99% of Worker-Master Communication in Distributed Optimization Is Not Needed

A Practical Riemannian Algorithm for Computing Dominant Generalized Eigenspace

A Simple Online Algorithm for Competing with Dynamic Comparators

A SUPER* Algorithm to Optimize Paper Bidding in Peer Review

Active Learning of Conditional Mean Embeddings via Bayesian Optimisation

Adapting Text Embeddings for Causal Inference

Adversarial Learning for 3D Matching

Amortized Bayesian Optimization over Discrete Spaces

Amortized Nesterov's Momentum: A Robust Momentum and Its Application to Deep Learning

Amortized variance reduction for doubly stochastic objective

An Interpretable and Sample Efficient Deep Kernel for Gaussian Process

Automated Dependence Plots

Batch norm with entropic regularization turns deterministic autoencoders into generative models

Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian

Bayesian Online Prediction of Change Points

Bounded Rationality in Las Vegas: Probabilistic Finite Automata Play Multi-Armed Bandits

Bounding the expected run-time of nonconvex optimization with early stopping

C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation

Causal screening in dynamical systems

Collapsible IDA: Collapsing Parental Sets for Locally Estimating Possible Causal Effects

Complete Dictionary Learning via norm Maximization

Complex Markov Logic Networks: Expressivity and Liftability

Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles

Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples

Deriving Bounds And Inequality Constraints Using Logical Relations Among Counterfactuals

Differentially Private Small Dataset Release Using Random Projections

Differentially Private Top-k Selection via Stability on Unknown Domain

Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series

Distortion estimates for approximate Bayesian inference

Divergence-Based Motivation for Online EM and Combining Hidden Variable Models

Dueling Posterior Sampling for Preference-Based Reinforcement Learning

Efficient Rollout Strategies for Bayesian Optimization

EiGLasso: Scalable Estimation of Cartesian Product of Sparse Inverse Covariance Matrices

Election Control by Manipulating Issue Significance

Estimation Rates for Sparse Linear Cyclic Causal Models

Fair Contextual Multi-Armed Bandits: Theory and Experiments

Faster algorithms for Markov equivalence

Finite-Memory Near-Optimal Learning for Markov Decision Processes with Long-Run Average Reward

Flexible Approximate Inference via Stratified Normalizing Flows

Flexible Prior Elicitation via the Prior Predictive Distribution

Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits

Graphical continuous Lyapunov models

Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series

High Dimensional Discrete Integration over the Hypergrid

How Private Are Commonly-Used Voting Rules?

IDA with Background Knowledge

Identifying causal effects in maximally oriented partially directed acyclic graphs

Improved Vector Pruning in Exact Algorithms for Solving POMDPs

Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty

Joint Stochastic Approximation and Its Application to Learning Discrete Latent Variable Models

Kernel Conditional Moment Test via Maximum Moment Restriction

Kidney Exchange with Inhomogeneous Edge Existence Uncertainty

Lagrangian Decomposition for Neural Network Verification

Learning Behaviors with Uncertain Human Feedback

Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect

Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence

Learning LWF Chain Graphs: A Markov Blanket Discovery Approach

Learning to learn generative programs with Memoised Wake-Sleep

Locally Masked Convolution for Autoregressive Models

MaskAAE: Latent space optimization for Adversarial Auto-Encoders

MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models

Measurement Dependence Inducing Latent Causal Models

Model-Augmented Conditional Mutual Information Estimation for Feature Selection

Multitask Soft Option Learning

Mutual Information Based Knowledge Transfer Under State-Action Dimension Mismatch

Neural Likelihoods via Cumulative Distribution Functions

No-regret Exploration in Contextual Reinforcement Learning

Non Parametric Graph Learning for Bayesian Graph Neural Networks

OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation

On Counterfactual Explanations under Predictive Multiplicity

On the design of consequential ranking algorithms

On the Relationship Between Probabilistic Circuits and Determinantal Point Processes

One-Bit Compressed Sensing via One-Shot Hard Thresholding

Online Parameter-Free Learning of Multiple Low Variance Tasks

Optimal Statistical Hypothesis Testing for Social Choice

Ordering Variables for Weighted Model Integration

PAC-Bayesian Contrastive Unsupervised Representation Learning

Permutation-Based Causal Structure Learning with Unknown Intervention Targets

PoRB-Nets: Poisson Process Radial Basis Function Networks

Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles

Probabilistic Safety for Bayesian Neural Networks

Provably Efficient Third-Person Imitation from Offline Observation

Randomized Exploration for Non-Stationary Stochastic Linear Bandits

Regret Analysis of Bandit Problems with Causal Background Knowledge

Regret Bounds for Decentralized Learning in Cooperative Multi-Agent Dynamical Systems

Relaxed Multivariate Bernoulli Distribution and Its Applications to Deep Generative Models

Risk Bounds for Low Cost Bipartite Ranking

Robust Collective Classification against Structural Attacks

Robust $k$-means++

Robust Spatial-Temporal Incident Prediction

Scalable and Flexible Clustering of Grouped Data via Parallel and Distributed Sampling

Selling Data at an Auction under Privacy Constraints

Semi-bandit Optimization in the Dispersed Setting

Semi-supervised learning, causality, and the conditional cluster assumption

Semi-Supervised Learning: the Case When Unlabeled Data is Equally Useful

Sensor Placement for Spatial Gaussian Processes with Integral Observations

Skewness Ranking Optimization for Personalized Recommendation

Slice Sampling for General Completely Random Measures

Spectral Methods for Ranking with Scarce Data

Stable Policy Optimization via Off-Policy Divergence Regularization

Static and Dynamic Values of Computation in MCTS

Statistically Efficient Greedy Equivalence Search

Streaming Nonlinear Bayesian Tensor Decomposition

Structure Learning for Cyclic Linear Causal Models

Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings

Testing Goodness of Fit of Conditional Density Models with Kernels

The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks

The Indian Chefs Process

Time Series Analysis using a Kernel based Multi-Modal Uncertainty Decomposition Framework

Towards Threshold Invariant Fair Classification

TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP

Unknown mixing times in apprenticeship and reinforcement learning

Walking on Two Legs: Learning Image Segmentation with Noisy Labels

What You See May Not Be What You Get: UCB Bandit Algorithms Robust to ε-Contamination

Zeroth Order Non-convex optimization with Dueling-Choice Bandits