AISTATS 2023

496 papers

Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding

Thomas Gebhart, Jakob Hansen, Paul Schrater

Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach

Syrine Belakaria, Janardhan Rao Doppa, Nicolo Fusi, Rishit Sheth

Second Order Path Variationals in Non-Stationary Online Learning

Dheeraj Baby, Yu-Xiang Wang

SoundSynp: Sound Source Detection from Raw Waveforms with Multi-Scale Synperiodic Filterbanks

Yuhang He, Andrew Markham

Learning to Optimize with Stochastic Dominance Constraints

Hanjun Dai, Yuan Xue, Niao He, Yixin Wang, Na Li

Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise

Haotian Ye, James Zou, Linjun Zhang

Adversarial robustness of VAEs through the lens of local geometry

Asif Khan, Amos Storkey

Federated Learning for Data Streams

Othmane MARFOQ, Giovanni Neglia, Laetitia Kameni, Richard Vidal

Context-Specific Causal Discovery for Categorical Data Using Staged Trees

Manuele Leonelli, Gherardo Varando

On the Calibration of Probabilistic Classifier Sets

Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman

Preferential Subsampling for Stochastic Gradient Langevin Dynamics

Srshti Putcha, Christopher Nemeth, Paul Fearnhead

Probabilistic Conformal Prediction Using Conditional Random Samples

Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David Blei

Nonstochastic Contextual Combinatorial Bandits

Lukas Zierahn, Dirk van der Hoeven, Nicolò Cesa-bianchi, Gergely Neu

Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo

Tom Huix, Matthew Zhang, Alain Durmus

Consistent Complementary-Label Learning via Order-Preserving Losses

Shuqi Liu, Yuzhou Cao, Qiaozhen Zhang, Lei Feng, Bo An

Learning Robust Graph Neural Networks with Limited Supervision

Abdullah Alchihabi, Yuhong Guo

Representation Learning in Deep RL via Discrete Information Bottleneck

Riashat Islam, Hongyu Zang, Manan Tomar, Aniket Didolkar, Md Mofijul Islam

Density Ratio Estimation and Neyman Pearson Classification with Missing Data

Josh Givens, Song Liu, Henry W. J. Reeve

Wasserstein Distributionally Robust Linear-Quadratic Estimation under Martingale Constraints

Kyriakos Lotidis, Nicholas Bambos, Jose Blanchet, JIAJIN LI

Loss-Curvature Matching for Dataset Selection and Condensation

Seungjae Shin, HeeSun Bae, Donghyeok Shin, Weonyoung Joo, Il-chul Moon

Causal Entropy Optimization

Nicola Branchini, Virginia Aglietti, Neil Dhir, Theodoros Damoulas

Discrete Distribution Estimation under User-level Local Differential Privacy

Jayadev Acharya, Yuhan Liu, Ziteng Sun

Scalable Unbalanced Sobolev Transport for Measures on a Graph

Tam Le, Truyen Nguyen, Kenji Fukumizu

How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm?

Haiyun He, Gholamali Aminian, Yuheng Bu, Miguel Rodrigues, Vincent Y. F. Tan

Overparameterized Random Feature Regression with Nearly Orthogonal Data

Zhichao Wang, Yizhe Zhu

Surveillance Evasion Through Bayesian Reinforcement Learning

Dongping Qi, David Bindel, Alexander Vladimirsky

CLIP-Lite: Information Efficient Visual Representation Learning with Language Supervision

Aman Shrivastava, Ramprasaath R. Selvaraju, Nikhil Naik, Vicente Ordonez

Principled Approaches for Private Adaptation from a Public Source

Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh

Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loops

Charles K. Assaad, Imad Ez-Zejjari, Lei Zan

A Targeted Accuracy Diagnostic for Variational Approximations

Yu Wang, Mikolaj Kasprzak, Jonathan H. Huggins

Tensor-based Kernel Machines with Structured Inducing Points for Large and High-Dimensional Data

Frederiek Wesel, Kim Batselier

Improved Representation Learning Through Tensorized Autoencoders

Pascal Esser, Satyaki Mukherjee, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar

Nonparametric Gaussian Process Covariances via Multidimensional Convolutions

Thomas M. Mcdonald, Magnus Ross, Michael T. Smith, Mauricio A. Álvarez

Nothing but Regrets — Privacy-Preserving Federated Causal Discovery

Osman Mian, David Kaltenpoth, Michael Kamp, Jilles Vreeken

Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference

Debangshu Banerjee, Avishek Ghosh, Sayak Ray Chowdhury, Aditya Gopalan

Pointwise sampling uncertainties on the Precision-Recall curve

Ralph E.Q. Urlus, Max Baak, Stéphane Collot, Ilan Fridman Rojas

Bures-Wasserstein Barycenters and Low-Rank Matrix Recovery

Tyler Maunu, Thibaut Le Gouic, Philippe Rigollet

Tighter PAC-Bayes Generalisation Bounds by Leveraging Example Difficulty

Felix Biggs, Benjamin Guedj

PAC-Bayesian Learning of Optimization Algorithms

Michael Sucker, Peter Ochs

Further Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits

Taira Tsuchiya, Shinji Ito, Junya Honda

Adaptive Tuning for Metropolis Adjusted Langevin Trajectories

Lionel Riou-Durand, Pavel Sountsov, Jure Vogrinc, Charles Margossian, Sam Power

Online Algorithms with Costly Predictions

Marina Drygala, Sai Ganesh Nagarajan, Ola Svensson

The Role of Codeword-to-Class Assignments in Error-Correcting Codes: An Empirical Study

Itay Evron, Ophir Onn, Tamar Weiss, Hai Azeroual, Daniel Soudry

Model-Based Uncertainty in Value Functions

Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters

Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum Games

Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Kentaro Toyoshima, Atsushi Iwasaki

Beyond Performative Prediction: Open-environment Learning with Presence of Corruptions

Jia-Wei Shan, Peng Zhao, Zhi-Hua Zhou

Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data

Alicia Curth, Mihaela van der Schaar

No time to waste: practical statistical contact tracing with few low-bit messages

Rob Romijnders, Yuki M. Asano, Christos Louizos, Max Welling

Revisiting Weighted Strategy for Non-stationary Parametric Bandits

Jing Wang, Peng Zhao, Zhi-Hua Zhou

Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems

Matthias Bitzer, Mona Meister, Christoph Zimmer

On the Complexity of Representation Learning in Contextual Linear Bandits

Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric

Rank-Based Causal Discovery for Post-Nonlinear Models

Grigor Keropyan, David Strieder, Mathias Drton

On the Consistency Rate of Decision Tree Learning Algorithms

Qin-Cheng Zheng, Shen-Huan Lyu, Shao-Qun Zhang, Yuan Jiang, Zhi-Hua Zhou

Fast Feature Selection with Fairness Constraints

Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen, Daniel Waddington

Data Augmentation for Imbalanced Regression

Samuel Stocksieker, Denys Pommeret, Arthur Charpentier

One Arrow, Two Kills: A Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits

Pierre Gaillard, Aadirupa Saha, Soham Dan

Risk-aware linear bandits with convex loss

Patrick Saux, Odalric Maillard

Vector Quantized Time Series Generation with a Bidirectional Prior Model

Daesoo Lee, Sara Malacarne, Erlend Aune

Global-Local Regularization Via Distributional Robustness

Hoang Phan, Trung Le, Trung Phung, Anh Tuan Bui, nhật Hồ

Multi-Agent congestion cost minimization with linear function approximations

Prashant Trivedi, Nandyala Hemachandra

Online Linearized LASSO

Shuoguang Yang, Yuhao Yan, Xiuneng Zhu, Qiang Sun

Improved Bound on Generalization Error of Compressed KNN Estimator

Hang Zhang, Ping Li

Optimal Algorithms for Latent Bandits with Cluster Structure

Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain

Retrospective Uncertainties for Deep Models using Vine Copulas

Natasa Tagasovska, Firat Ozdemir, Axel Brando

Minimax-Bayes Reinforcement Learning

Thomas Kleine Buening, Christos Dimitrakakis, Hannes Eriksson, Divya Grover, Emilio Jorge

Incremental Aggregated Riemannian Gradient Method for Distributed PCA

Xiaolu Wang, Yuchen Jiao, Hoi-To Wai, Yuantao Gu

Protecting Global Properties of Datasets with Distribution Privacy Mechanisms

Michelle Chen, Olga Ohrimenko

Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback

Fares Fourati, Vaneet Aggarwal, Christopher Quinn, Mohamed-Slim Alouini

Multi-Fidelity Bayesian Optimization with Unreliable Information Sources

Petrus Mikkola, Julien Martinelli, Louis Filstroff, Samuel Kaski

Identification of Blackwell Optimal Policies for Deterministic MDPs

Victor Boone, Bruno Gaujal

But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI

Charles Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon

Active Exploration via Experiment Design in Markov Chains

Mojmir Mutny, Tadeusz Janik, Andreas Krause

Prediction-Oriented Bayesian Active Learning

Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster

An Homogeneous Unbalanced Regularized Optimal Transport Model with Applications to Optimal Transport with Boundary

Theo Lacombe

Efficient Informed Proposals for Discrete Distributions via Newton’s Series Approximation

Yue Xiang, Dongyao Zhu, Bowen Lei, Dongkuan Xu, Ruqi Zhang

Explicit Regularization in Overparametrized Models via Noise Injection

Antonio Orvieto, Anant Raj, Hans Kersting, Francis Bach

Combining Graphical and Algebraic Approaches for Parameter Identification in Latent Variable Structural Equation Models

Ankur Ankan, Inge Wortel, Kenneth Bollen, Johannes Textor

Meta-Learning with Adjoint Methods

Shibo Li, Zheng Wang, Akil Narayan, Robert Kirby, Shandian Zhe

Faster Projection-Free Augmented Lagrangian Methods via Weak Proximal Oracle

Dan Garber, Tsur Livney, Shoham Sabach

Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles

Aldo Gael Carranza, Sanath Kumar Krishnamurthy, Susan Athey

Clustering High-dimensional Data with Ordered Weighted ℓ_1 Regularization

Chandramauli Chakraborty, Sayan Paul, Saptarshi Chakraborty, Swagatam Das

On the Accelerated Noise-Tolerant Power Method

Zhiqiang Xu

Learning Treatment Effects from Observational and Experimental Data

Sofia Triantafillou, Fattaneh Jabbari, Gregory F. Cooper

On double-descent in uncertainty quantification in overparametrized models

Lucas Clarte, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

SMCP3: Sequential Monte Carlo with Probabilistic Program Proposals

Alexander K. Lew, George Matheos, Tan Zhi-Xuan, Matin Ghavamizadeh, Nishad Gothoskar

Unifying local and global model explanations by functional decomposition of low dimensional structures

Munir Hiabu, Joseph T. Meyer, Marvin N. Wright

Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings

Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa

Thresholded linear bandits

Nishant A. Mehta, Junpei Komiyama, Vamsi K. Potluru, Andrea Nguyen, Mica Grant-Hagen

Recurrent Neural Networks and Universal Approximation of Bayesian Filters

Adrian N. Bishop, Edwin V. Bonilla

Differentiable Change-point Detection With Temporal Point Processes

Paramita Koley, Harshavardhan Alimi, Shrey Singla, Sourangshu Bhattacharya, Niloy Ganguly

Two-Sample Tests for Inhomogeneous Random Graphs in L_r Norm: Optimality and Asymptotics

Sayak Chatterjee, Dibyendu Saha, Soham Dan, Bhaswar B. Bhattacharya

Strong Lottery Ticket Hypothesis with ε–perturbation

Zheyang Xiong, Fangshuo Liao, Anastasios Kyrillidis

Convex Bounds on the Softmax Function with Applications to Robustness Verification

Dennis Wei, Haoze Wu, Min Wu, Pin-Yu Chen, Clark Barrett

Transport Reversible Jump Proposals

Laurence Davies, Robert Salomone, Matthew Sutton, Chris Drovandi

INO: Invariant Neural Operators for Learning Complex Physical Systems with Momentum Conservation

Ning Liu, Yue Yu, Huaiqian You, Neeraj Tatikola

Optimal Sample Complexity Bounds for Non-convex Optimization under Kurdyka-Lojasiewicz Condition

Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee

On Model Selection Consistency of Lasso for High-Dimensional Ising Models

Xiangming Meng, Tomoyuki Obuchi, Yoshiyuki Kabashima

Structure of Nonlinear Node Embeddings in Stochastic Block Models

Christopher Harker, Aditya Bhaskara

Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach

Vishnu Raj, Tianyu Cui, Markus Heinonen, Pekka Marttinen

Uncertainty-aware Unsupervised Video Hashing

Yucheng Wang, Mingyuan Zhou, Yu Sun, Xiaoning Qian