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Timezone: CET
Full Schedule Mon 3/28 Tue 3/29 Wed 3/30
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Oral
9:00 AM - 10:00 AM
4 Events in this session
Sampling from Arbitrary Functions via PSD Models
Ulysse Marteau-Ferey · Francis Bach · Alessandro Rudi
Orbital MCMC
Kirill Neklyudov · Max Welling
Hardness of Learning a Single Neuron with Adversarial Label Noise
Ilias Diakonikolas · Daniel Kane · Pasin Manurangsi · Lisheng Ren
Data-splitting improves statistical performance in overparameterized regimes
Nicole Mücke · Enrico Reiss · Jonas Rungenhagen · Markus Klein
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Oral
10:00 AM - 11:00 AM
4 Events in this session
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning
Yongchan Kwon · James Zou
Faster Rates, Adaptive Algorithms, and Finite-Time Bounds for Linear Composition Optimization and Gradient TD Learning
Anant Raj · Pooria Joulani · Andras Gyorgy · Csaba Szepesvari
A general class of surrogate functions for stable and efficient reinforcement learning
Sharan Vaswani · Olivier Bachem · Simone Totaro · Robert Müller · Shivam Garg · Matthieu Geist · Marlos C. Machado · Pablo Samuel Castro · Nicolas Le Roux
A Complete Characterisation of ReLU-Invariant Distributions
Jan Macdonald · Stephan Wäldchen
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Invited Talk
11:30 AM - 12:30 PM

Coming soon

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Speaker Bio
Mihaela van der Schaar
Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM). Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award. Mihaela is personally credited as inventor on 35 USA patents, many of which are still frequently cited and adopted in standards. She has made over 45 contributions to international standards for which she received 3 ISO Awards.
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Poster
12:30 PM - 2:00 PM
94 Events in this session
Acceleration in Distributed Optimization under Similarity
Ye Tian · Gesualdo Scutari · Tianyu Cao · Alexander Gasnikov
Adaptive A/B Test on Networks with Cluster Structures
Yang Liu · Yifan Zhou · Ping Li · Feifang Hu
Adversarially Robust Kernel Smoothing
Jia-Jie Zhu · Christina Kouridi · Yassine Nemmour · Bernhard Schölkopf
Conditional Gradients for the Approximately Vanishing Ideal
Elias Wirth · Sebastian Pokutta
Counterfactual Explanation Trees: Transparent and Consistent Actionable Recourse with Decision Trees
Kentaro Kanamori · Takuya Takagi · Ken Kobayashi · Yuichi Ike
Derivative-Based Neural Modelling of Cumulative Distribution Functions for Survival Analysis
Dominic Danks · Christopher Yau
Differentiable Bayesian inference of SDE parameters using a pathwise series expansion of Brownian motion
Sanmitra Ghosh · Paul J. Birrell · Daniela De Angelis
Differentially Private Federated Learning on Heterogeneous Data
Maxence Noble-Bourillot · Aurélien Bellet · Aymeric Dieuleveut
Equivariant Deep Dynamical Model for Motion Prediction
Bahar Azari · Deniz Erdogmus
Exploiting Correlation to Achieve Faster Learning Rates in Low-Rank Preference Bandits
Aadirupa Saha · Suprovat Ghoshal
Faster Rates, Adaptive Algorithms, and Finite-Time Bounds for Linear Composition Optimization and Gradient TD Learning
Anant Raj · Pooria Joulani · Andras Gyorgy · Csaba Szepesvari
Fast Rank-1 NMF for Missing Data with KL Divergence
Kazu Ghalamkari · Mahito Sugiyama
Federated Reinforcement Learning with Environment Heterogeneity
Hao Jin · Yang Peng · Wenhao Yang · Shusen Wang · Zhihua Zhang
Fixed Support Tree-Sliced Wasserstein Barycenter
Yuki Takezawa · Ryoma Sato · Zornitsa Kozareva · Sujith Ravi · Makoto Yamada
Gaussian Process Bandit Optimization with Few Batches
Zihan Li · Jonathan Scarlett
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects
Yao Zhang · Jeroen Berrevoets · Mihaela van der Schaar
Improved Approximation Algorithms for Individually Fair Clustering
Ali Vakilian · Mustafa Yalçıner
Increasing the accuracy and resolution of precipitation forecasts using deep generative models
Ilan Price · Stephan Rasp
Node Feature Kernels Increase Graph Convolutional Network Robustness
Mohamed El Amine Seddik · Changmin Wu · Johannes Lutzeyer · Michalis Vazirgiannis
On Some Fast And Robust Classifiers For High Dimension, Low Sample Size Data
Sarbojit Roy · Jyotishka Ray Choudhury · Subhajit Dutta
Optimal transport with $f$-divergence regularization and generalized Sinkhorn algorithm
Dávid Terjék · Diego González-Sánchez
Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games
Yulai Zhao · Yuandong Tian · Jason Lee · Simon Du
Sampling from Arbitrary Functions via PSD Models
Ulysse Marteau-Ferey · Francis Bach · Alessandro Rudi
Unifying Importance Based Regularisation Methods for Continual Learning
Frederik Benzing
A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds
Yan Shuo Tan · Abhineet Agarwal · Bin Yu
Accurate Shapley Values for explaining tree-based models
Salim I. Amoukou · Tangi Salaün · Nicolas Brunel
A Complete Characterisation of ReLU-Invariant Distributions
Jan Macdonald · Stephan Wäldchen
Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals
James Brofos · Marylou Gabrie · Marcus Brubaker · Roy Lederman
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets
Felix Opolka · Yin-Cong Zhi · Pietro Lió · Xiaowen Dong
Adaptive Importance Sampling meets Mirror Descent : a Bias-variance Tradeoff
Anna Korba · François Portier
Adaptive Multi-Goal Exploration
Jean Tarbouriech · Omar Darwiche Domingues · Pierre Ménard · Matteo Pirotta · Michal Valko · Alessandro Lazaric
A general sample complexity analysis of vanilla policy gradient
Rui YUAN · Robert Gower · Alessandro Lazaric
A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning
Youssef Diouane · Aurelien Lucchi · Vihang Patil
Approximate Function Evaluation via Multi-Armed Bandits
Tavor Baharav · Gary Cheng · Mert Pilanci · David Tse
A Single-Timescale Method for Stochastic Bilevel Optimization
Tianyi Chen · Yuejiao Sun · Quan Xiao · Wotao Yin
A Spectral Perspective of DNN Robustness to Label Noise
Oshrat Bar · Amnon Drory · Raja Giryes
Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes
Felix Opolka · Pietro Lió
Chernoff Sampling for Active Testing and Extension to Active Regression
Subhojyoti Mukherjee · Ardhendu Tripathy · Robert Nowak
Convergent Working Set Algorithm for Lasso with Non-Convex Sparse Regularizers
Alain Rakotomamonjy · Rémi Flamary · Joseph Salmon · Gilles Gasso
Coresets for Data Discretization and Sine Wave Fitting
Alaa Maalouf · Murad Tukan · Eric Price · Daniel Kane · Dan Feldman
Data-splitting improves statistical performance in overparameterized regimes
Nicole Mücke · Enrico Reiss · Jonas Rungenhagen · Markus Klein
Debiasing Samples from Online Learning Using Bootstrap
Ningyuan Chen · Xuefeng GAO · Yi Xiong
Differentially Private Densest Subgraph
Alireza Farhadi · MohammadTaghi Hajiaghai · Elaine Shi
Differentially Private Regression with Unbounded Covariates
Jason Milionis · Alkis Kalavasis · Dimitris Fotakis · Stratis Ioannidis
Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data
Matthew Engelhard · Ricardo Henao
Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes
Elvis Nava · Mojmir Mutny · Andreas Krause
Dual-Level Adaptive Information Filtering for Interactive Image Segmentation
Ervine Zheng · Qi Yu · Rui Li · Pengcheng Shi · Anne Haake
Effective Nonlinear Feature Selection Method based on HSIC Lasso and with Variational Inference
Kazuki Koyama · Keisuke Kiritoshi · Tomomi Okawachi · Tomonori Izumitani
Efficient Online Bayesian Inference for Neural Bandits
Gerardo Duran-Martin · Aleyna Kara · Kevin Murphy
Embedded Ensembles: infinite width limit and operating regimes
Maksim Velikanov · Roman Kail · Ivan Anokhin · Roman Vashurin · Maxim Panov · Alexey Zaytsev · Dmitry Yarotsky
Exploring Image Regions Not Well Encoded by an INN
Zenan Ling · Fan Zhou · Meng Wei · Quanshi Zhang
Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methods
Baturalp Yalçın · Haixiang Zhang · Javad Lavaei · Somayeh Sojoudi
Feature screening with kernel knockoffs
Benjamin Poignard · Peter Naylor · Héctor Climente-González · Makoto Yamada
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning
Elnur Gasanov · Ahmed Khaled · Samuel Horváth · Peter Richtarik
Generalized Group Testing
Xiwei Cheng · Sidharth Jaggi · Qiaoqiao Zhou
GraphAdaMix: Enhancing Node Representations with Graph Adaptive Mixtures
Da Sun Handason Tam · Siyue Xie · Wing Cheong Lau
Harmless interpolation in regression and classification with structured features
Andrew McRae · Santhosh Karnik · Mark Davenport · Vidya Muthukumar
How to Learn when Data Gradually Reacts to Your Model
Zachary Izzo · James Zou · Lexing Ying
Identification in Tree-shaped Linear Structural Causal Models
Benito van der Zander · Marcel Wienöbst · Markus Bläser · Maciej Liskiewicz
Implicitly Regularized RL with Implicit Q-values
Nino Vieillard · Marcin Andrychowicz · Anton Raichuk · Olivier Pietquin · Matthieu Geist
Kantorovich Mechanism for Pufferfish Privacy
Ni Ding
k-experts - Online Policies and Fundamental Limits
Samrat Mukhopadhyay · Sourav Sahoo · Abhishek Sinha
Learning a Single Neuron for Non-monotonic Activation Functions
Lei Wu
Learning Pareto-Efficient Decisions with Confidence
Sofia Ek · Dave Zachariah · Peter Stoica
Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback
Nghia Hoang · Anoop Deoras · Tong Zhao · Jin Li · George Karypis
Learning Proposals for Practical Energy-Based Regression
Fredrik Gustafsson · Martin Danelljan · Thomas Schön
Leveraging Time Irreversibility with Order-Contrastive Pre-training
Monica Agrawal · Hunter Lang · Michael Offin · Lior Gazit · David Sontag
Marginalising over Stationary Kernels with Bayesian Quadrature
Saad Hamid · Sebastian Schulze · Michael A. Osborne · Stephen Roberts
Marginalized Operators for Off-policy Reinforcement Learning
Yunhao Tang · Mark Rowland · Remi Munos · Michal Valko
Masked Training of Neural Networks with Partial Gradients
Amirkeivan Mohtashami · Martin Jaggi · Sebastian Stich
Model-agnostic out-of-distribution detection using combined statistical tests
Federico Bergamin · Pierre-Alexandre Mattei · Jakob Drachmann Havtorn · Hugo Sénétaire · Hugo Schmutz · Lars Maaløe · Soren Hauberg · Jes Frellsen
Multi-class classification in nonparametric active learning
Boris Ndjia Njike · Xavier Siebert
Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization
Guodong Zhang · Yuanhao Wang · Laurent Lessard · Roger Grosse
Near-Optimal Task Selection for Meta-Learning with Mutual Information and Online Variational Bayesian Unlearning
Yizhou Chen · Shizhuo Zhang · Bryan Kian Hsiang Low
Neural score matching for high-dimensional causal inference
Oscar Clivio · Fabian Falck · Brieuc Lehmann · George Deligiannidis · Chris Holmes
Nonstochastic Bandits and Experts with Arm-Dependent Delays
Dirk van der Hoeven · Nicolò Cesa-Bianchi
On Distributionally Robust Optimization and Data Rebalancing
Agnieszka Słowik · Leon Bottou
On Facility Location Problem in the Local Differential Privacy Model
Vincent Cohen-Addad · Yunus Esencayi · Chenglin Fan · Marco Gaboradi · Shi Li · Di Wang
On Global-view Based Defense via Adversarial Attack and Defense Risk Guaranteed Bounds
Trung Le · Anh Bui · LE Minh Tri Tue · He Zhao · Paul Montague · Quan Tran · Dinh Phung
On Linear Model with Markov Signal Priors
Lan Truong
Online Learning for Unknown Partially Observable MDPs
Mehdi Jafarnia · Rahul Jain · Ashutosh Nayyar
On Margins and Derandomisation in PAC-Bayes
Felix Biggs · Benjamin Guedj
On PAC-Bayesian reconstruction guarantees for VAEs
Badr-Eddine Chérief-Abdellatif · Yuyang Shi · Arnaud Doucet · Benjamin Guedj
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms
Nhat Ho · Tianyi Lin · Michael Jordan
On the complexity of the optimal transport problem with graph-structured cost
Jiaojiao Fan · Isabel Haasler · Johan Karlsson · Yongxin Chen
On the Generalization of Representations in Reinforcement Learning
Charline Le Lan · Stephen Tu · Adam Oberman · Rishabh Agarwal · Marc G. Bellemare
Optimal channel selection with discrete QCQP
Yeonwoo Jeong · Deokjae Lee · Gaon An · Changyong Son · Hyun Oh Song
Parallel MCMC Without Embarrassing Failures
Daniel Augusto de Souza · Diego Mesquita · Samuel Kaski · Luigi Acerbi
Physics Informed Deep Kernel Learning
Zheng Wang · Wei Xing · Robert Kirby · Shandian Zhe
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification
Ashwinee Panda · Saeed Mahloujifar · Arjun Nitin Bhagoji · Supriyo Chakraborty · Prateek Mittal
Structured variational inference in Bayesian state-space models
Honggang Wang · Anirban Bhattacharya · Debdeep Pati · Yun Yang
Super-Acceleration with Cyclical Step-sizes
Baptiste Goujaud · Damien Scieur · Aymeric Dieuleveut · Adrien Taylor · Fabian Pedregosa
System-Agnostic Meta-Learning for MDP-based Dynamic Scheduling via Descriptive Policy
Hyun-Suk Lee
Variational Continual Proxy-Anchor for Deep Metric Learning
Minyoung Kim · Ricardo Guerrero · Hai Pham · Vladimir Pavlovic
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Oral
3:00 PM - 4:00 PM
4 Events in this session
Minimax Optimization: The Case of Convex-Submodular
Arman Adibi · Aryan Mokhtari · Hamed Hassani
Doubly Mixed-Effects Gaussian Process Regression
Jun Ho Yoon · Daniel Jeong · Seyoung Kim
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes
Hugh Dance · Brooks Paige
Debiasing Samples from Online Learning Using Bootstrap
Ningyuan Chen · Xuefeng GAO · Yi Xiong
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Oral
4:00 PM - 5:00 PM
4 Events in this session
Entropy Regularized Optimal Transport Independence Criterion
Lang Liu · Soumik Pal · Zaid Harchaoui
Two-Sample Test with Kernel Projected Wasserstein Distance
Jie Wang · Rui Gao · Yao Xie
Estimating Functionals of the Out-of-Sample Error Distribution in High-Dimensional Ridge Regression
Pratik Patil · Alessandro Rinaldo · Ryan Tibshirani
Heavy-tailed Streaming Statistical Estimation
Che-Ping Tsai · Adarsh Prasad · Sivaraman Balakrishnan · Pradeep Ravikumar
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Poster
5:30 PM - 7:00 PM
104 Events in this session
A Bayesian Approach for Stochastic Continuum-armed Bandit with Long-term Constraints
Zai Shi · Atilla Eryilmaz
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons
Yue Wu · Tao Jin · Hao Lou · PAN XU · Farzad Farnoud · Quanquan Gu
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning
Yongchan Kwon · James Zou
Convex Analysis of the Mean Field Langevin Dynamics
Atsushi Nitanda · Denny Wu · Taiji Suzuki
Differentially Private Histograms under Continual Observation: Streaming Selection into the Unknown
Adrian Rivera Cardoso · Ryan Rogers
Efficient Kernelized UCB for Contextual Bandits
Houssam Zenati · Alberto Bietti · Eustache Diemert · Julien Mairal · Matthieu Martin · Pierre Gaillard
Many processors, little time: MCMC for partitions via optimal transport couplings
Tin Nguyen · Brian Trippe · Tamara Broderick
Meta Learning MDPs with linear transition models
Robert Müller · Aldo Pacchiano
Offline Policy Selection under Uncertainty
Sherry Yang · Bo Dai · Ofir Nachum · George Tucker · Dale Schuurmans
On Convergence of Lookahead in Smooth Games
Junsoo Ha · Gunhee Kim
Online Control of the False Discovery Rate under “Decision Deadlines”
Aaron Fisher
Optimizing Early Warning Classifiers to Control False Alarms via a Minimum Precision Constraint
Preetish Rath · Michael Hughes
Orbital MCMC
Kirill Neklyudov · Max Welling
QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning
Maxime Vono · Vincent Plassier · Alain Durmus · Aymeric Dieuleveut · Eric Moulines
Rapid Convergence of Informed Importance Tempering
Quan Zhou · Aaron Smith
Spiked Covariance Estimation from Modulo-Reduced Measurements
Elad Romanov · Or Ordentlich
Synthsonic: Fast, Probabilistic modeling and Synthesis of Tabular Data
Max Baak · Simon Brugman · Ilan Fridman Rojas · Lorraine d'Almeida · Ralph Urlus · Jean-Baptiste Oger
Unlabeled Data Help: Minimax Analysis and Adversarial Robustness
Yue Xing · Qifan Song · Guang Cheng
A Bandit Model for Human-Machine Decision Making with Private Information and Opacity
Sebastian Bordt · Ulrike von Luxburg
A Bayesian Model for Online Activity Sample Sizes
Thomas Richardson · Yu Liu · James McQueen · Doug Hains
A Contraction Theory Approach to Optimization Algorithms from Acceleration Flows
Pedro Cisneros · Francesco Bullo
Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE
Yuqing Zhu · Yu-Xiang Wang
A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence
Alex Dytso · Mario Goldenbaum · H. Vincent Poor · Shlomo Shamai
A Dual Approach to Constrained Markov Decision Processes with Entropy Regularization
Donghao Ying · Yuhao Ding · Javad Lavaei
Adversarial Tracking Control via Strongly Adaptive Online Learning with Memory
Zhiyu Zhang · Ashok Cutkosky · Ioannis Paschalidis
A general class of surrogate functions for stable and efficient reinforcement learning
Sharan Vaswani · Olivier Bachem · Simone Totaro · Robert Müller · Shivam Garg · Matthieu Geist · Marlos C. Machado · Pablo Samuel Castro · Nicolas Le Roux
A Manifold View of Adversarial Risk
Wenjia Zhang · Yikai Zhang · Xiaoling Hu · Mayank Goswami · Chao Chen · Dimitris Metaxas
An Information-Theoretic Justification for Model Pruning
Berivan Isik · Tsachy Weissman · Albert No
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization
Elan Rosenfeld · Pradeep Ravikumar · Andrej Risteski
Approximate Top-$m$ Arm Identification with Heterogeneous Reward Variances
Ruida Zhou · Chao Tian
A prior-based approximate latent Riemannian metric
Georgios Arvanitidis · Bogdan Georgiev · Bernhard Schölkopf
A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions
Ben Adlam · Jake Levinson · Jeffrey Pennington
Asynchronous Distributed Optimization with Stochastic Delays
Margalit Glasgow · Mary Wootters
A Unified View of SDP-based Neural Network Verification through Completely Positive Programming
Robin Brown · Edward Schmerling · Navid Azizan · Marco Pavone
A View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy
Kevin Bello · Chuyang Ke · Jean Honorio
Causally motivated shortcut removal using auxiliary labels
Maggie Makar · Ben Packer · Dan Moldovan · Davis Blalock · Yoni Halpern · Alexander D'Amour
Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning
Hsu Kao · Vijay Subramanian
Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization
Yujia Wang · Lu Lin · Jinghui Chen
Compressed Rule Ensemble Learning
Malte Nalenz · Thomas Augustin
Conditional Linear Regression for Heterogeneous Covariances
Leda Liang · Brendan Juba
Conditionally Tractable Density Estimation using Neural Networks
Hailiang Dong · Chiradeep Roy · Tahrima Rahman · Vibhav Gogate · Nicholas Ruozzi
Confident Least Square Value Iteration with Local Access to a Simulator
Botao Hao · Nevena Lazic · Dong Yin · Yasin Abbasi-Yadkori · Csaba Szepesvari
Contrasting the landscape of contrastive and non-contrastive learning
Ashwini Pokle · Jinjin Tian · Yuchen Li · Andrej Risteski
Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks
Amy Babay · Michael Dinitz · Aravind Srinivasan · Leonidas Tsepenekas · Anil Vullikanti
Convergence of Langevin Monte Carlo in Chi-Squared and Rényi Divergence
Murat Erdogdu · Rasa Hosseinzadeh · Shunshi Zhang
Convergence of online k-means
Geelon So · Gaurav Mahajan · Sanjoy Dasgupta
Cross-Loss Influence Functions to Explain Deep Network Representations
Andrew Silva · Rohit Chopra · Matthew Gombolay
Cycle Consistent Probability Divergences Across Different Spaces
Zhengxin Zhang · Youssef Mroueh · Ziv Goldfeld · Bharath Sriperumbudur
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection
Cristian Challu · Peihong Jiang · Ying Nian Wu · Laurent Callot
Deep Neyman-Scott Processes
Chengkuan Hong · Christian Shelton
Density Ratio Estimation via Infinitesimal Classification
Kristy Choi · Chenlin Meng · Yang Song · Stefano Ermon
Distributed Sparse Multicategory Discriminant Analysis
Hengchao Chen · Qiang Sun
Doubly Mixed-Effects Gaussian Process Regression
Jun Ho Yoon · Daniel Jeong · Seyoung Kim
Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression
Giacomo Meanti · Luigi Carratino · Ernesto De Vito · Lorenzo Rosasco
Entropy Regularized Optimal Transport Independence Criterion
Lang Liu · Soumik Pal · Zaid Harchaoui
Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms
Joshua Agterberg · Jeremias Sulam
Equivariance Discovery by Learned Parameter-Sharing
Raymond A. Yeh · Yuan-Ting Hu · Mark Hasegawa-Johnson · Alexander Schwing
Estimating Functionals of the Out-of-Sample Error Distribution in High-Dimensional Ridge Regression
Pratik Patil · Alessandro Rinaldo · Ryan Tibshirani
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis
Martin Pawelczyk · Chirag Agarwal · Shalmali Joshi · Sohini Upadhyay · Himabindu Lakkaraju
Expressivity of Neural Networks via Chaotic Itineraries beyond Sharkovsky's Theorem
Clayton Sanford · Vaggos Chatziafratis
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes
Hugh Dance · Brooks Paige
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization
Gideon Dresdner · Maria-Luiza Vladarean · Gunnar Rätsch · Francesco Locatello · Volkan Cevher · Alp Yurtsever
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity
Junchi Yang · Antonio Orvieto · Aurelien Lucchi · Niao He
Firebolt: Weak Supervision Under Weaker Assumptions
Charles Kwong · Chidubem Arachie · Bangyong Liang · Pradyumna Narayana · Giulia DeSalvo · Michael Quinn · Bert Huang · Geoffrey Downs · Yang Yang
Fundamental limits for rank-one matrix estimation with groupwise heteroskedasticity
Joshua Behne · Galen Reeves
Hardness of Learning a Single Neuron with Adversarial Label Noise
Ilias Diakonikolas · Daniel Kane · Pasin Manurangsi · Lisheng Ren
Heavy-tailed Streaming Statistical Estimation
Che-Ping Tsai · Adarsh Prasad · Sivaraman Balakrishnan · Pradeep Ravikumar
Hypergraph Simultaneous Generators
Bahman Pedrood · Carlotta Domeniconi · Kathryn Laskey
Learning and Generalization in Overparameterized Normalizing Flows
Kulin Shah · Amit Deshpande · Navin Goyal
Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits
Wenshuo Guo · Kumar Agrawal · Aditya Grover · Vidya Muthukumar · Ashwin Pananjady
Learning from Multiple Noisy Partial Labelers
Peilin Yu · Tiffany Ding · Stephen Bach
Learning in Stochastic Monotone Games with Decision-Dependent Data
Adhyyan Narang · Evan Faulkner · Dmitriy Drusvyatskiy · Maryam Fazel · Lillian Ratliff
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting
Youngsuk Park · Danielle Maddix · François-Xavier Aubet · Kelvin Kan · Jan Gasthaus · Yuyang Wang
Loss as the Inconsistency of a Probabilistic Dependency Graph: Choose Your Model, Not Your Loss Function
Oliver Richardson
Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference
AmirEmad Ghassami · Andrew Ying · Ilya Shpitser · Eric Tchetgen Tchetgen
Minimax Optimization: The Case of Convex-Submodular
Arman Adibi · Aryan Mokhtari · Hamed Hassani
One-bit Submission for Locally Private Quasi-MLE: Its Asymptotic Normality and Limitation
Hajime Ono · Kazuhiro Minami · Hideitsu Hino
On the Assumptions of Synthetic Control Methods
Claudia Shi · Dhanya Sridhar · Vishal Misra · David Blei
On the equivalence of Oja's algorithm and GROUSE
Laura Balzano
On the Implicit Bias of Gradient Descent for Temporal Extrapolation
Edo Cohen-Karlik · Avichai Ben David · Nadav Cohen · Amir Globerson
On the Oracle Complexity of Higher-Order Smooth Non-Convex Finite-Sum Optimization
Nicolas Emmenegger · Rasmus Kyng · Ahad N. Zehmakan
Optimal Compression of Locally Differentially Private Mechanisms
Abhin Shah · Wei-Ning Chen · Johannes Ballé · Peter Kairouz · Lucas Theis
Optimal Design of Stochastic DNA Synthesis Protocols based on Generative Sequence Models
Eli Weinstein · Alan Amin · Will Grathwohl · Daniel Kassler · Jean Disset · Debora Marks
Outcome Assumptions and Duality Theory for Balancing Weights
David Bruns-Smith · Avi Feller
Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis
Sloan Nietert · Ziv Goldfeld · Rachel Cummings
PAC Mode Estimation using PPR Martingale Confidence Sequences
Shubham A Jain · Rohan Shah · Sanit Gupta · Denil Mehta · Inderjeet Nair · Jian Vora · Sushil Khyalia · Sourav Das · Vinay Ribeiro · Shivaram Kalyanakrishnan
Parameter-Free Online Linear Optimization with Side Information via Universal Coin Betting
Jongha Jon Ryu · Alankrita Bhatt · Young-Han Kim
Policy Learning for Optimal Individualized Dose Intervals
Guanhua Chen · xiaomao li · Menggang Yu
Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions
ZIHAO DENG · Siddartha Devic · Brendan Juba
Projection Predictive Inference for Generalized Linear and Additive Multilevel Models
Alejandro Catalina · Paul Bürkner · Aki Vehtari
Provable Adversarial Robustness for Fractional Lp Threat Models
Alexander Levine · Soheil Feizi
Pulling back information geometry
Georgios Arvanitidis · Miguel González-Duque · Alison Pouplin · Dimitris Kalatzis · Soren Hauberg
Recoverability Landscape of Tree Structured Markov Random Fields under Symmetric Noise
Ashish Katiyar · Soumya Basu · Vatsal Shah · Constantine Caramanis
Sample-and-threshold differential privacy: Histograms and applications
Graham Cormode · Akash Bharadwaj
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Completion
Tian Tong · Cong Ma · Ashley Prater-Bennette · Erin Tripp · Yuejie Chi
Sketch-and-lift: scalable subsampled semidefinite program for K-means clustering
Yubo Zhuang · Xiaohui Chen · Yun Yang
TD-GEN: Graph Generation Using Tree Decomposition
Hamed Shirzad · Hossein Hajimirsadeghi · Amir Abdi · Greg Mori
The Fast Kernel Transform
John Ryan · Sebastian Ament · Carla Gomes · Anil Damle
The role of optimization geometry in single neuron learning
Nicholas Boffi · Stephen Tu · Jean-Jacques Slotine
The Tree Loss: Improving Generalization with Many Classes
Yujie Wang · Mike Izbicki
Towards an Understanding of Default Policies in Multitask Policy Optimization
Ted Moskovitz · Michael Arbel · Jack Parker-Holder · Aldo Pacchiano
Variational Autoencoders: A Harmonic Perspective
Alexander Camuto · Matthew Willetts
Weak Separation in Mixture Models and Implications for Principal Stratification
Nhat Ho · Avi Feller · Evan Greif · Luke Miratrix · Natesh Pillai
Zeroth-Order Methods for Convex-Concave Min-max Problems: Applications to Decision-Dependent Risk Minimization
Chinmay Maheshwari · Chih-Yuan Chiu · Eric Mazumdar · Shankar Sastry · Lillian Ratliff
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Affinity Event

Mentoring Retrospectives Workshop

Pablo Samuel Castro · Sarah Tan · Bilal Alsallakh · Matias Valdenegro-Toro · Laya Rafiee Sevyeri · Aaditya Ramdas · Li Chen
7:00 PM - 9:00 PM

[ Livestream and chat are lower on the page. ]

This event celebrates the submission mentorship program, with mentees presenting talks on their papers, and talks from experienced researchers in the ML community on good research practices. The workshop ends with an open social for AISTATS attendees.

Agenda (all times in UTC)

  • 17:00 - 17:20 (livestream) -- Learnings from AISTATS 2022 Submission Mentoring Program, Pablo Samuel Castro (Google Brain) and Sarah Tan (Meta)

  • 17:20 - 17:30 (livestream) -- Mentee Talks and Experience Sharing: AD-CGAN: Contrastive Generative Adversarial Network for Anomaly Detection, Laya Rafiee (Concordia University)

  • 17:30 - 17:45 (livestream) -- Good Research Practices: How to write a great paper, Bilal Alsallakh (Meta)

  • 17:45 - 18:00 (livestream) -- Good Research Practices: How to present your research, Li Chen (Meta)

  • 18:00 - 18:15 (livestream) -- Good Research Practices: How to read papers, Aaditya Ramdas (Carnegie Mellon University)

  • 18:15 - 18:30 (livestream) -- Good Research Practices: Tips for grad school research, Matias Valdenegro (University of Groningen)

  • 18:30 - 19:00 (no livestream) -- Open Social

... more