yusuke1997/dblp-id-title huggingface dataset:
DVAE++: Discrete Variational Autoencoders with Overlapping Transformations
Efficient Policy Learning from Surrogate-Loss Classification Reductions
Continuous Graph Neural Networks
Self-Supervised Representation Learning via Latent Graph Prediction
DROCC: Deep Robust One-Class Classification
Learning Linear Functions with Quadratic and Linear Multiplicative Updates
Not All Samples Are Created Equal: Deep Learning with Importance Sampling
DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization
Disparate Impact on Group Accuracy of Linearization for Private Inference
PaperBench: Evaluating AI's Ability to Replicate AI Research
Unsupervised Deep Embedding for Clustering Analysis
Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting Lambda
Aggregating From Multiple Target-Shifted Sources
Explaining and Generalizing Diagnostic Decisions
Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning
Actor-Attention-Critic for Multi-Agent Reinforcement Learning
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing
Finding Underlying Connections: A Fast Graph-Based Method for Link Analysis and Collaboration Queries
Graph construction and b -matching for semi-supervised learning
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures
A fast natural Newton method
On Connected Sublevel Sets in Deep Learning
Optimizing Robustness and Accuracy in Mixture of Experts: A Dual-Model Approach
Efficient Logit-based Knowledge Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deployment
Mastering Multiple-Expert Routing: Realizable H-Consistency and Strong Guarantees for Learning to Defer
Natural Analysts in Adaptive Data Analysis
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
Variational Inference with Normalizing Flows
Tell, Don't Show: Language Guidance Eases Transfer Across Domains in Images and Videos
Bit Allocation using Optimization
Riemann Tensor Neural Networks: Learning Conservative Systems with Physics-Constrained Networks
Gradient Projection Iterative Sketch for Large-Scale Constrained Least-Squares
Bounding Training Data Reconstruction in Private (Deep) Learning
Adaptive p-posterior mixture-model kernels for multiple instance learning
Causal Attribution Analysis for Continuous Outcomes
A Meta-learner for Heterogeneous Effects in Difference-in-Differences
Interactive Learning from Policy-Dependent Human Feedback
LLM Maybe LongLM: SelfExtend LLM Context Window Without Tuning
Constrained Offline Policy Optimization
Large Scale Private Learning via Low-rank Reparametrization
Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA
QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks
Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization
Does Sparsity Help in Learning Misspecified Linear Bandits?
Hybrid Models with Deep and Invertible Features
Global Context Vision Transformers
Peri-LN: Revisiting Normalization Layer in the Transformer Architecture
Fast Maximization of Non-Submodular, Monotonic Functions on the Integer Lattice
POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging
Projection-Free Online Convex Optimization with Time-Varying Constraints
Safe Screening with Variational Inequalities and Its Application to Lasso
Tandem Transformers for Inference Efficient LLMs
Adaptive Horizon Actor-Critic for Policy Learning in Contact-Rich Differentiable Simulation
Dynamic preferences in multi-criteria reinforcement learning
Logarithmic Regret for Online KL-Regularized Reinforcement Learning
Global Optimization with Parametric Function Approximation
Automatic Reward Shaping from Confounded Offline Data
An Information-Geometric Distance on the Space of Tasks
The Balanced-Pairwise-Affinities Feature Transform
Large-Scale Feature Learning With Spike-and-Slab Sparse Coding
Optimizing Language Models for Inference Time Objectives using Reinforcement Learning
Some Sparse Approximation Bounds for Regression Problems
ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval
Causal Proxy Models for Concept-based Model Explanations
Robust Inverse Graphics via Probabilistic Inference
From Importance Sampling to Doubly Robust Policy Gradient
Surface Snapping Optimization Layer for Single Image Object Shape Reconstruction
Are High-Quality AI-Generated Images More Difficult for Models to Detect?
Local Manifold Approximation and Projection for Manifold-Aware Diffusion Planning
SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference
Large Displacement Motion Transfer with Unsupervised Anytime Interpolation
APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference
Graph Optimal Transport for Cross-Domain Alignment
S4S: Solving for a Fast Diffusion Model Solver
Transfer and Marginalize: Explaining Away Label Noise with Privileged Information
Thompson Sampling with Less Exploration is Fast and Optimal
Outlier-aware Slicing for Post-Training Quantization in Vision Transformer
Uniformly Stable Algorithms for Adversarial Training and Beyond
CROW: Eliminating Backdoors from Large Language Models via Internal Consistency Regularization
On the difficulty of training recurrent neural networks
Efficient Online Reinforcement Learning for Diffusion Policy
Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer Evaluations
Efficient Locally Weighted Polynomial Regression Predictions
Almost surely constrained convex optimization
Near-Optimal Bounds for Cross-Validation via Loss Stability
Characterizing Fairness Over the Set of Good Models Under Selective Labels
Two Simple Ways to Learn Individual Fairness Metrics from Data
On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data
Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design
A trust-region method for stochastic variational inference with applications to streaming data
Scalable Exact Inference in Multi-Output Gaussian Processes
Adding seemingly uninformative labels helps in low data regimes
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebras
Recovery Guarantees for One-hidden-layer Neural Networks
Winner-takes-all learners are geometry-aware conditional density estimators
Learning Algorithms for Active Learning
Global Concavity and Optimization in a Class of Dynamic Discrete Choice Models
Reducing Sampling Error in Batch Temporal Difference Learning
Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer
Adaptive Conformal Predictions for Time Series
Linear Contextual Bandits With Interference
Grokking Group Multiplication with Cosets
Elucidating the Design Space of Multimodal Protein Language Models
Preference Adaptive and Sequential Text-to-Image Generation
Near-Linear Time Approximation Algorithms for k-means with Outliers
Position: AI-Powered Autonomous Weapons Risk Geopolitical Instability and Threaten AI Research
PID Accelerated Value Iteration Algorithm
Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018
TabLeak: Tabular Data Leakage in Federated Learning
Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups
Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters
MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases
AdaScale SGD: A User-Friendly Algorithm for Distributed Training
Fast rates for noisy interpolation require rethinking the effect of inductive bias
MaSS: Multi-attribute Selective Suppression for Utility-preserving Data Transformation from an Information-theoretic Perspective
TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning
Intersecting Faces: Non-negative Matrix Factorization With New Guarantees
Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network
Linear Regression with Limited Observation
SelfIE: Self-Interpretation of Large Language Model Embeddings
Inferring Relational Potentials in Interacting Systems
Rethinking Decision Transformer via Hierarchical Reinforcement Learning
Intersectional Unfairness Discovery
ASAP.SGD: Instance-based Adaptiveness to Staleness in Asynchronous SGD
More Robust Doubly Robust Off-policy Evaluation
A Machine Learning Framework for Programming by Example
VA-learning as a more efficient alternative to Q-learning
Value-at-Risk Optimization with Gaussian Processes
A Critical View of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignment
An Integrated Connectionist Approach to Reinforcement Learning for Robotic Control
Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup
When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models
Implicit Regularization for Tubal Tensor Factorizations via Gradient Descent
Accumulated Decoupled Learning with Gradient Staleness Mitigation for Convolutional Neural Networks
Not all solutions are created equal: An analytical dissociation of functional and representational similarity in deep linear neural networks
Better Locally Private Sparse Estimation Given Multiple Samples Per User
Provable Non-convex Phase Retrieval with Outliers: Median TruncatedWirtinger Flow
SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation
Fixed Point Quantization of Deep Convolutional Networks
A Robust Approach to Sequential Information Theoretic Planning
Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs
Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline
Stability and Generalization of Learning Algorithms that Converge to Global Optima
Query Complexity of Adversarial Attacks
Concurrent Reinforcement Learning with Aggregated States via Randomized Least Squares Value Iteration
One-Bit Compressed Sensing: Provable Support and Vector Recovery
Gaussian Process Classification and Active Learning with Multiple Annotators
Multi-criteria Reinforcement Learning
One Practical Algorithm for Both Stochastic and Adversarial Bandits
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Non-Asymptotic and Non-Lipschitzian Bounds on Optimal Values in Stochastic Optimization Under Heavy Tails
Model-Based Relational RL When Object Existence is Partially Observable
Improved Communication-Privacy Trade-offs in L2 Mean Estimation under Streaming Differential Privacy
Improved Nyström low-rank approximation and error analysis
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits
Latent Variable Estimation in Bayesian Black-Litterman Models
Lifelong Language Pretraining with Distribution-Specialized Experts
An Empirical Analysis of EBL Approaches for Learning Plan Schemata
MaskTwins: Dual-form Complementary Masking for Domain-Adaptive Image Segmentation
Self-Consuming Generative Models with Adversarially Curated Data
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds
Efficient Online ML API Selection for Multi-Label Classification Tasks
A Cross Modal Knowledge Distillation & Data Augmentation Recipe for Improving Transcriptomics Representations through Morphological Features
From Feature Interaction to Feature Generation: A Generative Paradigm of CTR Prediction Models
Comparing Several Linear-threshold Learning Algorithms on Tasks Involving Superfluous Attributes
Cooperation in Knowledge Base Refinement
An Incremental Method for Finding Multivariate Splits for Decision Trees
Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection
Low Bias Low Variance Gradient Estimates for Boolean Stochastic Networks
Thompson Sampling for Contextual Bandits with Linear Payoffs
Discovering symbolic policies with deep reinforcement learning
Generalization Performance of Ensemble Clustering: From Theory to Algorithm
IBCircuit: Towards Holistic Circuit Discovery with Information Bottleneck
Preserving Modes and Messages via Diverse Particle Selection
An epsilon-Optimal Grid-Based Algorithm for Partially Observable Markov Decision Processes
On Explainability of Graph Neural Networks via Subgraph Explorations
Optimal Budget Allocation: Theoretical Guarantee and Efficient Algorithm
Predicting the Generalization Performance of Cross Validatory Model Selection Criteria
Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams
Fast gradient-descent methods for temporal-difference learning with linear function approximation
Support Vector Machine Active Learning with Application sto Text Classification
Negative Sampling in Semi-Supervised learning
Problems with Shapley-value-based explanations as feature importance measures
What Good Are Experiments?
IBD-PSC: Input-level Backdoor Detection via Parameter-oriented Scaling Consistency
Revisiting Instance-Optimal Cluster Recovery in the Labeled Stochastic Block Model
Robust Offline Reinforcement Learning with Linearly Structured f-Divergence Regularization
The Canonical Distortion Measure for Vector Quantization and Function Approximation
Subtle Topic Models and Discovering Subtly Manifested Software Concerns Automatically
MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations
Compositional Image Decomposition with Diffusion Models
Evolving Semantic Prototype Improves Generative Zero-Shot Learning
Multi-Track Message Passing: Tackling Oversmoothing and Oversquashing in Graph Learning via Preventing Heterophily Mixing
False Discovery Rate Control and Statistical Quality Assessment of Annotators in Crowdsourced Ranking
Using Domain Knowledge to Aid Scientific Theory Revision
Predicting Multiprocessor Memory Access Patterns with Learning Models
Imitation Learning by Estimating Expertise of Demonstrators
Strengthen Out-of-Distribution Detection Capability with Progressive Self-Knowledge Distillation
MASS: Masked Sequence to Sequence Pre-training for Language Generation
Width Provably Matters in Optimization for Deep Linear Neural Networks
Winner-takes-all for Multivariate Probabilistic Time Series Forecasting
Determinate Literals in Inductive Logic Programming
WSNet: Compact and Efficient Networks Through Weight Sampling
How do Large Language Models Navigate Conflicts between Honesty and Helpfulness?
Viewing Transformers Through the Lens of Long Convolutions Layers
AuPair: Golden Example Pairs for Code Repair
Sign Gradient Descent-based Neuronal Dynamics: ANN-to-SNN Conversion Beyond ReLU Network
Transparency Promotion with Model-Agnostic Linear Competitors
Resolving Lexical Bias in Model Editing
From Robustness to Privacy and Back
Continual Reinforcement Learning with Complex Synapses
Memorization Through the Lens of Curvature of Loss Function Around Samples
GRU: Mitigating the Trade-off between Unlearning and Retention for LLMs
A Large-Scale Study of Probabilistic Calibration in Neural Network Regression
Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm
Bayesian leave-one-out cross-validation for large data
Formalizing Preferences Over Runtime Distributions
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
Active Manifolds: A non-linear analogue to Active Subspaces
BaxBench: Can LLMs Generate Correct and Secure Backends?
In-Context Deep Learning via Transformer Models
Graph Neural Networks Use Graphs When They Shouldn't
On Understanding Attention-Based In-Context Learning for Categorical Data
Parsimonious Learning-Augmented Approximations for Dense Instances of NP-hard Problems
Efficient On-Device Models using Neural Projections
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
Augmenting Decision with Hypothesis in Reinforcement Learning
Hierarchic Bayesian models for kernel learning
Scaling Up Ordinal Embedding: A Landmark Approach
De-coupled NeuroGF for Shortest Path Distance Approximations on Large Terrain Graphs
Differentiability and Optimization of Multiparameter Persistent Homology
On Deep Multi-View Representation Learning
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least
Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation
Local Vertex Colouring Graph Neural Networks
Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification
Revising Relational Domain Theories
Unnatural Languages Are Not Bugs but Features for LLMs
PPG Reloaded: An Empirical Study on What Matters in Phasic Policy Gradient
HPS: Hard Preference Sampling for Human Preference Alignment
Demonstration-Conditioned Reinforcement Learning for Few-Shot Imitation
Bimodal Modelling of Source Code and Natural Language
Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function
Adapting Kernel Representations Online Using Submodular Maximization
NETS: A Non-equilibrium Transport Sampler
Automatic Algorith/Model Class Selection
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems
Average Reward Optimization Objective In Partially Observable Domains
Early and Reliable Event Detection Using Proximity Space Representation
Automated inference of point of view from user interactions in collective intelligence venues
GistScore: Learning Better Representations for In-Context Example Selection with Gist Bottlenecks
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods
Trees with Attention for Set Prediction Tasks
Compositional Few-Shot Class-Incremental Learning
Speeding-up Nearest Neighbour Memories: The Template Tree Case Memory Organisation
Learning Portable Representations for High-Level Planning
Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data
Adaptive mesh compression in 3D computer graphics using multiscale manifold learning
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning
Theory Discovery and the Hypothesis Language
Predicting Choice with Set-Dependent Aggregation
Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation
Generative Social Choice: The Next Generation
Rethinking Momentum Knowledge Distillation in Online Continual Learning
Radio: Rate-Distortion Optimization for Large Language Model Compression
The projectron: a bounded kernel-based Perceptron
Accurate LoRA-Finetuning Quantization of LLMs via Information Retention
Bilevel Optimization with Coupled Decision-Dependent Distributions
Task Generalization with Autoregressive Compositional Structure: Can Learning from D Tasks Generalize to DT Tasks?
Robust Decision Trees Against Adversarial Examples
Online decoding of Markov models under latency constraints
Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization
VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix
Learning Controllable Degradation for Real-World Super-Resolution via Constrained Flows
Stochastic Bandits with ReLU Neural Networks
Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification
Harmonic Neural Networks
A worst-case comparison between temporal difference and residual gradient with linear function approximation
Datamodels: Understanding Predictions with Data and Data with Predictions
The Monge Gap: A Regularizer to Learn All Transport Maps
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
Asymmetric Loss Functions for Learning with Noisy Labels
Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation
Major-Minor Mean Field Multi-Agent Reinforcement Learning
Estimating Unknown Sparsity in Compressed Sensing
RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior
Learning Progress Driven Multi-Agent Curriculum
Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
Multi-instance tree learning
Effective Federated Graph Matching
SpaceMAP: Visualizing High-Dimensional Data by Space Expansion
Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks
Regret-Minimizing Double Oracle for Extensive-Form Games
Deep and Flexible Graph Neural Architecture Search
Learning Concepts by Synthesizing Minimal Threshold Gate Networks
Ranking individuals by group comparisons
How Spurious Features are Memorized: Precise Analysis for Random and NTK Features
Efficient Contextual Bandits with Uninformed Feedback Graphs
Structured Preconditioners in Adaptive Optimization: A Unified Analysis
Direct Motion Models for Assessing Generated Videos
Structured Prediction Energy Networks
Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving Regularization
Heterogeneous Label Shift: Theory and Algorithm
Optimal dimensionality of metric space for classification
REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective
An Equivalence Between Data Poisoning and Byzantine Gradient Attacks
Learning Physics Via Explanation-Based Learning of Correctness and Analogical Search Control
Efficient PAC Learning from the Crowd with Pairwise Comparisons
Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding
Learning interpretable continuous-time models of latent stochastic dynamical systems
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
An HDP-HMM for systems with state persistence
Tracking The Best Expert Privately
Planning by Incremental Dynamic Programming
Online dictionary learning for sparse coding
SoftSort: A Continuous Relaxation for the argsort Operator
Reduction: A Practical Mechanism of Searching for Regularity in Data
Evolution-Inspired Loss Functions for Protein Representation Learning
Learning Hierarchical Riffle Independent Groupings from Rankings
Entropy-Reinforced Planning with Large Language Models for Drug Discovery
Attention-Level Speculation
Lie Point Symmetry Data Augmentation for Neural PDE Solvers
Linear Explanations for Individual Neurons
Demystifying SGD with Doubly Stochastic Gradients
Linear and Parallel Learning of Markov Random Fields
The Computational Complexity of Concise Hypersphere Classification
ADIOS: Antibody Development via Opponent Shaping
Directional Bias Amplification
WISER: Weak Supervision and Supervised Representation Learning to Improve Drug Response Prediction in Cancer
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform
Learning Localized Spatio-Temporal Models From Streaming Data
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs
Interpretable Stability Bounds for Spectral Graph Filters
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization
Understanding the Forgetting of (Replay-based) Continual Learning via Feature Learning: Angle Matters
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo