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Timezone: Africa/Casablanca
Full Schedule Sat 5/2 Sun 5/3 Mon 5/4 Tue 5/5
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Registration Desk
8:30 AM - 4:30 PM
Invited Talk

Keynote: Taiji Suzuki

Taiji Suzuki
9:00 AM - 10:00 AM
Speaker Bio
Taiji Suzuki
Taiji Suzuki is a Professor in the Department of Mathematical Informatics at The University of Tokyo and a researcher with RIKEN’s Center for Advanced Intelligence Project. His work sits at the intersection of machine learning and statistics, with a focus on statistical learning theory, deep learning theory, kernel methods, nonparametric convergence analysis, and optimization; particularly stochastic optimization and optimization for deep learning. Previously, he worked as an Assistant Professor in the Department of Mathematical Informatics, the University of Tokyo, and then he was an Associate Professor in the Department of Mathematical and Computing Science, Tokyo Institute of Technology. He has been a frequent instructor in international summer schools on deep learning theory and optimization, and served as area chairs of premier conferences such as NeurIPS, ICML, ICLR and AISTATS, a program chair of ACML2019, and an action editor of the Annals of Statistics. He received the Outstanding Paper Award at ICLR in 2021, the JSPS prize, the MEXT Young Scientists’ Prize, and Outstanding Achievement Award in 2017 from the Japan Statistical Society.
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Oral
10:30 AM - 11:30 AM
8 Events in this session
Beyond Real Data: Synthetic Data through the Lens of Regularization
Amitis Shidani ⋅ Tyler Farghly ⋅ Yang SUN ⋅ Habib Ganjgahi ⋅ George Deligiannidis
Archetypal Graph Generative Models: Explainable and Identifiable Communities via Anchor-Dominant Convex Hulls
Nikolaos Nakis ⋅ Chrysoula Kosma ⋅ Panagiotis Promponas ⋅ Michail Chatzianastasis ⋅ Giannis Nikolentzos
Longitudinal Flow Matching for Trajectory Modeling
Mohammad Mohaiminul Islam ⋅ Thijs Kuipers ⋅ Sharvaree Vadgama ⋅ Coen de Vente ⋅ Afsana Khan ⋅ Clara Sánchez ⋅ Erik Bekkers
Denoising Score Matching with Random Features: Insights on Diffusion Models From Precise Learning Curves
Anand Jerry George ⋅ Rodrigo Veiga ⋅ Nicolas Macris
Simplex-to-Euclidean Bijections for Categorical Flow Matching
Bernardo Williams ⋅ Victor Yeom-Song ⋅ Marcelo Hartmann ⋅ Arto Klami
A Continuous Time Markov Chain Framework for Insertion Language Models
Dhruvesh Patel ⋅ Benjamin Rozonoyer ⋅ Soumitra Das ⋅ Tahira Naseem ⋅ Tim G. J. Rudner ⋅ Andrew McCallum
Explicit Density Approximation for Neural Implicit Samplers Using a Bernstein-Based Convex Divergence
José Manuel de Frutos ⋅ Pablo Martínez Olmos ⋅ Manuel Vázquez ⋅ Joaquín Míguez
High-Performance Self-Supervised Learning by Joint Training of Flow Matching
Kosuke Ukita ⋅ Tsuyoshi Okita
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Oral
11:30 AM - 12:30 PM
8 Events in this session
Orthogonal Representation Learning for Estimating Causal Quantities
Valentyn Melnychuk ⋅ Dennis Frauen ⋅ Jonas Schweisthal ⋅ Stefan Feuerriegel
On the Number of Conditional Independence Tests in Constraint-based Causal Discovery
Marc Franquesa Monés ⋅ Jiaqi Zhang ⋅ Caroline Uhler
A Semi-Supervised Kernel Two-Sample Test
Gyumin Lee ⋅ Shubhanshu Shekhar ⋅ Ilmun Kim
DP-SPRT: Differentially Private Sequential Probability Ratio Tests
Thomas Michel ⋅ Debabrota Basu ⋅ Emilie Kaufmann
On the Intrinsic Dimensions of Data in Kernel Learning
Rustem Takhanov
RealStats: A Rigorous Real-Only Statistical Framework for Fake Image Detection
Haim Zisman ⋅ Uri Shaham
The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification
Tavor Baharav ⋅ Spyridon Konstantinos Dragazis ⋅ Aldo Pacchiano
Distribution Free M-estimation
Felipe Areces ⋅ John Duchi
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Oral
2:00 PM - 3:00 PM
8 Events in this session
Panprediction: Optimal Predictions for Any Downstream Task and Loss
Sivaraman Balakrishnan ⋅ Nika Haghtalab ⋅ Daniel Hsu ⋅ Brian Lee ⋅ Eric Zhao
OEUVRE: OnlinE Unbiased Variance-Reduced loss Estimation
Kanad Pardeshi ⋅ Bryan Wilder ⋅ Aarti Singh
On the calibration of survival models with competing risks
Julie Alberge ⋅ Tristan Haugomat ⋅ Gaël Varoquaux ⋅ Judith Abécassis
Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detection
Hassan Baker ⋅ Austin J. Brockmeier
AMRM-Pure: Semantic-Preserving Adversarial Purification
Zhihao Dou ⋅ Zhiqiang Gao ⋅ Dongfei Cui ⋅ Weida Wang ⋅ Qinjian Zhao ⋅ Zhang Dinggen ⋅ Jun Yan ⋅ Zeke Xie ⋅ Shufei Zhang
Scalable Utility-Aware Multiclass Calibration
Mahmoud Hegazy ⋅ Michael Jordan ⋅ Aymeric Dieuleveut
Near-Optimal Dropout-Robust Sortiton
Maya Gambhir ⋅ Bailey Flanigan ⋅ Aaron Roth
SetPINNs: Set-based Physics-informed Neural Networks
Mayank Nagda ⋅ Phil Ostheimer ⋅ Thomas Specht ⋅ Frank Rhein ⋅ Fabian Jirasek ⋅ Stephan Mandt ⋅ Marius Kloft ⋅ Sophie Fellenz
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Poster
3:00 PM - 6:00 PM
199 Events in this session
Batch-Adaptive Causal Annotations
Ezinne Nwankwo ⋅ Lauri Goldkind ⋅ Angela Zhou
The Reasoning-Creativity Trade-off: Toward Creativity-Driven Problem Solving
Max Ruiz Luyten ⋅ Mihaela van der Schaar
Generalization Bounds for Spectral GNNs via Fourier Domain Analysis
Vahan Martirosyan ⋅ Daniele Malitesta ⋅ Hugues Talbot ⋅ Jhony Giraldo ⋅ Fragkiskos Malliaros
Efficient Model Performance Evaluation Using a Combination of Expert and Crowd-sourced Labels
Sam Corbett-Davies ⋅ Viet-An Nguyen ⋅ Udi Weinsberg
Deep Polynomial Chaos Expansion
Johannes Exenberger ⋅ Sascha Ranftl ⋅ Robert Peharz
On Global Convergence Rates for Federated Softmax Policy Gradient under Heterogeneous Environments
Safwan Labbi ⋅ Paul Mangold ⋅ Daniil Tiapkin ⋅ Eric Moulines
Model Selection for Average Reward RL with Application to Utility Maximization in Repeated Games
Alireza Masoumian ⋅ James Wright
RamPINN: Recovering Raman Spectra From Coherent Anti-Stokes Spectra Using Embedded Physics
Sai Karthikeya Vemuri ⋅ Adithya Ashok Chalain Valapil ⋅ Tim Büchner ⋅ Joachim Denzler
LLM-as-a-Judge on a Budget
Aadirupa Saha ⋅ Aniket Wagde ⋅ Branislav Kveton
Multiclass Local Calibration with the Jensen-Shannon Distance
Cesare Barbera ⋅ Lorenzo Perini ⋅ Giovanni De Toni ⋅ Andrea Passerini ⋅ Andrea Pugnana
Private and Efficient Federated Statistical Learning
Jaemu Heo ⋅ Xiwen Feng ⋅ Jeonghun Kang ⋅ Taehwan Kim ⋅ Changgee Chang
Learning Linear Regression with Low-Rank Tasks In-Context
Kaito Takanami ⋅ Takashi Takahashi ⋅ Yoshiyuki Kabashima
Conformal Margin Risk Minimization: An Envelope Framework for Robust Learning under Label Noise
Yuanjie Shi ⋅ Peihong Li ⋅ Zijian Zhang ⋅ Jana Doppa ⋅ Yan Yan
TESLA: Taylor Expansion of Sinusoidal Learnable Activations
Daehwa Ko ⋅ JaeHyeon Kim ⋅ SeungHyun Ham ⋅ Jay Hoon Jung
Aggregation on Learnable Manifolds for Asynchronous Federated Optimisation
Archie Licudi ⋅ Anshul Thakur ⋅ Soheila Molaei ⋅ Danielle Belgrave ⋅ David Clifton
Adversarial Debiasing for Parameter Recovery
Luke Sanford ⋅ Megan Ayers ⋅ Matthew Gordon ⋅ Eliana Stone
Randomized HyperSteiner: A Stochastic Delaunay Triangulation Heuristic for the Hyperbolic Steiner Minimal Tree
Aniss Aiman MEDBOUHI ⋅ Alejandro García-Castellanos ⋅ Giovanni Luca Marchetti ⋅ Daniel Pelt ⋅ Erik Bekkers ⋅ Danica Kragic
Empirically Calibrated Conditional Independence Tests
Milleno Pan ⋅ Antoine de Mathelin ⋅ Wesley Tansey
MineGrad: Gradient Inversion Attacks on LoRA Fine-Tuning
Hasin Us Sami ⋅ Swapneel Sen ⋅ Basak Guler
Discrete State Diffusion Models: A Sample Complexity Perspective
Aadithya Srikanth ⋅ Mudit ⋅ Vaneet Aggarwal
Optimized Projection-Free Algorithms for Online Learning: Construction and Worst-Case Analysis
Julien Weibel ⋅ Pierre Gaillard ⋅ Wouter Koolen ⋅ Adrien Taylor
BOAT: Navigating The Sea of in Silico Predictors for Antibody Design via Multi-Objective Bayesian Optimization
Jackie Rao ⋅ Ferran Gonzalez ⋅ Leon Gerard ⋅ Alexandra Gessner
Tractable Uncertainty-Aware Meta-Learning
Young-Jin Park ⋅ Cesar Almecija ⋅ Apoorva Sharma ⋅ Navid Azizan
CAWI: Copula-Aligned Weight Initialization for Randomized Neural Networks
Mushir Akhtar ⋅ M. Tanveer ⋅ Mohd. Arshad
GRANITE: A Generalized Regional Framework for Identifying Agreement in Feature-Based Explanations
Julia Herbinger ⋅ Gabriel Laberge ⋅ Maximilian Muschalik ⋅ Yann Pequignot ⋅ Marvin N. Wright ⋅ Fabian Fumagalli
Adaptive A/B Testing under Nonstationary Dynamics using State-Space Models
Junzhe Shao ⋅ Waverly Wei ⋅ Jingshen Wang
Fundamental Limits for Weighted Empirical Approximations of Exponentially Tilted Distributions
Sarvesh Iyer ⋅ Himadri Mandal ⋅ Dhruman Gupta ⋅ Rushil Gupta ⋅ Agniv Bandyopadhyay ⋅ Achal Bassamboo ⋅ Sandeep Juneja ⋅ Varun Gupta
Network Inversion for Extreme-Case Training-Like Data Reconstruction
Pirzada Suhail ⋅ Sunny Gupta ⋅ Amit Sethi
Robust Federated Clustering under Heterogeneity and Adversaries
Martín Bravo ⋅ Sebastian Dalleiger
On the Neural Feature Ansatz for Deep Neural Networks
Edward Tansley ⋅ Estelle Massart ⋅ Coralia Cartis
Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization
Kristi Topollai ⋅ Anna Choromanska
A New Perspective on Minimum-Norm Interpolation Under Gaussian Covariates
Gil Kur ⋅ Zong Shang ⋅ Paul Simanjuntak ⋅ Guillaume Lecué ⋅ Reese Pathak
Open Multi-agent Multi-armed Bandit with Applications in Permissionless Blockchain
Mengfan Xu ⋅ Diego Klabjan
CoreSPECT: Enhancing Clustering Algorithms via an Interplay of Density and Geometry
Chandra Sekhar Mukherjee ⋅ Joonyoung Bae ⋅ Jiapeng Zhang
Eliciting Truthful Feedback for Preference-Based Learning via the VCG Mechanism
Leo Landolt ⋅ Anna Maddux ⋅ Andreas Schlaginhaufen ⋅ Saurabh Vaishampayan ⋅ Maryam Kamgarpour
Dual Averaging Converges for Nonconvex Smooth Stochastic Optimization
Tuo Liu ⋅ El Mehdi Saad ⋅ Wojciech Kotlowski ⋅ Francesco Orabona
Differentially Private and Federated Structure Learning in Bayesian Networks
Ghita Fassy El Fehri ⋅ Aurélien Bellet ⋅ Philippe Bastien
Adaptive Diffusion Guidance via Stochastic Optimal Control
Iskander Azangulov ⋅ Peter Potaptchik ⋅ Qinyu Li ⋅ Eddie Aamari ⋅ George Deligiannidis ⋅ Judith Rousseau
Replicable Machine Learning: Theory and Algorithms for Stochastic Convex and Non-Convex Optimization
Raman Arora ⋅ Kaibo Zhang
Efficient Bilevel Optimization with KFAC-Based Hypergradients
Disen Liao ⋅ Felix Dangel ⋅ Yaoliang Yu
A Polynomial-Time Approximation for Pairwise Fair $k$-Median Clustering
Sayan Bandyapadhyay ⋅ Eden Chlamtáč ⋅ Zachary Friggstad ⋅ Mahya Jamshidian ⋅ Yury Makarychev ⋅ Ali Vakilian
UniPROT: Uniform Prototype Selection via Partial Optimal Transport with Submodular Guarantees
Prateek Chanda ⋅ Prayas Agrawal ⋅ Karthik Gurumoorthy ⋅ Ganesh Ramakrishnan ⋅ Bamdev Mishra ⋅ Pratik Jawanpuria
Bandits in Flux: Dynamic Regret under Adversarial Constraints
Tareq Si Salem
Generalization Bounds under Heavy-Tailed Losses
Gholamali Aminian
Amortized In-Context Mixed Effect Transformer Models: A Zero-Shot Approach for Pharmacokinetics
César Ali Ojeda Marin ⋅ Ramses Sanchez ⋅ Wilhelm Huisinga ⋅ Niklas Hartung
Policy Learning with Abstention
Ayush Sawarni ⋅ Jikai Jin ⋅ Justin Whitehouse ⋅ Vasilis Syrgkanis
Tractable Gaussian Phase Retrieval with Heavy Tails and Adversarial Corruption with Near-Linear Sample Complexity
Santanu Das ⋅ jatin batra
On the convergence and straightness of Rectified Flow
Vansh Bansal ⋅ Saptarshi Roy ⋅ Alessandro Rinaldo ⋅ Purnamrita Sarkar
Fast and Robust Convergence Rate for TD(0) with Linear Function Approximation, Universal Learning Steps and I.I.D. Samples
Ziad Kobeissi ⋅ Eloïse Berthier
From Restless to Contextual: A Thresholding Bandit Reformulation for Finite-horizon Improvement
Jiamin Xu ⋅ Ivan Nazarov ⋅ Aditya Rastogi ⋅ Africa Perianez Santiago ⋅ Kyra Gan
Functional Properties of the Focal-Entropy
Jaimin Shah ⋅ Martina Cardone ⋅ Alex Dytso
DRAUN: An Optimization-Agnostic Data Reconstruction Attack on Federated Unlearning
Hithem Lamri ⋅ Manaar Alam ⋅ Haiyan Jiang ⋅ Michail Maniatakos
Spectral Thresholds in Correlated Spiked Models and Fundamental Limits of Partial Least Squares
Pierre Mergny ⋅ Lenka Zdeborova
Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation
Neeraj Gangwar ⋅ Anshuka Rangi ⋅ Rishabh Deshmukh ⋅ Holakou Rahmanian ⋅ Yesh Dattatreya ⋅ Nickvash Kani
Leveraging Machine-Learned Advice in Strategic Interactions with No-Regret Learners
Tinashe Handina ⋅ Tongxin Li ⋅ Kishan Panaganti ⋅ Eric Mazumdar ⋅ Adam Wierman
Gradient Descent with Provably Tuned Learning-rate Schedules
Dravyansh Sharma
On the Bias of Variational Resampling
Axel Finke ⋅ Oskar Kviman ⋅ Nicola Branchini ⋅ Victor Elvira
Q-ShiftDP: A Differentially Private Parameter-Shift Rule for Quantum Machine Learning
Hoang Ngo ⋅ Nhat Hoang-Xuan ⋅ Quan Nguyen ⋅ Nguyen Do ⋅ Incheol Shin ⋅ My T. Thai
Learning Equivariant Functions via Quadratic Forms
Pavan Karjol ⋅ Vivek Kashyap ⋅ Rohan Kashyap ⋅ Prathosh AP
Multi-Armed Sampling Problem and the End of Exploration
Mohammad Pedramfar ⋅ Siamak Ravanbakhsh
Fast Private Adaptive Query Answering for Large Data Domains
Miguel Fuentes ⋅ Brett Mullins ⋅ Yingtai Xiao ⋅ Daniel Kifer ⋅ Cameron Musco ⋅ Daniel Sheldon
Is Supervised Learning Really That Different from Unsupervised?
Oskar Allerbo ⋅ Thomas Schön
Scalable Policy Maximization Under Network Interference
Aidan Gleich ⋅ Eric Laber ⋅ Alexander Volfovsky
Adaptive Candidate Point Thompson Sampling for High-Dimensional Bayesian Optimization
Donney Fan ⋅ Geoff Pleiss
Disentangling Federated Learning Heterogeneity: A Dual-Perspective Analysis of Quantifying Skew versus Scarcity
Wenkai Zeng ⋅ NAN YANG ⋅ Zhiyu Zhu ⋅ Zhibo Jin ⋅ Dong Yuan
Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective
Soo Min Kwon ⋅ Alec Xu ⋅ Can Yaras ⋅ Laura Balzano ⋅ Qing Qu
Active Measuring in Reinforcement Learning With Delayed Negative Effects
Daiqi Gao ⋅ Ziping Xu ⋅ Aseel Rawashdeh ⋅ Predrag Klasnja ⋅ Susan Murphy
Atlas-based Manifold Representations for Interpretable Riemannian Machine Learning
Ryan Robinett ⋅ Sophia Madejski ⋅ Kyle Ruark ⋅ Samantha Riesenfeld ⋅ Lorenzo Orecchia
Robust Learning of A Group DRO Neuron
Guyang Cao ⋅ Shuyao Li ⋅ Sushrut Karmalkar ⋅ Jelena Diakonikolas
Provable FDR Control for Deep Feature Selection: Deep MLPs and Beyond
Kazuma Sawaya
Clustering-Based Edge Augmentation for Minimizing the Kirchhoff Index
Prasanth Yalamanchili ⋅ Aditya Bhaskara
Inverse-Free Sparse Variational Gaussian Processes
Stefano Cortinovis ⋅ Laurence Aitchison ⋅ Stefanos Eleftheriadis ⋅ Mark van der Wilk
Orthogonal Representation Learning for Estimating Causal Quantities
Valentyn Melnychuk ⋅ Dennis Frauen ⋅ Jonas Schweisthal ⋅ Stefan Feuerriegel
Panprediction: Optimal Predictions for Any Downstream Task and Loss
Sivaraman Balakrishnan ⋅ Nika Haghtalab ⋅ Daniel Hsu ⋅ Brian Lee ⋅ Eric Zhao
OEUVRE: OnlinE Unbiased Variance-Reduced loss Estimation
Kanad Pardeshi ⋅ Bryan Wilder ⋅ Aarti Singh
On the Number of Conditional Independence Tests in Constraint-based Causal Discovery
Marc Franquesa Monés ⋅ Jiaqi Zhang ⋅ Caroline Uhler
On the calibration of survival models with competing risks
Julie Alberge ⋅ Tristan Haugomat ⋅ Gaël Varoquaux ⋅ Judith Abécassis
Archetypal Graph Generative Models: Explainable and Identifiable Communities via Anchor-Dominant Convex Hulls
Nikolaos Nakis ⋅ Chrysoula Kosma ⋅ Panagiotis Promponas ⋅ Michail Chatzianastasis ⋅ Giannis Nikolentzos
Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detection
Hassan Baker ⋅ Austin J. Brockmeier
A Semi-Supervised Kernel Two-Sample Test
Gyumin Lee ⋅ Shubhanshu Shekhar ⋅ Ilmun Kim
Empirical PAC-Bayes Bounds for Markov Chains
Vahe Karagulyan ⋅ Pierre Alquier
Auditing Pay-Per-Token in Large Language Models
Ander Artola Velasco ⋅ Stratis Tsirtsis ⋅ Manuel Gomez Rodriguez
Longitudinal Flow Matching for Trajectory Modeling
Mohammad Mohaiminul Islam ⋅ Thijs Kuipers ⋅ Sharvaree Vadgama ⋅ Coen de Vente ⋅ Afsana Khan ⋅ Clara Sánchez ⋅ Erik Bekkers
DP-SPRT: Differentially Private Sequential Probability Ratio Tests
Thomas Michel ⋅ Debabrota Basu ⋅ Emilie Kaufmann
Scalable Utility-Aware Multiclass Calibration
Mahmoud Hegazy ⋅ Michael Jordan ⋅ Aymeric Dieuleveut
Near Optimal Dropout-Robust Sortion
Maya Gambhir ⋅ Bailey Flanigan ⋅ Aaron Roth
Beyond Real Data: Synthetic Data through the Lens of Regularization
Amitis Shidani ⋅ Tyler Farghly ⋅ Yang SUN ⋅ Habib Ganjgahi ⋅ George Deligiannidis
Denoising Score Matching with Random Features: Insights on Diffusion Models From Precise Learning Curves
Anand Jerry George ⋅ Rodrigo Veiga ⋅ Nicolas Macris
Simplex-to-Euclidean Bijections for Categorical Flow Matching
Bernardo Williams ⋅ Victor Yeom-Song ⋅ Marcelo Hartmann ⋅ Arto Klami
On the Intrinsic Dimensions of Data in Kernel Learning
Rustem Takhanov
Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation
David Holzmüller, Francis Bach
Differentially Private Clipped-SGD: High-Probability Convergence with Arbitrary Clipping Level
Saleh Khah ⋅ Savelii Chezhegov ⋅ Shahrokh Farahmand ⋅ Samuel Horvath ⋅ Eduard Gorbunov
SetPINNs: Set-based Physics-informed Neural Networks
Mayank Nagda ⋅ Phil Ostheimer ⋅ Thomas Specht ⋅ Frank Rhein ⋅ Fabian Jirasek ⋅ Stephan Mandt ⋅ Marius Kloft ⋅ Sophie Fellenz
A Continuous Time Markov Chain Framework for Insertion Language Models
Dhruvesh Patel ⋅ Benjamin Rozonoyer ⋅ Soumitra Das ⋅ Tahira Naseem ⋅ Tim G. J. Rudner ⋅ Andrew McCallum
RealStats: A Rigorous Real-Only Statistical Framework for Fake Image Detection
Haim Zisman ⋅ Uri Shaham
The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification
Tavor Baharav ⋅ Spyridon Konstantinos Dragazis ⋅ Aldo Pacchiano
Distribution free M-estimation
Felipe Areces ⋅ John Duchi
High-Performance Self-Supervised Representation Learning by Joint Training of Flow Matching and Representation Encoder
Kosuke Ukita ⋅ Tsuyoshi Okita
VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference
Sakshi Agarwal ⋅ Gabriel Hope ⋅ Jimin Heo ⋅ Erik B. Sudderth
Efficient Subgroup Analysis via Optimal Trees with Global Parameter Fusion
Zhongming Xie ⋅ Joseph Giorgio ⋅ Jingshen Wang
KQ-SVD: Compressing the KV Cache with Provable Guarantees on Attention Fidelity
Damien Lesens ⋅ Beheshteh T. Rakhshan ⋅ Guillaume Rabusseau
Robustness and Generalization in Uncertainty-Aware Message Passing Neural Networks
Alesia Chernikova ⋅ Moritz Laber ⋅ Narayan G. Sabhahit ⋅ Tina Eliassi-Rad
Loss-Driven Bayesian Active Learning
Zhuoyue Huang ⋅ Freddie Bickford Smith ⋅ Tom Rainforth
Improving Adaptive Moment Optimization via Preconditioner Diagonalization
Son Nguyen ⋅ Bo Liu ⋅ Lizhang Chen ⋅ qiang liu
Understanding the Benefits of SimCLR Pre-Training in Two-Layer Convolutional Neural Networks
Han Zhang ⋅ Yuan Cao
Differentially Private Linear Regression and Synthetic Data Generation with Statistical Guarantees
Shurong Lin ⋅ Aleksandra Slavkovic ⋅ Deekshith Bhoomireddy
Guided by the Experts: Provable Feature Learning Dynamic of Soft-Routed Mixture-of-Experts
Fangshuo Liao ⋅ Anastasios Kyrillidis
Regularizing attention scores with bootstrapping
Neo Christopher Chung ⋅ Maxim Laletin
HGT-FD: Hypergraph transformer for Fraud Detection
Yintao Cai ⋅ Yunjiong Liu ⋅ Zelong Yang ⋅ Shuyang Fang ⋅ Xiaoping Min
BASTION: A Bayesian Framework for Trend and Seasonality Decomposition
Jason Cho ⋅ David Matteson
PolarQuant: Vector Quantization with Polar Transformation
Insu Han ⋅ Praneeth Kacham ⋅ Amin Karbasi ⋅ Vahab Mirrokni ⋅ Amir Zandieh
Latent-IMH: Efficient Bayesian Inference for Inverse Problems with Approximate Operators
Youguang Chen ⋅ George Biros
Multi-Agent Lipschitz Bandits
Sourav Chakraborty ⋅ Amit Rege ⋅ Claire Monteleoni ⋅ Lijun Chen
Direct Preference Optimization With Unobserved Preference Heterogeneity
Keertana Chidambaram ⋅ Karthik Seetharaman ⋅ Vasilis Syrgkanis
Black-Box Optimization from Small Offline Datasets via Meta Learning with Synthetic Tasks
Azza Fadhel ⋅ The Hung Tran ⋅ Nghia Hoang ⋅ Jana Doppa
Amortized Structural Variational Inference
Shitao Fan ⋅ Carlos Misael Madrid Padilla ⋅ Yun Yang ⋅ Lizhen Lin
Uncovering Hidden Training Dynamics in Neural Networks via Inter-Sample Influence Graphs
Dylan Tai ⋅ Jiayin Zhang ⋅ Rohan Ghosh ⋅ Mehul Motani
Numerical Fragility in Transformers: A Layer-wise Theory for Risk Estimation and Selective Stabilization
Jinwoo Baek
One-Step Diffusion Samplers via Self-Distillation and Deterministic Flow
Pascal Jutras-Dube ⋅ Jiaru Zhang ⋅ Ziran Wang ⋅ Ruqi Zhang
Learning to Explore with Lagrangians for Bandits under Unknown Constraints
Udvas Das ⋅ Debabrota Basu
SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning
Zelin He ⋅ Boran Han ⋅ Xiyuan Zhang ⋅ Shuai Zhang ⋅ Haotian Lin ⋅ Qi Zhu ⋅ Haoyang Fang ⋅ Danielle Maddix ⋅ Abdul Fatir Ansari ⋅ Akash Chandrayan ⋅ Abhinav Pradhan ⋅ Bernie Wang ⋅ Matthew Reimherr
Visual Prompting Reimagined: The Power of Activation Prompts
Yihua Zhang ⋅ Hongkang Li ⋅ Yuguang Yao ⋅ Aochuan Chen ⋅ Shuai Zhang ⋅ Pin-Yu Chen ⋅ Meng Wang ⋅ Sijia Liu
Optimistic Reinforcement Learning with Quantile Objectives
Mohammad Alipour-Vaezi ⋅ Huaiyang Zhong ⋅ Kwok-leung Tsui ⋅ Sajad Khodadadian
The Unseen Adversaries: Robust and Generalized Defense Against Adversarial Patches
Vishesh Kumar ⋅ Akshay Agarwal
ConDiSim: Conditional Diffusion Models for Simulation-Based Inference
Mayank Nautiyal ⋅ Andreas Hellander ⋅ Prashant Singh
Deep Feedback Models
David Calhas ⋅ Arlindo Oliveira
WSBD: Freezing-Based Optimizer for Quantum Neural Networks
Christopher Kverne ⋅ Mayur Akewar ⋅ Yuqian Huo ⋅ Tirthak Patel ⋅ Janki Bhimani
Auto-Regressive Masked Diffusion Models
Mahdi Karami ⋅ Ali Ghodsi
A Unifying Framework for Unsupervised Concept Extraction
Chandler Squires
Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs
Hen Davidov ⋅ Shai Feldman ⋅ Gilad Freidkin ⋅ Yaniv Romano
Convex Markov Games and Beyond: New Proof of Existence, Characterization and Learning Algorithms for Nash Equilibria
Anas Barakat ⋅ Ioannis Panageas ⋅ Antonios Varvitsiotis
Provable Affine Identifiability of Nonlinear CCA under Latent Distributional Priors
Zhiwei Han ⋅ Stefan Matthes ⋅ Hao Shen
A Gaussian Process View on Observation Noise and Initialization in Wide Neural Networks
Sergio Calvo Ordoñez ⋅ Jonathan Plenk ⋅ Richard Bergna ⋅ Alvaro Cartea ⋅ Jose Miguel Hernandez-Lobato ⋅ Konstantina Palla ⋅ Kamil Ciosek
High-Probability Bounds for Heterogeneous Local Differential Privacy
Maryam Aliakbarpour ⋅ Alireza Fallah ⋅ Swaha Roy ⋅ Ria Stevens
Accelerated Learning on Large-Scale Screens using Generative Library Models
Eli Weinstein ⋅ Andrei Slabodkin ⋅ Mattia Gollub ⋅ Elizabeth Wood
Recovery Guarantees for Continual Learning of Dependent Tasks: Memory, Data-Dependent Regularization, and Data-Dependent Weights
Liangzu Peng ⋅ Uday Kiran Reddy Tadipatri ⋅ Ziqing Xu ⋅ Eric Eaton ⋅ Rene Vidal
TENDE: Transfer Entropy Neural Diffusion Estimation
Simon Pedro Galeano Munoz ⋅ Maurizio Filippone ⋅ Giulio Franzese ⋅ Mustapha Bounoua ⋅ Pietro Michiardi
$\epsilon$-Identifiability of Causal Quantities
Ang Li ⋅ Scott Mueller ⋅ Xin Shu ⋅ Judea Pearl
Calibrated Principal Component Regression
Yixuan Florence Wu ⋅ Yilun Zhu ⋅ Lei Cao ⋅ Naichen Shi
Modeling Multi-Objective Tradeoffs with Monotonic Utility Functions
Edward Chen ⋅ Natalie Dullerud ⋅ Thomas Niedermayr ⋅ Elizabeth Kidd ⋅ Ransalu Senanayake ⋅ Pang Wei Koh ⋅ Sanmi Koyejo ⋅ Carlos Guestrin
Explicit Density Approximation for Neural Implicit Samplers Using a Bernstein-Based Convex Divergence
José Manuel de Frutos ⋅ Pablo Martínez Olmos ⋅ Manuel Vázquez ⋅ Joaquín Míguez
On the Normalization of Confusion Matrices: Methods and Geometric Interpretations
Johan Erbani ⋅ Sonia Ben Mokhtar ⋅ Pierre-Edouard Portier ⋅ Elöd Egyed-Zsigmond ⋅ Diana Nurbakova
How to Approximate Inference with Subtractive Mixture Models
Lena Zellinger ⋅ Nicola Branchini ⋅ Lennert De Smet ⋅ Victor Elvira ⋅ Nikolay Malkin ⋅ Antonio Vergari
Securing Model Weights Against Eavesdropping Adversaries in Federated Learning Using Quantization
Kushal Chakrabarti ⋅ Dipankar Maity
Frequency-Based Hyperparameter Selection in Games
Aniket Sanyal ⋅ Baraah Sidahmed ⋅ Rebekka Burkholz ⋅ Tatjana Chavdarova
ACE-KT: Cascaded Cognitive Modeling for Stage-wise Knowledge Tracing
Teng Guo ⋅ Yubin Xia ⋅ Jinsen Ke ⋅ Mingliang Hou ⋅ Jiaqi Zheng ⋅ Zitao Liu
Shift is Good: Mismatched Data Mixing Improves Test Performance
Marko Medvedev ⋅ Kaifeng Lyu ⋅ Zhiyuan Li ⋅ Nathan Srebro
Impact of Positional Encoding: Clean and Adversarial Rademacher Complexity for Transformers under In-Context Regression
Weiyi He ⋅ Yue Xing
Beyond Spectral Clustering: Probabilistic Cuts for Differentiable Graph Partitioning
Ayoub Ghriss
Deliberate-When-Needed: Flow-Reasoner for Neuro-Symbolic Continuous Thought
Wenjie Shen ⋅ Boyang Li ⋅ Chao Yang ⋅ Shuang Li
Finite-Time Analysis of Gradient Descent for Shallow Transformers
Enes Arda ⋅ Semih Cayci ⋅ Atilla Eryilmaz
Predictive Deep Sets
Alex Hämäläinen ⋅ Sammie Katt ⋅ Samuel Kaski
ProxRouter: Proximity-Weighted LLM Query Routing for Improved Robustness to Outliers
Shivam Patel ⋅ Neharika Jali ⋅ Ankur Mallick ⋅ Gauri Joshi
Active Measurement of Two-Point Correlations
Max Hamilton ⋅ Daniel Sheldon ⋅ Subhransu Maji
Optimal Learning in Games under Delayed Feedback
Ruotong Zhuang ⋅ Taira Tsuchiya ⋅ Shinji Ito
CONTEXTUAL RANKING AND MATCHING. OPTIMAL REGRET UNDER LST
Hafedh El Ferchichi ⋅ Vianney Perchet ⋅ Matthieu LERASLE
Beyond the Ideal: Analyzing the Inexact Muon Update
Egor Shulgin ⋅ AlRashed ⋅ Peter Richtarik ⋅ Francesco Orabona
Two mathematical models of knowledge distillation
Audrey Xie ⋅ Ludwig Schmidt ⋅ John Duchi
Slithering Through Gaps: Capturing Discrete Isolated Modes via Logistic Bridging
Pinaki Mohanty ⋅ Ruqi Zhang
Multiple Invertible and Partial-Equivariant Function for Latent Vector Transformation to Enhance Disentanglement in VAEs
Hee-Jun Jung ⋅ Jaehyoung Jeong ⋅ Kangil Kim
Q-Learning with Shift-Aware Upper Confidence Bound in Non-Stationary Reinforcement Learning
Ha Manh Bui ⋅ Felix Parker ⋅ Kimia Ghobadi ⋅ Anqi Liu
Counterfactually Fair Conformal Prediction
Ozgur Guldogan ⋅ Neeraj Sarna ⋅ Yuanyuan Li ⋅ Michael Berger
Learning Markov Processes as Sum-of-Square Forms for Analytical Belief Propagation
Peter Amorese ⋅ Morteza Lahijanian
Regularized $f-$Divergence Kernel Tests
Mónica Ribero ⋅ Antonin Schrab ⋅ Arthur Gretton
Proof of The TAP Free Energy for High-Dimensional Linear Regression with Spherical Priors at All Temperatures
Zhiyuan Yu ⋅ Jingbo Liu
Understanding SAM's Robustness to Noisy Labels through Gradient Down-weighting
Hoang-Chau Luong ⋅ Thuc Nguyen-Quang ⋅ Dat Tran ⋅ Minh-Triet Tran
CADENT: Gated Hybrid Distillation for Sample-Efficient Transfer in Reinforcement Learning
Mahyar Alinejad ⋅ Yue Wang ⋅ George Atia
Confidence-Guided Self-Training for Gradual Domain Adaptation
Akram Heidarizadeh ⋅ Akram Awad ⋅ HanQin Cai ⋅ George Atia
Meta-probabilistic Modeling
Kevin Zhang ⋅ Yixin Wang
Nearly Optimal Best Arm Identification for Semiparametric Bandits
Seok-Jin Kim
Regression Descent: A Statistical Framework for Neural Network Optimization
Kamaljeet Singh ⋅ Nicolas Hengartner ⋅ Hao Zhang ⋅ Brian Bell ⋅ James Hyman
SQuaT: Self-Supervised Knowledge Distillation via Student-Aware Quantized Teacher Features
Hyeon Jun Lee ⋅ Hyeonsik Jo ⋅ Jinwoo Chung ⋅ Jangho Kim
On the Finite-Sample Bias of Minimizing Expected Wasserstein Loss Between Empirical Distributions
Cheongjae Jang ⋅ Yung-Kyun Noh
ZipMoE: A Theoretically-Grounded Mixture of Experts Approach forParameter-Efficient Deep Learning
Lin Chen ⋅ Kyriakos Axiotis ⋅ Gang Fu ⋅ Kaiyuan Wang ⋅ MohammadHossein Bateni ⋅ Vahab Mirrokni
It’s All In The (Exponential) Family: An Equivalence Between Maximum Likelihood Estimation and Control Variates For Sketching Algorithms
Keegan Kang ⋅ Kerong Wang ⋅ Ding Zhang ⋅ Rameshwar Pratap ⋅ Bhisham Verma ⋅ Benedict Wong
On propagation of chaos for the Fisher-Rao gradient flow in entropic mean-field optimization
Petra Lazić ⋅ Linshan Liu ⋅ Mateusz Majka
Three-Step Nav: A Hierarchical Global–Local Planner for Zero-Shot Vision-and-Language Navigation
Wanrong Zheng ⋅ Yunhao Ge ⋅ Laurent Itti
Efficient Flow Matching using Latent Variables
Anirban Samaddar ⋅ Yixuan Sun ⋅ Viktor Nilsson ⋅ Sandeep Madireddy
Closed-Form Coordinate Ascent Variational Inference for Student-t Process Regression with Student-t Likelihood
Keisuke Onoue ⋅ Takatomi Kubo ⋅ Kazushi Ikeda
An Illusion of Unlearning? Assessing Machine Unlearning Through Internal Representations
Yichen Gao ⋅ Altay Unal ⋅ Akshay Rangamani ⋅ Zhihui Zhu
From Counts to Preferences: Preference-Driven Models for Spatio-Temporal Event Data
Chao Yang ⋅ Yiling Kuang ⋅ Shuang Li
A Correlation Analysis Approach to Finding Interpretable Latent Representations via Conditional Generative Models
James Buenfil ⋅ Eardi Lila
Exact Tensor Completion Beyond Isotropy and Invertibility
Li Ge ⋅ Lin Chen ⋅ Yudong Chen ⋅ Xue Jiang
Learning Right Monotone Permutation Matrices for Neural Subsequence Search
Bhavya Kohli ⋅ Soutrik Sarangi ⋅ Aziz Shameem ⋅ Abir De
Non-Stationary Functional Bilevel Optimization
Jason Bohne ⋅ Ieva Petrulionytė ⋅ Michael Arbel ⋅ Julien Mairal ⋅ Pawel Polak
Provably Efficient and Agile Randomized Q-Learning
He Wang ⋅ Xingyu Xu ⋅ Yuejie Chi
Monotone and Conservative Policy Iteration Beyond the Tabular Case
S R Eshwar ⋅ Gugan Chandrashekhar Mallika Thoppe ⋅ Ananyabrata Barua ⋅ Aditya Gopalan ⋅ Gal Dalal
Retrieval Augmented Time Series Forecasting
Kutay Tire ⋅ Ege Onur Taga ⋅ Muhammed Emrullah Ildiz ⋅ Samet Oymak
The Information Geometry of Local Generalization Dynamics
Emmanouil-Marios Athanasakos
Breaking Data Symmetry is Needed For Generalization in Feature Learning Kernels
Marcel Bernal ⋅ Neil Mallinar ⋅ Mikhail Belkin
Quantifying Epistemic Uncertainty in Diffusion Models
Aditi Gupta ⋅ Raphael Meyer ⋅ Yotam Yaniv ⋅ Elynn Chen ⋅ N. Benjamin Erichson
Bayesian Fourier Features for Reduced Rank Gaussian Processes
Cristian A. Galvis-Florez ⋅ George Whittle ⋅ Michael A. Osborne ⋅ Simo Särkkä
PowerSoftmax: Towards Secure LLM Inference Over Encrypted Data
Itamar Zimerman ⋅ Allon Adir ⋅ Ehud Aharoni ⋅ Matan Avitan ⋅ Moran Baruch ⋅ Nir Drucker ⋅ Jenny Lerner ⋅ Ramy Masalha ⋅ Reut Moshe ⋅ Omri Soceanu
Adversary-Free Counterfactual Prediction via Information-Regularized Representations
Shiqin Tang ⋅ Rong Feng ⋅ Shuxin Zhuang ⋅ Youzhi Zhang ⋅ Hongzong LI
Accelerating PDE Surrogates via RL-Guided Mesh Optimization
yang meng ⋅ Ruoxi Jiang ⋅ Zhuokai Zhao ⋅ Chong Liu ⋅ Rebecca Willett ⋅ Yuxin Chen
Optimal Transport Guarantees to Nonparametric Regression for Locally Stationary Time Series
Jan Nino Tinio ⋅ Mokhtar Alaya ⋅ Salim Bouzebda
On the Hardness of Auditing Model Properties Under Updates: Complexity of Property-Preserving Updates
Ayoub Ajarra ⋅ Debabrota Basu
Unsupervised Ensemble Learning Through Deep Energy-based Models
Ariel Maymon ⋅ Yanir Buznah ⋅ Uri Shaham
Value Gradient Sampler: Learning Invariant Value Functions for Equivariant Diffusion Sampling
Himchan Hwang ⋅ Hyeokju Jeong ⋅ Dong Shin ⋅ Che-Sang Park ⋅ Sehee Kweon ⋅ Sangwoong Yoon ⋅ Frank Park
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