Carnegie Mellon University at NeurIPS 2024

Stylus: Automatic Adapter Selection for Diffusion Models

Authors: Michael Luo, Justin Wong, Brandon Trabucco, Yanping Huang, Joseph Gonzalez, Zhifeng Chen, Ruslan Salakhutdinov, Ion Stoica

This paper explores an alternative approach to generating high-fidelity, customized images at reduced costs using fine-tuned adapters instead of simply scaling base models with additional data or parameters. Over time, the open-source community has created a large collection of more than 100,000 adapters—small modules that fine-tune base models for specific tasks. However, many of these adapters are highly customized and lack clear descriptions, making them challenging to use effectively. To address this, the paper introduces Stylus, a system designed to match prompts with relevant adapters and automatically compose them for better image generation. Building on recent research showing the benefits of combining multiple adapters, Stylus uses a three-stage process: summarizing adapters with improved descriptions and embeddings, retrieving relevant adapters, and composing adapters based on prompt keywords to ensure a strong match. The authors also present StylusDocs, a curated dataset of 75,000 adapters with pre-computed embeddings, for evaluation. Testing Stylus on popular Stable Diffusion checkpoints shows that it achieves better CLIP/FID Pareto efficiency and is twice as preferred by human and multimodal evaluators compared to the base model.

The Sample-Communication Complexity Trade-off in Federated Q-Learning

Authors: Sudeep Salgia, Yuejie Chi

This work examines the problem of Federated Q-learning, where multiple agents collaboratively learn the optimal Q-function for an unknown infinite-horizon Markov Decision Process with finite state and action spaces. The focus is on understanding the trade-off between sample complexity (the number of data samples needed for learning) and communication complexity (the amount of data exchanged between agents) for intermittent communication algorithms, a commonly used approach in federated settings.

The authors first establish a fundamental limitation: any Federated Q-learning algorithm that achieves linear speedup in sample complexity relative to the number of agents must incur a communication cost of at least Ω(1/1−γ), where γ is the discount factor. They then introduce a new algorithm, Fed-DVR-Q, which is the first to achieve both optimal sample complexity and communication complexity simultaneously. Together, these results provide a comprehensive understanding of the trade-offs between sample and communication efficiency in Federated Q-learning.

Aligner Encoders: Self-Attention Transformers Can Be Self-Transducers

Authors: Adam Stooke, Rohit Prabhavalkar, Khe Sim, Pedro Moreno Mengibar

The paper introduces a new transformer-based approach to automatic speech recognition (ASR) that simplifies the alignment process between audio input and text output. Unlike traditional models, the encoder itself aligns audio information internally, reducing the complexity of decoding. The proposed “Aligner-Encoder” model combines efficient training techniques and a lightweight decoder, resulting in significantly faster performance while maintaining competitive accuracy. Notably, the alignment process is evident in the self-attention weights of the model, showcasing its ability to handle the task efficiently.

Approximating the Top Eigenvector in Random Order Streams

Authors: Praneeth Kacham, David Woodruff

This work focuses on streaming algorithms for approximating the top eigenvector of a matrix when its rows are presented in a random order. The authors introduce a new algorithm that works efficiently when there is a sufficient gap between the largest and second-largest eigenvalues of the matrix. Their approach uses a small amount of memory, depending on the number of “heavy rows” (rows with large norms), and produces highly accurate results. They also show that using this heavy-row-based parameterization is necessary for achieving high accuracy and improve on prior methods by reducing the gap requirement for random-order streams, though their method assumes the rows are presented in a random order rather than any order.

Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning

Authors: Shentong Mo, Peter Tong

Recent advancements in unsupervised visual representation learning have highlighted the Joint-Embedding Predictive Architecture (JEPA) as an effective method for extracting visual features from unlabeled images using masking strategies. However, JEPA faces two key challenges: its reliance on Exponential Moving Average (EMA) fails to prevent model collapse, and its predictions struggle to accurately capture the average representation of image patches. To address these issues, this work introduces C-JEPA, a new framework that combines JEPA with a variance-invariance-covariance regularization strategy called VICReg. This approach improves stability, prevents collapse, and ensures better learning of consistent representations. Experiments show that C-JEPA achieves faster convergence and higher performance on standard benchmarks when pre-trained on ImageNet-1K.

CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics

Authors: Jiawei Gao, Ziqin Wang, Zeqi Xiao, Jingbo Wang, Tai Wang, Jinkun Cao, Xiaolin Hu, Si Liu, Jifeng Dai, Jiangmiao Pang

This work addresses the challenge of enabling humanoid robots to collaborate on tasks like moving large furniture, which require coordination between multiple robots. Existing methods struggle due to a lack of motion capture data for multi-humanoid collaboration and the inefficiency of training multiple agents together. To overcome this, the authors introduce Cooperative Human-Object Interaction (CooHOI), a framework that uses a two-phase learning approach: first, individual humanoids learn object interaction skills from human motion data, and then they learn to work together using multi-agent reinforcement learning. By focusing on shared object dynamics and decentralized execution, the robots achieve coordination through implicit communication. Unlike previous tracking-based methods, CooHOI is efficient, does not rely on multi-humanoid motion data, and can easily scale to more participants and diverse object types.

DiffTOP: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learning

Authors: Weikang Wan, Ziyu Wang, Yufei Wang, Zackory Erickson, David Held

This paper presents DiffTORI, a framework that uses differentiable trajectory optimization as a policy representation for reinforcement and imitation learning. Trajectory optimization, a common tool in control, is parameterized by a cost and a dynamics function, and recent advances now allow gradients of the loss to be computed with respect to these parameters. This enables DiffTORI to learn cost and dynamics functions end-to-end, addressing the “objective mismatch” in previous model-based RL methods by aligning the dynamics model with task performance. Benchmarking on robotic manipulation tasks with high-dimensional sensory inputs, DiffTORI demonstrates superior performance over prior methods, including feedforward policies, energy-based models, and diffusion models, across a wide range of reinforcement and imitation learning tasks.

Don’t Look Twice: Faster Video Transformers with Run-Length Tokenization

Authors: Rohan Choudhury, Guanglei Zhu, Sihan Liu, Koichiro Niinuma, Kris Kitani, László Jeni

Video transformers are notoriously slow to train due to the large number of input tokens, many of which are repeated across frames. Existing methods to remove redundant tokens often introduce significant overhead or require dataset-specific tuning, limiting their practicality. This work introduces Run-Length Tokenization (RLT), a simple and efficient method inspired by run-length encoding, which identifies and removes repeated patches in video frames before inference. By replacing repeated patches with a single token and a positional encoding to reflect its duration, RLT reduces redundancy without requiring tuning or adding significant computational cost. It accelerates training by 30%, maintains baseline performance, and increases throughput by 35% with minimal accuracy loss, while reducing token counts by up to 80% on longer videos.

ICAL: Continual Learning of Multimodal Agents by Transforming Trajectories into Actionable Insights

Authors: Gabriel Sarch, Lawrence Jang, Michael Tarr, William Cohen, Kenneth Marino, Katerina Fragkiadaki

This work introduces In-Context Abstraction Learning (ICAL), a method that enables large-scale language and vision-language models (LLMs and VLMs) to generate high-quality task examples from imperfect demonstrations. ICAL uses a vision-language model to analyze and improve inefficient task trajectories by abstracting key elements like causal relationships, object states, and temporal goals, with iterative refinement through human feedback. These improved examples, when used as prompts, enhance decision-making and reduce reliance on human input over time, making the system more efficient. ICAL outperforms state-of-the-art models in tasks like instruction following, web navigation, and action forecasting, demonstrating its ability to improve performance without heavy manual prompt engineering.

Is Your LiDAR Placement Optimized for 3D Scene Understanding?

Authors: Ye Li, Lingdong Kong, Hanjiang Hu, Xiaohao Xu, Xiaonan Huang

This work focuses on improving the reliability of driving perception systems under challenging and unexpected conditions, particularly with multi-LiDAR setups. Most existing datasets rely on single-LiDAR systems and are collected in ideal conditions, making them insufficient for real-world applications. To address this, the authors introduce Place3D, a comprehensive pipeline that optimizes LiDAR placement, generates data, and evaluates performance. Their approach includes three key contributions: a new metric called the Surrogate Metric of the Semantic Occupancy Grids (M-SOG) for assessing multi-LiDAR configurations, an optimization strategy to improve LiDAR placements based on M-SOG, and the creation of a 280,000-frame dataset capturing both clean and adverse conditions. Experiments show that their optimized placements lead to significant improvements in tasks like semantic segmentation and 3D object detection, even in challenging scenarios with harsh weather or sensor failures.

Learn To be Efficient: Build Structured Sparsity in Large Language Models

Authors: Haizhong Zheng, Xiaoyan Bai, Xueshen Liu, Zhuoqing Morley Mao, Beidi Chen, Fan Lai, Atul Prakash

The paper explores how Large Language Models (LLMs), known for their impressive capabilities but high computational costs, can be made more efficient. It highlights that while activation sparsity—where only some model parameters are used during inference—naturally occurs, current methods fail to maximize its potential during training. The authors propose a novel training algorithm, Learn-To-be-Efficient (LTE), that encourages LLMs to activate fewer neurons, striking a balance between efficiency and performance. Their approach, applicable to models beyond traditional ReLU-based ones, demonstrates improved results across various tasks and reduces inference latency by 25% for LLaMA2-7B at 50% sparsity.

Learning Social Welfare Functions

Authors: Kanad Pardeshi, Itai Shapira, Ariel Procaccia, Aarti Singh

This work explores whether it is possible to understand or replicate a policymaker’s reasoning by analyzing their past decisions. The problem is framed as learning social welfare functions from the family of power mean functions. Two learning tasks are considered: one uses utility vectors of actions and their corresponding social welfare values, while the other uses pairwise comparisons of welfares for different utility vectors. The authors demonstrate that power mean functions can be learned efficiently, even when the social welfare data is noisy. They also propose practical algorithms for these tasks and evaluate their effectiveness.

Metric Transforms and Low Rank Representations of Kernels

Authors: Timothy Chu, Josh Alman, Gary L. Miller, Shyam Narayanan, Mark Sellke, Zhao Song

The authors introduce a linear-algebraic tool based on group representation theory to solve three important problems in machine learning. First, they investigate fast attention algorithms for large language models and prove that only low-degree polynomials can produce the low-rank matrices required for subquadratic attention, thereby showing that polynomial-based approximations are essential. Second, they extend the classification of positive definite kernels from Euclidean distances to Manhattan distances, offering a broader foundation for kernel methods. Finally, they classify all functions that transform Manhattan distances into Manhattan distances, generalizing earlier work on Euclidean metrics and introducing new results about stable-rank-preserving functions with potential applications in algorithm design.

Sample-Efficient Private Learning of Mixtures of Gaussians

Authors: Hassan Ashtiani, Mahbod Majid, Shyam Narayanan

This work examines the problem of learning mixtures of Gaussians while ensuring approximate differential privacy. The authors demonstrate that it is possible to learn a mixture of k arbitrary d-dimensional Gaussians with significantly fewer samples than previous methods, achieving optimal performance when the dimensionality d is much larger than the number of components k. For univariate Gaussians, they establish the first optimal bound, showing that the sample complexity scales linearly with k, improving upon earlier methods that required a quadratic dependence on k. Their approach leverages advanced techniques, including the inverse sensitivity mechanism, sample compression for distributions, and volume bounding methods, to achieve these results.

Sequoia: Scalable and Robust Speculative Decoding

Authors: Zhuoming Chen, Avner May, Ruslan Svirschevski, Yu-hsun Huang, Max Ryabinin, Zhihao Jia, Beidi Chen

As the use of large language models (LLMs) increases, serving them quickly and efficiently has become a critical challenge. Speculative decoding offers a promising solution, but existing methods struggle to scale with larger workloads or adapt to different settings. This paper introduces Sequoia, a scalable and robust algorithm for speculative decoding. By employing a dynamic programming algorithm, Sequoia optimizes the tree structure for speculated tokens, improving scalability. It also introduces a novel sampling and verification method that enhances robustness across various decoding temperatures. Sequoia achieves significant speedups, improving decoding speed on models like Llama2-7B, Llama2-13B, and Vicuna-33B by up to 4.04x, 3.73x, and 2.27x, respectively, and reducing per-token latency for Llama3-70B-Instruct on a single GPU by 9.5x compared to DeepSpeed-Zero-Inference.

Slight Corruption in Pre-training Data Makes Better Diffusion Models

Authors: Hao Chen, Yujin Han, Diganta Misra, Xiang Li, Kai Hu, Difan Zou, Masashi Sugiyama, Jindong Wang, Bhiksha Raj

Diffusion models have demonstrated impressive capabilities in generating high-quality images, audio, and videos, largely due to pre-training on large datasets that pair data with conditions, such as image-text or image-class pairs. However, even with careful filtering, these datasets often include corrupted pairs where the conditions do not accurately represent the data. This paper provides the first comprehensive study of how such corruption affects diffusion model training. By synthetically corrupting datasets like ImageNet-1K and CC3M, the authors show that slight corruption in pre-training data can surprisingly enhance image quality, diversity, and fidelity across various models. They also provide theoretical insights, demonstrating that slight condition corruption increases entropy and reduces the 2-Wasserstein distance to the ground truth distribution. Building on these findings, the authors propose a method called condition embedding perturbations, which improves diffusion model performance during both pre-training and downstream tasks, offering new insights into the training process.

Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models

Authors: Sanae Lotfi, Yilun Kuang, Marc Finzi, Brandon Amos, Micah Goldblum, Andrew Wilson

Large language models (LLMs) with billions of parameters are highly effective at predicting the next token in a sequence. While recent research has computed generalization bounds for these models using compression-based techniques, these bounds often fail to apply to billion-parameter models or rely on restrictive methods that produce low-quality text. Existing approaches also tie the tightness of bounds to the number of independent documents in the training set, ignoring the larger number of dependent tokens, which could offer better bounds. This work uses properties of martingales to derive generalization bounds that leverage the vast number of tokens in LLM training sets. By using more flexible compression techniques like Monarch matrices, Kronecker factorizations, and post-training quantization, the authors achieve meaningful generalization bounds for large-scale models, including LLaMA2-70B, marking the first successful bounds for practical, high-quality text-generating models.

Causal Inference in the Closed-Loop: Marginal Structural Models for Sequential Excursion Effects

Authors: Alexander Levis, Gabriel Loewinger, Francisco Pereira

Causal Temporal Representation Learning with Nonstationary Sparse Transition

Authors: Xiangchen Song, Zijian Li, Guangyi Chen, Yujia Zheng, Yewen Fan, Xinshuai Dong, Kun Zhang

Discovery of the Hidden World with Large Language Models

Authors: Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang

From Causal to Concept-Based Representation Learning

Authors: Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar

Identifying General Mechanism Shifts in Linear Causal Representations

Authors: Tianyu Chen, Kevin Bello, Francesco Locatello, Bryon Aragam, Pradeep Ravikumar

Identifying Selections for Unsupervised Subtask Discovery

Authors: Yiwen Qiu, Yujia Zheng, Kun Zhang

Interventional Causal Discovery in a Mixture of DAGs

Authors: Burak Varıcı, Dmitriy Katz, Dennis Wei, Prasanna Sattigeri, Ali Tajer

Learning Discrete Concepts in Latent Hierarchical Models

Authors: Lingjing Kong, Guangyi Chen, Biwei Huang, Eric Xing, Yuejie Chi, Kun Zhang

Learning Discrete Latent Variable Structures with Tensor Rank Conditions

Authors: Zhengming Chen, Ruichu Cai, Feng Xie, Jie Qiao, Anpeng Wu, Zijian Li, Zhifeng Hao, Kun Zhang

Likelihood-based differentiable structure learning

Authors: Chang Deng, Kevin Bello, Pradeep Ravikumar, Bryon Aragam

Linear Causal Representation Learning from Unknown Multi-node Interventions

Authors: Burak Varıcı, Emre Acartürk, Karthikeyan Shanmugam, Ali Tajer

Mutli-Armed Bandits with Network Interference

Authors: Abhineet Agarwal, Anish Agarwal, Lorenzo Masoero, Justin Whitehouse

Natural Counterfactuals With Necessary Backtracking

Authors: Guang-yuan Hao, Jiji Zhang, Biwei Huang, Hao Wang, Kun Zhang

On Causal Discovery in the Presence of Deterministic Relations

Authors: Loka Li, Haoyue Dai, Hanin Al Ghothani, Biwei Huang, Jiji Zhang, Shahar Harel, Isaac Bentwich, Guangyi Chen, Kun Zhang

Sample Complexity of Interventional Causal Representation Learning

Authors: Emre Acartürk, Burak Varıcı, Karthikeyan Shanmugam, Ali Tajer

Protein-Nucleic Acid Complex Modeling with Frame Averaging Transformer

Authors: Tinglin Huang, Zhenqiao Song, Rex Ying, Wengong Jin

Adaptive Visual Scene Understanding: Incremental Scene Graph Generation

Authors: Naitik Khandelwal, Xiao Liu, Mengmi Zhang

Crafting Hierarchical Strand-based Hair Geometry with Frequency-decomposed Representative Guide Curves

Authors: Yunlu Chen, Francisco Vicente Carrasco, Christian Häne, Giljoo Nam, Jean-charles Bazin, Fernando D De La Torre

DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features

Authors: Letian Wang, Seung Wook Kim, Jiawei Yang, Cunjun Yu, Boris Ivanovic, Steven Waslander, Yue Wang, Sanja Fidler, Marco Pavone, Peter Karkus

EAGLE: Efficient Adaptive Geometry-based Learning in Cross-view Understanding

Authors: Thanh-dat Truong, Utsav Prabhu, Dongyi Wang, Bhiksha Raj, Susan Gauch, Jeyamkondan Subbiah, Khoa Luu

Hamba: Single-view 3D Hand Reconstruction with Graph-guided Bi-Scanning Mamba

Authors: Haoye Dong, Aviral Chharia, Wenbo Gou, Francisco Vicente Carrasco, Fernando D De La Torre

Lexicon3D: Probing Visual Encoding Models for Complex 3D Scene Understanding

Authors: Yunze Man, Shuhong Zheng, Zhipeng Bao, Martial Hebert, Liangyan Gui, Yu-xiong Wang

MGF: Mixed Gaussian Flow for Diverse Trajectory Prediction

Authors: Jiahe Chen, Jinkun Cao, Dahua Lin, Kris Kitani, Jiangmiao Pang

Metric from Human: Zero-shot Monocular Metric Depth Estimation via Test-time Adaptation

Authors: Yizhou Zhao, Hengwei Bian, Kaihua Chen, Pengliang Ji, Liao Qu, Shao-yu Lin, Weichen Yu, Haoran Li, Hao Chen, Jun Shen, Bhiksha Raj, Min Xu

Sparse-view Pose Estimation and Reconstruction via Analysis by Generative Synthesis

Authors: Qitao Zhao, Shubham Tulsiani

Vision Foundation Model Enables Generalizable Object Pose Estimation

Authors: Kai Chen, Yiyao Ma, Xingyu Lin, Stephen James, Jianshu Zhou, Yun-hui Liu, Pieter Abbeel, Dou Qi

Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks

Authors: Victor Boutin, Rishav Mukherji, Aditya Agrawal, Sabine Muzellec, Thomas Fel, Thomas Serre, Rufin Vanrullen

4Real: Towards Photorealistic 4D Scene Generation via Video Diffusion Models

Authors: Heng Yu, Chaoyang Wang, Peiye Zhuang, Willi Menapace, Aliaksandr Siarohin, Junli Cao, László Jeni, Sergey Tulyakov, Hsin-ying Lee

A Versatile Diffusion Transformer with Mixture of Noise Levels for Audiovisual Generation

Authors: Gwanghyun Kim, Alonso Martinez, Yu-chuan Su, Brendan Jou, Jose Lezama, Agrim Gupta, Lijun Yu, Lu Jiang, Aren Jansen, Jacob Walker, Se Young Chun, Krishna Somandepalli

DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular Videos

Authors: Wen-hsuan Chu, Lei Ke, Katerina Fragkiadaki

HENASY: Learning to Assemble Scene-Entities for Interpretable Egocentric Video-Language Model

Authors: Khoa Vo, Thinh Phan, Kashu Yamazaki, Minh Tran, Ngan Le

Data Distribution Valuation

Authors: Xinyi Xu, Shuaiqi Wang, Chuan Sheng Foo, Bryan Kian Hsiang Low, Giulia Fanti

Visual Data Diagnosis and Debiasing with Concept Graphs

Authors: Rwiddhi Chakraborty, Yinong O Wang, Jialu Gao, Runkai Zheng, Cheng Zhang, Fernando D De La Torre

Turning Indirect Knowledge into Direct Demonstrations for Computer Agents at Scale

Authors: Tianyue Ou, Frank F. Xu, Aman Madaan, Jiarui Liu, Robert Lo, Abishek Sridhar, Sudipta Sengupta, Dan Roth, Graham Neubig, Shuyan Zhou

MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models

Authors: Zichun Yu, Spandan Das, Chenyan Xiong

Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios

Authors: Shantanu Jaiswal, Debaditya Roy, Basura Fernando, Cheston Tan

On the Inductive Bias of Stacking Towards Improving Reasoning

Authors: Nikunj Saunshi, Stefani Karp, Shankar Krishnan, Sobhan Miryoosefi, Sashank Jakkam Reddi, Sanjiv Kumar

RGMDT: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space

Authors: Jingdi Chen, Hanhan Zhou, Yongsheng Mei, Carlee Joe-wong, Nathaniel Bastian, Tian Lan

Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding

Authors: Zhenyu Zhang, Runjin Chen, Shiwei Liu, Zhewei Yao, Olatunji Ruwase, Beidi Chen, Xiaoxia Wu, Zhangyang “atlas” Wang

Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length

Authors: Xuezhe Ma, Xiaomeng Yang, Wenhan Xiong, Beidi Chen, Lili Yu, Hao Zhang, Jonathan May, Luke Zettlemoyer, Omer Levy, Chunting Zhou

Towards Understanding the Mechanisms of Associative Memory in Transformers

Authors: Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam

FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations

Authors: Ziyao Wang, Zheyu Shen, Yexiao He, Guoheng Sun, Hongyi Wang, Lingjuan Lyu, Ang Li

HORSE: Hierarchical Representation for Large-Scale Neural Subset Selection

Authors: Binghui Xie, Yixuan Wang, Yongqiang Chen, Kaiwen Zhou, Yu Li, Wei Meng, James Cheng

Hydra: Bidirectional State Space Models Through Generalized Matrix Mixers

Authors: Sukjun Hwang, Aakash Sunil Lahoti, Ratish Puduppully, Tri Dao, Albert Gu

MINI-SEQUENCE TRANSFORMER: Optimizing Intermediate Memory for Long Sequences Training

Authors: Cheng Luo, Jiawei Zhao, Zhuoming Chen, Beidi Chen, Animashree Anandkumar

Mixture of Nested Experts: Adaptive Processing of Visual Tokens

Authors: Gagan Jain, Nidhi Hegde, Aditya Kusupati, Arsha Nagrani, Shyamal Buch, Prateek Jain, Anurag Arnab, Sujoy Paul

SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning

Authors: Yexiao He, Ziyao Wang, Zheyu Shen, Guoheng Sun, Yucong Dai, Yongkai Wu, Hongyi Wang, Ang Li

Towards Understanding Extrapolation: a Causal Lens

Authors: Lingjing Kong, Guangyi Chen, Petar Stojanov, Haoxuan Li, Eric Xing, Kun Zhang

Who Needs Features? On the Surprising Effectiveness of Attention Transfer for Vision Transformers

Authors: Alex Li, Yuandong Tian, Beidi Chen, Deepak Pathak, Xinlei Chen

Achieving Domain-Independent Certified Robustness via Knowledge Continuity

Authors: Alan Sun, Chiyu Ma, Kenneth Ge, Soroush Vosoughi

Predicting the Performance of Foundation Models via Agreement-on-the-Line

Authors: Rahul Saxena, Taeyoun Kim, Aman Mehra, Christina Baek, J. Zico Kolter, Aditi Raghunathan

ProTransformer: Robustify Transformers via Plug-and-Play Paradigm

Authors: Zhichao Hou, Weizhi Gao, Yuchen Shen, Feiyi Wang, Xiaorui Liu

Fair Wasserstein Coresets

Authors: Zikai Xiong, Niccolo Dalmasso, Shubham Sharma, Freddy Lecue, Daniele Magazzeni, Vamsi Potluru, Tucker Balch, Manuela Veloso

Mitigating Biases in Blackbox Feature Extractors for Image Classification Tasks

Authors: Abhipsa Basu, Saswat Subhajyoti Mallick, Venkatesh Babu R

On Socially Fair Low-Rank Approximation and Column Subset Selection

Authors: Zhao Song, Ali Vakilian, David Woodruff, Samson Zhou

SureMap: Simultaneous mean estimation for single-task and multi-task disaggregated evaluation

Authors: Misha Khodak, Lester Mackey, Miro Dudik, Alexandra Chouldechova

A Critical Evaluation of AI Feedback for Aligning Large Language Models

Authors: Archit Sharma, Sedrick Scott Keh, Eric Mitchell, Chelsea Finn, Kushal Arora, Thomas Kollar

Data Attribution for Text-to-Image Models by Unlearning Synthesized Images

Authors: Sheng-yu Wang, Alexei Efros, Aaron Hertzmann, Jun-yan Zhu, Richard Zhang

Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching

Authors: Yasi Zhang, Peiyu Yu, Yaxuan Zhu, Yingshan Chang, Feng Gao, Ying Nian Wu, Oscar Leong

Nearest Neighbor Speculative Decoding for LLM Generation and Attribution

Authors: Minghan Li, Xilun Chen, Ari Holtzman, Beidi Chen, Jimmy Lin, Scott Yih, Victoria Lin

Diffusing Differentiable Representations

Authors: Yash Savani, Marc Finzi, J. Zico Kolter

Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion

Authors: Boyuan Chen, Diego Martí Monsó, Yilun Du, Max Simchowitz, Russ Tedrake, Vincent Sitzmann

Improving the Training of Rectified Flows

Authors: Sangyun Lee, Zinan Lin, Giulia Fanti

Model-based Diffusion for Trajectory Optimization

Authors: Chaoyi Pan, Zeji Yi, Guanya Shi, Guannan Qu

Permutation-Invariant Autoregressive Diffusion for Graph Generation

Authors: Lingxiao Zhao, Xueying Ding, Leman Akoglu

Understanding Hallucinations in Diffusion Models through Mode Interpolation

Authors: Sumukh K Aithal, Pratyush Maini, Zachary Lipton, J. Zico Kolter

Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive Training

Authors: Yunshu Wu, Yingtao Luo, Xianghao Kong, Vagelis Papalexakis, Greg Ver Steeg

Can large language models explore in-context?

Authors: Akshay Krishnamurthy, Keegan Harris, Dylan J Foster, Cyril Zhang, Aleksandrs Slivkins

Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning

Authors: Xuechen Zhang, Zijian Huang, Ege Onur Taga, Carlee Joe-wong, Samet Oymak, Jiasi Chen

MixEval: Fast and Dynamic Human Preference Approximation with LLM Benchmark Mixtures

Authors: Jinjie Ni, Fuzhao Xue, Xiang Yue, Yuntian Deng, Mahir Shah, Kabir Jain, Graham Neubig, Yang You

AutoMix: Automatically Mixing Language Models

Authors: Pranjal Aggarwal, Aman Madaan, Ankit Anand, Srividya Pranavi Potharaju, Swaroop Mishra, Pei Zhou, Aditya Gupta, Dheeraj Rajagopal, Karthik Kappaganthu, Yiming Yang, Shyam Upadhyay, Manaal Faruqui, Mausam

Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision

Authors: Zhiqing Sun, Longhui Yu, Yikang Shen, Weiyang Liu, Yiming Yang, Sean Welleck, Chuang Gan

Recursive Introspection: Teaching Foundation Model Agents How to Self-Improve

Authors: Yuxiao Qu, Tianjun Zhang, Naman Garg, Aviral Kumar

Transformers Can Do Arithmetic with the Right Embeddings

Authors: Sean Mcleish, Arpit Bansal, Alex Stein, Neel Jain, John Kirchenbauer, Brian Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Jonas Geiping, Avi Schwarzschild, Tom Goldstein

Even Sparser Graph Transformers

Authors: Hamed Shirzad, Honghao Lin, Balaji Venkatachalam, Ameya Velingker, David Woodruff, Danica J. Sutherland

Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning

Authors: Zebang Cheng, Zhi-qi Cheng, Jun-yan He, Kai Wang, Yuxiang Lin, Zheng Lian, Xiaojiang Peng, Alexander Hauptmann

Harmonizing Stochasticity and Determinism: Scene-responsive Diverse Human Motion Prediction

Authors: Zhenyu Lou, Qiongjie Cui, Tuo Wang, Zhenbo Song, Luoming Zhang, Cheng Cheng, Haofan Wang, Xu Tang, Huaxia Li, Hong Zhou

Diffusion PID: Interpreting Diffusion via Partial Information Decomposition

Authors: Shaurya Dewan, Rushikesh Zawar, Prakanshul Saxena, Yingshan Chang, Andrew Luo, Yonatan Bisk

Model Lego: Creating Models Like Disassembling and Assembling Building Blocks

Authors: Jiacong Hu, Jing Gao, Jingwen Ye, Yang Gao, Xingen Wang, Zunlei Feng, Mingli Song

IQA-EVAL: Automatic Evaluation of Human-Model Interactive Question Answering

Authors: Ruosen Li, Ruochen Li, Barry Wang, Xinya Du

Aligning to Thousands of Varying Preferences via System Message Generalization

Authors: Seongyun Lee, Sue Hyun Park, Seungone Kim, Minjoon Seo

Alignment for Honesty

Authors: Yuqing Yang, Ethan Chern, Xipeng Qiu, Graham Neubig, Pengfei Liu

Accelerating ERM for data-driven algorithm design using output-sensitive techniques

Authors: Maria-florina Balcan, Christopher Seiler, Dravyansh Sharma

On the Comparison between Multi-modal and Single-modal Contrastive Learning

Authors: Wei Huang, Andi Han, Yongqiang Chen, Yuan Cao, Zhiqiang Xu, Taiji Suzuki

Oracle-Efficient Differentially Private Learning with Public Data

Authors: Adam Block, Mark Bun, Rathin Desai, Abhishek Shetty, Steven Wu

Sample-Efficient Agnostic Boosting

Authors: Udaya Ghai, Karan Singh

Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity

Authors: Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason Lee

Post-Hoc Reversal: Are We Selecting Models Prematurely?

Authors: Rishabh Ranjan, Saurabh Garg, Mrigank Raman, Carlos Guestrin, Zachary Lipton

Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices

Authors: Andres Potapczynski, Shikai Qiu, Marc Finzi, Christopher Ferri, Charlie Chen, Micah Goldblum, C. Bayan Bruss, Christopher De Sa, Andrew Wilson

Implicit Regularization Paths of Weighted Neural Representations

Authors: Jin-hong Du, Pratik Patil

Continual Audio-Visual Sound Separation

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Do CLIP Models Always Generalize Better than ImageNet Models?

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Dual Prototype Evolving for Test-Time Generalization of Vision-Language Models

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FlexCap: Describe Anything in Images in Controllable Detail

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Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning

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Divergences between Language Models and Human Brains

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MiSO: Optimizing brain stimulation to create neural activity states

Authors: Yuki Minai, Joana Soldado-magraner, Matthew Smith, Byron M Yu

Communication Bounds for the Distributed Experts Problem

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Global Rewards in Restless Multi-Armed Bandits

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Optimal Top-Two Method for Best Arm Identification and Fluid Analysis

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Regret Minimization in Stackelberg Games with Side Information

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Binary Search Tree with Distributional Predictions

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SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization

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John Ellipsoids via Lazy Updates

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Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability

Authors: Ming Xiang, Stratis Ioannidis, Edmund Yeh, Carlee Joe-wong, Lili Su

LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing

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Warm-starting Push-Relabel

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A Local Method for Satisfying Interventional Fairness with Partially Known Causal Graphs

Authors: Haoxuan Li, Yue Liu, Zhi Geng, Kun Zhang

Active, anytime-valid risk controlling prediction sets

Authors: Ziyu Xu, Nikos Karampatziakis, Paul Mineiro

Aligning Audio-Visual Joint Representations with an Agentic Workflow

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Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting

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Convergence of $log(1/epsilon)$ for Gradient-Based Algorithms in Zero-Sum Games without the Condition Number: A Smoothed Analysis

Authors: Ioannis Anagnostides, Tuomas Sandholm

Efficient LLM Jailbreak via Adaptive Dense-to-sparse Constrained Optimization

Authors: Kai Hu, Weichen Yu, Tianjun Yao, Xiang Li, Wenhe Liu, Lijun Yu, Yining Li, Kai Chen, Zhiqiang Shen, Matt Fredrikson

Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning

Authors: Tong Yang, Shicong Cen, Yuting Wei, Yuxin Chen, Yuejie Chi

GL-NeRF: Gauss-Laguerre Quadrature Enables Training-Free NeRF Acceleration

Authors: Silong Yong, Yaqi Xie, Simon Stepputtis, Katia Sycara

Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization

Authors: Boshi Wang, Xiang Yue, Yu Su, Huan Sun

Hierarchical and Density-based Causal Clustering

Authors: Kwangho Kim, Jisu Kim, Larry Wasserman, Edward Kennedy

Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations

Authors: Hao Chen, Ankit Shah, Jindong Wang, Ran Tao, Yidong Wang, Xiang Li, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj

In-Context Learning with Representations: Contextual Generalization of Trained Transformers

Authors: Tong Yang, Yu Huang, Yingbin Liang, Yuejie Chi

Invisible Image Watermarks Are Provably Removable Using Generative AI

Authors: Xuandong Zhao, Kexun Zhang, Zihao Su, Saastha Vasan, Ilya Grishchenko, Christopher Kruegel, Giovanni Vigna, Yu-xiang Wang, Lei Li

MAmmoTH2: Scaling Instructions from the Web

Authors: Xiang Yue, Tianyu Zheng, Ge Zhang, Wenhu Chen

MergeMinds: Boosting Multilingual Reasoning with the Built-in Capabilities of LLMs

Authors: Zixian Huang, Wenhao Zhu, Gong Cheng, Lei Li, Fei Yuan

Neural Collapse Inspired Feature Alignment for Out-of-Distribution Generalization

Authors: Zhikang Chen, Min Zhang, Sen Cui, Haoxuan Li, Gang Niu, Mingming Gong, Changshui Zhang, Kun Zhang

On the Parameter Identifiability of Partially Observed Linear Causal Models

Authors: Xinshuai Dong, Ignavier Ng, Biwei Huang, Yuewen Sun, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang

One-Step Diffusion Distillation through Score Implicit Matching

Authors: Weijian Luo, Zemin Huang, Zhengyang Geng, J. Zico Kolter, Guo-jun Qi

Private and Personalized Frequency Estimation in a Federated Setting

Authors: Amrith Setlur, Vitaly Feldman, Kunal Talwar

Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction

Authors: Xingyu Xu, Yuejie Chi

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity

Authors: Xinyu Yang, Jixuan Leng, Geyang Guo, Jiawei Zhao, Ryumei Nakada, Linjun Zhang, Huaxiu Yao, Beidi Chen

SIRIUS : Contexual Sparisty with Correction for Efficient LLMs

Authors: Yang Zhou, Zhuoming Chen, Zhaozhuo Xu, Victoria Lin, Beidi Chen

Sequential Harmful Shift Detection Without Labels

Authors: Salim I. Amoukou, Tom Bewley, Saumitra Mishra, Freddy Lecue, Daniele Magazzeni, Manuela Veloso

SpecExec: Massively Parallel Speculative Decoding For Interactive LLM Inference on Consumer Devices

Authors: Ruslan Svirschevski, Avner May, Zhuoming Chen, Beidi Chen, Zhihao Jia, Max Ryabinin

Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generation

Authors: Ruihan Gao, Kangle Deng, Gengshan Yang, Wenzhen Yuan, Jun-yan Zhu

Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models

Authors: Aviv Bick, Kevin Li, Eric Xing, J. Zico Kolter, Albert Gu

When and How Does Synthetic Data Improve Reasoning Capabilities of Language Models?

Authors: Amrith Setlur, Saurabh Garg, Naman Garg, Xinyang Geng, Virginia Smith, Aviral Kumar

LLM Dataset Inference: Detect Datasets, not Strings

Authors: Pratyush Maini, Hengrui Jia, Nicolas Papernot, Adam Dziedzic

No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices

Authors: Qi Pang, Shengyuan Hu, Wenting Zheng, Virginia Smith

On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift

Authors: Pratiksha Thaker, Amrith Setlur, Steven Wu, Virginia Smith

Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable

Authors: Martin Bertran, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Steven Wu

Adaptive $Q$-Aid for Conditional Supervised Learning in Offline Reinforcement Learning

Authors: Jeonghye Kim, Suyoung Lee, Woojun Kim, Youngchul Sung

BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning

Authors: Haohong Lin, Wenhao Ding, Jian Chen, Laixi Shi, Jiacheng Zhu, Bo Li, Ding Zhao

OASIS: Conditional Distribution Shaping for Offline Safe Reinforcement Learning

Authors: Yihang Yao, Zhepeng Cen, Wenhao Ding, Haohong Lin, Shiqi Liu, Tingnan Zhang, Wenhao Yu, Ding Zhao

Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation

Authors: Daehee Lee, Minjong Yoo, Woo Kyung Kim, Wonje Choi, Honguk Woo

REBEL: Reinforcement Learning via Regressing Relative Rewards

Authors: Zhaolin Gao, Jonathan Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, Drew Bagnell, Jason Lee, Wen Sun

Understanding Preference Learning Through the Lens of Coverage

Authors: Yuda Song, Gokul Swamy, Aarti Singh, J. Bagnell, Wen Sun

Language Grounded Multi-Agent Communication for Ad-hoc Teamwork

Authors: Huao Li, Hossein Nourkhiz Mahjoub, Behdad Chalaki, Vaishnav Tadiparthi, Kwonjoon Lee, Ehsan Moradi Pari, Charles Lewis, Katia Sycara

Multi-Agent Imitation Learning: Value is Easy, Regret is Hard

Authors: Jingwu Tang, Gokul Swamy, Fei Fang, Steven Wu

Identifying Latent State-Transition Processes for Individualized Reinforcement Learning

Authors: Yuewen Sun, Biwei Huang, Yu Yao, Donghuo Zeng, Xinshuai Dong, Songyao Jin, Boyang Sun, Roberto Legaspi, Kazushi Ikeda, Peter Spirtes, Kun Zhang

Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference

Authors: Benjamin Eysenbach, Vivek Myers, Ruslan Salakhutdinov, Sergey Levine

BehaviorGPT: Smart Agent Simulation for Autonomous Driving with Next-Patch Prediction

Authors: Zikang Zhou, Hu Haibo, Xinhong Chen, Jianping Wang, Nan Guan, Kui Wu, Yung-hui Li, Yu-kai Huang, Chun Jason Xue

Simulated Humanoid Grasping on Diverse Objects

Authors: Zhengyi Luo, Jinkun Cao, Sammy Christen, Alexander Winkler, Kris Kitani, Weipeng Xu

Adversarially Robust Dense-Sparse Tradeoffs via Heavy-Hitters

Authors: David Woodruff, Samson Zhou

Analytically Computing Partial Information Decomposition

Authors: Chaitanya Goswami, Amanda Merkley

Aggregating Quantitative Relative Judgments: From Social Choice to Ranking Prediction

Authors: Yixuan Xu, Hanrui Zhang, Yu Cheng, Vincent Conitzer

Bias Detection via Signaling

Authors: Yiling Chen, Tao Lin, Ariel Procaccia, Aaditya Ramdas, Itai Shapira

Efficient $Phi$-Regret Minimization with Low-Degree Swap Deviations in Extensive-Form Games

Authors: Brian Zhang, Ioannis Anagnostides, Gabriele Farina, Tuomas Sandholm

The Secretary Problem with Predicted Additive Gap

Authors: Alexander Braun, Sherry Sarkar

A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning

Authors: Tom Yan, Zachary Lipton

Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification

Authors: Junru Chen, Tianyu Cao, Jing Xu, Jiahe Li, Zhilong Chen, Tao Xiao, Yang Yang

Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift

Authors: Jiayun Wu, Jiashuo Liu, Peng Cui, Steven Wu

Improved Few-Shot Jailbreaking Can Circumvent Aligned Language Models and Their Defenses

Authors: Xiaosen Zheng, Tianyu Pang, Chao Du, Qian Liu, Jing Jiang, Min Lin

Improving Alignment and Robustness with Short Circuiting

Authors: Andy Zou, Long Phan, Justin Wang, Derek Duenas, Maxwell Lin, Maksym Andriushchenko, J. Zico Kolter, Matt Fredrikson, Dan Hendrycks

Lips Are Lying: Spotting the Temporal Inconsistency between Audio and Visual in Lip-Syncing DeepFakes

Authors: Weifeng Liu, Tianyi She, Jiawei Liu, Run Wang, Dongyu Yao, 子游 梁, Boheng Li

Rethinking LLM Memorization through the Lens of Adversarial Compression

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Test-Time Adaptation Induces Stronger Accuracy and Agreement-on-the-Line

Authors: Eungyeup Kim, Mingjie Sun, Christina Baek, Aditi Raghunathan, J. Zico Kolter

Towards Calibrated Robust Fine-Tuning of Vision-Language Models

Authors: Changdae Oh, Hyesu Lim, Mijoo Kim, Dongyoon Han, Sangdoo Yun, Jaegul Choo, Alexander Hauptmann, Zhi-qi Cheng, Kyungwoo Song

WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models

Authors: Liwei Jiang, Kavel Rao, Seungju Han, Allyson Ettinger, Faeze Brahman, Sachin Kumar, Niloofar Mireshghallah, Ximing Lu, Maarten Sap, Nouha Dziri, Yejin Choi