yusuke1997/dblp-id-title huggingface dataset:
Minimax Differential Dynamic Programming: An Application to Robust Biped Walking
Weak Form Generalized Hamiltonian Learning
TrAct: Making First-layer Pre-Activations Trainable
Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation
Siamese Masked Autoencoders
Matrix Completion from Power-Law Distributed Samples
Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models
An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch
RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars
Manipulating a Learning Defender and Ways to Counteract
Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?
Convergence of Gradient EM on Multi-component Mixture of Gaussians
A Single Recipe for Online Submodular Maximization with Adversarial or Stochastic Constraints
Training and Analysing Deep Recurrent Neural Networks
One for All: Multi-Domain Joint Training for Point Cloud Based 3D Object Detection
Sensitivity analysis in HMMs with application to likelihood maximization
Towards Best-of-All-Worlds Online Learning with Feedback Graphs
Combiner: Full Attention Transformer with Sparse Computation Cost
Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation
Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models
Using Neural Networks to Improve Cochlear Implant Speech Perception
Your representations are in the network: composable and parallel adaptation for large scale models
Transfer Learning with Neural AutoML
Stochastic bandits with groups of similar arms
Coded Sequential Matrix Multiplication For Straggler Mitigation
Dynamic Causal Bayesian Optimization
Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution
Multiwavelet-based Operator Learning for Differential Equations
Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases
Regret Bounds for Learning State Representations in Reinforcement Learning
SCAMPS: Synthetics for Camera Measurement of Physiological Signals
Stable adaptive control with online learning
Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting
Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity
What the Vec? Towards Probabilistically Grounded Embeddings
Modeling Interactions of the Rat's Place and Head Direction Systems
Why are Visually-Grounded Language Models Bad at Image Classification?
Infinite State Bayes-Nets for Structured Domains
Black-Box Optimization with Local Generative Surrogates
Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation
Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space
Sample Complexity of Uniform Convergence for Multicalibration
Automatic Generation of Social Tags for Music Recommendation
StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences
The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning
Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks
Differentially Private Covariance Revisited
Meta-Reinforcement Learning with Universal Policy Adaptation: Provable Near-Optimality under All-task Optimum Comparator
Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks
Deep Variational Instance Segmentation
Evolutionary Neural Architecture Search for Transformer in Knowledge Tracing
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
Robust Fine-tuning of Zero-shot Models via Variance Reduction
Retinal Processing Emulation in a Programmable 2-Layer Analog Array Processor CMOS Chip
Shared Context Probabilistic Transducers
FUSE: Fast Unified Simulation and Estimation for PDEs
The Image Local Autoregressive Transformer
Feature Construction for Inverse Reinforcement Learning
CheXphoto: 10, 000+ Photos and Transformations of Chest X-rays for Benchmarking Deep Learning Robustness
Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents
Quantification of Uncertainty with Adversarial Models
A Rapid Graph-based Method for Arbitrary Transformation-Invariant Pattern Classification
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote
When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits
Outlier Detection with One-class Kernel Fisher Discriminants
Double Gumbel Q-Learning
Copeland Dueling Bandits
Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
Provable Guarantees for Neural Networks via Gradient Feature Learning
Sparse Coding of Natural Images Using an Overcomplete Set of Limited Capacity Units
Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions
Amortized Inference for Heterogeneous Reconstruction in Cryo-EM
A Logic for Expressing Log-Precision Transformers
Speakers optimize information density through syntactic reduction
Dual Discriminator Generative Adversarial Nets
Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes
Symbolic Graph Reasoning Meets Convolutions
Where Do Large Learning Rates Lead Us?
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
Fast and accurate randomized algorithms for low-rank tensor decompositions
Neural Information Processing Systems (NIPS) Mini-Symposium on Causality in Time Series, Vancouver, Canada, December 10, 2009
ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization
Feint Behaviors and Strategies: Formalization, Implementation and Evaluation
PDEBench: An Extensive Benchmark for Scientific Machine Learning
Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization
Weakly Supervised Dense Event Captioning in Videos
Bridging RL Theory and Practice with the Effective Horizon
Large Margin Hidden Markov Models for Automatic Speech Recognition
New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence
A Lower Bound of Hash Codes' Performance
NeuMA: Neural Material Adaptor for Visual Grounding of Intrinsic Dynamics
Predicting Complex Behavior in Sparse Asymmetric Networks
Learning from Distributions via Support Measure Machines
Temporal coding in the sub-millisecond range: Model of barn owl auditory pathway
Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection
A nonparametric variable clustering model
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability
ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution
DreamSteerer: Enhancing Source Image Conditioned Editability using Personalized Diffusion Models
Oracle-Efficient Online Learning for Smoothed Adversaries
SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation
Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices
Luckiness in Multiscale Online Learning
On the Downstream Performance of Compressed Word Embeddings
Mean Estimation with User-level Privacy under Data Heterogeneity
Robot Policy Learning with Temporal Optimal Transport Reward
Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel
Influence Patterns for Explaining Information Flow in BERT
To update or not to update? Neurons at equilibrium in deep models
Set-based Meta-Interpolation for Few-Task Meta-Learning
What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks
Optimality Criteria for LMS and Backpropagation
Discovering, Learning and Exploiting Relevance
Hyperbolic Neural Networks
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection
Independent Policy Gradient Methods for Competitive Reinforcement Learning
AID: Attention Interpolation of Text-to-Image Diffusion
Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation
Causal Inference for Event Pairs in Multivariate Point Processes
A State Representation for Diminishing Rewards
The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance
Neural Attentive Circuits
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting
Bayesian Network Induction via Local Neighborhoods
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
Training sparse natural image models with a fast Gibbs sampler of an extended state space
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler
Convex Relaxations of Latent Variable Training
GraB: Finding Provably Better Data Permutations than Random Reshuffling
Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences
Formalizing locality for normative synaptic plasticity models
Estimating Entropy of Distributions in Constant Space
The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning
Data-Driven Offline Decision-Making via Invariant Representation Learning
Nonzero-sum Adversarial Hypothesis Testing Games
Continual Learning in the Frequency Domain
Search for Information Bearing Components in Speech
Rotated Binary Neural Network
Searching the Search Space of Vision Transformer
Multi-task Batch Reinforcement Learning with Metric Learning
LF-Net: Learning Local Features from Images
TripleBlind: A Privacy-Preserving Framework for Decentralized Data and Algorithms
Learning without the Phase: Regularized PhaseMax Achieves Optimal Sample Complexity
Supervised Kernel Thinning
The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information
Multi-layer State Evolution Under Random Convolutional Design
Data-Efficient Instance Generation from Instance Discrimination
Learning compositional functions via multiplicative weight updates
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
Unsupervised Classifiers, Mutual Information and 'Phantom Targets'
Support Recovery of Sparse Signals from a Mixture of Linear Measurements
Optical Implementation of a Self-Organizing Feature Extractor
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning
Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective
Detecting Bugs with Substantial Monetary Consequences by LLM and Rule-based Reasoning
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization
Copula Multi-label Learning
Provably sample-efficient RL with side information about latent dynamics
Falcon: Fast Spectral Inference on Encrypted Data
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications
An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare
Learning Optimal Reserve Price against Non-myopic Bidders
Autobidder's Dilemma: Why More Sophisticated Autobidders Lead to Worse Auction Efficiency
A Large-Scale Database for Graph Representation Learning
3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction
Federated Learning via Meta-Variational Dropout
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks
Analog VLSI Circuits for Attention-Based, Visual Tracking
Graph Learning for Numeric Planning
Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization
Event-Driven Simulation of Networks of Spiking Neurons
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC
Heavy-tailed Representations, Text Polarity Classification & Data Augmentation
An Extended Level Method for Efficient Multiple Kernel Learning
Towards Deep Conversational Recommendations
Deep Synoptic Monte-Carlo Planning in Reconnaissance Blind Chess
Empirical performance maximization for linear rank statistics
Statistical Estimation in the Spiked Tensor Model via the Quantum Approximate Optimization Algorithm
Temporally Disentangled Representation Learning
A Polylog Pivot Steps Simplex Algorithm for Classification
Language and Visual Entity Relationship Graph for Agent Navigation
Evolution Algorithm and Online Learning for Racing Drone
Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs
MPNet: Masked and Permuted Pre-training for Language Understanding
Towards Robust and Expressive Whole-body Human Pose and Shape Estimation
The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data
Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language Models
Adversarial Examples in Multi-Layer Random ReLU Networks
Direct Preference-based Policy Optimization without Reward Modeling
Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models
bit2bit: 1-bit quanta video reconstruction via self-supervised photon prediction
Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study
Data Quality in Imitation Learning
Neuromorphic Networks Based on Sparse Optical Orthogonal Codes
Cross-validation Confidence Intervals for Test Error
Learning Expressive Meta-Representations with Mixture of Expert Neural Processes
Segregated Graphs and Marginals of Chain Graph Models
Clustering with Bregman Divergences: an Asymptotic Analysis
Kernel Measures of Independence for non-iid Data
Expanding Small-Scale Datasets with Guided Imagination
Maximum Likelihood Competitive Learning
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model
Animal-Bench: Benchmarking Multimodal Video Models for Animal-centric Video Understanding
Probabilistic Belief Revision with Structural Constraints
Online Robust PCA via Stochastic Optimization
Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update
FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification
On the Stochastic Stability of Deep Markov Models
How Tight Can PAC-Bayes be in the Small Data Regime?
Provable Defense against Backdoor Policies in Reinforcement Learning
Improvisation and Learning
Sequential Predictive Two-Sample and Independence Testing
Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity
Topological Attention for Time Series Forecasting
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization
Online and Stochastic Gradient Methods for Non-decomposable Loss Functions
Primal-Dual Mesh Convolutional Neural Networks
Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL
A nonparametric method for gradual change problems with statistical guarantees
Curriculum-guided Hindsight Experience Replay
Do LLMs Build World Representations? Probing Through the Lens of State Abstraction
Discovering High Order Features with Mean Field Modules
Efficient coding provides a direct link between prior and likelihood in perceptual Bayesian inference
Recurrent World Models Facilitate Policy Evolution
Random Function Descent
Augmented Rescorla-Wagner and Maximum Likelihood Estimation
Learning Disentangled Representation for Robust Person Re-identification
Pre-training of Recurrent Neural Networks via Linear Autoencoders
Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization
On the K-Winners-Take-All Network
Continuous-Time Functional Diffusion Processes
Multi-Label Prediction via Compressed Sensing
Best-Arm Identification in Linear Bandits
Fast and Guaranteed Tensor Decomposition via Sketching
PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference
Variance-Aware Machine Translation Test Sets
Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis
On Optimal Generalizability in Parametric Learning
Learning Deformable Tetrahedral Meshes for 3D Reconstruction
Conditional Controllable Image Fusion
Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization
Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions
Error Compensated Distributed SGD Can Be Accelerated
Going Beyond Linear RL: Sample Efficient Neural Function Approximation
SciCode: A Research Coding Benchmark Curated by Scientists
Planning with Goal-Conditioned Policies
Domain Generalization by Learning and Removing Domain-specific Features
Stepwise Alignment for Constrained Language Model Policy Optimization
A Functional Architecture for Motion Pattern Processing in MSTd
Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning
Convolutional Neural Operators for robust and accurate learning of PDEs
Generalization Bounds for (Wasserstein) Robust Optimization
Unsupervised Anomaly Detection with Rejection
Faster Boosting with Smaller Memory
Dimension-Free Iteration Complexity of Finite Sum Optimization Problems
Training Stochastic Model Recognition Algorithms as Networks can Lead to Maximum Mutual Information Estimation of Parameters
Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada
Noether's Razor: Learning Conserved Quantities
Identification of Functional Clusters in the Striatum Using Infinite Relational Modeling
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization
Conformal Symplectic and Relativistic Optimization
Channel Gating Neural Networks
Multisensory Encoding, Decoding, and Identification
Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation
Federated Submodel Optimization for Hot and Cold Data Features
Robust Reinforcement Learning in Motion Planning
Improving Self-Supervised Learning by Characterizing Idealized Representations
Lg Depth Estimation and Ripple Fire Characterization
Generalization by Weight-Elimination with Application to Forecasting
Learning Commonality, Divergence and Variety for Unsupervised Visible-Infrared Person Re-identification
Inferring a Semantic Representation of Text via Cross-Language Correlation Analysis
Byzantine Spectral Ranking
Learning to Categorize Objects Using Temporal Coherence
Unifying Homophily and Heterophily for Spectral Graph Neural Networks via Triple Filter Ensembles
Virtual Class Enhanced Discriminative Embedding Learning
Latent Field Discovery in Interacting Dynamical Systems with Neural Fields
VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images
VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance
Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation
Compressive spectral embedding: sidestepping the SVD
Cardinality constrained submodular maximization for random streams
Convergence of a Neural Network Classifier
Learning Black-Box Attackers with Transferable Priors and Query Feedback
Closed-Loop Visuomotor Control with Generative Expectation for Robotic Manipulation
HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces
Direct Preference-Based Evolutionary Multi-Objective Optimization with Dueling Bandits
Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models
VectorAdam for Rotation Equivariant Geometry Optimization
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning
LISA: Learning Interpretable Skill Abstractions from Language
A Unified Approach to Interpreting Model Predictions
The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network
Stable Minima Cannot Overfit in Univariate ReLU Networks: Generalization by Large Step Sizes
Contextual Gaussian Process Bandit Optimization
Self-Erasing Network for Integral Object Attention
GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising
Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence
FedNAR: Federated Optimization with Normalized Annealing Regularization
Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models
A Sublinear-Time Spectral Clustering Oracle with Improved Preprocessing Time
Fast detection of multiple change-points shared by many signals using group LARS
Learning How to Teach or Selecting Minimal Surface Data
Module-wise Training of Neural Networks via the Minimizing Movement Scheme
Evaluating Search Engines by Modeling the Relationship Between Relevance and Clicks
First Order Stochastic Optimization with Oblivious Noise
BARTScore: Evaluating Generated Text as Text Generation
Stochastic Approximation Algorithms for Systems of Interacting Particles
Auditing for Human Expertise
A Multi-step Inertial Forward-Backward Splitting Method for Non-convex Optimization
Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch
Towards Enabling Meta-Learning from Target Models
Hierarchical Clustering of a Mixture Model
Implicit Regularization in Over-Parameterized Support Vector Machine
Online Learning via Global Feedback for Phrase Recognition
TAIA: Large Language Models are Out-of-Distribution Data Learners
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
Smoothed Online Learning for Prediction in Piecewise Affine Systems
Core-sets for Fair and Diverse Data Summarization
Compositional Foundation Models for Hierarchical Planning
Evaluating Cognitive Maps and Planning in Large Language Models with CogEval
The Complete Lasso Tradeoff Diagram
On the Importance of Feature Separability in Predicting Out-Of-Distribution Error
Learning metrics for persistence-based summaries and applications for graph classification
Learning to delegate for large-scale vehicle routing
Relevant sparse codes with variational information bottleneck
Softassign versus Softmax: Benchmarks in Combinatorial Optimization
Predictive Coding with Neural Nets: Application to Text Compression
Neurosymbolic Deep Generative Models for Sequence Data with Relational Constraints
Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data
Polyhedral Complex Derivation from Piecewise Trilinear Networks
Regret Minimization via Saddle Point Optimization
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio
Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation
Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images
Bridging Discrete and Backpropagation: Straight-Through and Beyond
Hardware Design and Accurate Simulation of Structured-Light Scanning for Benchmarking of 3D Reconstruction Algorithms
BuckTales: A multi-UAV dataset for multi-object tracking and re-identification of wild antelopes
A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions
End-to-End Learning and Intervention in Games
Gorilla: Large Language Model Connected with Massive APIs
One-to-Multiple: A Progressive Style Transfer Unsupervised Domain-Adaptive Framework for Kidney Tumor Segmentation
Maximum Margin Clustering
Exactly Minimax-Optimal Locally Differentially Private Sampling
RashomonGB: Analyzing the Rashomon Effect and Mitigating Predictive Multiplicity in Gradient Boosting
Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition
Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada
A Sparse Non-Parametric Approach for Single Channel Separation of Known Sounds
Link Prediction in Relational Data
Asymptotic Theory for Regularization: One-Dimensional Linear Case
Revisiting Time Series Outlier Detection: Definitions and Benchmarks
Deep Markov Factor Analysis: Towards Concurrent Temporal and Spatial Analysis of fMRI Data
Association Pattern-aware Fusion for Biological Entity Relationship Prediction