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MON 28 MAR
midnight
Community Activities and Mentorship:
Affinity Groups Speed Networking Social
(ends 1:30 AM)
1:30 a.m.
Oral 1: Learning theory / General ML [1:30-2:30]
Orals 1:30-2:30
[1:30] Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients
[1:45] Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably
[2:00] Survival regression with proper scoring rules and monotonic neural networks
[2:15] Multivariate Quantile Function Forecaster
(ends 2:30 AM)
2:30 a.m.
Oral 2: Bayesian methods / Sampling methods [2:30-3:30]
Orals 2:30-3:30
[2:30] Differentiable Bayesian inference of SDE parameters using a pathwise series expansion of Brownian motion
[2:45] Nonparametric Relational Models with Superrectangulation
[3:00] Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
[3:15] Unifying Importance Based Regularisation Methods for Continual Learning
(ends 3:30 AM)
4:30 a.m.
Poster Session 1 [4:30-6:00]
Posters 4:30-6:00
On the Interplay between Information Loss and Operation Loss in Representations for Classification
On a Connection Between Fast and Sparse Oblivious Subspace Embeddings
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
Adaptively Partitioning Max-Affine Estimators for Convex Regression
Nonparametric Relational Models with Superrectangulation
An Alternate Policy Gradient Estimator for Softmax Policies
Can we Generalize and Distribute Private Representation Learning?
Aligned Multi-Task Gaussian Process
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations
Modeling Conditional Dependencies in Multiagent Trajectories
Conditionally Gaussian PAC-Bayes
Non-stationary Online Learning with Memory and Non-stochastic Control
Optimal partition recovery in general graphs
The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data?
Robustness and Reliability When Training With Noisy Labels
Permutation Equivariant Layers for Higher Order Interactions
On the Consistency of Max-Margin Losses
Neural Enhanced Dynamic Message Passing
Robust Probabilistic Time Series Forecasting
Triple-Q: A Model-Free Algorithm for Constrained Reinforcement Learning with Sublinear Regret and Zero Constraint Violation
MLDemon:Deployment Monitoring for Machine Learning Systems
Primal-Dual Stochastic Mirror Descent for MDPs
SAN: Stochastic Average Newton Algorithm for Minimizing Finite Sums
DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search
Tight bounds for minimum $\ell_1$-norm interpolation of noisy data
Crowdsourcing Regression: A Spectral Approach
Efficient interventional distribution learning in the PAC framework
Improved Algorithms for Misspecified Linear Markov Decision Processes
p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets
Provable Continual Learning via Sketched Jacobian Approximations
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference
Obtaining Causal Information by Merging Datasets with MAXENT
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests
Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods
Federated Functional Gradient Boosting
Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data
Robust Deep Learning from Crowds with Belief Propagation
Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications
Last Layer Marginal Likelihood for Invariance Learning
ExactBoost: Directly Boosting the Margin in Combinatorial and Non-decomposable Metrics
AdaBlock: SGD with Practical Block Diagonal Matrix Adaptation for Deep Learning
Multi-armed Bandit Algorithm against Strategic Replication
Point Cloud Generation with Continuous Conditioning
PAC Top-$k$ Identification under SST in Limited Rounds
CATVI: Conditional and Adaptively Truncated Variational Inference for Hierarchical Bayesian Nonparametric Models
Rejection sampling from shape-constrained distributions in sublinear time
Fast and accurate optimization on the orthogonal manifold without retraction
Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies
Standardisation-function Kernel Stein Discrepancy: A Unifying View on Kernel Stein Discrepancy Tests for Goodness-of-fit
On the Convergence Rate of Off-Policy Policy Optimization Methods with Density-Ratio Correction
Momentum Accelerates the Convergence of Stochastic AUPRC Maximization
Safe Active Learning for Multi-Output Gaussian Processes
Randomized Stochastic Gradient Descent Ascent
Semi-Implicit Hybrid Gradient Methods with Application to Adversarial Robustness
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations
REPID: Regional Effect Plots with implicit Interaction Detection
Uncertainty Quantification for Bayesian Optimization
Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably
Optimal estimation of Gaussian DAG models
Weighted Gaussian Process Bandits for Non-stationary Environments
Non-separable Spatio-temporal Graph Kernels via SPDEs
Modelling Non-Smooth Signals with Complex Spectral Structure
LIMESegment: Meaningful, Realistic Time Series Explanations
Two-way Sparse Network Inference for Count Data
Gap-Dependent Bounds for Two-Player Markov Games
Regret, stability & fairness in matching markets with bandit learners
Random Effect Bandits
Grassmann Stein Variational Gradient Descent
Statistical and computational thresholds for the planted k-densest sub-hypergraph problem
Double Control Variates for Gradient Estimation in Discrete Latent Variable Models
Resampling Base Distributions of Normalizing Flows
Information-Theoretic Analysis of Epistemic Uncertainty in Bayesian Meta-learning
An Unsupervised Hunt for Gravitational Lenses
Sinkformers: Transformers with Doubly Stochastic Attention
Proximal Optimal Transport Modeling of Population Dynamics
Using time-series privileged information for provably efficient learning of prediction models
Off-Policy Risk Assessment for Markov Decision Processes
Multivariate Quantile Function Forecaster
Common Failure Modes of Subcluster-based Sampling in Dirichlet Process Gaussian Mixture Models - and a Deep-learning Solution
On Learning Mixture Models with Sparse Parameters
Pairwise Supervision Can Provably Elicit a Decision Boundary
Reward-Free Policy Space Compression for Reinforcement Learning
On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization
Survival regression with proper scoring rules and monotonic neural networks
State Dependent Performative Prediction with Stochastic Approximation
Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximations
Spectral Pruning for Recurrent Neural Networks
An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift
Generative Models as Distributions of Functions
Learning Sparse Fixed-Structure Gaussian Bayesian Networks
VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition
Reconstructing Test Labels from Noisy Loss Functions
(ends 6:00 AM)
6 a.m.
Oral 3: Causality / Trustworthy ML [6:00-7:00]
Orals 6:00-7:00
[6:00] Almost Optimal Universal Lower Bound for Learning Causal DAGs with Atomic Interventions
[6:15] Variance Minimization in the Wasserstein Space for Invariant Causal Prediction
[6:30] On the Assumptions of Synthetic Control Methods
[6:45] Differentially Private Densest Subgraph
(ends 7:00 AM)
7 a.m.
Community Activities and Mentorship:
MeMentor Mentoring
(ends 8:00 AM)
Oral 4: Bandits / Reinforcement learning [7:00-8:00]
Orals 7:00-8:00
[7:00] Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits
[7:15] Nonstochastic Bandits and Experts with Arm-Dependent Delays
[7:30] Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise
[7:45] Towards an Understanding of Default Policies in Multitask Policy Optimization
(ends 8:00 AM)
9 a.m.
Remarks:
Opening Remarks
(ends 9:15 AM)
9:15 a.m.
Invited Talk:
New Methods for Taming Endogeneity Bias in Observational Studies: Proxies, Bespoke Instruments and Invalid Instruments
Eric Tchetgen Tchetgen
(ends 10:15 AM)
10:15 a.m.
Poster Session 2 [10:15-11:45]
Posters 10:15-11:45
Sobolev Norm Learning Rates for Conditional Mean Embeddings
Reinforcement Learning with Fast Stabilization in Linear Dynamical Systems
Variational Marginal Particle Filters
Safe Optimal Design with Applications in Off-Policy Learning
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent
Nuances in Margin Conditions Determine Gains in Active Learning
Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent
Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?
Spectral Robustness for Correlation Clustering Reconstruction in Semi-Adversarial Models
Sample Complexity of Robust Reinforcement Learning with a Generative Model
Two-Sample Test with Kernel Projected Wasserstein Distance
How and When Random Feedback Works: A Case Study of Low-Rank Matrix Factorization
Stochastic Extragradient: General Analysis and Improved Rates
Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise
Model-free Policy Learning with Reward Gradients
Neural Contextual Bandits without Regret
Gap-Dependent Unsupervised Exploration for Reinforcement Learning
On perfectness in Gaussian graphical models
A New Notion of Individually Fair Clustering: $\alpha$-Equitable $k$-Center
Fast Sparse Classification for Generalized Linear and Additive Models
Parametric Bootstrap for Differentially Private Confidence Intervals
Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates
Learning Revenue-Maximizing Auctions With Differentiable Matching
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation
Learning Interpretable, Tree-Based Projection Mappings for Nonlinear Embeddings
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm
Optimal Accounting of Differential Privacy via Characteristic Function
Faster Unbalanced Optimal Transport: Translation invariant Sinkhorn and 1-D Frank-Wolfe
On the Convergence of Continuous Constrained Optimization for Structure Learning
Sample Complexity of Policy-Based Methods under Off-Policy Sampling and Linear Function Approximation
On Combining Bags to Better Learn from Label Proportions
PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge
Beyond the Policy Gradient Theorem for Efficient Policy Updates in Actor-Critic Algorithms
Independent Natural Policy Gradient always converges in Markov Potential Games
Deep Non-crossing Quantiles through the Partial Derivative
Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond
On the Global Optimum Convergence of Momentum-based Policy Gradient
Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time
Decoupling Local and Global Representations of Time Series
Learning Competitive Equilibria in Exchange Economies with Bandit Feedback
Near Instance Optimal Model Selection for Pure Exploration Linear Bandits
Uncertainty Quantification for Low-Rank Matrix Completion with Heterogeneous and Sub-Exponential Noise
A Predictive Approach to Bayesian Nonparametric Survival Analysis
Iterative Alignment Flows
Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation
Towards Federated Bayesian Network Structure Learning with Continuous Optimization
Wide Mean-Field Bayesian Neural Networks Ignore the Data
Norm-Agnostic Linear Bandits
Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective
Measuring the robustness of Gaussian processes to kernel choice
Sensing Cox Processes via Posterior Sampling and Positive Bases
Towards Return Parity in Markov Decision Processes
Deep Multi-Fidelity Active Learning of High-Dimensional Outputs
Finding Dynamics Preserving Adversarial Winning Tickets
Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape
Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size
Towards Understanding Biased Client Selection in Federated Learning
Orthogonal Multi-Manifold Enriching of Directed Networks
On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging
Self-training Converts Weak Learners to Strong Learners in Mixture Models
Nearly Optimal Algorithms for Level Set Estimation
The Importance of Future Information in Credit Card Fraud Detection
Relational Neural Markov Random Fields
Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits
Finding Nearly Everything within Random Binary Networks
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs
Variance Minimization in the Wasserstein Space for Invariant Causal Prediction
Tuning-Free Generalized Hamiltonian Monte Carlo
Strategic ranking
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations
Multiway Spherical Clustering via Degree-Corrected Tensor Block Models
Computing D-Stationary Points of $\rho$-Margin Loss SVM
Flexible Accuracy for Differential Privacy
Hierarchical Bayesian Bandits
On the Value of Prior in Online Learning to Rank
Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures
Online Page Migration with ML Advice
Federated Learning with Buffered Asynchronous Aggregation
Transductive Robust Learning Guarantees
The Curse of Passive Data Collection in Batch Reinforcement Learning
Second-Order Sensitivity Analysis for Bilevel Optimization
Structured Multi-task Learning for Molecular Property Prediction
Privacy Amplification by Subsampling in Time Domain
Federated Myopic Community Detection with One-shot Communication
Learning Inconsistent Preferences with Gaussian Processes
Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization
Thompson Sampling with a Mixture Prior
On Coresets for Fair Regression and Individually Fair Clustering
Laplacian Constrained Precision Matrix Estimation: Existence and High Dimensional Consistency
Vanishing Curvature in Randomly Initialized Deep ReLU Networks
Margin-distancing for safe model explanation
Solving Marginal MAP Exactly by Probabilistic Circuit Transformations
Provable Lifelong Learning of Representations
Fast Fourier Transform Reductions for Bayesian Network Inference
Pareto Optimal Model Selection in Linear Bandits
Performative Prediction in a Stateful World
Mitigating Bias in Calibration Error Estimation
Fair Disaster Containment via Graph-Cut Problems
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL
Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes
Policy Learning and Evaluation with Randomized Quasi-Monte Carlo
Online Competitive Influence Maximization
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach
Variational Gaussian Processes: A Functional Analysis View
Moment Matching Deep Contrastive Latent Variable Models
Solving Multi-Arm Bandit Using a Few Bits of Communication
Amortized Rejection Sampling in Universal Probabilistic Programming
(ends 11:45 AM)
TUE 29 MAR
1 a.m.
Poster Session 3 [1:00-2:30]
Posters 1:00-2:30
Distributionally Robust Structure Learning for Discrete Pairwise Markov Networks
k-Pareto Optimality-Based Sorting with Maximization of Choice
Lagrangian manifold Monte Carlo on Monge patches
Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity
Almost Optimal Universal Lower Bound for Learning Causal DAGs with Atomic Interventions
Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization
Efficient Algorithms for Extreme Bandits
Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domains by Adaptive Discretization
A Cramér Distance perspective on Quantile Regression based Distributional Reinforcement Learning
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits
Efficient and passive learning of networked dynamical systems driven by non-white exogenous inputs
Diversity and Generalization in Neural Network Ensembles
Are All Linear Regions Created Equal?
Particle-based Adversarial Local Distribution Regularization
Improved analysis of randomized SVD for top-eigenvector approximation
Deep Layer-wise Networks Have Closed-Form Weights
Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics
Asymptotically Optimal Locally Private Heavy Hitters via Parameterized Sketches
PAC Learning of Quantum Measurement Classes : Sample Complexity Bounds and Universal Consistency
Dropout as a Regularizer of Interaction Effects
Forward Looking Best-Response Multiplicative Weights Update Methods for Bilinear Zero-sum Games
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning
Identity Testing of Reversible Markov Chains
Predictive variational Bayesian inference as risk-seeking optimization
A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality
Minimal Expected Regret in Linear Quadratic Control
Corruption-robust Offline Reinforcement Learning
Learning Tensor Representations for Meta-Learning
Feature Collapsing for Gaussian Process Variable Ranking
On Multimarginal Partial Optimal Transport: Equivalent Forms and Computational Complexity
Certifiably Robust Variational Autoencoders
An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints
Stateful Offline Contextual Policy Evaluation and Learning
Improving Attribution Methods by Learning Submodular Functions
A Witness Two-Sample Test
Nearly Tight Convergence Bounds for Semi-discrete Entropic Optimal Transport
Top K Ranking for Multi-Armed Bandit with Noisy Evaluations
Quadric Hypersurface Intersection for Manifold Learning in Feature Space
Spectral risk-based learning using unbounded losses
Learning to Plan Variable Length Sequences of Actions with a Cascading Bandit Click Model of User Feedback
Complex Momentum for Optimization in Games
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks
Maillard Sampling: Boltzmann Exploration Done Optimally
Generalised GPLVM with Stochastic Variational Inference
Data Appraisal Without Data Sharing
Pick-and-Mix Information Operators for Probabilistic ODE Solvers
Differential privacy for symmetric log-concave mechanisms
Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation
A Non-asymptotic Approach to Best-Arm Identification for Gaussian Bandits
MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption
Dimensionality Reduction and Prioritized Exploration for Policy Search
Mean Nyström Embeddings for Adaptive Compressive Learning
Predicting the impact of treatments over time with uncertainty aware neural differential equations.
Causal Effect Identification with Context-specific Independence Relations of Control Variables
Bayesian Classifier Fusion with an Explicit Model of Correlation
Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding
Mode estimation on matrix manifolds: Convergence and robustness
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks
Transfer Learning with Gaussian Processes for Bayesian Optimization
Duel-based Deep Learning system for solving IQ tests
Warping Layer: Representation Learning for Label Structures in Weakly Supervised Learning
Encrypted Linear Contextual Bandit
New Coresets for Projective Clustering and Applications
Analysis of a Target-Based Actor-Critic Algorithm with Linear Function Approximation
Calibration Error for Heterogeneous Treatment Effects
LocoProp: Enhancing BackProp via Local Loss Optimization
Sequential Multivariate Change Detection with Calibrated and Memoryless False Detection Rates
Pairwise Fairness for Ordinal Regression
Best Arm Identification with Safety Constraints
Exact Community Recovery over Signed Graphs
Label differential privacy via clustering
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation
Finite Sample Analysis of Mean-Volatility Actor-Critic for Risk-Averse Reinforcement Learning
Efficient computation of the the volume of a polytope in high-dimensions using Piecewise Deterministic Markov Processes
Beyond Data Samples: Aligning Differential Networks Estimation with Scientific Knowledge
A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits
Bias-Variance Decompositions for Margin Losses
Investigating the Role of Negatives in Contrastive Representation Learning
Learning Quantile Functions for Temporal Point Processes with Recurrent Neural Splines
Being a Bit Frequentist Improves Bayesian Neural Networks
Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? A Theoretical Analysis
Privacy Amplification by Decentralization
Estimators of Entropy and Information via Inference in Probabilistic Models
ContextGen: Targeted Data Generation for Low Resource Domain Specific Text Classification
Metalearning Linear Bandits by Prior Update
Can Functional Transfer Methods Capture Simple Inductive Biases?
Lifted Division for Lifted Hugin Belief Propagation
Online Continual Adaptation with Active Self-Training
How to scale hyperparameters for quickshift image segmentation
Jointly Efficient and Optimal Algorithms for Logistic Bandits
Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients
Co-Regularized Adversarial Learning for Multi-Domain Text Classification
(ends 2:30 AM)
2:30 a.m.
Oral 5: Kernels / Optimization / Deep learning [2:30-3:30]
Orals 2:30-3:30
[2:30] Adversarially Robust Kernel Smoothing
[2:45] A Single-Timescale Method for Stochastic Bilevel Optimization
[3:00] Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization
[3:15] Generative Models as Distributions of Functions
(ends 3:30 AM)
3:30 a.m.
Oral 6: Learning theory / Sampling methods [3:30-4:30]
Orals 3:30-4:30
[3:30] Amortized Rejection Sampling in Universal Probabilistic Programming
[3:45] Adaptive Importance Sampling meets Mirror Descent : a Bias-variance Tradeoff
[4:00] Loss as the Inconsistency of a Probabilistic Dependency Graph: Choose Your Model, Not Your Loss Function
[4:15] On the Consistency of Max-Margin Losses
(ends 4:30 AM)
5:30 a.m.
Community Activities and Mentorship:
Affinity Group Supported Pathways to ML Research Panel and Social
(ends 7:00 AM)
7 a.m.
Invited Talk:
Machine Learning for Ocean and Climate Modeling: Advances & Challenges
Laure Zanna
(ends 8:00 AM)
8 a.m.
Oral 7: Bayesian methods / Deep learning [8:00-9:00]
Orals 8:00-9:00
[8:00] Many processors, little time: MCMC for partitions via optimal transport couplings
[8:15] Rapid Convergence of Informed Importance Tempering
[8:30] Projection Predictive Inference for Generalized Linear and Additive Multilevel Models
[8:45] Density Ratio Estimation via Infinitesimal Classification
(ends 9:00 AM)
9:45 a.m.
Award Ceremony:
Award Ceremony
(ends 10:00 AM)
10 a.m.
Test Of Time:
Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation
(ends 11:00 AM)
11 a.m.
Community Activities and Mentorship:
WiML - CWS Social
(ends 12:00 PM)
WED 30 MAR
midnight
Oral 8: Learning theory / Sampling methods [12:00-1:00]
Orals 12:00-1:00
[12:00] Sampling from Arbitrary Functions via PSD Models
[12:15] Orbital MCMC
[12:30] Hardness of Learning a Single Neuron with Adversarial Label Noise
[12:45] Data-splitting improves statistical performance in overparameterized regimes
(ends 1:00 AM)
1 a.m.
Oral 9: Reinforcement learning / Deep learning [1:00-2:00]
Orals 1:00-2:00
[1:00] Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning
[1:15] Faster Rates, Adaptive Algorithms, and Finite-Time Bounds for Linear Composition Optimization and Gradient TD Learning
[1:30] A general class of surrogate functions for stable and efficient reinforcement learning
[1:45] A Complete Characterisation of ReLU-Invariant Distributions
(ends 2:00 AM)
2:30 a.m.
Invited Talk:
Using ML to Discover the Underlying Models of Medicine
Mihaela van der Schaar
(ends 3:30 AM)
3:30 a.m.
Poster Session 4 [3:30-5:00]
Posters 3:30-5:00
Differentiable Bayesian inference of SDE parameters using a pathwise series expansion of Brownian motion
Federated Reinforcement Learning with Environment Heterogeneity
Conditional Gradients for the Approximately Vanishing Ideal
Improved Approximation Algorithms for Individually Fair Clustering
Unifying Importance Based Regularisation Methods for Continual Learning
Counterfactual Explanation Trees: Transparent and Consistent Actionable Recourse with Decision Trees
Derivative-Based Neural Modelling of Cumulative Distribution Functions for Survival Analysis
Optimal transport with $f$-divergence regularization and generalized Sinkhorn algorithm
On Some Fast And Robust Classifiers For High Dimension, Low Sample Size Data
Faster Rates, Adaptive Algorithms, and Finite-Time Bounds for Linear Composition Optimization and Gradient TD Learning
Exploiting Correlation to Achieve Faster Learning Rates in Low-Rank Preference Bandits
Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games
Differentially Private Federated Learning on Heterogeneous Data
Adaptive A/B Test on Networks with Cluster Structures
Fixed Support Tree-Sliced Wasserstein Barycenter
Fast Rank-1 NMF for Missing Data with KL Divergence
Acceleration in Distributed Optimization under Similarity
Sampling from Arbitrary Functions via PSD Models
Gaussian Process Bandit Optimization with Few Batches
Increasing the accuracy and resolution of precipitation forecasts using deep generative models
Node Feature Kernels Increase Graph Convolutional Network Robustness
Adversarially Robust Kernel Smoothing
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects
Equivariant Deep Dynamical Model for Motion Prediction
Nonstochastic Bandits and Experts with Arm-Dependent Delays
Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals
On Facility Location Problem in the Local Differential Privacy Model
Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes
On the complexity of the optimal transport problem with graph-structured cost
GraphAdaMix: Enhancing Node Representations with Graph Adaptive Mixtures
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning
Debiasing Samples from Online Learning Using Bootstrap
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets
Data-splitting improves statistical performance in overparameterized regimes
Marginalized Operators for Off-policy Reinforcement Learning
Coresets for Data Discretization and Sine Wave Fitting
Convergent Working Set Algorithm for Lasso with Non-Convex Sparse Regularizers
Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback
A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning
Efficient Online Bayesian Inference for Neural Bandits
Neural score matching for high-dimensional causal inference
Multi-class classification in nonparametric active learning
Online Learning for Unknown Partially Observable MDPs
On Global-view Based Defense via Adversarial Attack and Defense Risk Guaranteed Bounds
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification
Optimal channel selection with discrete QCQP
On the Generalization of Representations in Reinforcement Learning
On Distributionally Robust Optimization and Data Rebalancing
Effective Nonlinear Feature Selection Method based on HSIC Lasso and with Variational Inference
Learning a Single Neuron for Non-monotonic Activation Functions
On PAC-Bayesian reconstruction guarantees for VAEs
Variational Continual Proxy-Anchor for Deep Metric Learning
Physics Informed Deep Kernel Learning
Approximate Function Evaluation via Multi-Armed Bandits
Learning Pareto-Efficient Decisions with Confidence
Near-Optimal Task Selection for Meta-Learning with Mutual Information and Online Variational Bayesian Unlearning
Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methods
Differentially Private Densest Subgraph
Embedded Ensembles: infinite width limit and operating regimes
Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data
Harmless interpolation in regression and classification with structured features
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms
Feature screening with kernel knockoffs
A Spectral Perspective of DNN Robustness to Label Noise
Learning Proposals for Practical Energy-Based Regression
Parallel MCMC Without Embarrassing Failures
Identification in Tree-shaped Linear Structural Causal Models
A Single-Timescale Method for Stochastic Bilevel Optimization
Kantorovich Mechanism for Pufferfish Privacy
Adaptive Importance Sampling meets Mirror Descent : a Bias-variance Tradeoff
Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization
Dual-Level Adaptive Information Filtering for Interactive Image Segmentation
Adaptive Multi-Goal Exploration
Implicitly Regularized RL with Implicit Q-values
Accurate Shapley Values for explaining tree-based models
On Margins and Derandomisation in PAC-Bayes
Differentially Private Regression with Unbounded Covariates
A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds
Leveraging Time Irreversibility with Order-Contrastive Pre-training
How to Learn when Data Gradually Reacts to Your Model
Super-Acceleration with Cyclical Step-sizes
Exploring Image Regions Not Well Encoded by an INN
Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes
On Linear Model with Markov Signal Priors
k-experts - Online Policies and Fundamental Limits
Structured variational inference in Bayesian state-space models
A Complete Characterisation of ReLU-Invariant Distributions
Marginalising over Stationary Kernels with Bayesian Quadrature
Generalized Group Testing
A general sample complexity analysis of vanilla policy gradient
Chernoff Sampling for Active Testing and Extension to Active Regression
System-Agnostic Meta-Learning for MDP-based Dynamic Scheduling via Descriptive Policy
Masked Training of Neural Networks with Partial Gradients
Model-agnostic out-of-distribution detection using combined statistical tests
(ends 5:00 AM)
6 a.m.
Oral 10: Gaussian processes / Optimization / Online ML [6:00-7:00]
Orals 6:00-7:00
[6:00] Minimax Optimization: The Case of Convex-Submodular
[6:15] Doubly Mixed-Effects Gaussian Process Regression
[6:30] Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes
[6:45] Debiasing Samples from Online Learning Using Bootstrap
(ends 7:00 AM)
7 a.m.
Oral 11: Learning theory / Kernels [7:00-8:00]
Orals 7:00-8:00
[7:00] Entropy Regularized Optimal Transport Independence Criterion
[7:15] Two-Sample Test with Kernel Projected Wasserstein Distance
[7:30] Estimating Functionals of the Out-of-Sample Error Distribution in High-Dimensional Ridge Regression
[7:45] Heavy-tailed Streaming Statistical Estimation
(ends 8:00 AM)
8:30 a.m.
Poster Session 5 [8:30-10:00]
Posters 8:30-10:00
Unlabeled Data Help: Minimax Analysis and Adversarial Robustness
A Bayesian Approach for Stochastic Continuum-armed Bandit with Long-term Constraints
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning
Synthsonic: Fast, Probabilistic modeling and Synthesis of Tabular Data
Meta Learning MDPs with linear transition models
Differentially Private Histograms under Continual Observation: Streaming Selection into the Unknown
Many processors, little time: MCMC for partitions via optimal transport couplings
Optimizing Early Warning Classifiers to Control False Alarms via a Minimum Precision Constraint
Online Control of the False Discovery Rate under “Decision Deadlines”
Efficient Kernelized UCB for Contextual Bandits
Offline Policy Selection under Uncertainty
QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning
Orbital MCMC
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons
Spiked Covariance Estimation from Modulo-Reduced Measurements
On Convergence of Lookahead in Smooth Games
Rapid Convergence of Informed Importance Tempering
Convex Analysis of the Mean Field Langevin Dynamics
Density Ratio Estimation via Infinitesimal Classification
Sketch-and-lift: scalable subsampled semidefinite program for K-means clustering
Learning and Generalization in Overparameterized Normalizing Flows
Outcome Assumptions and Duality Theory for Balancing Weights
Convergence of Langevin Monte Carlo in Chi-Squared and Rényi Divergence
PAC Mode Estimation using PPR Martingale Confidence Sequences
Conditionally Tractable Density Estimation using Neural Networks
The Fast Kernel Transform
Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks
Conditional Linear Regression for Heterogeneous Covariances
Learning from Multiple Noisy Partial Labelers
Firebolt: Weak Supervision Under Weaker Assumptions
Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression
A prior-based approximate latent Riemannian metric
Expressivity of Neural Networks via Chaotic Itineraries beyond Sharkovsky's Theorem
Minimax Optimization: The Case of Convex-Submodular
Zeroth-Order Methods for Convex-Concave Min-max Problems: Applications to Decision-Dependent Risk Minimization
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection
Asynchronous Distributed Optimization with Stochastic Delays
Fundamental limits for rank-one matrix estimation with groupwise heteroskedasticity
The role of optimization geometry in single neuron learning
Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions
Optimal Compression of Locally Differentially Private Mechanisms
Distributed Sparse Multicategory Discriminant Analysis
Compressed Rule Ensemble Learning
Entropy Regularized Optimal Transport Independence Criterion
Confident Least Square Value Iteration with Local Access to a Simulator
Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms
Learning in Stochastic Monotone Games with Decision-Dependent Data
Towards an Understanding of Default Policies in Multitask Policy Optimization
A general class of surrogate functions for stable and efficient reinforcement learning
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization
Sample-and-threshold differential privacy: Histograms and applications
On the Implicit Bias of Gradient Descent for Temporal Extrapolation
On the Oracle Complexity of Higher-Order Smooth Non-Convex Finite-Sum Optimization
Causally motivated shortcut removal using auxiliary labels
A View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy
Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization
A Bandit Model for Human-Machine Decision Making with Private Information and Opacity
A Unified View of SDP-based Neural Network Verification through Completely Positive Programming
Provable Adversarial Robustness for Fractional Lp Threat Models
Optimal Design of Stochastic DNA Synthesis Protocols based on Generative Sequence Models
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity
Policy Learning for Optimal Individualized Dose Intervals
Doubly Mixed-Effects Gaussian Process Regression
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes
Parameter-Free Online Linear Optimization with Side Information via Universal Coin Betting
Hardness of Learning a Single Neuron with Adversarial Label Noise
TD-GEN: Graph Generation Using Tree Decomposition
Recoverability Landscape of Tree Structured Markov Random Fields under Symmetric Noise
Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning
On the equivalence of Oja's algorithm and GROUSE
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Completion
Hypergraph Simultaneous Generators
Variational Autoencoders: A Harmonic Perspective
Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits
On the Assumptions of Synthetic Control Methods
The Tree Loss: Improving Generalization with Many Classes
Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis
A Bayesian Model for Online Activity Sample Sizes
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting
Weak Separation in Mixture Models and Implications for Principal Stratification
Deep Neyman-Scott Processes
A Dual Approach to Constrained Markov Decision Processes with Entropy Regularization
Cross-Loss Influence Functions to Explain Deep Network Representations
Approximate Top-$m$ Arm Identification with Heterogeneous Reward Variances
A Contraction Theory Approach to Optimization Algorithms from Acceleration Flows
Contrasting the landscape of contrastive and non-contrastive learning
Loss as the Inconsistency of a Probabilistic Dependency Graph: Choose Your Model, Not Your Loss Function
A Manifold View of Adversarial Risk
Pulling back information geometry
Estimating Functionals of the Out-of-Sample Error Distribution in High-Dimensional Ridge Regression
An Information-Theoretic Justification for Model Pruning
Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE
Cycle Consistent Probability Divergences Across Different Spaces
Projection Predictive Inference for Generalized Linear and Additive Multilevel Models
Heavy-tailed Streaming Statistical Estimation
Adversarial Tracking Control via Strongly Adaptive Online Learning with Memory
Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference
Equivariance Discovery by Learned Parameter-Sharing
A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence
A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions
One-bit Submission for Locally Private Quasi-MLE: Its Asymptotic Normality and Limitation
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis
Convergence of online k-means
(ends 10:00 AM)
10 a.m.
Community Activities and Mentorship:
Mentoring Retrospectives Workshop
(ends 12:00 PM)