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Timezone: Europe/Madrid
Full Schedule Thu 5/2 Fri 5/3 Sat 5/4
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Registration Desk
7:30 AM - 5:00 PM
Remarks
8:45 AM - 9:00 AM
Invited Talk

Invited Talk - Matt Hoffman

Matthew D. Hoffman
9:00 AM - 10:00 AM
Speaker Bio
Matthew D. Hoffman
Matt Hoffman is a Research Scientist at Google. His main research focus is in probabilistic modeling and approximate inference algorithms. He has worked on various applications including music information retrieval, speech enhancement, topic modeling, learning to rank, computer vision, user interfaces, user behavior modeling, social network analysis, digital imaging, and astronomy. He is a co-creator of the widely used statistical modeling package Stan, and a contributor to the TensorFlow Probability library.
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Oral
10:30 AM - 11:30 AM
4 Events in this session
Conformal Contextual Robust Optimization
Yash Patel · Sahana Rayan · Ambuj Tewari
Model-based Policy Optimization under Approximate Bayesian Inference
Chaoqi Wang · Yuxin Chen · Kevin Murphy
Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games
Yang Cai · Haipeng Luo · Chen-Yu Wei · Weiqiang Zheng
Online Learning of Decision Trees with Thompson Sampling
Ayman Chaouki · Jesse Read · Albert Bifet
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Panel

Establishing your own research path in the age of big compute

Aaditya Ramdas · Matthew D. Hoffman · Jennifer Dy · Alexia Jolicoeur-Martineau
11:30 AM - 12:30 PM
Oral
2:00 PM - 3:15 PM
5 Events in this session
Absence of spurious solutions far from ground truth: A low-rank analysis with high-order losses
Ziye Ma · Ying Chen · Javad Lavaei · Somayeh Sojoudi
Graph Partitioning with a Move Budget
Mina Dalirrooyfard · Elaheh Fata · Majid Behbahani · Yuriy Nevmyvaka
Learning-Based Algorithms for Graph Searching Problems
Adela DePavia · Erasmo Tani · Ali Vakilian
Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements
Emmanouil Vasileios Vlatakis-Gkaragkounis · Angeliki Giannou · Yudong Chen · Qiaomin Xie
The sample complexity of ERMs in stochastic convex optimization
Daniel Carmon · Amir Yehudayoff · Roi Livni
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Oral
3:45 PM - 5:00 PM
5 Events in this session
Generative Flow Networks as Entropy-Regularized RL
Daniil Tiapkin · Nikita Morozov · Alexey Naumov · Dmitry Vetrov
Intrinsic Gaussian Vector Fields on Manifolds
Daniel Robert-Nicoud · Andreas Krause · Slava Borovitskiy
Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes
Haoming Yang · Ali Hasan · Yuting Ng · Vahid Tarokh
Reparameterized Variational Rejection Sampling
Martin Jankowiak · Du Phan
Robust Approximate Sampling via Stochastic Gradient Barker Dynamics
Lorenzo Mauri · Giacomo Zanella
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Poster
5:00 PM - 5:30 PM
182 Events in this session
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models
Shivvrat Arya · Tahrima Rahman · Vibhav Gogate
Scalable Higher-Order Tensor Product Spline Models
David Rügamer
Fast Dynamic Sampling for Determinantal Point Processes
Zhao Song · Junze Yin · Lichen Zhang · Ruizhe Zhang
Optimal Zero-Shot Detector for Multi-Armed Attacks
Federica Granese · Marco Romanelli · Pablo Piantanida
Conformal Contextual Robust Optimization
Yash Patel · Sahana Rayan · Ambuj Tewari
From Data Imputation to Data Cleaning --- Automated Cleaning of Tabular Data Improves Downstream Predictive Performance
Sebastian Jäger · Felix Biessmann
On-Demand Federated Learning for Arbitrary Target Class Distributions
Isu Jeong · Seulki Lee
Manifold-Aligned Counterfactual Explanations for Neural Networks
Asterios Tsiourvas · Wei Sun · Georgia Perakis
On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problem
Georg Pichler · Marco Romanelli · Divya Prakash Manivannan · Prashanth Krishnamurthy · Farshad khorrami · Siddharth Garg
Simple and scalable algorithms for cluster-aware precision medicine
Amanda M. Buch · Conor Liston · Logan Grosenick
HintMiner: Automatic Question Hints Mining From Q\&A Web Posts with Language Model via Self-Supervised Learning
Zhenyu Zhang · yang jiudong
Enhancing In-context Learning via Linear Probe Calibration
Momin Abbas · Yi Zhou · Parikshit Ram · Nathalie Baracaldo · Horst Samulowitz · Theodoros Salonidis · Tianyi Chen
TenGAN: Pure Transformer Encoders Make an Efficient Discrete GAN for De Novo Molecular Generation
Chen Li · Yoshihiro Yamanishi
Best Arm Identification with Resource Constraints
ZITIAN LI · Wang Chi Cheung
LEDetection: A Simple Framework for Semi-Supervised Few-Shot Object Detection
Phi Vu Tran
Fusing Individualized Treatment Rules Using Secondary Outcomes
Daiqi Gao · Yuanjia Wang · Donglin Zeng
A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Models
Zhongliang Guo · Weiye Li · Yifei Qian · Ognjen Arandjelovic · Lei Fang
When No-Rejection Learning is Consistent for Regression with Rejection
Xiaocheng Li · Shang Liu · Chunlin Sun · Hanzhao Wang
Efficient Graph Laplacian Estimation by Proximal Newton
Yakov Medvedovsky · Eran Treister · Tirza Routtenberg
Simulation-Free Schrödinger Bridges via Score and Flow Matching
Alexander Tong · Nikolay Malkin · Kilian Fatras · Lazar Atanackovic · Yanlei Zhang · Guillaume Huguet · Guy Wolf · Yoshua Bengio
Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks
Soheila Molaei · Anshul Thakur · Ghazaleh Niknam · Andrew Soltan · Hadi Zare · David Clifton
Cross-model Mutual Learning for Exemplar-based Medical Image Segmentation
Qing En · Yuhong Guo
Provable local learning rule by expert aggregation for a Hawkes network
Sophie Jaffard · Samuel Vaiter · Alexandre Muzy · Patricia Reynaud-Bouret
Ethics in Action: Training Reinforcement Learning Agents for Moral Decision-making In Text-based Adventure Games
Weichen Li · Rati Devidze · Waleed Mustafa · Sophie Fellenz
A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning
Kihyuk Hong · Yuhang Li · Ambuj Tewari
Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint
Haoyue Tang · Tian Xie · Aosong Feng · Hanyu Wang · Chenyang Zhang · Yang Bai
Safe and Interpretable Estimation of Optimal Treatment Regimes
Harsh Parikh · Quinn Lanners · Zade Akras · Sahar Zafar · M Brandon Westover · Cynthia Rudin · Alexander Volfovsky
MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization
Nguyen Do · Tanmoy Chowdhury · Chen Ling · Liang Zhao · My T. Thai
Bayesian Online Learning for Consensus Prediction
Sam Showalter · Alex Boyd · Padhraic Smyth · Mark Steyvers
BlockBoost: Scalable and Efficient Blocking through Boosting
Thiago Ramos · Rodrigo Schuller · Alex Akira Okuno · Lucas Nissenbaum · Roberto Oliveira · Paulo Orenstein
Estimating treatment effects from single-arm trials via latent-variable modeling
Manuel Haußmann · Tran Le · Viivi Halla-aho · Samu Kurki · Jussi Leinonen · Miika Koskinen · Samuel Kaski · Harri Lähdesmäki
ALAS: Active Learning for Autoconversion Rates Prediction from Satellite Data
Maria Carolina Novitasari · Johannes Quaas · Miguel Rodrigues
Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation
Vinod Kumar Chauhan · Jiandong Zhou · Ghadeer Ghosheh · Soheila Molaei · David Clifton
Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations
Krzysztof Kacprzyk · Mihaela van der Schaar
Attention-based Multi-instance Mixed Models
Jan Engelmann · Alessandro Palma · Jakub Tomczak · Fabian Theis · Francesco Paolo Casale
On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation
Jiawei Huang · Batuhan Yardim · Niao He
Learning to Rank for Optimal Treatment Allocation Under Resource Constraints
Fahad Kamran · Maggie Makar · Jenna Wiens
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine Learning
Amey Pasarkar · Adji Bousso Dieng
Contextual Bandits with Budgeted Information Reveal
Kyra Gan · Esmaeil Keyvanshokooh · Xueqing Liu · Susan Murphy
Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection
Mohammad Mahmudul Alam · Edward Raff · Stella Biderman · Tim Oates · James Holt
Deep Learning-Based Alternative Route Computation
Alex Zhai · Dee Guo · Sreenivas Gollapudi · Kostas Kollias · Daniel Delling
Uncertainty-aware Continuous Implicit Neural Representations for Remote Sensing Object Counting
Siyuan Xu · Yucheng Wang · Mingzhou Fan · Byung-Jun Yoon · Xiaoning Qian
Towards a Complete Benchmark on Video Moment Localization
Jinyeong Chae · Donghwa Kim · Kwanseok Kim · Doyeon Lee · Sangho Lee · Seongsu Ha · Jonghwan Mun · Wooyoung Kang · Byungseok Roh · Joonseok Lee
SADI: Similarity-Aware Diffusion Model-Based Imputation for Incomplete Temporal EHR Data
Zongyu Dai · Emily Getzen · Qi Long
The Effective Number of Shared Dimensions Between Paired Datasets
Hamza Giaffar · Camille Rullán Buxó · Mikio Aoi
Auditing Fairness under Unobserved Confounding
Yewon Byun · Dylan Sam · Michael Oberst · Zachary Lipton · Bryan Wilder
Sample Efficient Learning of Factored Embeddings of Tensor Fields
Taemin Heo · Chandrajit Bajaj
Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach
Juanwu Lu · Wei Zhan · Masayoshi TOMIZUKA · Yeping Hu
Density Uncertainty Layers for Reliable Uncertainty Estimation
Yookoon Park · David Blei
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?
Kyurae Kim · Yian Ma · Jacob Gardner
Neural McKean Vlasov Processes: Distributional Dependence in Diffusion Models
Haoming Yang · Ali Hasan · Yuting Ng · Vahid Tarokh
DNNLasso: Scalable Graph Learning for Matrix-Variate Data
Meixia Lin · Yangjing Zhang
Error bounds for any regression model using Gaussian processes with gradient information
Rafael Savvides · Hoang Phuc Hau Luu · Kai Puolamäki
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
Dongxia Wu · Tsuyoshi Ide · Georgios Kollias · Jiri Navratil · Aurelie Lozano · Naoki Abe · Yian Ma · Rose Yu
Looping in the Human: Collaborative and Explainable Bayesian Optimization
Masaki Adachi · Brady Planden · David Howey · Michael A. Osborne · Sebastian Orbell · Natalia Ares · Krikamol Muandet · Siu Lun Chau
VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates
Guillaume Braun · Masashi Sugiyama
Theory-guided Message Passing Neural Network for Probabilistic Inference
Zijun Cui · Hanjing Wang · Tian Gao · Kartik Talamadupula · Qiang Ji
Reparameterized Variational Rejection Sampling
Martin Jankowiak · Du Phan
Riemannian Laplace Approximation with the Fisher Metric
Hanlin Yu · Marcelo Hartmann · Bernardo Williams Moreno Sanchez · Mark Girolami · Arto Klami
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support
Tim Reichelt · Luke Ong · Tom Rainforth
Categorical Generative Model Evaluation via Synthetic Distribution Coarsening
Florence Regol · Mark Coates
Data-Adaptive Probabilistic Likelihood Approximation for Ordinary Differential Equations
Mohan Wu · Martin Lysy
Intrinsic Gaussian Vector Fields on Manifolds
Daniel Robert-Nicoud · Andreas Krause · Slava Borovitskiy
A Unifying Variational Framework for Gaussian Process Motion Planning
Lucas Cosier · Rares Iordan · Sicelukwanda Zwane · Giovanni Franzese · James Wilson · Marc Deisenroth · Alexander Terenin · Yasemin Bekiroglu
Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning
Jörn Tebbe · Christoph Zimmer · Ansgar Steland · Markus Lange-Hegermann · Fabian Mies
Subsampling Error in Stochastic Gradient Langevin Diffusions
Kexin Jin · Chenguang Liu · Jonas Latz
Robust Offline Reinforcement Learning with Heavy-Tailed Rewards
Jin Zhu · Runzhe Wan · Zhengling Qi · Shikai Luo · Chengchun Shi
Self-Supervised Quantization-Aware Knowledge Distillation
Kaiqi Zhao · Ming Zhao
Joint control variate for faster black-box variational inference
Xi Wang · · Justin Domke
Adaptivity of Diffusion Models to Manifold Structures
Rong Tang · Yun Yang
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean
Anton Thielmann · René-Marcel Kruse · Thomas Kneib · Benjamin Säfken
Diagonalisation SGD: Fast \& Convergent SGD for Non-Differentiable Models via Reparameterisation and Smoothing
Dominik Wagner · Basim Khajwal · Luke Ong
Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting
Louis Sharrock · Daniel Dodd · Christopher Nemeth
Bayesian Semi-structured Subspace Inference
Daniel Dold · David Rügamer · Beate Sick · Oliver Dürr
Structural perspective on constraint-based learning of Markov networks
Tuukka Korhonen · Fedor Fomin · Pekka Parviainen
Variational Gaussian Process Diffusion Processes
Prakhar Verma · Vincent Adam · Arno Solin
Scalable Meta-Learning with Gaussian Processes
Petru Tighineanu · Lukas Grossberger · Paul Baireuther · Kathrin Skubch · Stefan Falkner · Julia Vinogradska · Felix Berkenkamp
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation
Yingru Li · Zhiquan Luo
Training Implicit Generative Models via an Invariant Statistical Loss
José Manuel de Frutos · Pablo Olmos · Manuel Alberto Vazquez Lopez · Joaquín Míguez
GmGM: a fast multi-axis Gaussian graphical model
Bailey Andrew · David Westhead · Luisa Cutillo
Learning Sparse Codes with Entropy-Based ELBOs
Dmytro Velychko · Simon Damm · Asja Fischer · Jörg Lücke
Robust Approximate Sampling via Stochastic Gradient Barker Dynamics
Lorenzo Mauri · Giacomo Zanella
Probabilistic Integral Circuits
Gennaro Gala · Cassio de Campos · Robert Peharz · Antonio Vergari · Erik Quaeghebeur
Backward Filtering Forward Deciding in Linear Non-Gaussian State Space Models
Yun-Peng Li · Hans-Andrea Loeliger
Stochastic Approximation with Biased MCMC for Expectation Maximization
Samuel Gruffaz · Kyurae Kim · Alain Durmus · Jacob Gardner
Mixed variational flows for discrete variables
Gian C. Diluvi · Benjamin Bloem-Reddy · Trevor Campbell
Minimizing Convex Functionals over Space of Probability Measures via KL Divergence Gradient Flow
Rentian Yao · Linjun Huang · Yun Yang
Surrogate Bayesian Networks for Approximating Evolutionary Games
Vincent Hsiao · Dana Nau · Bobak Pezeshki · Rina Dechter
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias
Ambroise Odonnat · Vasilii Feofanov · Ievgen Redko
Large-Scale Gaussian Processes via Alternating Projection
Kaiwen Wu · Jonathan Wenger · Haydn Jones · Geoff Pleiss · Jacob Gardner
Clustering Items From Adaptively Collected Inconsistent Feedback
Shubham Gupta · Peter Staar · Christian de Sainte Marie
Fair k-center Clustering with Outliers
Daichi Amagata
Deep anytime-valid hypothesis testing
Teodora Pandeva · Patrick Forré · Aaditya Ramdas · Shubhanshu Shekhar
Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters
Zhenyu Sun · Xiaochun Niu · Ermin Wei
A General Algorithm for Solving Rank-one Matrix Sensing
Lianke Qin · Zhao Song · Ruizhe Zhang
Recovery Guarantees for Distributed-OMP
Chen Amiraz · Robert Krauthgamer · Boaz Nadler
Making Better Use of Unlabelled Data in Bayesian Active Learning
Freddie Bickford Smith · Adam Foster · Tom Rainforth
BOBA: Byzantine-Robust Federated Learning with Label Skewness
Wenxuan Bao · Jun Wu · Jingrui He
Autoregressive Bandits
Francesco Bacchiocchi · Gianmarco Genalti · Davide Maran · Marco Mussi · Marcello Restelli · Nicola Gatti · Alberto Maria Metelli
MINTY: Rule-based models that minimize the need for imputing features with missing values
Lena Stempfle · Fredrik Johansson
Graph Machine Learning through the Lens of Bilevel Optimization
Amber Yijia Zheng · Tong He · Yixuan Qiu · Minjie Wang · David Wipf
Towards Achieving Sub-linear Regret and Hard Constraint Violation in Model-free RL
Arnob Ghosh · Xingyu Zhou · Ness Shroff
Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization
Mathieu Even · Anastasia Koloskova · Laurent Massoulié
Scalable Algorithms for Individual Preference Stable Clustering
Ron Mosenzon · Ali Vakilian
Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles
Fan Yang · Pierre Le Bodic · Michael Kamp · Mario Boley
Adaptive Experiment Design with Synthetic Controls
Alihan Hüyük · Zhaozhi Qian · Mihaela van der Schaar
Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization
Alejandro de la Concha Duarte · Nicolas Vayatis · Argyris Kalogeratos
Scalable Learning of Item Response Theory Models
Susanne Frick · Amer Krivošija · Alexander Munteanu
Corruption-Robust Offline Two-Player Zero-Sum Markov Games
Andi Nika · Debmalya Mandal · Adish Singla · Goran Radanovic
Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models
Frederiek Wesel · Kim Batselier
Fair Soft Clustering
Rune D. Kjærsgaard · Pekka Parviainen · Saket Saurabh · MADHUMITA KUNDU · Line Clemmensen
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels
Raphaël Carpintero Perez · Sébastien Da Veiga · Josselin Garnier · Brian Staber
MMD-based Variable Importance for Distributional Random Forest
Clément Bénard · Jeffrey Näf · julie Josse
Better Batch for Deep Probabilistic Time Series Forecasting
Zhihao Zheng · Seongjin Choi · Lijun Sun
Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification
Marzi Heidari · Abdullah Alchihabi · Qing En · Yuhong Guo
Online Calibrated and Conformal Prediction Improves Bayesian Optimization
Shachi Shailesh Deshpande · Charles Marx · Volodymyr Kuleshov
Offline Policy Evaluation and Optimization Under Confounding
Chinmaya Kausik · Yangyi Lu · Kevin Tan · Maggie Makar · Yixin Wang · Ambuj Tewari
Asynchronous Randomized Trace Estimation
Vasileios Kalantzis · Shashanka Ubaru · Chai Wah Wu · Georgios Kollias · Lior Horesh
FedFisher: Leveraging Fisher Information for One-Shot Federated Learning
Divyansh Jhunjhunwala · Shiqiang Wang · Gauri Joshi
Feasible $Q$-Learning for Average Reward Reinforcement Learning
Ying Jin · Ramki Gummadi · Zhengyuan Zhou · Jose Blanchet
Smoothness-Adaptive Dynamic Pricing with Nonparametric Demand Learning
Zeqi Ye · Hansheng Jiang
Sample Complexity Characterization for Linear Contextual MDPs
JUNZE DENG · Yuan Cheng · Shaofeng Zou · Yingbin Liang
Queuing dynamics of asynchronous Federated Learning
Louis Leconte · Matthieu Jonckheere · Sergey Samsonov · Eric Moulines
Learning Populations of Preferences via Pairwise Comparison Queries
Gokcan Tatli · Yi Chen · Ramya Korlakai Vinayak
Distributionally Robust Model-based Reinforcement Learning with Large State Spaces
Shyam Sundhar Ramesh · Pier Giuseppe Sessa · Yifan Hu · Andreas Krause · Ilija Bogunovic
A Neural Architecture Predictor based on GNN-Enhanced Transformer
Xunzhi Xiang · Kun Jing · Jungang Xu
Robust Data Clustering with Outliers via Transformed Tensor Low-Rank Representation
Tong Wu
Regret Bounds for Risk-sensitive Reinforcement Learning with Lipschitz Dynamic Risk Measures
Hao Liang · Zhiquan Luo
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference
Vo Nguyen Le Duy · Hsuan-Tien Lin · Ichiro Takeuchi
Multitask Online Learning: Listen to the Neighborhood Buzz
Juliette Achddou · Nicolò Cesa-Bianchi · Pierre Laforgue
Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model
Nikolaos Nakis · Abdulkadir Celikkanat · Louis Boucherie · Sune Lehmann · Morten Mørup
Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks
Marcus September · Francesco Sanna Passino · Leonie Goldmann · Anton Hinel
Classifier Calibration with ROC-Regularized Isotonic Regression
Eugène Berta · Francis Bach · Michael Jordan
RL in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound under the Local Access Model
Junyi FAN · Yuxuan Han · JIALIN ZENG · Jian-Feng Cai · Yang Wang · Yang Xiang · Jiheng Zhang
Multiclass Learning from Noisy Labels for Non-decomposable Performance Measures
Mingyuan Zhang · Shivani Agarwal
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
Tamim El Ahmad · Luc Brogat-Motte · Pierre Laforgue · Florence d'Alché-Buc
Efficient Model-Based Concave Utility Reinforcement Learning through Greedy Mirror Descent
Bianca Marin Moreno · Margaux Bregere · Pierre Gaillard · Nadia Oudjane
GRAWA: Gradient-based Weighted Averaging for Distributed Training of Deep Learning Models
Tolga Dimlioglu · Anna Choromanska
A Doubly Robust Approach to Sparse Reinforcement Learning
Wonyoung Kim · Garud Iyengar · Assaf Zeevi
Analysis of Kernel Mirror Prox for Measure Optimization
Pavel Dvurechenskii · Jia-Jie Zhu
Online Learning in Contextual Second-Price Pay-Per-Click Auctions
Mengxiao Zhang · Haipeng Luo
Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients
Chris Cundy · Rishi Desai · Stefano Ermon
Information Theoretically Optimal Sample Complexity of Learning Dynamical Directed Acyclic Graphs
Mishfad Shaikh Veedu · Deepjyoti Deka · Murti Salapaka
Local Causal Discovery with Linear non-Gaussian Cyclic Models
Haoyue Dai · Ignavier Ng · Yujia Zheng · Zhengqing Gao · Kun Zhang
An Improved Algorithm for Learning Drifting Discrete Distributions
Alessio Mazzetto
An Online Bootstrap for Time Series
Nicolai Palm · Thomas Nagler
Achieving Fairness through Separability: A Unified Framework for Fair Representation Learning
Taeuk Jang · Hongchang Gao · Pengyi Shi · Xiaoqian Wang
Is this model reliable for everyone? Testing for strong calibration
Jean Feng · Alexej Gossmann · Romain Pirracchio · Nicholas Petrick · Gene Pennello · Berkman Sahiner
Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations
Hanjiang Hu · Zuxin Liu · Linyi Li · Jiacheng Zhu · DING ZHAO
Emergent specialization from participation dynamics and multi-learner retraining
Sarah Dean · Mihaela Curmei · Lillian Ratliff · Jamie Morgenstern · Maryam Fazel
Optimal Sparse Survival Trees
Rui Zhang · Rui Xin · Margo Seltzer · Cynthia Rudin
Explanation-based Training with Differentiable Insertion/Deletion Metric-aware Regularizers
Yuya Yoshikawa · Tomoharu Iwata
Online Distribution Learning with Local Privacy Constraints
Jin Sima · Changlong Wu · Olgica Milenkovic · Wojciech Szpankowski
The Risks of Recourse in Binary Classification
Hidde Fokkema · Damien Garreau · Tim van Erven
Quantifying Uncertainty in Natural Language Explanations of Large Language Models
Sree Harsha Tanneru · Chirag Agarwal · Himabindu Lakkaraju
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective
Yue Xing · Xiaofeng Lin · Qifan Song · Yi Xu · Belinda Zeng · Guang Cheng
Effect of Ambient-Intrinsic Dimension Gap on Adversarial Vulnerability
Rajdeep Haldar · Yue Xing · Qifan Song
Fast and Adversarial Robust Kernelized SDU Learning
Yajing Fan · wanli shi · Yi Chang · Bin Gu
Uncertainty Matters: Stable Conclusions under Unstable Assessment of Fairness Results
Ainhize Barrainkua · Paula Gordaliza · Jose A Lozano · Novi Quadrianto
Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions
Zulqarnain Khan · Davin Hill · Aria Masoomi · Joshua Bone · Jennifer Dy
Certified private data release for sparse Lipschitz functions
Konstantin Donhauser · Johan Lokna · Amartya Sanyal · March Boedihardjo · Robert Hönig · Fanny Yang
Analysis of Privacy Leakage in Federated Large Language Models
Minh Vu · Truc Nguyen · Tre' Jeter · My T. Thai
Confident Feature Ranking
Bitya Neuhof · Yuval Benjamini
Taming False Positives in Out-of-Distribution Detection with Human Feedback
Harit Vishwakarma · Heguang Lin · Ramya Korlakai Vinayak
On the Privacy of Selection Mechanisms with Gaussian Noise
Jonathan Lebensold · Doina Precup · Borja Balle
Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry
Serena Wang · Stephen Bates · P Aronow · Michael Jordan
Solving Attention Kernel Regression Problem via Pre-conditioner
Zhao Song · Junze Yin · Lichen Zhang
Thompson Sampling Itself is Differentially Private
Tingting Ou · Rachel Cummings · Marco Avella
Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes
Jinwon Sohn · Qifan Song · Guang Lin
DAGnosis: Localized Identification of Data Inconsistencies using Structures
Nicolas HUYNH · Jeroen Berrevoets · Nabeel Seedat · Jonathan Crabbé · Zhaozhi Qian · Mihaela van der Schaar
Interpretability Guarantees with Merlin-Arthur Classifiers
Stephan Wäldchen · Kartikey Sharma · Berkant Turan · Max Zimmer · Sebastian Pokutta
Tackling the XAI Disagreement Problem with Regional Explanations
Gabriel Laberge · Yann Batiste Pequignot · Mario Marchand · Foutse Khomh
Sparse and Faithful Explanations Without Sparse Models
Yiyang Sun · Zhi Chen · Vittorio Orlandi · Tong Wang · Cynthia Rudin
Enhancing Distributional Stability among Sub-populations
Jiashuo Liu · Jiayun Wu · Jie Peng · xiaoyu wu · Yang Zheng · Bo Li · Peng Cui
Agnostic Multi-Robust Learning using ERM
Saba Ahmadi · Avrim Blum · Omar Montasser · Kevin Stangl
Imposing Fairness Constraints in Synthetic Data Generation
Mahed Abroshan · Andrew Elliott · Mohammad Mahdi Khalili
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
Miguel Fuentes · Brett Mullins · Ryan McKenna · Gerome Miklau · Daniel Sheldon
DHMConv: Directed Hypergraph Momentum Convolution Framework
WENBO ZHAO · ZITONG MA · Zhe Yang
Estimation of partially known Gaussian graphical models with score-based structural priors
Martín Sevilla · Antonio Marques · Santiago Segarra
Data-Driven Online Model Selection With Regret Guarantees
Chris Dann · Claudio Gentile · Aldo Pacchiano
Monitoring machine learning-based risk prediction algorithms in the presence of performativity
Jean Feng · Alexej Gossmann · Gene Pennello · Nicholas Petrick · Berkman Sahiner · Romain Pirracchio
Directed Hypergraph Representation Learning for Link Prediction
ZITONG MA · WENBO ZHAO · Zhe Yang
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Affinity Workshop

LatinX Social

Laura Montoya · Brayan Impata
6:00 PM - 8:00 PM