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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 - 5:00 PM
Invited Talk

Keynote: Taiji Suzuki

Taiji Suzuki
9:00 AM - 10:00 AM
Speaker Bio
Taiji Suzuki
Daiji 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. He has been a frequent instructor in international summer schools on deep learning theory and optimization, and previously held faculty appointments at Tokyo Institute of Technology before joining the University of Tokyo.
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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 Representation Learning by Joint Training of Flow Matching and Representation Encoder
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 Sortion
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
206 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, Efficient, and Robust 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 · Jay Hoon Jung · SeungHyun Ham · JaeHyeon Kim
Aggregation on Learnable Manifolds for Asynchronous Federated Optimisation
Archie Licudi · Anshul Thakur · Soheila Molaei · Danielle Belgrave · David Clifton
Differential Privacy in Kernelized Contextual Bandits via Random Projections
Nikola Pavlovic · Sudeep Salgia · Qing Zhao
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
Frequency-Based Hyperparameter Selection in Games
Aniket Sanyal · Baraah Sidahmed · Rebekka Burkholz · Tatjana Chavdarova
Numerical Fragility in Transformers: A Layer-wise Theory for Explaining, Forecasting, and Mitigating Instability
Jinwoo Baek
BOAT: Navigating The Sea of in Silico Predictors for Antibody Design via Multi-Objective Bayesian Optimization
Jackie Rao · Ferran Gonzalez · Leon Gerard · Alexandra Gessner
Amortized Structural Variational Inference
Shitao Fan · Carlos Misael Madrid Padilla · Yun Yang · Lizhen Lin
Open Multi-agent Multi-armed Bandit with Applications in Permissionless Blockchain
Mengfan Xu · Diego Klabjan
Uncertainty-Aware Meta-Learning with Analytically Tractable Posterior
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 Least-Norm Interpolation Under Gaussian Covariates
Gil Kur · Zong Shang · Paul Simanjuntak · Guillaume Lecué · Reese Pathak
TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer
Jiayu Li · Bingyin Zhao · Zilong Zhao · Uzair Javaid · Biplab Sikdar
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 · Niklas Hartung · Wilhelm Huisinga · Ramses Sanchez
One-Step Diffusion Samplers via Self-Distillation and Deterministic Flow
Pascal Jutras-Dube · Jiaru Zhang · Ziran Wang · Ruqi Zhang
Provable Affine Identifiability of Nonlinear CCA under Latent Distributional Priors
Zhiwei Han · Stefan Matthes · Hao Shen
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 flow matching models
Saptarshi Roy · Vansh Bansal · Purnamrita Sarkar · Alessandro Rinaldo
Model-independent O(1/k)-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
Spectral Text Fusion: A Frequency-Aware Approach to Multimodal Time-Series Forecasting
Huu Hiep Nguyen · Minh Nguyen · Dung Nguyen · Hung Le
On the Identifiability of Tensor Ranks via Prior Predictive Matching
Eliezer de Souza da Silva · Arto Klami · Diego Mesquita · Iñigo Urteaga
Learning Equivariant Functions via Quadratic Forms
Pavan Karjol · Vivek Kashyap · Rohan Kashyap · Prathosh AP
TexTSC: Class-Texture Preserving Data Condensation for Time Series Classification
Pouya Hosseinzadeh · Peiyu Li · Omar Bahri · Soukaina Filali Boubrahimi · Shah Muhammad Hamdi
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
HGT-FD: Hypergraph transformer for Fraud Detection
Yintao Cai · Yunjiong Liu · Zelong Yang · Shuyang Fang · Xiaoping Min
KQ-SVD: Compressing the KV Cache with Provable Guarantees on Attention Fidelity
Damien Lesens · Beheshteh T. Rakhshan · Guillaume Rabusseau
Is Supervised Learning Really That Different from Unsupervised?
Oskar Allerbo · Thomas Schön
The Cross-Context Threshold Test: Detecting Discrimination Under Environmental Shifts
Jun Yuan · Xinyue Ye
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
AMRM-Pure: Semantic-Preserving Adversarial Purification
Zhihao Dou · Zhiqiang Gao · Dongfei Cui · Weida Wang · Qinjian Zhao · Zhang Dinggen · Jun Yan · Zeke Xie · Shufei Zhang
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
From Decision to Acquisition: Loss-Driven Bayesian Active Learning
Zhuoyue Huang · Freddie Bickford Smith · Tom Rainforth
Regularizing attention scores with the bootstrapping
Maxim Laletin · Neo Christopher Chung
Direct Preference Optimization With Unobserved Preference Heterogeneity
Keertana Chidambaram · Karthik Seetharaman · Vasilis Syrgkanis
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
Uncovering Hidden Training Dynamics in Neural Networks via Inter-Sample Influence Graphs
Dylan Tai · Jiayin Zhang · Rohan Ghosh · Mehul Motani
VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference
Sakshi Agarwal · Gabriel Hope · Jimin Heo · Erik B. Sudderth
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
Guided by the Experts: Provable Feature Learning Dynamic of Soft-Routed Mixture-of-Experts
Fangshuo Liao · Anastasios Kyrillidis
Robustness and Generalization in Uncertainty-Aware Message Passing Neural Networks
Alesia Chernikova · Moritz Laber · Narayan G. Sabhahit · Tina Eliassi-Rad
Refining Covariance Matrix Estimation in Stochastic Gradient Descent through Bias Reduction
Ziyang Wei · Wanrong Zhu · Jingyang Lyu · Wei Biao Wu
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
SQuaT: Self-Supervised Knowledge Distillation via Student-Aware Quantized Teacher Features
Hyeon Jun Lee · Hyeonsik Jo · Jinwoo Chung · Jangho Kim
Shift is Good: Mismatched Data Mixing Improves Test Performance
Marko Medvedev · Kaifeng Lyu · Zhiyuan Li · Nathan Srebro
Confidence-Guided Self-Training for Gradual Domain Adaptation
Akram Heidarizadeh · Akram Awad · HanQin Cai · George Atia
ACE-KT: Cascaded Cognitive Modeling for Stage-wise Knowledge Tracing
Teng Guo · Yubin Xia · Jinsen Ke · Mingliang Hou · Jiaqi Zheng · Zitao Liu
Differentially Private Linear Regression and Synthetic Data Generation with Statistical Guarantees
Shurong Lin · Aleksandra Slavkovic · Deekshith Bhoomireddy
Improving Adaptive Moment Optimization via Preconditioner Diagonalization
Son Nguyen · Bo Liu · Lizhang Chen · qiang liu
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
BASTION: A Bayesian Framework for Trend and Seasonality Decomposition
Jason Cho · David Matteson
Understanding the Benefits of SimCLR Pre-Training in Two-Layer Convolutional Neural Networks
Han Zhang · Yuan Cao
Auto-Regressive Masked Diffusion Models
Mahdi Karami · Ali Ghodsi
Optimal Learning in Two-Player Zero-Sum 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
Adversarial Debiasing for Parameter Recovery
Luke Sanford · Megan Ayers · Matthew Gordon · Eliana Stone
PolarQuant: Vector Quantization with Polar Transformation
Amir Zandieh · Praneeth Kacham · Insu Han · Vahab Mirrokni · Amin Karbasi
Latent-IMH: Efficient Bayesian Inference for Inverse Problems with Approximate Operators
Youguang Chen · George Biros
Securing Model Weights Against Eavesdropping Adversaries in Federated Learning Using Quantization
Kushal Chakrabarti · Dipankar Maity
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
Multi-Agent Lipschitz Bandits
Sourav Chakraborty · Amit Rege · Lijun Chen · Claire Monteleoni
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 Representation
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
CADENT: Gated Hybrid Distillation for Sample-Efficient Transfer in Reinforcement Learning
Mahyar Alinejad · Yue Wang · George Atia
Adversary-Free Counterfactual Prediction via Information-Regularized Representations
Shiqin Tang · Rong Feng · Shuxin Zhuang · Hongzong LI · Youzhi Zhang
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
Active Measurement of Two-Point Correlations
Max Hamilton · Daniel Sheldon · Subhransu Maji
Black-Box Optimization from Small Offline Datasets via Meta Learning with Synthetic Tasks
Azza Fadhel · The Hung Tran · Nghia Hoang · Jana Doppa
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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