Most Cited ICML "image autoencoders" Papers
5,975 papers found • Page 17 of 30
Conference
Stay-Positive: A Case for Ignoring Real Image Features in Fake Image Detection
Anirudh Sundara Rajan, Yong Jae Lee
AdaDecode: Accelerating LLM Decoding with Adaptive Layer Parallelism
Zhepei Wei, Wei-Lin Chen, Xinyu Zhu et al.
Computing Optimal Transport Maps and Wasserstein Barycenters Using Conditional Normalizing Flows
Gabriele Visentin, Patrick Cheridito
Beyond Atoms: Enhancing Molecular Pretrained Representations with 3D Space Modeling
Shuqi Lu, Xiaohong Ji, Bohang Zhang et al.
Diffusion Counterfactual Generation with Semantic Abduction
Rajat Rasal, Avinash Kori, Fabio De Sousa Ribeiro et al.
Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales
Ju-Seung Byun, Andrew Perrault
Stochastic Smoothed Primal-Dual Algorithms for Nonconvex Optimization with Linear Inequality Constraints
Ruichuan Huang, Jiawei Zhang, Ahmet Alacaoglu
No Soundness in the Real World: On the Challenges of the Verification of Deployed Neural Networks
Attila Szász, Balázs Bánhelyi, Mark Jelasity
Universal Approximation of Mean-Field Models via Transformers
Shiba Biswal, Karthik Elamvazhuthi, Rishi Sonthalia
LASER: Attention with Exponential Transformation
Sai Surya Duvvuri, Inderjit Dhillon
Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation
Tianyi Zhang, Junda Su, Aditya Desai et al.
Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias
Yuanzhe Hu, Kinshuk Goel, Vlad Killiakov et al.
Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models
Yuan Li, Zhengzhong Liu, Eric Xing
Ranked from Within: Ranking Large Multimodal Models Without Labels
Weijie Tu, Weijian Deng, Dylan Campbell et al.
When to Forget? Complexity Trade-offs in Machine Unlearning
Martin Van Waerebeke, Marco Lorenzi, Giovanni Neglia et al.
Direct Density Ratio Optimization: A Statistically Consistent Approach to Aligning Large Language Models
Rei Higuchi, Taiji Suzuki
PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization
Xinyi Wan, Penghui Qi, Guangxing Huang et al.
Defending LVLMs Against Vision Attacks Through Partial-Perception Supervision
Qi Zhou, Dongxia Wang, Tianlin Li et al.
Overcoming Multi-step Complexity in Multimodal Theory-of-Mind Reasoning: A Scalable Bayesian Planner
Chunhui Zhang, Zhongyu Ouyang, Kwonjoon Lee et al.
Compositional Flows for 3D Molecule and Synthesis Pathway Co-design
Tony Shen, Seonghwan Seo, Ross Irwin et al.
Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models
Jialin Zhao, Yingtao Zhang, Carlo Cannistraci
Targeted Unlearning with Single Layer Unlearning Gradient
Zikui Cai, Yaoteng Tan, M. Salman Asif
It's My Data Too: Private ML for Datasets with Multi-User Training Examples
Arun Ganesh, Ryan McKenna, Hugh B McMahan et al.
Maximum Total Correlation Reinforcement Learning
Bang You, Puze Liu, Huaping Liu et al.
An End-to-End Model for Logits-Based Large Language Models Watermarking
KA HIM WONG, Jicheng Zhou, Jiantao Zhou et al.
Logits are All We Need to Adapt Closed Models
Gaurush Hiranandani, Haolun Wu, Subhojyoti Mukherjee et al.
BARK: A Fully Bayesian Tree Kernel for Black-box Optimization
Toby Boyne, Jose Pablo Folch, Robert Lee et al.
Unbiased Evaluation of Large Language Models from a Causal Perspective
Meilin Chen, Jian Tian, Liang Ma et al.
O-MAPL: Offline Multi-agent Preference Learning
The Viet Bui, Tien Mai, Thanh Nguyen
A Variational Information Theoretic Approach to Out-of-Distribution Detection
Sudeepta Mondal, Zhuolin Jiang, Ganesh Sundaramoorthi
De-AntiFake: Rethinking the Protective Perturbations Against Voice Cloning Attacks
Wei Fan, Kejiang Chen, Chang Liu et al.
Kandinsky Conformal Prediction: Beyond Class- and Covariate-Conditional Coverage
Konstantina Bairaktari, Jiayun Wu, Steven Wu
Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach
Yang Xu, Vaneet Aggarwal
Policy Guided Tree Search for Enhanced LLM Reasoning
Yang Li
Learning In-context $n$-grams with Transformers: Sub-$n$-grams Are Near-Stationary Points
Aditya Vardhan Varre, Gizem Yüce, Nicolas Flammarion
From Local Details to Global Context: Advancing Vision-Language Models with Attention-Based Selection
Lincan Cai, Jingxuan Kang, Shuang Li et al.
Gradient-based Explanations for Deep Learning Survival Models
Sophie Hanna Langbein, Niklas Koenen, Marvin N. Wright
Tokenized Bandit for LLM Decoding and Alignment
Suho Shin, Chenghao Yang, Haifeng Xu et al.
A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control
Zilin Kang, Chenyuan Hu, Yu Luo et al.
Non-Asymptotic Length Generalization
Thomas Chen, Tengyu Ma, Zhiyuan Li
Speeding up Policy Simulation in Supply Chain RL
Vivek Farias, Joren Gijsbrechts, Aryan Khojandi et al.
Gandalf the Red: Adaptive Security for LLMs
Niklas Pfister, Václav Volhejn, Manuel Knott et al.
Rethink the Role of Deep Learning towards Large-scale Quantum Systems
Yusheng Zhao, Chi Zhang, Yuxuan Du
Pareto-Optimality, Smoothness, and Stochasticity in Learning-Augmented One-Max-Search
Ziyad Benomar, Lorenzo Croissant, Vianney Perchet et al.
EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions
Huayu Deng, Xiangming Zhu, Yunbo Wang et al.
BCE vs. CE in Deep Feature Learning
Qiufu Li, Huibin Xiao, Linlin Shen
Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces
ERIC EATON, Marcel Hussing, Michael Kearns et al.
Safety Alignment Can Be Not Superficial With Explicit Safety Signals
Jianwei Li, Jung-Eun Kim
Learning Cascade Ranking as One Network
Yunli Wang, ZhenZhang, Zhiqiang Wang et al.
Enhancing Graph Contrastive Learning for Protein Graphs from Perspective of Invariance
YUSONG WANG, Shiyin Tan, Jialun Shen et al.
Lightweight Online Adaption for Time Series Foundation Model Forecasts
Thomas Lee, William Toner, Rajkarn Singh et al.
Random Feature Representation Boosting
Nikita Zozoulenko, Thomas Cass, Lukas Gonon
Complex Wavelet Mutual Information Loss: A Multi-Scale Loss Function for Semantic Segmentation
Renhao Lu
Learning to Match Unpaired Data with Minimum Entropy Coupling
Mustapha Bounoua, Giulio Franzese, Pietro Michiardi
Holistic Physics Solver: Learning PDEs in a Unified Spectral-Physical Space
Xihang Yue, Yi Yang, Linchao Zhu
Tackling View-Dependent Semantics in 3D Language Gaussian Splatting
Jiazhong Cen, Xudong Zhou, Jiemin Fang et al.
POROver: Improving Safety and Reducing Overrefusal in Large Language Models with Overgeneration and Preference Optimization
Batuhan K. Karaman, ishmam zabir, Alon Benhaim et al.
G-Adaptivity: optimised graph-based mesh relocation for finite element methods
James Rowbottom, Georg Maierhofer, Teo Deveney et al.
Distributed Event-Based Learning via ADMM
Guner Dilsad ER, Sebastian Trimpe, Michael Muehlebach
Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets
Charita Dellaporta, Patrick O'Hara, Theodoros Damoulas
Navigating Conflicting Views: Harnessing Trust for Learning
Jueqing Lu, Wray Buntine, Yuanyuan Qi et al.
DocVXQA: Context-Aware Visual Explanations for Document Question Answering
Mohamed Ali Souibgui, Changkyu Choi, Andrey Barsky et al.
MixMin: Finding Data Mixtures via Convex Minimization
Anvith Thudi, Evianne Rovers, Yangjun Ruan et al.
GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction
Muleilan Pei, Shaoshuai Shi, Lu Zhang et al.
Modulated Diffusion: Accelerating Generative Modeling with Modulated Quantization
Weizhi Gao, Zhichao Hou, Junqi Yin et al.
Think Twice, Act Once: A Co-Evolution Framework of LLM and RL for Large-Scale Decision Making
Xu Wan, Wenyue Xu, Chao Yang et al.
Lightweight Protocols for Distributed Private Quantile Estimation
Anders Aamand, Fabrizio Boninsegna, Abigail Gentle et al.
Accurate and Efficient World Modeling with Masked Latent Transformers
Maxime Burchi, Radu Timofte
GCAL: Adapting Graph Models to Evolving Domain Shifts
Ziyue Qiao, Qianyi Cai, Hao Dong et al.
Position: Enough of Scaling LLMs! Lets Focus on Downscaling
Yash Goel, Ayan Sengupta, Tanmoy Chakraborty
Avoiding Catastrophe in Online Learning by Asking for Help
Benjamin Plaut, Hanlin Zhu, Stuart Russell
Robust Universal Adversarial Perturbations
Changming Xu, Gagandeep Singh
Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error
Haoran Li, Zicheng Zhang, Wang Luo et al.
Adversarial Attacks on Combinatorial Multi-Armed Bandits
Rishab Balasubramanian, Jiawei Li, Tadepalli Prasad et al.
PruNeRF: Segment-Centric Dataset Pruning via 3D Spatial Consistency
Yeonsung Jung, Heecheol Yun, Joonhyung Park et al.
Simplicity Bias via Global Convergence of Sharpness Minimization
Khashayar Gatmiry, Zhiyuan Li, Sashank J. Reddi et al.
A3S: A General Active Clustering Method with Pairwise Constraints
Xun Deng, Junlong Liu, Han Zhong et al.
Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement Learning
Donghu Kim, Hojoon Lee, Kyungmin Lee et al.
Achieving Margin Maximization Exponentially Fast via Progressive Norm Rescaling
Mingze Wang, Zeping Min, Lei Wu
No Dimensional Sampling Coresets for Classification
Meysam Alishahi, Jeff Phillips
A Rate-Distortion View of Uncertainty Quantification
Ifigeneia Apostolopoulou, Benjamin Eysenbach, Frank Nielsen et al.
Monotone Individual Fairness
Yahav Bechavod
Naive Bayes Classifiers over Missing Data: Decision and Poisoning
Song Bian, Xiating Ouyang, ZHIWEI FAN et al.
Biharmonic Distance of Graphs and its Higher-Order Variants: Theoretical Properties with Applications to Centrality and Clustering
Mitchell Black, Lucy Lin, Weng-Keen Wong et al.
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples
Dake Bu, Wei Huang, Taiji Suzuki et al.
Feasibility Consistent Representation Learning for Safe Reinforcement Learning
Zhepeng Cen, Yihang Yao, Zuxin Liu et al.
Feature Importance Disparities for Data Bias Investigations
Peter Chang, Leor Fishman, Seth Neel
Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting
Serina Chang, Frederic Koehler, Zhaonan Qu et al.
MaSS: Multi-attribute Selective Suppression for Utility-preserving Data Transformation from an Information-theoretic Perspective
Yizhuo Chen, Chun-Fu (Richard) Chen, Hsiang Hsu et al.
High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization
Yihang Chen, Fanghui Liu, Taiji Suzuki et al.
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes
Yingyi Chen, Qinghua Tao, Francesco Tonin et al.
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
Yu Chen, XiangCheng Zhang, Siwei Wang et al.
Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameters
Brian Cho, Yaroslav Mukhin, Kyra Gan et al.
Safe Reinforcement Learning using Finite-Horizon Gradient-based Estimation
Juntao Dai, Yaodong Yang, Qian Zheng et al.
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction
Riccardo De Santi, Federico Arangath Joseph, Noah Liniger et al.
An Unsupervised Approach for Periodic Source Detection in Time Series
Berken Utku Demirel, Christian Holz
Trust Regions for Explanations via Black-Box Probabilistic Certification
Amit Dhurandhar, Swagatam Haldar, Dennis Wei et al.
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Benjamin Eyre, Elliot Creager, David Madras et al.
Fast White-Box Adversarial Streaming Without a Random Oracle
Ying Feng, Aayush Jain, David Woodruff
Explaining Probabilistic Models with Distributional Values
Luca Franceschi, Michele Donini, Cedric Archambeau et al.
Testing the Feasibility of Linear Programs with Bandit Feedback
Aditya Gangrade, Aditya Gopalan, Venkatesh Saligrama et al.
Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy
Riqiang Gao, Florin-Cristian Ghesu, Simon Arberet et al.
DMTG: One-Shot Differentiable Multi-Task Grouping
Yuan Gao, Shuguo Jiang, Moran Li et al.
Decoupling Learning and Decision-Making: Breaking the $\mathcal{O}(\sqrt{T})$ Barrier in Online Resource Allocation with First-Order Methods
Wenzhi Gao, Chunlin Sun, Chenyu Xue et al.
Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model
Chenyin Gao, ZHIMING ZHANG, Shu Yang
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates
Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
MAGNOLIA: Matching Algorithms via GNNs for Online Value-to-go Approximation
Alexandre Hayderi, Amin Saberi, Ellen Vitercik et al.
Quantum Algorithm for Online Exp-concave Optimization
Jianhao He, Chengchang Liu, Xutong Liu et al.
Removing Spurious Concepts from Neural Network Representations via Joint Subspace Estimation
Floris Holstege, Bram Wouters, Noud van Giersbergen et al.
Loss Shaping Constraints for Long-Term Time Series Forecasting
Ignacio Hounie, Javier Porras-Valenzuela, Alejandro Ribeiro
An Information Theoretic Approach to Interaction-Grounded Learning
Xiaoyan Hu, Farzan Farnia, Ho-fung Leung
Faster Adaptive Decentralized Learning Algorithms
Feihu Huang, jianyu zhao
Faster Sampling via Stochastic Gradient Proximal Sampler
Xunpeng Huang, Difan Zou, Hanze Dong et al.
Finite Volume Features, Global Geometry Representations, and Residual Training for Deep Learning-based CFD Simulation
Loh S.E. Jessica, Naheed Anjum Arafat, Wei Xian Lim et al.
FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data
Shusen Jing, Anlan Yu, Shuai Zhang et al.
Fair Classification with Partial Feedback: An Exploration-Based Data Collection Approach
Vijay Keswani, Anay Mehrotra, L. Elisa Celis
Attribute Based Interpretable Evaluation Metrics for Generative Models
Dongkyun Kim, Mingi Kwon, Youngjung Uh
Polynomial-based Self-Attention for Table Representation Learning
Jayoung Kim, Yehjin Shin, Jeongwhan Choi et al.
PcLast: Discovering Plannable Continuous Latent States
ANURAG KOUL, Shivakanth Sujit, Shaoru Chen et al.
Improving Instruction Following in Language Models through Proxy-Based Uncertainty Estimation
JoonHo Lee, Jae Oh Woo, Juree Seok et al.
Learning Causal Domain-Invariant Temporal Dynamics for Few-Shot Action Recognition
Yuke Li, Guangyi Chen, Ben Abramowitz et al.
Completing Visual Objects via Bridging Generation and Segmentation
Xiang Li, Yinpeng Chen, Chung-Ching Lin et al.
A Differentiable Partially Observable Generalized Linear Model with Forward-Backward Message Passing
Chengrui Li, Weihan Li, Yule Wang et al.
Harnessing Neural Unit Dynamics for Effective and Scalable Class-Incremental Learning
Depeng Li, Tianqi Wang, Junwei Chen et al.
A Single-Loop Robust Policy Gradient Method for Robust Markov Decision Processes
Zhenwei Lin, Chenyu Xue, Qi Deng et al.
DIDI: Diffusion-Guided Diversity for Offline Behavioral Generation
Jinxin Liu, Xinghong Guo, Zifeng Zhuang et al.
Floating Anchor Diffusion Model for Multi-motif Scaffolding
Ke Liu, Weian Mao, Shuaike Shen et al.
Causal Discovery via Conditional Independence Testing with Proxy Variables
Mingzhou Liu, Xinwei Sun, YU QIAO et al.
Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point
David Martínez-Rubio, Christophe Roux, Sebastian Pokutta
Deep Fusion: Efficient Network Training via Pre-trained Initializations
Hanna Mazzawi, Xavi Gonzalvo, Michael Wunder et al.
PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect
Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh et al.
Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces
Brahma Pavse, Matthew Zurek, Yudong Chen et al.
Graph Automorphism Group Equivariant Neural Networks
Edward Pearce-Crump, William J. Knottenbelt
Embarrassingly Parallel GFlowNets
Tiago Silva, Luiz Carvalho, Amauri Souza et al.
DFA-RAG: Conversational Semantic Router for Large Language Model with Definite Finite Automaton
Yiyou Sun, Junjie Hu, Wei Cheng et al.
Fourier Controller Networks for Real-Time Decision-Making in Embodied Learning
Hengkai Tan, LIU SONGMING, Kai Ma et al.
Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval
Qiwei Tian, Chenhao Lin, Zhengyu Zhao et al.
MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence
Hongduan Tian, Feng Liu, Tongliang Liu et al.
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring
Taira Tsuchiya, Shinji Ito, Junya Honda
Improving Antibody Humanness Prediction using Patent Data
Talip Ucar, Aubin Ramon, Dino Oglic et al.
Highway Value Iteration Networks
Yuhui Wang, Weida Li, Francesco Faccio et al.
Exact Soft Analytical Side-Channel Attacks using Tractable Circuits
Thomas Wedenig, Rishub Nagpal, Gaëtan Cassiers et al.
DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation
Yinjun Wu, Mayank Keoliya, Kan Chen et al.
AND: Audio Network Dissection for Interpreting Deep Acoustic Models
Tung-Yu Wu, Yu-Xiang Lin, Lily Weng
Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value
Young Wu, Jeremy McMahan, Yiding Chen et al.
Automating the Selection of Proxy Variables of Unmeasured Confounders
Feng Xie, Zhengming Chen, Shanshan Luo et al.
Pricing with Contextual Elasticity and Heteroscedastic Valuation
Jianyu Xu, Yu-Xiang Wang
Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation
Yuchen Yang, Yingdong Shi, Cheems Wang et al.
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms
Ming Yang, Xiyuan Wei, Tianbao Yang et al.
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning
Shuai Zhang, Heshan Fernando, Miao Liu et al.
Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation
Wanpeng Zhang, Yilin Li, Boyu Yang et al.
Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
Lei Zhao, Mengdi Wang, Yu Bai
Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning
Tianchen Zhou, Hairi, Haibo Yang et al.
Online Isolation Forest
Filippo Leveni, Guilherme Weigert Cassales, Bernhard Pfahringer et al.
REMEDI: Corrective Transformations for Improved Neural Entropy Estimation
Viktor Nilsson, Anirban Samaddar, Sandeep Madireddy et al.
On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning
Jeongheon Oh, Kibok Lee
Lookbehind-SAM: k steps back, 1 step forward
Gonçalo Mordido, Pranshu Malviya, Aristide Baratin et al.
Mathematical Framework for Online Social Media Auditing
Wasim Huleihel, Yehonathan Refael
MC-GTA: Metric-Constrained Model-Based Clustering using Goodness-of-fit Tests with Autocorrelations
Zhangyu Wang, Gengchen Mai, Krzysztof Janowicz et al.
A Neural-Preconditioned Poisson Solver for Mixed Dirichlet and Neumann Boundary Conditions
Kai Weixian Lan, Elias Gueidon, Ayano Kaneda et al.
Hybrid Neural Representations for Spherical Data
Hyomin Kim, Yunhui Jang, Jaeho Lee et al.
On Stronger Computational Separations Between Multimodal and Unimodal Machine Learning
Ari Karchmer
Sparse Dimensionality Reduction Revisited
Mikael Møller Høgsgaard, Lior Kamma, Kasper Green Larsen et al.
A New Theoretical Perspective on Data Heterogeneity in Federated Optimization
Jiayi Wang, Shiqiang Wang, Rong-Rong Chen et al.
Subhomogeneous Deep Equilibrium Models
Pietro Sittoni, Francesco Tudisco
Evaluating Model Bias Requires Characterizing its Mistakes
Isabela Albuquerque, Jessica Schrouff, David Warde-Farley et al.
Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method
Qinghua Tao, Francesco Tonin, Alex Lambert et al.
Saliency strikes back: How filtering out high frequencies improves white-box explanations
Sabine Muzellec, Thomas FEL, Victor Boutin et al.
SFC: Achieve Accurate Fast Convolution under Low-precision Arithmetic
Liulu He, yufei zhao, rui gao et al.
Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions
Harrie Oosterhuis, Lijun Lyu, Avishek Anand
Joint Composite Latent Space Bayesian Optimization
Natalie Maus, Zhiyuan Jerry Lin, Maximilian Balandat et al.
Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training
Tehila Dahan, Kfir Levy
Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning
Takayuki Osa, Tatsuya Harada
ELF: Encoding Speaker-Specific Latent Speech Feature for Speech Synthesis
Jungil Kong, Junmo Lee, Jeongmin Kim et al.
GFlowNet Training by Policy Gradients
Puhua Niu, Shili Wu, Mingzhou Fan et al.
Transitional Uncertainty with Layered Intermediate Predictions
Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib
Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching
Rico Angell, Andrew McCallum
Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency
Alan Amin, Andrew Wilson
GATE: How to Keep Out Intrusive Neighbors
Nimrah Mustafa, Rebekka Burkholz
On Gradient-like Explanation under a Black-box Setting: When Black-box Explanations Become as Good as White-box
Yi Cai, Gerhard Wunder
Risk Aware Benchmarking of Large Language Models
Apoorva Nitsure, Youssef Mroueh, Mattia Rigotti et al.
Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples
Andrew C. Cullen, Shijie Liu, Paul Montague et al.
Disentanglement Learning via Topology
Nikita Balabin, Daria Voronkova, Ilya Trofimov et al.
Efficient Mixture Learning in Black-Box Variational Inference
Alexandra Hotti, Oskar Kviman, Ricky Molén et al.
Indirectly Parameterized Concrete Autoencoders
Alfred Nilsson, Klas Wijk, Sai bharath chandra Gutha et al.
Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization
Hao Wang, Kaifeng Yang, Michael Affenzeller
Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
Yvonne Zhou, Mingyu Liang, Ivan Brugere et al.
Quality-Diversity with Limited Resources
Ren-Jian Wang, Ke Xue, Cong Guan et al.
Directly Denoising Diffusion Models
Dan Zhang, Jingjing Wang, Feng Luo
Integrated Hardware Architecture and Device Placement Search
Irene Wang, Jakub Tarnawski, Amar Phanishayee et al.
Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization
Sam Reifenstein, Timothee Leleu, Yoshihisa Yamamoto
Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities
Golnoosh Farnadi, Mohammad Havaei, Negar Rostamzadeh
From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions
Johannes Resin
Learning to Infer Generative Template Programs for Visual Concepts
R. Kenny Jones, Siddhartha Chaudhuri, Daniel Ritchie
Unveiling the Dynamics of Information Interplay in Supervised Learning
Kun Song, Zhiquan Tan, Bochao Zou et al.
Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity
Hyunki Seong, Hyunchul Shim
CF-OPT: Counterfactual Explanations for Structured Prediction
Germain Vivier-Ardisson, Alexandre Forel, Axel Parmentier et al.
Clustering via Self-Supervised Diffusion
Roy Uziel, Irit Chelly, Oren Freifeld et al.
Distributed Bilevel Optimization with Communication Compression
Yutong He, Jie Hu, Xinmeng Huang et al.
Can We Predict Performance of Large Models across Vision-Language Tasks?
Qinyu Zhao, Ming Xu, Kartik Gupta et al.