uncertainty of Neural Networks in Sparse Information Form
Temporal Difference Learning of Backgammon Strategy
On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization
Asynchronous Decentralized Optimization With Implicit Stochastic Variance Reduction
MARS: Unleashing the Power of Variance Reduction for Training Large Models
Streaming and Distributed Algorithms for Robust Column Subset Selection
A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point Processes
Heterogeneous Uncertainty Sampling for Supervised Learning
Online Conformal Prediction via Online Optimization
MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation
Directed Chain Generative Adversarial Networks
Abstraction Mechanisms Predict Generalization in Deep Neural Networks
Training Flexible Models of Genetic Variant Effects from Functional Annotations using Accelerated Linear Algebra
Score Matching with Missing Data
Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription
Optimizing abstaining classifiers using ROC analysis
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search
Learning Optimal Multimodal Information Bottleneck Representations
Multi-instance learning by treating instances as non-I.I.D. samples
Scaling Up Coordinate Descent Algorithms for Large ℓ 1 Regularization Problems
Most likely heteroscedastic Gaussian process regression
Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning
Value Function Based Production Scheduling
Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory
Guaranteed Tensor Decomposition: A Moment Approach
Taming MAML: Efficient unbiased meta-reinforcement learning
Fast methods for estimating the Numerical rank of large matrices
Cheap Bandits
Data spectroscopy: learning mixture models using eigenspaces of convolution operators
Generalized Leverage Scores: Geometric Interpretation and Applications
Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics
Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval
Learning-Rate-Free Learning by D-Adaptation
Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective
Algorithmic Collective Action in Machine Learning
On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies
Strongly-Typed Agents are Guaranteed to Interact Safely
An Efficient Approach for Assessing Hyperparameter Importance
Antidote: Post-fine-tuning Safety Alignment for Large Language Models against Harmful Fine-tuning Attack
Robust Regression on MapReduce
DIDI: Diffusion-Guided Diversity for Offline Behavioral Generation
Variational Inference for Sparse and Undirected Models
Interpretable Stein Goodness-of-fit Tests on Riemannian Manifold
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints
Dense Reward for Free in Reinforcement Learning from Human Feedback
Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis
Semi-Cyclic Stochastic Gradient Descent
Learning Force Control Policies for Compliant Robotic Manipulation
Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians
Distributional Rank Aggregation, and an Axiomatic Analysis
Estimating Structured Vector Autoregressive Models
Discrete Chebyshev Classifiers
Policy-Regret Minimization in Markov Games with Function Approximation
Evaluation methods for topic models
Meta Variance Transfer: Learning to Augment from the Others
I/O Complexity of Attention, or How Optimal is FlashAttention?
Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
Optimizing Non-decomposable Performance Measures: A Tale of Two Classes
LLM Data Selection and Utilization via Dynamic Bi-level Optimization
Random Teachers are Good Teachers
Active Evaluation Acquisition for Efficient LLM Benchmarking
Guided Policy Search
ModelDiff: A Framework for Comparing Learning Algorithms
Online Learning with Feedback Graphs: The True Shape of Regret
Fleet of Agents: Coordinated Problem Solving with Large Language Models
Amortized Equation Discovery in Hybrid Dynamical Systems
Delegating classifiers
Discrete Probabilistic Inverse Optimal Transport
Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning
Learning user preferences for sets of objects
Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference
Constructing States for Reinforcement Learning
Deep learning from temporal coherence in video
Resisting Stochastic Risks in Diffusion Planners with the Trajectory Aggregation Tree
Path Neural Networks: Expressive and Accurate Graph Neural Networks
An Enhancer for Reactive Plans
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
Pixel2Feature Attack (P2FA): Rethinking the Perturbed Space to Enhance Adversarial Transferability
A Unified View on PAC-Bayes Bounds for Meta-Learning
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training
Sanity Checking Causal Representation Learning on a Simple Real-World System
Incremental learning of linear model trees
Unsupervised Skill Discovery for Learning Shared Structures across Changing Environments
Offline Reinforcement Learning with Fisher Divergence Critic Regularization
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD
Fixing a Broken ELBO
Feasible Action Search for Bandit Linear Programs via Thompson Sampling
One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training
A Reduction from Linear Contextual Bandit Lower Bounds to Estimation Lower Bounds
Abstracting Imperfect Information Away from Two-Player Zero-Sum Games
Differentiable Combinatorial Scheduling at Scale
Plug-in martingales for testing exchangeability on-line
Sparse multiscale gaussian process regression
Generalizing Causal Effects from Randomized Controlled Trials to Target Populations across Diverse Environments
Straight to the Gradient: Learning to Use Novel Tokens for Neural Text Generation
To the Max: Reinventing Reward in Reinforcement Learning
The underlying structures of self-attention: symmetry, directionality, and emergent dynamics in Transformer training
Improving Neural Logic Machines via Failure Reflection
Zero-Shot Adaptation of Parameter-Efficient Fine-Tuning in Diffusion Models
Safely Learning Optimal Auctions: A Testable Learning Framework for Mechanism Design
Combinatorial Reinforcement Learning with Preference Feedback
Progressive Inference: Explaining Decoder-Only Sequence Classification Models Using Intermediate Predictions
Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios
Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control
Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets
On the Convergence of Projected Bures-Wasserstein Gradient Descent under Euclidean Strong Convexity
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits
Learning to Intervene on Concept Bottlenecks
Distributed Value Functions
Consistent Polyhedral Surrogates for Top-k Classification and Variants
Online Learning with Imperfect Hints
On Path to Multimodal Generalist: General-Level and General-Bench
Accelerating Large-Scale Inference with Anisotropic Vector Quantization
Multi-Source Conformal Inference Under Distribution Shift
Deep Tensor Convolution on Multicores
FAB-MAP: Appearance-Based Place Recognition and Mapping using a Learned Visual Vocabulary Model
LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction
A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation
Optimal Algorithm for Max-Min Fair Bandit
On Penalty-based Bilevel Gradient Descent Method
Towards Black-Box Membership Inference Attack for Diffusion Models
Diameter-Based Active Learning
Continuation Path Learning for Homotopy Optimization
Breaking the Barrier of Hard Samples: A Data-Centric Approach to Synthetic Data for Medical Tasks
From Crowdsourced Data to High-quality Benchmarks: Arena-Hard and Benchbuilder Pipeline
Diffusion Kernels on Graphs and Other Discrete Input Spaces
Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing
Active Nearest Neighbors in Changing Environments
Generative Temporal Models with Spatial Memory for Partially Observed Environments
Learning to Convolve: A Generalized Weight-Tying Approach
Trajectory Diversity for Zero-Shot Coordination
Learning to cluster using local neighborhood structure
Pitfalls in the use of Parallel Inference for the Dirichlet Process
MuLan: Adapting Multilingual Diffusion Models for Hundreds of Languages with Negligible Cost
DNA: Domain Generalization with Diversified Neural Averaging
Learning Discrete Representations via Information Maximizing Self-Augmented Training
Causal Strategic Classification: A Tale of Two Shifts
Attention-based Deep Multiple Instance Learning
One-Pass Feature Evolvable Learning with Theoretical Guarantees
Quantitative Universal Approximation Bounds for Deep Belief Networks
Pruning Adaptive Boosting
Activation Space Interventions Can Be Transferred Between Large Language Models
Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm
Outlier-Efficient Hopfield Layers for Large Transformer-Based Models
Data-Driven Selection of Instrumental Variables for Additive Nonlinear, Constant Effects Models
Performance Analysis of a Probabilistic Inductive Learning System
Anomaly Ranking as Supervised Bipartite Ranking
Low Complexity Homeomorphic Projection to Ensure Neural-Network Solution Feasibility for Optimization over (Non-)Convex Set
A Generative Process for Contractive Auto-Encoders
Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic
Set Based Stochastic Subsampling
Federated Learning of User Verification Models Without Sharing Embeddings
Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events
Online Feature Selection using Grafting
Relational Instance Based Regression for Relational Reinforcement Learning
Comprehensible Interpretation of Relief's Estimates
Memory Efficient Kernel Approximation
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
Consistent Multilabel Ranking through Univariate Losses
Batch mode active learning and its application to medical image classification
Breaking Inter-Layer Co-Adaptation by Classifier Anonymization
CoPINN: Cognitive Physics-Informed Neural Networks
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism
Asynchronous Coagent Networks
Tutorial summary: Active learning
Convergent TREE BACKUP and RETRACE with Function Approximation
Improved Approximations for Hard Graph Problems using Predictions
Robust Bayesian Classification Using An Optimistic Score Ratio
Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations
PinNet: Pinpoint Instructive Information for Retrieval Augmented Code-to-Text Generation
Vanilla Bayesian Optimization Performs Great in High Dimensions
The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning
Convergence Rates for Differentially Private Statistical Estimation
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems
Near-Optimal BRL using Optimistic Local Transitions
Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?
Stochastic Gradient Descent under Markovian Sampling Schemes
Private Lossless Multiple Release
Diffusion Based Representation Learning
Mu 2 SLAM: Multitask, Multilingual Speech and Language Models
ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias
Reified Context Models
Feasible Reachable Policy Iteration
Proper Network Interpretability Helps Adversarial Robustness in Classification
Ripple Attention for Visual Perception with Sub-quadratic Complexity
Byzantine-Resilient Federated Alternating Gradient Descent and Minimization for Partly-Decoupled Low Rank Matrix Learning
International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA
A scalable framework for discovering coherent co-clusters in noisy data
Adaptive Multi-prompt Contrastive Network for Few-shot Out-of-distribution Detection
Habitizing Diffusion Planning for Efficient and Effective Decision Making
Compositional Planning Using Optimal Option Models
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction
Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints
Correlated Errors in Large Language Models
Model Performance Scaling with Multiple Data Sources
Online Kernel Learning with a Near Optimal Sparsity Bound
Attentive Group Equivariant Convolutional Networks
Data-Efficient Molecular Generation with Hierarchical Textual Inversion
Verification of Machine Unlearning is Fragile
A Constraint-based Induction Algorithm in FOL
Parameter-Efficient Fine-Tuning with Discrete Fourier Transform
The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization
Bounding the fairness and accuracy of classifiers from population statistics
QuantTree: Histograms for Change Detection in Multivariate Data Streams
PEINR: A Physics-enhanced Implicit Neural Representation for High-Fidelity Flow Field Reconstruction
Correlation Clustering in Data Streams
Earley-Driven Dynamic Pruning for Efficient Structured Decoding
Dimensionality Reduction for Tukey Regression
Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer
Learning Adversarially Fair and Transferable Representations