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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: Emma Brunskill

Emma Brunskill
9:00 AM - 10:00 AM
Speaker Bio
Emma Brunskill
Emma is an Associate Professor (tenured) in the Computer Science Department at Stanford University, where she leads a research group developing AI systems that learn from few samples and make robust decisions, motivated by applications in healthcare and education. Her lab is part of the Stanford AI Lab, the Stanford Statistical ML group, and AI Safety @Stanford, and she is an Associate Director of the Stanford Causal Science Center. Previously, she was an Assistant Professor at Carnegie Mellon University. Her work has been recognized with early-career awards from the National Science Foundation, the Office of Naval Research, and Microsoft Research, and has received multiple best paper nominations and awards across venues including UAI, CHI, EDM, LAK, RLDM, and ITS. She serves on the International Machine Learning Society Board and advisory and leadership committees including the Khan Academy Research Advisory Board and the Stanford Faculty Women’s Forum, and she was Co-Program Chair for ICML 2023.
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Session
11:30 AM - 12:30 PM
Poster
3:00 PM - 6:00 PM
187 Events in this session
A Bayesian Information-Theoretic Approach to Data Attribution
Dharmesh Tailor · Nicolò Felicioni · Kamil Ciosek
Adaptive Coverage Policies in Conformal Prediction
Etienne Gauthier · Francis Bach · Michael Jordan
Linear Reasoning vs. Proof by Cases: Obstacles for Large Language Models in FOL Problem Solving
Yuliang Ji · Fuchen Shen · Jian Wu · Qiujie Xie · Yue Zhang
Lipschitz Multiscale Deep Equilibrium Models: A Theoretically Guaranteed and Accelerated Approach
Naoki Sato · Hideaki Iiduka
LLMPhy: Parameter-Identifiable Physical Reasoning Combining Large Language Models and Physics Engines
Anoop Cherian · Radu Corcodel · Siddarth Jain · Diego Romeres
Local Causal Discovery for Statistically Efficient Causal Inference
Mátyás Schubert · Tom Claassen · Sara Magliacane
Local Regression on Path Spaces with Signature Metrics
Davit Gogolashvili · Christian Bayer · Luca Pelizzari
Loss Gaps Parity for Fairness in Heterogeneous Federated Learning
Brahim Erraji · Michaël Perrot · Aurélien Bellet
Lower Bounds for Public-Private Learning under Distribution Shift
Amrith Setlur · Pratiksha Thaker · Jonathan Ullman
Mask-Conditional Conformal Prediction: Valid Uncertainty For All Missing Data Mechanisms
JIARONG FAN · Juhyun Park · Thi Vo · Nicolas Brunel
Minimax-Optimal Two-Sample Test with Sliced Wasserstein
Binh Thuan Tran · Nicolas Schreuder
Minimizing Human Intervention in Online Classification
William Réveillard · Vasileios Saketos · Alexandre Proutiere · Richard Combes
Adversarial Robustness in One-Stage Learning-to-Defer
Yannis Montreuil · Lai Xing Ng · Axel Carlier · Wei Ooi · Letian Yu
Momentum SVGD-EM for Accelerated Maximum Marginal Likelihood Estimation
Adam Rozzio · Rafael Athanasiades · O. Deniz Akyildiz
Moonwalk: Inverse-Forward Differentiation
Dmitrii Krylov · Armin Karamzade · Roy Fox
Multi-Metric Adaptive Experimental Design under a Fixed Budget with Validation
Qining Zhang · Tanner Fiez · Yi Liu · Wenyang Liu
Multilayer Correlation Clustering
Atsushi Miyauchi · Florian Adriaens · Francesco Bonchi · Nikolaj Tatti
Near-optimal Rank Adaptive Inference of High Dimensional Matrices
Frédéric Zheng · Yassir Jedra · Alexandre Proutiere
Near-Optimal Swap Regret in Combinatorial Bandits
Andreas Kontogiannis · Vasilis Pollatos · Panayotis Mertikopoulos · Ioannis Panageas
NeST-BO: Fast Local Bayesian Optimization via Newton-Step Targeting of Gradient and Hessian Information
Wei-Ting Tang · Akshay Kudva · Joel Paulson
Neural Doubly Robust Proximal Causal Estimation
Ruolin Meng · Dhanajit Brahma · Ricardo Henao · Lawrence Carin Duke
Neuron Block Dynamics for XOR Classification with Zero-Margin
Guillaume Braun · Masaaki Imaizumi
Nonparametric Multi Change Point Detection for Markov Chains via Adaptive Clustering
Imon Banerjee · Jiaqi Lei · Sanjay Mehrotra
AlphaFold's Bayesian Roots in Probability Kinematics
Thomas Hamelryck · Kanti Mardia
On the Convergence and Stability of Distributed Sub-model Training
Yuyang Deng · Fuli Qiao · Mehrdad Mahdavi
On the Misinformation in a Statistical Experiment
Jake Callahan · Tommie Catanach
On the optimal regret of collaborative personalized linear bandits
Bruce Huang · Ruida Zhou · Lin Yang · Suhas Diggavi
On the Role of Depth in the Expressivity of RNNs
Maude Lizaire · Michael Rizvi-Martel · Éric Dupuis · Guillaume Rabusseau
Online Learning-to-Defer with Varying Experts
Yannis Montreuil · Hoang Dang · Maxime Meyer · Lai Xing Ng · Axel Carlier · Wei Ooi
Optimal Local Convergence Rates of Stochastic First-Order Methods under Local $\alpha$-PL
Mohammadsaeed Masiha · Saber Salehkaleybar · Niao He · Negar Kiyavash · Patrick Thiran
Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees
Yannis Montreuil · Yeo Heng · Lai Xing Ng · Axel Carlier · Wei Ooi
Optimistic Actor-Critic with Parametric Policies: Unifying Sample Efficiency and Practicality
Max Lin · Reza Asad · Kevin Tan · Haque Ishfaq · Csaba Szepesvari · Sharan Vaswani
Parameter-Free Dynamic Regret for Unconstrained Linear Bandits
Alberto Rumi · Andrew Jacobsen · Nicolò Cesa-Bianchi · Fabio Vitale
Partial Monotonicity for Submodular Maximization with a Knapsack Constraint
Tong Cheng · Xueyan Tang
An Evaluation of Cost Functions for Algorithmic Recourse
Eoin Kenny · Allan Anzagira · Tom Bewley · Freddy Lecue · Manuela Veloso
Partial VOROS: A Cost-aware Performance Metric for Binary Classifiers with Precision and Capacity Constraints
Christopher Ratigan · Kyle Heuton · Carissa Wang · Lenore Cowen · Michael Hughes
Personalized Incentive Alignment: Correcting Utility-Driven Selection Bias in A/B Tests
Jiachun Li · yang meng · David Simchi-Levi · Chonghuan Wang
Power Transform Revisited: Numerically Stable, and Federated
Xuefeng Xu · Graham Cormode
Private Synthetic Graph Generation and Fused Gromov-Wasserstein Distance
Leoni Carla Wirth · Gholamali Aminian · Gesine Reinert
Process-Tensor Tomography of SGD: Measuring Non-Markovian Memory via Back-Flow of Distinguishability
Vasileios Sevetlidis · George Pavlidis
Provable Accelerated Bayesian Optimization with Knowledge Transfer
Haitao Lin · Boxin Zhao · Mladen Kolar · Chong Liu
Provably Efficient and Agile Randomized Q-Learning
He Wang · Xingyu Xu · Yuejie Chi
Provably Efficient Reinforcement Learning for Sparse Dynamical Systems with Non-Gaussian Noise
Davide Maran · Gianmarco Tedeschi · Enea Gusmeroli · Marcello Restelli
Pure Exploration with Infinite Answers
Riccardo Poiani · Martino Bernasconi · Andrea Celli
Rank Lifting and Random Non-Linear Maps
Andrea Drago · Maria Sofia Bucarelli · Francesco Caso · Marius Michetti · Federico Siciliano · Fabrizio Silvestri · Luca Becchetti
An Information-Geometric Approach to Artificial Curiosity
Alexander Nedergaard · Pablo Morales
Rashomon Effect for Visualizing High-Dimensional Data
Yiyang Sun · Haiyang Huang · Gaurav Rajesh Parikh · Cynthia Rudin
Rate optimal learning of equilibria from data
Till Freihaut · Luca Viano · Emanuele Nevali · Volkan Cevher · Matthieu Geist · Giorgia Ramponi
Recency Biased Causal Attention for Time-series Forecasting
Kareem Hegazy · Michael Mahoney · N. Benjamin Erichson
Reconciling Communication Compression and Byzantine-Robustness in Distributed Learning
Diksha Gupta · Antonio Honsell · Chuan Xu · Nirupam Gupta · Giovanni Neglia
Reinforcement Learning Using Known Invariances
Alexandru Cioba · Aya Kayal · Laura Toni · Sattar Vakili · Alberto Bernacchia
Representation Learning via Non-Contrastive Mutual Information
Zhaohan Daniel Guo · Bernardo Avila Pires · Khimya Khetarpal · Dale Schuurmans · Bo Dai
Representative, Informative, and De-Amplifying: Requirements for Robust Bayesian Active Learning under Model Misspecification
Roubing Tang · Sabina Sloman · Samuel Kaski
ReTrack: Data Unlearning in Diffusion Models through Redirecting the Denoising Trajectory
Qitan Shi · Cheng Jin · Jiawei Zhang · Yuantao Gu
Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data
Rickard Karlsson · Piersilvio De Bartolomeis · Issa Dahabreh · Jesse Krijthe
Scalable Model-Based Clustering with Sequential Monte Carlo
Connie Trojan · Pavel Myshkov · Paul Fearnhead · James Hensman · Tom Minka · Christopher Nemeth
Asymptotic optimality theory of confidence intervals of the mean
Vikas Deep · Achal Bassamboo · Sandeep Juneja
Semi-Random Noisy and One-Bit Matrix Completion With Nonconvex Primal-Dual Framework
Xing Gao · Binhao Chen · Yu Cheng
Sequential Off-Policy Learning with Logarithmic Smoothing
Maxime Haddouche · Otmane Sakhi
Set to Be Fair: Demographic Parity Constraints for Set-Valued Classification
Eyal Cohen · Christophe Denis · Mohamed Hebiri
Sharp risk bounds for early-stopping in Gaussian linear regression
Tobias Wegel · Gil Kur · Patrick Rebeschini
SiGHT: A Self-Supervised Graph-based Hallucination DeTection Framework for Domain-Specific LLMs
Chen Ziying · Meng-Fen Chiang · Wen-Chih Peng
Stationarity-Aware Causal Discovery in Time Series via Minimal Separating Sets
Shanyun Gao · Raghavendra Addanki · Tong Yu · Ryan Rossi · Qifan Song · Murat Kocaoglu
Statistical Inference for Explainable Boosting Machines
Haimo Fang · Kevin Tan · Jonathan Pipping · Giles Hooker
Statistical-computational gap in multiple Gaussian graph alignment
Bertrand Even · Luca Ganassali
Stick-Breaking Embedded Topic Model with Continuous Optimal Transport for Online Analysis of Document Streams
Federica Granese · Serena Villata · Charles Bouveyron
Structured Matching via Cost-Regularized Unbalanced Optimal Transport
Emanuele Pardini · Katerina Papagiannouli
Bad Values but Good Behavior: Learning Highly Misspecified Bandits with Function Approximation
Debangshu Banerjee · Aditya Gopalan
Structured Temporal Inference in State-Space Models
hamidreza hashemp
T$_k$CP: Context-Aware Pooling via Top-k% Activation Selection
Seo-Yeon Choi · Kyungsu Lee
Tensor Gaussian Processes: Efficient Solvers for Nonlinear PDEs
Qiwei Yuan · Zhitong Xu · Yinghao Chen · Yiming Xu · Houman Owhadi · Shandian Zhe
The Role of Causal Features in Strategic Classification for Robustness and Alignment
Sophia Gunluk · Antonio Gois · Nir Rosenfeld · Nidhi Hegde · Simon Lacoste-Julien · Dhanya Sridhar
Thompson Sampling-like Algorithms for Stochastic Rising Bandits
Marco Fiandri · Alberto Maria Metelli · Francesco Trovò
Three-operator splitting with stale gradients for faster non-linear optimal transport
Jacob Lindbäck · David Alvarez-Melis · Mikael Johansson
Tight Analysis of Decentralized SGD: a Markov Chain Perspective
Lucas Versini · Paul Mangold · Aymeric Dieuleveut
Tight Lower Bounds and Optimal Algorithms for Stochastic Nonconvex Optimization with Heavy-Tailed Noise
Adrien Fradin · Abdurakhmon Sadiev · Laurent Condat · Peter Richtarik
Tight Regret Upper and Lower Bounds for Optimistic Hedge in Two-Player Zero-Sum Games
Taira Tsuchiya
Time Series Forecasting with Hahn Kolmogorov-Arnold Networks
Zahidul Hasan · Abdessamad Ben Hamza · Nizar Bouguila
Bayesian Inverse Transition Learning: Learning Dynamics from Near-Optimal Trajectories
Leo Benac · Sonali Parbhoo · Abhishek Sharma · Finale Doshi-Velez
Time-Aware Synthetic Control
Saeyoung Rho · Cyrus Illick · Samhitha Narasipura · Alberto Abadie · Daniel Hsu · Vishal Misra
Towards Blackwell Optimality: Bellman Optimality Is All You Can Get
Victor Boone · Adrienne Tuynman
Towards Characterizing the Complexity of Riemannian Online Convex Optimization
Hibiki Fukushima · Hiroshi Hirai · Shinji Ito
Towards Sensitivity-Aware Language Models
Dren Fazlija · Iyiola E. Olatunji · Daniel Kudenko · Sandipan Sikdar
Tractable Shapley Values and Interactions via Tensor Networks
Farzaneh Heidari · Chao Li · Guillaume Rabusseau
Training Latent Diffusion Models with Interacting Particle Algorithms
Tim Y. J. Wang · Juan Kuntz · O. Deniz Akyildiz
Transportability without Graphs: A Bayesian Approach to Identifying s-Admissible Backdoor Sets
Konstantina Lelova · Gregory Cooper · Sofia Triantafillou
Uncertainty Quantification for Named Entity Recognition via Conformal Prediction
Matthew Singer · Karl Pazdernik · Srijan Sengupta
Undersmoothing Black-Box Models for Functional Estimation
Yue Yu · Debarghya Mukherjee · Moulinath Banerjee · Yaacov Ritov
Understanding and Steering Large Generative Models Using Constitutions for Atomic Concept Edits
Neha Kalibhat · Zi Wang · Prasoon Bajpai · Drew Proud · Wenjun Zeng · Been Kim · Mani Malek
Best Policy Learning from Trajectory Preference Feedback
Akhil Agnihotri · Rahul Jain · Deepak Ramachandran · Zheng Wen
Unified Causal Discovery and Missing Data Imputation
Osman Ali Mian · Jens Kleesiek · Michael Kamp
Unmixing Mean Embeddings for Domain Adaptation with Target Label Proportion
Alain Rakotomamonjy · Maxime Berar · Mokhtar Alaya
Variational Grey-Box Dynamics Matching
Gurjeet Singh · Frantzeska Lavda · Giangiacomo Mercatali · Alexandros Kalousis
Variational inference via radial transport
Luca Ghafourpour · Sinho Chewi · Alessio Figalli · Aram-Alexandre Pooladian
We Still Don’t Understand High-Dimensional Bayesian Optimization
Colin Doumont · Donney Fan · Natalie Maus · Jacob Gardner · Henry Moss · Geoff Pleiss
Where the Score Lives: A Wavelet View of Diffusion
Emma Finn · Binxu Wang · T. Keller · Demba Ba
Why is prompting hard? Understanding prompts on binary sequence predictors
Li Wenliang · Anian Ruoss · Jordi Grau-Moya · Marcus Hutter · Tim Genewein
Zeroth-Order Stochastic Compositional Gradient Descent: Towards Black-Box Sparse AUC Maximization
WENKANG WANG · Dongxu Liu · Bin Gu
Beyond Binning: Soft Task Reformulation for Deep Regression
Lawrence Stewart · Francis Bach · Quentin Berthet
A Divergence-Based Method for Weighting and Averaging Model Predictions
Olav Benjamin Vassend
Beyond ReLU: How Activations Affect Neural Kernels and Random Wide Networks
David Holzmüller · Max Schölpple
Boosted GFlowNets: Improving Exploration via Sequential Learning
Pedro Dall'Antonia · Tiago Silva · Daniel Augusto de Souza · César Lincoln Mattos · Diego Mesquita
Bounds and Identification of Joint Probabilities of Potential Outcomes and Observed Variables under Monotonicity Assumptions
Naoya Hashimoto · Yuta Kawakami · Jin Tian
Busemann Functions in the Wasserstein Space: Existence, Closed-Forms, and Applications to Slicing
Clément Bonet · Elsa Cazelles · Lucas Drumetz · Nicolas Courty
Canopy Tree Height Estimation using Quantile Regression: Modeling and Evaluating Uncertainty in Remote Sensing
Karsten Schrödter · Jan Pauls · Fabian Gieseke
Certifying Reading Comprehension in Large Language Models
Isha Chaudhary · Vedaant Jain · Gagandeep Singh
Clustering in Mixtures of Ergodic Markov Chains: Fundamental Limit and Two-Stage Algorithm
Junghyun Lee · Yassir Jedra · Alexandre Proutiere · Se-Young Yun
Community-Enhanced Semi-seeded Network Alignment (CESSNA): A Robust Method with Application to Microbiome Networks
Sijing Yu · Vince Lyzinski · Daniel Sussman
Complexity-Aware Deep Symbolic Regression with Robust Risk-Seeking Policy Gradients
Zachary Bastiani · Mike Kirby · Jacob Hochhalter · Shandian Zhe
Computationally Lightweight Classifiers with Frequentist Bounds on Prediction Errors
Shreeram Murali · Cristian Rojas · Dominik Baumann
A Geometric Approach to Optimal Experimental Design
Gavin Kerrigan · Christian Andersson Naesseth · Tom Rainforth
Conditional Flow Matching for Bayesian Posterior Sampling
Percy Zhai · Sowon Jeong · Veronika Rockova
ConMeZO: Adaptive Descent-Direction Sampling for Gradient-Free Finetuning of Large Language Models
Lejs Deen Behric · Liang Zhang · Bingcong Li · Kiran Thekumparampil
Connectome-Guided Optimization for Deep Networks
Peilin He · Tananun Songdechakraiwut
Conservative Inference in Switchback Experiments
Jose Blanchet · Peter Glynn · Ramesh Johari · Linjia Wu · Wenqian Xing
Corruption-robust Offline Multi-agent Reinforcement Learning from Human Feedback
Andi Nika · Debmalya Mandal · Parameswaran Kamalaruban · Adish Singla · Goran Radanovic
Counterfactual Explanations via Latent Structure for Time Series Classification
Akihiro Yamaguchi · Shizuo Kaji · Kaname Matsue · Ryusei Shingaki
Creator Incentives in Recommender Systems: A Cooperative Game-Theoretic Approach for Stable and Fair Collaboration in Multi-Agent Bandits
Ramakrishnan Krishnamurthy · Arpit Agarwal · Lakshmi Subramanian · Maximilian Nickel
CTRLS: Chain-of-Thought Reasoning via Latent State-Transition
Junda Wu · Yuxin Xiong · Xintong Li · Sheldon Yu · Zhengmian Hu · Tong Yu · Rui Wang · Xiang Chen · Jingbo Shang · Julian McAuley
Data Distribution Valuation Using Generalized Bayesian Inference
Ngoc Cuong Nguyen · Cuong V. Nguyen
Deformed Decomposition for Non-negative Tensors
Kazu Ghalamkari · Petr Taborsky · Morten Mørup
A Goemans-Williamson type algorithm for identifying subcohorts in clinical trials
Pratik Worah
Demystifying Transition Matching: When and Why It Can Beat Flow Matching
Jaihoon Kim · Rajarshi Saha · Youngsuk Park · Minhyuk Sung
Differentially Private E-Values
Daniel Csillag · Diego Mesquita
DIVERSED: Relaxed Speculative Decoding via Dynamic Ensemble Verification
Ziyi Wang · Siva Rajesh Kasa · Ankith M S · SANTHOSH KASA · Jiaru Zou · Nan Jiang · Sumit Negi · Ruqi Zhang · Qifan Song
Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants
Abdoulaye SAKHO · Emmanuel Malherbe · Erwan Scornet
ECAI: Efficient Convolution Activation Inversion for Constant-Memory Convolutional Neural Networks Training
Changhyeon Lee · Seulki Lee
Efficient and Accurate Tensor Compression via Recursive Sketching
Amit Sharma · Mohammad Azhar Khan · Rameshwar Pratap
Efficient Inference for Coupled Hidden Markov Models in Continuous Time and Discrete Space
Giosue Migliorini · Padhraic Smyth
Efficient Logistic Regression with Mixture of Sigmoids
Federico Di Gennaro · Saptarshi Chakraborty · Nikita Zhivotovskiy
Efficient Uncoupled Learning Dynamics with $\tilde{O}\left(T^{-1/4}\right)$ Last-Iterate Convergence in Bilinear Saddle-Point Problems over Convex Sets under Bandit Feedback
ARNAB MAITI · Claire Jie Zhang · Kevin Jamieson · Jamie Morgenstern · Ioannis Panageas · Lillian Ratliff
Enforcing Fair Predicted Scores on Intervals of Percentiles by Difference-of-Convex Constraints
Yutian He · Yankun Huang · Yao Yao · Qihang Lin
A Modularized Framework for Piecewise-Stationary Restless Bandits
Kuan-Ta Li · Chia-Chun Lin · Ping-Chun Hsieh · Yu-Chih Huang
Entropic Projection Alignment: Estimating, Explaining, and Improving Model Performance Under Tabular Distribution Shift
Salim I. Amoukou · Emanuele Albini · Tom Bewley · Saumitra Mishra · Manuela Veloso
Ergodic and Subhomogeneous Dynamics in Hyperbolic Neural Networks
Nico Alvarado · Sebastian Burgos
Evaluation of Large Language Models via Coupled Token Generation
Nina Corvelo Benz · Stratis Tsirtsis · Eleni Straitouri · Ivi Chatzi · Ander Artola Velasco · Suhas Thejaswi · Manuel Gomez Rodriguez
EventFlow: Forecasting Temporal Point Processes with Flow Matching
Gavin Kerrigan · Kai Nelson · Padhraic Smyth
Exact and Approximate MCMC for Doubly-intractable Probabilistic Graphical Models Leveraging the Underlying Independence Model
Antik Chakraborty · Anindya Bhadra · Yujie Chen
Explore-then-Commit for Nonstationary Linear Bandits with Latent Dynamics
Sunmook Choi · Yahya Sattar · Yassir Jedra · Maryam Fazel · Sarah Dean
Fact-Augmented Lookahead for LLM Agents: Simple Online Memory, No Finetuning
Samuel Holt · Max Ruiz Luyten · Thomas Pouplin · Mihaela van der Schaar
FairSHAP: Preprocessing for Fairness Through Attribution-Based Data Augmentation
Lin Zhu · Yijun Bian · Lei You
Fast and Robust Simulation-Based Inference With Optimization Monte Carlo
Vasilis Gkolemis · Christos Diou · Michael Gutmann
Fast Quasar-Convex Optimization with Constraints
David Martínez-Rubio
Accelerated Distributed Optimization with Compression and Error Feedback
Yuan Gao · Anton Rodomanov · Jeremy Rack · Sebastian Stich
Faster parallel MCMC: Metropolis adjustment is best served warm
Jakob Robnik · Uros Seljak
FastRank: Fast Tensor Rank Approximation based on Spectral Energy
Konstantinos Bougiatiotis · Georgios Paliouras
FlowPINNs: A Variational Framework for PDE Parameter Inference and Uncertainty Quantification
David Dalton · Hao Gao · Dirk Husmeier
Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
Dennis Wagner · Arjun Nair · Billy Franks · Justus Arweiler · Aparna Muraleedharan · Indra Jungjohann · Fabian Hartung · Andriy Balinskyy · Saurabh Varshneya · Mayank Ahuja · Nabeel Hussain Syed · Mayank Nagda · Philipp Liznerski · Steffen Reithermann · Maja Waldron · Sebastian Vollmer · Ralf Schulz · Torsten Katz · Stephan Mandt · Michael Bortz · Heike Leitte · Daniel Neider · Jakob Burger · Fabian Jirasek · Hans Hasse · Sophie Fellenz · Marius Kloft
Free Random Projection for In-Context Reinforcement Learning
Tomohiro Hayase · Benoit Collins · Nakamasa Inoue
From Cells to Sentences: An End-to-End Framework for Table Understanding
Deepak Vijaykeerhty · Arvind Agarwal
From Hawkes Processes to Attention: Time-Modulated Mechanisms for Event Sequences
Xinzi Tan · Kejian Zhang · Junhan Yu · Doudou Zhou
From Token Imbalance to Balanced Routing: An ELBO-Regularized Probabilistic Framework for Contrastive Multimodal Learning
Habibeh Naderi · Behrouz Haji Soleimani · Stan Matwin
From Transformers to State Spaces: GeoMamba-SE(3) for Fast and Accurate Molecular Learning
Jiayu Qin · Zhengquan Luo · Jian Chen · Xuhui Li · Jiayi Chen · Zhiqiang Xu
Fundamental Limits of Non-Adaptive Group Testing With Markovian Correlation
Aditya Narayan Ravi · Ilan Shomorony
Accelerating PDE Surrogates via RL-Guided Mesh Optimization
yang meng · Ruoxi Jiang · Zhuokai Zhao · Chong Liu · Rebecca Willett · Yuxin Chen
Generalized Correlation Shifting for Lasso
Izuru Miyazaki · Hironori Fujisawa
GL-LowPopArt: A Nearly Instance-Wise Minimax Estimator for Generalized Low-Rank Trace Regression
Junghyun Lee · Kyoungseok Jang · Kwang-Sung Jun · Milan Vojnovic · Se-Young Yun
Gradient Regularized Natural Gradients
Satya Dash · Hossein Abdi · Wei Pan · Samuel Kaski · Mingfei Sun
Gradient-flow SDEs have unique transient population dynamics
Vincent Guan · Joseph Janssen · Nicolas Lanzetti · Antonio Terpin · Geoffrey Schiebinger · Elina Robeva
Graph Learning is Suboptimal in Causal Bandits
Mohammad Shahverdikondori · Jalal Etesami · Negar Kiyavash
Hybrid Meta-Learners for Estimating Heterogeneous Treatment Effects
Zhongyuan Liang · Lars van der Laan · Ahmed Alaa
Hyperbolic Learning with Supervision from any Granularity
Mina Atigh · Max van Spengler · Teng Long · Melika Ayoughi · Tejaswi Kasarla · Pascal Mettes
Hyperbolic Part-Whole Image Segmentation
Mikhail Vlasenko · Mina Atigh · Pascal Mettes
Hypergraph Neural Networks Accelerate MUS Enumeration
Hiroya Ijima · Koichiro Yawata
i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data
Chen Ma · Wanjie Wang · Shuhao Fan
Active Subspaces in Infinite Dimension
Poorbita Kundu · Nathan Wycoff
Identification and Estimation of "Probabilities of Causation" in the Presence of Confounding and Selection Bias
Ryusei Shingaki · Haruka Yoshida · Manabu Kuroki
In-Context Function Learning in Large Language Models
Elif Akata · Konstantinos Voudouris · Vincent Fortuin · Eric Schulz
In-Context Learning for Discrete Optimal Transport: Can Transformers Sort?
Hadi Daneshmand
Incentivizing Truthful Submissions in a Data Marketplace for Mean Estimation
Keran Chen · Alex Clinton · Kirthevasan Kandasamy
Incoherence in goal-conditioned autoregressive models
Jacek Karwowski · Raymond Douglas
Information-theoretic error bounds for source localization in neural sensing
Leighton Barnes · Yuxin Guo · Alex Dytso · Pulkit Grover
Injecting Measurement Information Yields a Fast and Noise-Robust Diffusion-Based Inverse Problem Solver
Jonathan Patsenker · Henry Li · Myeongseob Ko · Ruoxi Jia · Yuval Kluger
Interpretable DNA Sequence Classification via Dynamic Feature Generation in Decision Trees
Nicolas HUYNH · Krzysztof Kacprzyk · Ryan Sheridan · David Bentley · Mihaela van der Schaar
Kernel Treatment Effects with Adaptively Collected Data
Houssam Zenati · Bariscan Bozkurt · Arthur Gretton
Lag Operator SSMs: A Geometric Framework for Structured State Space Modeling
Sutashu Tomonaga · Kenji Doya · Noboru Murata
Adaptive Combinatorial Experimental Design: Pareto Optimality for Decision-Making and Inference
Hongrui Xie · Junyu Cao · Kan Xu
LAMP: Extracting Local Decision Surfaces from Large Language Models
Ryan Chen · Youngmin Ko · Catherine Cho · Zeyu Zhang · Mauro Giuffrè · Sunny Chung · Dennis Shung · Bradly Stadie
LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data
Antony Sikorski · Michael Ivanitskiy · Nathan Lenssen · Douglas Nychka · Daniel McKenzie
Learnability with partial labels and adaptive nearest neighbors
Nicolás Errandonea · Santiago Mazuelas · Jose A Lozano · Sanjoy Dasgupta
Learning Geometry and Topology via Multi-Chart Flows
Hanlin Yu · Soren Hauberg · Marcelo Hartmann · Arto Klami · Georgios Arvanitidis
Learning How Deep to Go: Self-Scaling Deep Reinforcement Learning
Michelangelo Vegliò · Marco Fantozzi · Antonio Di Cecco · Carlo Metta · Flora Angileri · Simone Treccani · Adrienne Macazar · Silvia Galfre' · Maurizio Parton · Francesco Morandin
Learning Physical Operators using Neural Operators
Vignesh Gopakumar · Ander Gray · Daniel Giles · Lorenzo Zanisi · Matt Kusner · Timo Betcke · Stanislas Pamela · Marc Deisenroth
Learning to Bid in Discriminatory Auctions with Budget Constraints
Negin Golrezaei · Sourav Sahoo
Learning to Choose or Choosing to Learn: Best-of-N vs. Supervised Fine-Tuning for Bit String Generation
Seamus Seamus · Vinod Raman · Unique Subedi · Yuekai Sun
Learning under Moral Hazard with Instrumental Regression and Generalized Method of Moments
Shiliang Zuo
Learning When Not to Learn: Risk-Sensitive Abstention in Bandits with Unbounded Rewards
Sarah Liaw · Benjamin Plaut
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