Skip to yearly menu bar Skip to main content


Timezone: Africa/Casablanca
Full Schedule Sat 5/2 Sun 5/3 Mon 5/4 Tue 5/5
Filter Events
Registration Desk
7:30 AM - 5:00 PM
Remarks
8:30 AM - 9:00 AM
Invited Talk

Keynote: Eric Xing

Eric Xing
9:00 AM - 10:00 AM
Speaker Bio
Eric Xing
Professor Eric P. Xing is the founding President and Professor at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), where he has led the University’s rapid growth in fundamental AI research and the recruitment of a world-class faculty. At MBZUAI, he has built a strong research platform aligned with national priorities while maintaining excellence in both basic research and translational R&D, and has helped establish broad partnerships across academia, industry, and government. A world-leading computer scientist, Professor Xing’s contributions span statistical machine learning and large-scale ML systems, including foundational work in distance metric learning, network and graphical models, and distributed machine learning. His recent efforts include advancing foundation and world models and open, reproducible large language models, as well as AI for science initiatives in computational biology and healthcare. He is a recipient of major early-career awards including the NSF CAREER Award and a Sloan Fellowship, and is a Fellow of AAAI, IEEE, and ASA, and an ACM Fellow (2023). Professor Xing holds PhDs from Rutgers University and UC Berkeley, and completed his undergraduate studies at Tsinghua University.
... more
Poster
3:00 PM - 6:00 PM
203 Events in this session
$k$-PCA for (non-squared) Euclidean Distances: Deterministic Polynomial Time Approximation
Daniel Greenhut · Dan Feldman
A Scalable Lift-and-Project Differentiable Approach For the Maximum Cut Problem
Ismail Alkhouri · Mian Wu · CUNXI YU · Jia Liu · Rongrong Wang · Alvaro Velasquez
Linearly Separable Features in Shallow Nonlinear Networks: Width Scales Polynomially with Intrinsic Data Dimension
Alec Xu · Can Yaras · Peng Wang · Qing Qu
LLMs Judging LLMs: A Simplex Perspective
Patrick Vossler · Fan Xia · Yifan Mai · Adarsh Subbaswamy · Jean Feng
Lloyd's $K$-Means Clustering Algorithm is Frank-Wolfe in Disguise
Michael Pokojovy · J. Jobe · Simon Lacoste-Julien
Local Inconsistency Resolution: The Interplay between Attention and Control in Probabilistic Models
Oliver Richardson · Abdessamad Kabid · Mandana Samiei · Mehran Shakerinava · Joseph Viviano · Ali Parviz · Yoshua Bengio
Low Rank Based Subspace Inference for the Laplace Approximation of Bayesian Neural Networks
Josua Faller · Jörg Martin
Low-Complexity and Consistent Graphon Estimation from Multiple Networks
Roland Boniface SOGAN · Tabea Rebafka
Low-rank bias, weight decay, and model merging in neural networks
Ilja Kuzborskij · Yasin Abbasi-Yadkori
Lyapunov-Guided Self-Alignment: Test-Time Adaptation for Offline Safe Reinforcement Learning
Seungyub Han · HyungJin Kim · Jungwoo Lee
MDPs with a State Sensing Cost
Vansh Kapoor · Jayakrishnan Nair
Meet Me at the Arm: The Cooperative Multi Armed Bandits Problem with Shareable Arms
Xinyi Hu · Aldo Pacchiano
Accelerating Byzantine-Robust Distributed Learning with Compressed Communication via Double Momentum and Variance Reduction
Yanghao Li · Changxin Liu · Yuhao Yi
Meta Sparse Principal Component Analysis
Imon Banerjee · Jean Honorio
Minimax generalized cross-entropy
Kartheek Bondugula · Santiago Mazuelas · Aritz Pérez · Anqi Liu
Mixture Proportion Estimation and Weakly-supervised Kernel Test for Conditional Independence
Yushi Hirose · Akito Narahara · Takafumi Kanamori
MLorc: Momentum Low-rank Compression for Large Language Model Adaptation
Wei Shen · Yaxiang Zhang · Minhui Huang · Mengfan Xu · Jiawei Zhang · Cong Shen
Multi-Component VAE with Gaussian Markov Random Field
Fouad Oubari · Mohamed El Baha · Raphaël Meunier · Rodrigue Décatoire · Mathilde MOUGEOT
Narrowing Action Choices with AI Improves Human Sequential Decisions
Eleni Straitouri · Stratis Tsirtsis · Ander Artola Velasco · Manuel Gomez Rodriguez
Near-Optimal Sample Complexities of Divergence-based S-rectangular Distributionally Robust Reinforcement Learning
Zhenghao Li · Shengbo Wang · NIAN SI
Neural Additive Experts: Context-Gated Experts for Controllable Model Additivity
Guangzhi Xiong · Sanchit Sinha · Aidong Zhang
Neural Variance-aware Dueling Bandits with Deep Representation and Shallow Exploration
Youngmin Oh · Jinje Park · Taejin Paik
Noise-Free Dynamic Rank-Adaptation via Riemannian Methods in Federated Fine-Tuning
Zihan Zhou · Yang Zhou · Tianshi Che · Zeru Zhang · Jiaxiang Ren · Da Yan · Zhe Jiang · yelong shen · Ruoming Jin · Jianfeng Gao
Active learning for stochastic contextual linear bandits
Emma Brunskill · Ishani Karmarkar · Zhaoqi Li
Non-Asymptotic Generalization and Optimization Bounds for Stochastic Gauss-Newton in Deep Neural Networks
Semih Cayci
Off-policy Distributional Q($\lambda$): Distributional RL without Importance Sampling
Yunhao Tang · Mark Rowland · Rémi Munos · Bernardo Avila Pires · Will Dabney
On Barycenter Computation: Analyzing Semi-Unbalanced Optimal Transport-based Method on Bures-Wasserstein manifold.
Ngoc-Hai Nguyen · Le Dung · Hoang-Phi Nguyen · Tung Pham · Nhat Ho
On Computational Limits of FlowAR Models: Expressivity and Efficiency
Yang Cao · Chengyue Gong · Yekun Ke · Xiaoyu Li · Yingyu Liang · Zhizhou Sha · Zhenmei Shi · Zhao Song
On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Models
Philipp M. Faller · Dominik Janzing
On Kernel based Variational Autoencoders
Tian Qin · Wei-Min Huang
On Relation-Aware Slicing in Cross-Domain Alignment
Dhruv Sarkar · Aprameyo Chakrabartty · Anish Chakrabarty · Swagatam Das
On the Expressivity of Selective State-Space Layers: A Multivariate Polynomial Approach
Edo Cohen-Karlik · Itamar Zimerman · Liane Galanti · Ido Atad · Amir Globerson · Lior Wolf
On the hardness of Reinforcement Learning with Transition Lookahead
Corentin Pla · Hugo Richard · Marc Abeille · Nadav Merlis · Vianney Perchet
On the Interplay of Priors and Overparametrization in Bayesian Neural Network Posteriors
Julius Kobialka · Emanuel Sommer · Chris Kolb · Juntae Kwon · Daniel Dold · David Rügamer
Adaptive Replay Buffer for Offline-to-Online Reinforcement Learning
Chihyeon Song · Jaewoo Lee · Jinkyoo Park
On the Latent Information Geometry of the Grassmann Manifold
Lorenzo Cazzella · Soren Hauberg · Georgios Arvanitidis · Matteo Matteucci
On the Weight Density of L2-Regularized Linear Classification and Regression
He Zhe Lin · Zhi-Bao Lu · Sheng-Wei Chen · Cheng-Hung Liu · Chih-Jen Lin
Optimal Arm Elimination Algorithms for Combinatorial Bandits
Yuxiao Wen · Yanjun Han · Zhengyuan Zhou
Optimal Posterior Sampling for Policy Identification in Tabular Markov Decision Processes
Cyrille Kone · Kevin Jamieson
Optimal rates for density and mode estimation with expand-and-sparsify representations
Kaushik Sinha · Christopher Tosh
Optimal Variance and Covariance Estimation under Differential Privacy in the Add-Remove Model and Beyond
Shokichi Takakura · Seng Pei Liew · Satoshi Hasegawa
Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributions
Timothée Loranchet · Charles ASSAAD
PAC-Bayesian Bounds on Constrained $f$-Entropic Risk Measures
Hind Atbir · Farah Cherfaoui · Guillaume Metzler · Emilie Morvant · Paul Viallard
Partially Lazy Gradient Descent for Smoothed Online Learning
Naram Mhaisen · George Iosifidis
PENGUIN: Enhancing Transformer with Periodic-Nested Group Attention for Long-term Time Series Forecasting
Tian Sun · Yuqi Chen · Weiwei Sun
ADOPT: Additive Optimal Transport Regression
Wookyeong Song · Hans-Georg Müller
Policy Testing in Markov Decision Processes
Kaito Ariu · Pai-An WANG · Alexandre Proutiere · Kenshi Abe
Policy-Oriented Binary Classification: Improving (KD-)CART Final Splits for Subpopulation Targeting
Bill Wang · Zhenbang Jiao · Fangyi Wang
Practical Attribution Guidance for Rashomon Sets
Sichao Li · Quanling Deng · Amanda Barnard
Precise Dynamics of Diagonal Linear Networks: A Unifying Analysis by Dynamical Mean-Field Theory
Sota Nishiyama · Masaaki Imaizumi
Preconditioned Attention: Enhancing Efficiency in Transformer Blocks
Hemanth Saratchandran
Preference-based Conditional Treatment Effects and Policy Learning
Dovid Parnas · Mathieu Even · julie Josse · Uri Shalit
Prior Knowledge Makes It Possible: From Sublinear Graph Algorithms to LLM Test-Time Methods
Avrim Blum · Daniel Hsu · Cyrus Rashtchian · Donya Saless
Prior shift estimation for positive unlabeled data through the lens of kernel embedding
Jan Mielniczuk · Paweł Teisseyre · Wojciech Rejchel
Probabilistic multi-dimensional classification with incomplete data at the prediction time
Thu Ha DO · Vu-Linh Nguyen · Yves Grandvalet
Prognostic Scores and Representation Learning for Causal Effect Estimation with Weak Overlap
Oscar Clivio · Alexander D'Amour · Alexander Franks · David Bruns-Smith · Chris Holmes · Avi Feller
Almost Sure Convergence of Differential Temporal Difference Learning for Average Reward Markov Decision Processes
Ethan Blaser · Jiuqi Wang · Shangtong Zhang
Projection-free Algorithms for Online Convex Optimization with Adversarial Constraints
Dhruv Sarkar · Aprameyo Chakrabartty · Subhamon Supantha · Palash Dey · Abhishek Sinha
Provable Effects of Data Replay in Continual Learning: A Feature Learning Perspective
Meng Ding · Jinhui Xu · Kaiyi Ji
Provable Guarantees for Estimating Covariances between Latent Variables with Application to Precision Matrix Estimation
Haichi Long · Qifan Song · Jean Honorio
Provable Target Sample Complexity Improvements as Pre‑Trained Models Scale
Kazuto Fukuchi · Ryuichiro Hataya · Kota Matsui
Random Features for Operator-Valued Kernels: Bridging Kernel Methods and Neural Operators
Mike Nguyen · Nicole Mücke
Rate-optimal Design for Anytime Best Arm Identification
Junpei Komiyama · Kyoungseok Jang · Junya Honda
Regret Guarantees for Linear Contextual Stochastic Shortest Path
Dor Polikar · Alon Cohen
Regularized Operator Extrapolation Method For Stochastic Bilevel Variational Inequality Problems
Mohammad Khalafi · Digvijay Boob
Regularizing Extrapolation in Causal Inference
David Arbour · Harsh Parikh · Bijan Niknam · Elizabeth Stuart · Kara Rudolph · Avi Feller
Rethinking Cross-Modal Fine-Tuning: Optimizing the Interaction between Feature Alignment and Target Fitting
Trong Khiem Tran · Manh Dao · Phi Le Nguyen · Thao Nguyen Truong · Nghia Hoang
Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies from Simulated Nonparametric Functions
Cen-You Li · Marc Toussaint · Barbara Rakitsch · Christoph Zimmer
Rethinking Intrinsic Dimension Estimation in Neural Representations
Rickmer Schulte · David Rügamer
Rethinking Probabilistic Circuit Parameter Learning
Anji Liu · Zilei Shao · Guy Van den Broeck
Revisiting Social Welfare in Bandits: UCB is (Nearly) All You Need
Dhruv Sarkar · Nishant Pandey · Sayak Ray Chowdhury
RL-finetuning LLMs from on- and off-policy data with a single algorithm
Yunhao Tang · Taco Cohen · David Zhang · Gabriel Synnaeve · Rémi Munos
Robust Estimation of a Sparse Linear Model: Provable Guarantees with Non-convexity
Deepak Maurya · Adarsh Barik · Jean Honorio
Robust Generalization with Adaptive Optimal Transport Priors for Decision-Focused Learning
Haixiang Sun · Andrew Liu
Root cause analysis of outliers in unknown cyclic graphs
Daniela Schkoda · Dominik Janzing
RoseCDL: Robust and Scalable Convolutional Dictionary Learning for rare-event and anomaly detection
Jad Yehya · Mansour Benbakoura · Cédric Allain · Benoît Malézieux · Matthieu Kowalski · Thomas Moreau
Sample Average Approximation for Alpha-Divergence Minimization with Exponential Convergence Guarantees
François Bertholom · François Roueff · randal douc
Scalable Learning of Multivariate Distributions via Coresets
Zeyu Ding · Katja Ickstadt · Nadja Klein · Alexander Munteanu · Simon Omlor
An Indicator of Membership Inference Security in Post-Training Quantized Models
Eric AUBINAIS · Philippe Formont · Pablo Piantanida · Elisabeth Gassiat
Scalable Spatiotemporal Inference with Biased Scan Attention Transformer Neural Processes
Daniel Jenson · Jhonathan Navott · Piotr Grynfelder · Mengyan Zhang · Makkunda Sharma · Elizaveta Semenova · Seth Flaxman
Secretary problem with predictions and ordering
Yiming Kang
Semi-Implicit Variational Inference via Kernelized Path Gradient Descent
Tobias Pielok · Bernd Bischl · David Rügamer
Sequential 1-bit Mean Estimation with Near-Optimal Sample Complexity
Ivan Lau · Jonathan Scarlett
SFBD Flow: A Continuous-Optimization Framework for Training Diffusion Models with Noisy Samples
Haoye Lu · Darren Lo · Yaoliang Yu
Sparse Linear Bandits with Blocking Constraints
Adit Jain · Soumyabrata Pal · Sunav Choudhary · Ramasuri Narayanam · Harshita Chopra · Vikram Krishnamurthy
Sparse Linear Bandits with Fixed Sparsity Support: Adversarial and Stochastic Regimes
Kyoungseok Jang · Nam Tran · Nicolò Cesa-Bianchi
Sparse Offline Reinforcement Learning with Corruption Robustness
Nam Tran · Andi Nika · Goran Radanovic · Long Tran-Thanh · Debmalya Mandal
Spectral Clustering for Directed Graphs via Likelihood Estimation on Stochastic Block Models
Ning Zhang · Xiaowen Dong · Mihai Cucuringu
SPIRE: Conditional Personalization for Federated Diffusion Generative Models
Kaan Ozkara · Ruida Zhou · Suhas Diggavi
An Information-Theoretic Approach to Understanding Transformers' In-Context Learning of Variable-Order Markov Chains
Ruida Zhou · Chao Tian · Suhas Diggavi
Split-Flows: Measure Transport and Information Loss Across Molecular Resolutions
Sander Hummerich · Ullrich Köthe · Tristan Bereau
Standard Acquisition is Sufficient for Asynchronous Bayesian Optimization
Ben Riegler · James Odgers · Vincent Fortuin
Stochastic Bandits on Mixture Distributions: Metrics & Regret Bounds
Adit Jain · Sujay Bhatt · Alec Koppel
Structural Alignment Improves Graph Test-Time Adaptation
Hans Hao-Hsun Hsu · Shikun Liu · Han Zhao · Pan Li
Structured Difference-of-Q via Orthogonal Learning
Defu Cao · Angela Zhou
Structured Matrix Scaling for Multi-Class Calibration
Eugène Berta · David Holzmüller · Michael Jordan · Francis Bach
Support Basis: Fast Attention Beyond Bounded Entries
Maryam Aliakbarpour · Vladimir Braverman · Junze Yin · Haochen Zhang
TAS-EGNN: Task-Aware Spectral Ego-Graphs for Efficient GNNs-Based Classification
Mebarka Allaoui · Rachid Hedjam · Sonia Gupta
The Majority Vote Paradigm Shift: When Popular Meets Optimal
Antonio Purificato · Maria Sofia Bucarelli · Anil Nelakanti · Andrea Bacciu · Fabrizio Silvestri · Amin Mantrach
The Minimax Lower Bound of Kernel Stein Discrepancy Estimation
Jose Cribeiro-Ramallo · Agnideep Aich · Florian Kalinke · Ashit Baran Aich · Zoltan Szabo
Balanced and Robust Multi-Treatment Experimental Designs via Randomized Differencing
Qing Chen · Jing Jia · Peng Zhang
The Riemannian Geometry associated to Gradient Flows of Linear Convolutional Networks
El Mehdi Achour · Kathlén Kohn · Holger Rauhut
Topological Alignment of Shared Vision-Language Embedding Space
Junwon You · Kang Dasol · Jae-Hun Jung
Towards Motion-aware Referring Image Segmentation
Chaeyun Kim · Seunghoon Yi · Yejin Kim · Yohan Jo · Joonseok Lee
Train Less, Infer Faster: Efficient Model Finetuning and Compression via Structured Sparsity
Jonathan Svirsky · Yehonathan Refael · Ofir Lindenbaum
Tyler’s M-estimator Through the Lens Of Convex-Concave Programming
Daniel Cederberg
Uncertainty-Aware Meta-Learning with Analytically Tractable Posterior
Young-Jin Park · Cesar Almecija · Apoorva Sharma · Navid Azizan
Understanding Generalization in Node and Link Prediction
Antonis Vasileiou · Timo Stoll · Christopher Morris
Unsupervised Ensemble Learning Through Deep Energy-based Models
Ariel Maymon · Yanir Buznah · Uri Shaham
Variance Constrained Distribution Alignment in Few-shot Models
Xiaohong Cai · Yi SUN · Zhaowen Lin · Tianwei Cai
Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling
Daniil Medyakov · Gleb Molodtsov · Savelii Chezhegov · Alexey Rebrikov · Aleksandr Beznosikov
A Consequentialist Critique of Binary Classification Evaluation Practices: Theory, Practice, and Tools
Gerardo Flores · Alyssa Smith · Abigail Schiff · Julia Fukuyama · Ashia Wilson
Bandit-based Maximum Inner Product Search with Data-Dependent Confidence Intervals
Yoichi Sasaki · Yuzuru Okajima
Weighted quantization using MMD: From mean field to mean shift using gradient flows
Ayoub Belhadji · Daniel Sharp · Youssef Marzouk
Welfare-Centric Clustering
Claire Jie Zhang · Seyed Esmaeili · Jamie Morgenstern
When Can Federated Learning Match Centralized Learning? A PAC-Bayesian Generalization Gap Analysis
Xuanyu Chen · Shuai Wang · NAN YANG · Dong Yuan
Where You Place the Norm Matters: From Prejudiced to Neutral Initializations
Emanuele Francazi · Francesco Pinto · Aurelien Lucchi · Marco Baity-Jesi
Bayesian Model Selection via a Data-Emphasized Variational Objective
Ethan Harvey · Mikhail Petrov · Michael Hughes
Beyond Binary Out of Distribution Detection: Characterizing Distributional Shifts with Multi-Statistic Diffusion Trajectories
Achref Jaziri · Martin Rogmann · Martin Mundt · Visvanathan Ramesh
Beyond Black-Box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networks
Anton Thielmann · Arik Reuter · Benjamin Säfken
Beyond Johnson-Lindenstrauss: Uniform Bounds for Sketched Bilinear Forms
Rohan Deb · Qiaobo Li · Mayank Shrivastava · Arindam Banerjee
Beyond Pooling: Matching for Robust Generalization under Data Heterogeneity
Ayush Roy · Rudrasis Chakraborty · Lav R. Varshney · Vishnu Lokhande
Boltzmann Exploration for Heavy-Tailed Bandits
Hyeon-jun Park · Yoon-Sik Cho · Kyungjae Lee
Brenier Isotonic Regression
Han Bao · Amirreza Eshraghi · Yutong Wang
Catoni-Style Change Point Detection for Regret Minimization in Piecewise-Stationary Heavy-Tailed Bandits
Gianmarco Genalti · Sujay Bhatt · Nicola Gatti · Alberto Maria Metelli
Causal Additive Models with Unobserved Causal Paths and Backdoor Paths
Thong Pham · Takashi Nicholas Maeda · Shohei Shimizu
A Covering Framework for Offline POMDPs Learning using Belief Space Metric
Youheng Zhu · Yiping Lu
Causal Partial Identification via Conditional Optimal Transport
Sirui Lin · Zijun Gao · Jose Blanchet · Peter Glynn
Causal-DRF: Conditional Kernel Treatment Effect Estimation using Distributional Random Forest
Jeffrey NÄF · Junhyung Park · Herbert Susmann
Closed-Form Coordinate Ascent Variational Inference for Student-t Process Regression with Student-t Likelihood
Keisuke Onoue · Takatomi Kubo · Kazushi Ikeda
Composable Coresets for Constrained Determinant Maximization and Beyond
Sepideh Mahabadi · Thuy-Duong Vuong
Conditional Vendi Score: Prompt-Aware Diversity Evaluation for Text-Guided Generative AI Models
Mohammad Jalali · Azim Ospanov · Amin Gohari · Farzan Farnia
Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity
Thomas Mortier · Alireza Javanmardi · Yusuf Sale · Eyke Hüllermeier · Willem Waegeman
Conformal Robust Control of Linear Systems
Yash Patel · Sahana Rayan · Ambuj Tewari
Consistent PCA and Spectral Clustering
Satoshi Hara · Yuichi Yoshida
Contraction rates for generalized posteriors based on $f$-divergences: a diffusion process approach
Enric Boloix · Ioar Casado-Telletxea
Convergence of projected stochastic natural gradient variational inference: Balancing speed, computational effort, and accuracy
Thomas Guilmeau · Hadrien Hendrikx · Florence Forbes
A Finite Time Analysis of Thompson Sampling for Bayesian Optimization with Preferential Feedback
Sattar Vakili · Joseph Lazzaro · Davide Buffelli · Da-shan Shiu
Convexified Message-Passing Graph Neural Networks
Saar Cohen · Noa Agmon · Uri Shaham
Corruption Robust Thompson Sampling for Gaussian Bandits
Yinglun Xu · ZHIWEI WANG · Gagandeep Singh
Counterfactual Credit Guided Bayesian Optimization
Qiyu Wei · Haowei Wang · Richard Allmendinger · Mauricio Álvarez
Dashed Line Defense: Plug-And-Play Defense Against Adaptive Score-Based Query Attacks
Yanzhang Fu · Zizheng Guo · Jizhou Luo
DeepRV: Accelerating spatiotemporal inference with pre-trained neural priors
Jhonathan Navott · Daniel Jenson · Seth Flaxman · Elizaveta Semenova
Dendrograms of Mixing Measures for Softmax-Gated Gaussian Mixture of Experts: Consistency without Model Sweeps
TienHai Do · Trung Nguyen · TrungTin Nguyen · Nhat Ho · Binh Nguyen · Chris Drovandi
Design-Based Finite-Sample Analysis for Regression Adjustment
Dogyoon Song
Differential Privacy in Kernelized Contextual Bandits via Random Projections
Nikola Pavlovic · Sudeep Salgia · Qing Zhao
Differentially Private Algorithms for the Stochastic Compositional Optimization Problem
Zhuanghua Liu · Weida Li · Xiaokui Xiao · Bryan Kian Hsiang Low
Differentially Private Clustering in Data Streams
Alessandro Epasto · Tamalika Mukherjee · Peilin Zhong
A projection-based framework for gradient-free and parallel learning
Andreas Bergmeister · Manish Krishan Lal · Stefanie Jegelka · Suvrit Sra
Differentially Private Minimum Spanning Tree in Euclidean Graphs
Zongrui Zou · Alessandro Epasto · Chenglin Fan · Rudrajit Das
DISPO: Enhancing Training Efficiency and Stability in Reinforcement Learning for Large Language Model Mathematical Reasoning
Batuhan Karaman · Aditya Rawal · Mohammad Ghavamzadeh · Suhaila Shakiah · Arijit Biswas · Ruida Zhou
Doctor Rashomon and the UNIVERSE of Madness: Variable Importance with Unobserved Confounding and the Rashomon Effect
Jon Donnelly · Srikar Katta · Emanuele Borgonovo · Cynthia Rudin
Dualformer: Time-Frequency Dual Domain Learning for Long-term Time Series Forecasting
Jingjing Bai · Yoshinobu Kawahara
Duality-based Residual Estimation for Fully Offline Value-based Reinforcement Learning
Kohei Miyaguchi
Dyno-Net: A Dynamic Feature Extraction Model for Gastrointestinal Polyp Detection
Zijie Song · Jingjing Wan · Xianchun Meng · Qingye Hua · Wenjie Zhu · Bolun Chen · WEI SHAO
E-Scores for (In)Correctness Assessment of Generative Model Outputs
Guneet Singh Dhillon · Javier Gonzalez · Teodora Pandeva · Alicia Curth
Efficient Learning of Stationary Diffusions with Stein-type Discrepancies
Fabian Bleile · Sarah Lumpp · Mathias Drton
Efficient Subgroup Analysis via Optimal Trees with Global Parameter Fusion
Zhongming Xie · Joseph Giorgio · Jingshen Wang
Empirical PAC-Bayes bounds for Markov chains
Vahe Karagulyan · Pierre Alquier
A Proof of Learning Rate Transfer under $\mu$P
Soufiane Hayou
Enhancing LLM Safety through a Theoretical Minimax Game Lens
Yihe Deng · Yu Yang · Junkai Zhang · Wei Wang · Bo Li
Examining the Bias of In-Batch Sampling in Similarity Learning with Two-Tower Models
Yaxu Liu · Li-Chung Lin · Chih-Jen Lin
Explanation Design in Strategic Learning: Sufficient Explanations that Induce Non-harmful Responses
Kiet Vo · Siu Lun Chau · Masahiro Kato · Yixin Wang · Krikamol Muandet
Fair Clustering via Hierarchical Fair-Dirichlet Prior
Abhisek Chakraborty · Anirban Bhattacharya · Debdeep Pati
Feature Importance via Sets of Locally Performant Linear Models
Fatemeh Tohidian · Davin Hill · Aria Masoomi · Peter Castaldi · Jennifer Dy
FedCCA: Federated Canonical Correlation Analysis
Zhengquan Luo · Kai Fong Ernest Chong · Pengfei Wei · Changyou Chen · Peilin Zhao · Renmin Han · Chunlai Zhou · Yunlong Wang · Zhiqiang Xu
FedDuA: Doubly Adaptive Federated Learning
Shokichi Takakura · Seng Pei Liew · Satoshi Hasegawa
FIELDING: Clustered Federated Learning with Data Drift
Minghao Li · Dmitrii Avdiukhin · Rana Shahout · Nikita Ivkin · Vladimir Braverman · Minlan Yu
Filter, Augment, Forecast: Online Data Selection for Robust Time Series Forecasting
Ege Taga · Halil Gozeten · Kutay Tire · Rahul Dalvi · Reinhard Heckel · Samet Oymak
FocusViT: Faithful Explanations for Vision Transformers via Gradient-Guided Layer-Skipping
Mohsin Ali · Haider Raza · John Gan · Muhammad Haris Khan
A pure hypothesis test for inhomogeneous random graph models based on a kernelised Stein discrepancy
Anum Fatima · Gesine Reinert
From Guess2Graph: When and How Can Unreliable Experts Safely Boost Causal Discovery in Finite Samples?
Sujai Hiremath · Dominik Janzing · Philipp M. Faller · Patrick Bloebaum · Elke Kirschbaum · Shiva Kasiviswanathan · Kyra Gan
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent
Marina Sheshukova · Sergey Samsonov · Denis Belomestny · Eric Moulines · Qi-Man Shao · Zhuo-Song Zhang · Alexey Naumov
Gaussian Equivalence for Self-Attention: Asymptotic Spectral Analysis of Attention Matrix
Tomohiro Hayase · Benoit Collins · Ryo Karakida
General Weighted Averaging in Stochastic Gradient Descent: CLT and Adaptive Optimality
Ziyang Wei · Wanrong Zhu · Wei Biao Wu
Generalized and Optimal Straight-Through Estimators
James Hooper · Alexander Shekhovtsov
Generalizing Behavior via Inverse Reinforcement Learning with Closed-Form Reward Centroids
Filippo Lazzati · Alberto Maria Metelli
GeoTTER: Leveraging Local Geometry of Optimal Transport for Zero-Shot Classification
Wei-Yang Lee · Rudrasis Chakraborty · Vishnu Lokhande
GiVA: Gradient-Informed Bases for Vector-Based Adaptation
Neeraj Gangwar · Rishabh Deshmukh · Michael Shavlovsky · Hancao Li · Vivek Mittal · Lexing Ying · Nickvash Kani
Graphon Mixtures
Sevvandi Kandanaarachchi · Cheng Soon Ong
Happiness as a Measure of Fairness
Georg Pichler · Marco Romanelli · Pablo Piantanida
A Recovery Theory for Diffusion Priors: Deterministic Analysis of the Implicit Prior Algorithm
Oscar Leong · Yann Traonmilin
Harnessing the Power of Reinforcement Learning for Adaptive MCMC
Congye Wang · Matthew Fisher · Heishiro Kanagawa · Wilson Ye Chen · Chris Oates
Hellinger Multimodal Variational Autoencoders
Huyen Khanh Vo · Isabel Valera
High Effort, Low Gain: Fundamental Limits of Active Learning for Linear Dynamical Systems
Nicolas Chatzikiriakos · Kevin Jamieson · Andrea Iannelli
High-Dimensional Analysis of Bootstrap Ensemble Classifiers
Hamza Cherkaoui · Malik Tiomoko · Mohamed El Amine Seddik · Cosme Louart · Ekkehard Schnoor · Balázs Kégl
High-dimensional Learning with Noisy Labels
Aymane Firdoussi · Mohamed El Amine Seddik
High-dimensional Level Set Estimation with Trust Regions and Double Acquisition Functions
Giang Ngo · Dat Phan Trong · Dang Nguyen · Sunil Gupta
Identifiability of potentially degenerate Gaussian Mixture Models with piecewise affine mixing
Danru Xu · Sebastien Lachapelle · Sara Magliacane
Implicit Updates for Average-Reward Temporal Difference Learning
Hwanwoo Kim · Dongkyu Cho · Eric Laber
Improved Algorithms for Clustering with Noisy Distance Oracles
Pinki Pradhan · Anup Bhattacharya · Ragesh Jaiswal
Improving Coverage in Combined Prediction Sets with Weighted p-values
Gina Wong · Drew Prinster · Suchi Saria · Rama Chellappa · Anqi Liu
A Regularized Actor-Critic Algorithm for Bi-Level Reinforcement Learning
Sihan Zeng · Sujay Bhatt · Sumitra Ganesh · Alec Koppel
Improving Semantic Uncertainty Quantification in Language Models via Token-Level Temperature Scaling
Tom A. Lamb · Desi Ivanova · Philip Torr · Tim G. J. Rudner
Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphs
Zachris Björkman · Jorge Loria · Sophie Wharrie · Samuel Kaski
Influence Attributions can be Systematically Altered by Model Manipulation
Chhavi Yadav · Ruihan Wu · Kamalika Chaudhuri
Information Hidden in Gradients of Regression with Target Noise
Arash Jamshidi · Katsiaryna Haitsiukevich · Kai Puolamäki
Integrating Feature Correlation in Differential Privacy with Applications in DP-ERM
Tianyu Wang · Luhao Zhang · Rachel Cummings
Laplace approximation for Bayesian variable selection via Le Cam's one-step procedure
Tianrui Hou · Aguemon Atchade
Learning in Continuous State-Space MDPs for Network Inventory Management
Hansheng Jiang · Shunan Jiang · Zuo-Jun Shen
Learning with Incomplete Context: Linear Contextual Bandits with Pretrained Imputation
Hao Yan · Heyan Zhang · Yongyi Guo
Likelihood-Free Inference via Structured Score Matching
Haoyu Jiang · Yuexi Wang · Yun Yang
Linear Convergence of the Frank-Wolfe Algorithm over Product Polytopes
Gabriele Iommazzo · David Martínez-Rubio · Francisco Criado · Elias Wirth · Sebastian Pokutta
Go to Event Page
Reception
6:00 PM - 8:00 PM