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1sRuv4cnuZ | Multi-Track Message Passing: Tackling Oversmoothing and Oversquashing in Graph Learning via Preventing Heterophily Mixing | https://openreview.net/forum?id=1sRuv4cnuZ | [
"Hongbin Pei",
"Yu Li",
"Huiqi Deng",
"Jingxin Hai",
"Pinghui Wang",
"Jie Ma",
"Jing Tao",
"Yuheng Xiong",
"Xiaohong Guan"
] | Spotlight | null | The advancement toward deeper graph neural networks is currently obscured by two inherent issues in message passing, *oversmoothing* and *oversquashing*. We identify the root cause of these issues as information loss due to *heterophily mixing* in aggregation, where messages of diverse category semantics are mixed. We ... | [] | null | 9,699 | null | null |
ngcZhfXCBW | RLVF: Learning from Verbal Feedback without Overgeneralization | https://openreview.net/forum?id=ngcZhfXCBW | [
"Moritz Pascal Stephan",
"Alexander Khazatsky",
"Eric Mitchell",
"Annie S Chen",
"Sheryl Hsu",
"Archit Sharma",
"Chelsea Finn"
] | Poster | null | The diversity of contexts in which large language models (LLMs) are deployed requires the ability to modify or customize default model behaviors to incorporate nuanced requirements and preferences. A convenient interface to specify such model adjustments is high-level verbal feedback, such as “Don’t use emojis when dra... | [] | null | 9,698 | 2402.10893 | title_snapshot |
jdRIaUu3xY | BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models | https://openreview.net/forum?id=jdRIaUu3xY | [
"Haotian Sun",
"Yuchen Zhuang",
"Wei Wei",
"Chao Zhang",
"Bo Dai"
] | Spotlight | null | Adapting state-of-the-art Large Language Models (LLMs) like GPT-4 and Gemini for specific tasks is challenging. Due to the opacity in their parameters, embeddings, and even output probabilities, existing fine-tuning adaptation methods are inapplicable. Consequently, adapting these black-box LLMs is only possible throug... | [] | null | 9,696 | 2402.08219 | title_snapshot |
QAGRPiC3FS | RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content | https://openreview.net/forum?id=QAGRPiC3FS | [
"Zhuowen Yuan",
"Zidi Xiong",
"Yi Zeng",
"Ning Yu",
"Ruoxi Jia",
"Dawn Song",
"Bo Li"
] | Poster | null | Recent advancements in Large Language Models (LLMs) have showcased remarkable capabilities across various tasks in different domains. However, the emergence of biases and the potential for generating harmful content in LLMs, particularly under malicious inputs, pose significant challenges. Current mitigation strategies... | [] | null | 9,695 | 2403.13031 | title_snapshot |
pVyOchWUBa | Position: Understanding LLMs Requires More Than Statistical Generalization | https://openreview.net/forum?id=pVyOchWUBa | [
"Patrik Reizinger",
"Szilvia Ujváry",
"Anna Mészáros",
"Anna Kerekes",
"Wieland Brendel",
"Ferenc Huszár"
] | Spotlight | null | The last decade has seen blossoming research in deep learning theory attempting to answer, ``Why does deep learning generalize?" A powerful shift in perspective precipitated this progress: the study of overparametrized models in the interpolation regime. In this paper, we argue that another perspective shift is due, si... | [] | null | 9,692 | 2405.01964 | title_snapshot |
ghYrfdJfjK | PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels | https://openreview.net/forum?id=ghYrfdJfjK | [
"Praneeth Kacham",
"Vahab Mirrokni",
"Peilin Zhong"
] | Poster | null | The quadratic time and memory complexity inherent to self-attention mechanisms, with respect to sequence length, presents a critical computational bottleneck in the training and deployment of large-scale Transformer-based language models. Recent theoretical results indicate the intractability of sub-quadratic softmax a... | [] | null | 9,690 | 2310.01655 | title_snapshot |
64I29YeQdt | Quality-Diversity with Limited Resources | https://openreview.net/forum?id=64I29YeQdt | [
"Ren-Jian Wang",
"Ke Xue",
"Cong Guan",
"Chao Qian"
] | Poster | null | Quality-Diversity (QD) algorithms have emerged as a powerful optimization paradigm with the aim of generating a set of high-quality and diverse solutions. To achieve such a challenging goal, QD algorithms require maintaining a large archive and a large population in each iteration, which brings two main issues, sample ... | [] | null | 9,689 | 2406.03731 | title_snapshot |
cMige5MK1N | Accelerating Heterogeneous Federated Learning with Closed-form Classifiers | https://openreview.net/forum?id=cMige5MK1N | [
"Eros Fanì",
"Raffaello Camoriano",
"Barbara Caputo",
"Marco Ciccone"
] | Poster | null | Federated Learning (FL) methods often struggle in highly statistically heterogeneous settings. Indeed, non-IID data distributions cause client drift and biased local solutions, particularly pronounced in the final classification layer, negatively impacting convergence speed and accuracy. To address this issue, we intro... | [] | null | 9,674 | 2406.01116 | title_snapshot |
Gp5F6qzwGK | Iterative Regularized Policy Optimization with Imperfect Demonstrations | https://openreview.net/forum?id=Gp5F6qzwGK | [
"Gong Xudong",
"Feng Dawei",
"Kele Xu",
"Yuanzhao Zhai",
"Chengkang Yao",
"Weijia Wang",
"Bo Ding",
"Huaimin Wang"
] | Poster | null | Imitation learning heavily relies on the quality of provided demonstrations. In scenarios where demonstrations are imperfect and rare, a prevalent approach for refining policies is through online fine-tuning with reinforcement learning, in which a Kullback–Leibler (KL) regularization is often employed to stabilize the ... | [] | null | 9,665 | null | null |
B1W712hMBi | NExT: Teaching Large Language Models to Reason about Code Execution | https://openreview.net/forum?id=B1W712hMBi | [
"Ansong Ni",
"Miltiadis Allamanis",
"Arman Cohan",
"Yinlin Deng",
"Kensen Shi",
"Charles Sutton",
"Pengcheng Yin"
] | Poster | null | A fundamental skill among human developers is the ability to understand and reason about program execution. As an example, a programmer can mentally simulate code execution in natural language to debug and repair code (aka. rubber duck debugging). However, large language models (LLMs) of code are typically trained on t... | [] | null | 9,658 | 2404.14662 | title_snapshot |
FYQIgQWH3d | 3D Geometric Shape Assembly via Efficient Point Cloud Matching | https://openreview.net/forum?id=FYQIgQWH3d | [
"Nahyuk Lee",
"Juhong Min",
"Junha Lee",
"Seungwook Kim",
"Kanghee Lee",
"Jaesik Park",
"Minsu Cho"
] | Poster | null | Learning to assemble geometric shapes into a larger target structure is a pivotal task in various practical applications. In this work, we tackle this problem by establishing local correspondences between point clouds of part shapes in both coarse- and fine-levels. To this end, we introduce Proxy Match Transform (PMT),... | [] | null | 9,656 | 2407.10542 | title_snapshot |
vGHOFeUQi8 | Simulation-Based Inference with Quantile Regression | https://openreview.net/forum?id=vGHOFeUQi8 | [
"He Jia"
] | Poster | null | We present Neural Quantile Estimation (NQE), a novel Simulation-Based Inference (SBI) method based on conditional quantile regression. NQE autoregressively learns individual one dimensional quantiles for each posterior dimension, conditioned on the data and previous posterior dimensions. Posterior samples are obtained ... | [] | null | 9,653 | 2401.02413 | title_snapshot |
oRLwyayrh1 | DRCT: Diffusion Reconstruction Contrastive Training towards Universal Detection of Diffusion Generated Images | https://openreview.net/forum?id=oRLwyayrh1 | [
"Baoying Chen",
"Jishen Zeng",
"Jianquan Yang",
"Rui Yang"
] | Spotlight | null | Diffusion models have made significant strides in visual content generation but also raised increasing demands on generated image detection. Existing detection methods have achieved considerable progress, but they usually suffer a significant decline in accuracy when detecting images generated by an unseen diffusion mo... | [] | null | 9,651 | null | null |
37xFIeYgE0 | Explaining Probabilistic Models with Distributional Values | https://openreview.net/forum?id=37xFIeYgE0 | [
"Luca Franceschi",
"Michele Donini",
"Cedric Archambeau",
"Matthias Seeger"
] | Spotlight | null | A large branch of explainable machine learning is grounded in cooperative game theory. However, research indicates that game-theoretic explanations may mislead or be hard to interpret. We argue that often there is a critical mismatch between what one wishes to explain (e.g. the output of a classifier) and what current ... | [] | null | 9,644 | 2402.09947 | title_snapshot |
nDps3Q8j2l | Fourier Controller Networks for Real-Time Decision-Making in Embodied Learning | https://openreview.net/forum?id=nDps3Q8j2l | [
"Hengkai Tan",
"Songming Liu",
"Kai Ma",
"Chengyang Ying",
"Xingxing Zhang",
"Hang Su",
"Jun Zhu"
] | Poster | null | Transformer has shown promise in reinforcement learning to model time-varying features for obtaining generalized low-level robot policies on diverse robotics datasets in embodied learning. However, it still suffers from the issues of low data efficiency and high inference latency. In this paper, we propose to investiga... | [] | null | 9,642 | 2405.19885 | title_snapshot |
2cXzNDe614 | PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming | https://openreview.net/forum?id=2cXzNDe614 | [
"Bingheng Li",
"Linxin Yang",
"Yupeng Chen",
"Senmiao Wang",
"Haitao Mao",
"Qian Chen",
"Yao Ma",
"Akang Wang",
"Tian Ding",
"Jiliang Tang",
"Ruoyu Sun"
] | Poster | null | Solving large-scale linear programming (LP) problems is an important task in various areas such as communication networks, power systems, finance and logistics. Recently, two distinct approaches have emerged to expedite LP solving: (i) First-order methods (FOMs); (ii) Learning to optimize (L2O). In this work, we propos... | [] | null | 9,641 | 2406.01908 | title_snapshot |
5kGfm3Pa41 | Recurrent Distance Filtering for Graph Representation Learning | https://openreview.net/forum?id=5kGfm3Pa41 | [
"Yuhui Ding",
"Antonio Orvieto",
"Bobby He",
"Thomas Hofmann"
] | Poster | null | Graph neural networks based on iterative one-hop message passing have been shown to struggle in harnessing the information from distant nodes effectively. Conversely, graph transformers allow each node to attend to all other nodes directly, but lack graph inductive bias and have to rely on ad-hoc positional encoding. I... | [] | null | 9,640 | 2312.01538 | title_snapshot |
9U29U3cDKq | Adaptively Perturbed Mirror Descent for Learning in Games | https://openreview.net/forum?id=9U29U3cDKq | [
"Kenshi Abe",
"Kaito Ariu",
"Mitsuki Sakamoto",
"Atsushi Iwasaki"
] | Poster | null | This paper proposes a payoff perturbation technique for the Mirror Descent (MD) algorithm in games where the gradient of the payoff functions is monotone in the strategy profile space, potentially containing additive noise. The optimistic family of learning algorithms, exemplified by optimistic MD, successfully achieve... | [] | null | 9,636 | 2305.16610 | title_snapshot |
8ERo4jph0A | Attack-free Evaluating and Enhancing Adversarial Robustness on Categorical Data | https://openreview.net/forum?id=8ERo4jph0A | [
"Yujun Zhou",
"Yufei Han",
"Haomin Zhuang",
"Hongyan Bao",
"Xiangliang Zhang"
] | Poster | null | Research on adversarial robustness has predominantly focused on continuous inputs, leaving categorical inputs, especially tabular attributes, less examined. To echo this challenge, our work aims to evaluate and enhance the robustness of classification over categorical attributes against adversarial perturbations throug... | [] | null | 9,630 | null | null |
5kXNMDpUVF | A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization | https://openreview.net/forum?id=5kXNMDpUVF | [
"Ashwinee Panda",
"Xinyu Tang",
"Saeed Mahloujifar",
"Vikash Sehwag",
"Prateek Mittal"
] | Poster | null | An open problem in differentially private deep learning is hyperparameter optimization (HPO). DP-SGD introduces new hyperparameters and complicates existing ones, forcing researchers to painstakingly tune hyperparameters with hundreds of trials, which in turn makes it impossible to account for the privacy cost of HPO w... | [] | null | 9,629 | 2212.04486 | title_snapshot |
JNN6QHhLHB | Measuring Stochastic Data Complexity with Boltzmann Influence Functions | https://openreview.net/forum?id=JNN6QHhLHB | [
"Nathan Hoyen Ng",
"Roger Baker Grosse",
"Marzyeh Ghassemi"
] | Poster | null | Estimating the uncertainty of a model’s prediction on a test point is a crucial part of ensuring reliability and calibration under distribution shifts.A minimum description length approach to this problem uses the predictive normalized maximum likelihood (pNML) distribution, which considers every possible label for a d... | [] | null | 9,617 | 2406.02745 | title_snapshot |
17ZwoHl65h | PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-Performer | https://openreview.net/forum?id=17ZwoHl65h | [
"Chang Chen",
"Junyeob Baek",
"Fei Deng",
"Kenji Kawaguchi",
"Caglar Gulcehre",
"Sungjin Ahn"
] | Poster | null | Despite the recent advancements in offline RL, no unified algorithm could achieve superior performance across a broad range of tasks. Offline *value function learning*, in particular, struggles with sparse-reward, long-horizon tasks due to the difficulty of solving credit assignment and extrapolation errors that accumu... | [] | null | 9,611 | 2406.06793 | title_snapshot |
ia5XvxFUJT | Gated Linear Attention Transformers with Hardware-Efficient Training | https://openreview.net/forum?id=ia5XvxFUJT | [
"Songlin Yang",
"Bailin Wang",
"Yikang Shen",
"Rameswar Panda",
"Yoon Kim"
] | Poster | null | Transformers with linear attention allow for efficient parallel training but can simultaneously be formulated as an RNN with 2D (matrix-valued) hidden states, thus enjoying linear-time inference complexity. However, linear attention generally underperforms ordinary softmax attention. Moreover, current implementations o... | [] | null | 9,608 | 2312.06635 | title_snapshot |
FQQ4476dT2 | FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning | https://openreview.net/forum?id=FQQ4476dT2 | [
"Wenzhe Li",
"Zihan Ding",
"Seth Karten",
"Chi Jin"
] | Poster | null | Recent advances in reinforcement learning (RL) heavily rely on a variety of well-designed benchmarks, which provide environmental platforms and consistent criteria to evaluate existing and novel algorithms. Specifically, in multi-agent RL (MARL), a plethora of benchmarks based on cooperative games have spurred the deve... | [] | null | 9,606 | 2406.02081 | title_snapshot |
Ln3moCobjO | Relaxing the Accurate Imputation Assumption in Doubly Robust Learning for Debiased Collaborative Filtering | https://openreview.net/forum?id=Ln3moCobjO | [
"Haoxuan Li",
"Chunyuan Zheng",
"Shuyi Wang",
"Kunhan Wu",
"Hao Wang",
"Peng Wu",
"Zhi Geng",
"Xu Chen",
"Xiao-Hua Zhou"
] | Spotlight | null | Recommender system aims to recommend items or information that may interest users based on their behaviors and preferences. However, there may be sampling selection bias in the data collection process, i.e., the collected data is not a representative of the target population. Many debiasing methods are developed based ... | [] | null | 9,604 | null | null |
c0LoolDFw4 | A Language Model’s Guide Through Latent Space | https://openreview.net/forum?id=c0LoolDFw4 | [
"Dimitri von Rütte",
"Sotiris Anagnostidis",
"Gregor Bachmann",
"Thomas Hofmann"
] | Poster | null | Concept guidance has emerged as a cheap and simple way to control the behavior of language models by probing their hidden representations for concept vectors and using them to perturb activations at inference time. While the focus of previous work has largely been on *truthfulness*, in this paper we extend this framewo... | [] | null | 9,601 | 2402.14433 | title_snapshot |
o8AaRKbP9K | Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning? | https://openreview.net/forum?id=o8AaRKbP9K | [
"Khashayar Gatmiry",
"Nikunj Saunshi",
"Sashank J. Reddi",
"Stefanie Jegelka",
"Sanjiv Kumar"
] | Poster | null | Transformers to do reasoning and few-shot learning, without any fine-tuning, is widely conjectured to stem from their ability to implicitly simulate a multi-step algorithms -- such as gradient descent -- with their weights in a single forward pass. Recently, there has been progress in understanding this complex phenome... | [] | null | 9,597 | 2410.08292 | title_snapshot |
XLlQb24X2o | Test-Time Degradation Adaptation for Open-Set Image Restoration | https://openreview.net/forum?id=XLlQb24X2o | [
"Yuanbiao Gou",
"Haiyu Zhao",
"Boyun Li",
"Xinyan Xiao",
"Xi Peng"
] | Spotlight | null | In contrast to close-set scenarios that restore images from a predefined set of degradations, open-set image restoration aims to handle the unknown degradations that were unforeseen during the pretraining phase, which is less-touched as far as we know. This work study this challenging problem and reveal its essence as ... | [] | null | 9,594 | 2312.02197 | title_snapshot |
5NTTCCO74S | Regression with Multi-Expert Deferral | https://openreview.net/forum?id=5NTTCCO74S | [
"Anqi Mao",
"Mehryar Mohri",
"Yutao Zhong"
] | Spotlight | null | Learning to defer with multiple experts is a framework where the learner can choose to defer the prediction to several experts. While this problem has received significant attention in classification contexts, it presents unique challenges in regression due to the infinite and continuous nature of the label space. In t... | [] | null | 9,593 | 2403.19494 | title_snapshot |
L0VoOdjCUb | Learning with 3D rotations, a hitchhiker's guide to SO(3) | https://openreview.net/forum?id=L0VoOdjCUb | [
"Andreas René Geist",
"Jonas Frey",
"Mikel Zhobro",
"Anna Levina",
"Georg Martius"
] | Poster | null | Many settings in machine learning require the selection of a rotation representation. However, choosing a suitable representation from the many available options is challenging. This paper acts as a survey and guide through rotation representations. We walk through their properties that harm or benefit deep learning wi... | [] | null | 9,590 | 2404.11735 | title_snapshot |
GqsRKEhelH | Indirectly Parameterized Concrete Autoencoders | https://openreview.net/forum?id=GqsRKEhelH | [
"Alfred Nilsson",
"Klas Wijk",
"Sai bharath chandra Gutha",
"Erik Englesson",
"Alexandra Hotti",
"Carlo Saccardi",
"Oskar Kviman",
"Jens Lagergren",
"Ricardo Vinuesa Motilva",
"Hossein Azizpour"
] | Poster | null | Feature selection is a crucial task in settings where data is high-dimensional or acquiring the full set of features is costly. Recent developments in neural network-based embedded feature selection show promising results across a wide range of applications. Concrete Autoencoders (CAEs), considered state-of-the-art in ... | [] | null | 9,582 | 2403.00563 | title_snapshot |
7uwLvFvpis | Vector Quantization Pretraining for EEG Time Series with Random Projection and Phase Alignment | https://openreview.net/forum?id=7uwLvFvpis | [
"Haokun GUI",
"Xiucheng Li",
"Xinyang Chen"
] | Poster | null | In this paper, we propose a BERT-style self-supervised learning model, VQ-MTM (Vector Quantization Masked Time-Series Modeling), for the EEG time series data analysis. At its core, VQ-MTM comprises a theoretically grounded random-projection quantization module and a phase-aligning module guided by the Time-Phase-Shift ... | [] | null | 9,569 | null | null |
wmljUnbjy6 | Unsupervised Parameter-free Simplicial Representation Learning with Scattering Transforms | https://openreview.net/forum?id=wmljUnbjy6 | [
"Hiren Madhu",
"Sravanthi Gurugubelli",
"Sundeep Prabhakar Chepuri"
] | Poster | null | Simplicial neural network models are becoming popular for processing and analyzing higher-order graph data, but they suffer from high training complexity and dependence on task-specific labels. To address these challenges, we propose simplicial scattering networks (SSNs), a parameter-free model inspired by scattering t... | [] | null | 9,568 | null | null |
MWZWUyfFHC | TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge | https://openreview.net/forum?id=MWZWUyfFHC | [
"Young D. Kwon",
"Rui Li",
"Stylianos Venieris",
"Jagmohan Chauhan",
"Nicholas Donald Lane",
"Cecilia Mascolo"
] | Poster | null | On-device training is essential for user personalisation and privacy. With the pervasiveness of IoT devices and microcontroller units (MCUs), this task becomes more challenging due to the constrained memory and compute resources, and the limited availability of labelled user data. Nonetheless, prior works neglect the d... | [] | null | 9,556 | 2307.09988 | title_snapshot |
beXQVQorse | High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling | https://openreview.net/forum?id=beXQVQorse | [
"Yuxuan Yin",
"Yu Wang",
"Peng Li"
] | Spotlight | null | We introduce a novel semi-supervised learning approach, named Teacher-Student Bayesian Optimization ($\texttt{TSBO}$), integrating the teacher-student paradigm into BO to minimize expensive labeled data queries for the first time. $\texttt{TSBO}$ incorporates a teacher model, an unlabeled data sampler, and a student mo... | [] | null | 9,555 | 2305.02614 | title_snapshot |
NeotatlYOL | SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN | https://openreview.net/forum?id=NeotatlYOL | [
"kang you",
"Zekai Xu",
"Chen Nie",
"Zhijie Deng",
"Qinghai Guo",
"Xiang Wang",
"Zhezhi He"
] | Poster | null | Spiking neural network (SNN) has attracted great attention due to its characteristic of high efficiency and accuracy. Currently, the ANN-to-SNN conversion methods can obtain ANN on-par accuracy SNN with ultra-low latency (8 time-steps) in CNN structure on computer vision (CV) tasks. However, as Transformer-based networ... | [] | null | 9,550 | 2406.03470 | title_snapshot |
9ZkUFSwlUH | Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts | https://openreview.net/forum?id=9ZkUFSwlUH | [
"Onur Celik",
"Aleksandar Taranovic",
"Gerhard Neumann"
] | Poster | null | Reinforcement learning (RL) is a powerful approach for acquiring a good-performing policy. However, learning diverse skills is challenging in RL due to the commonly used Gaussian policy parameterization. We propose Diverse Skill Learning (Di-SkilL), an RL method for learning diverse skills using Mixture of Experts, whe... | [] | null | 9,547 | 2403.06966 | title_snapshot |
Rp8R9C0Sth | AutoOS: Make Your OS More Powerful by Exploiting Large Language Models | https://openreview.net/forum?id=Rp8R9C0Sth | [
"Huilai Chen",
"Yuanbo Wen",
"Limin Cheng",
"Shouxu Kuang",
"Yumeng Liu",
"Weijia Li",
"Ling Li",
"Rui Zhang",
"Xinkai Song",
"Wei Li",
"Qi Guo",
"Yunji Chen"
] | Poster | null | With the rapid development of Artificial Intelligence of Things (AIoT), customizing and optimizing operating system (OS) kernel configurations for various AIoT application scenarios is crucial for maximizing system performance. However, existing approaches falter due to the overwhelming problem complexity (i.e., over 1... | [] | null | 9,546 | null | null |
efzkSbpyRw | Conformal Predictions under Markovian Data | https://openreview.net/forum?id=efzkSbpyRw | [
"Frédéric Zheng",
"Alexandre Proutiere"
] | Poster | null | We study the split Conformal Prediction method when applied to Markovian data. We quantify the gap in terms of coverage induced by the correlations in the data (compared to exchangeable data). This gap strongly depends on the mixing properties of the underlying Markov chain, and we prove that it typically scales as $\s... | [] | null | 9,543 | 2407.15277 | title_snapshot |
hKdJPMQvew | Hyperbolic Active Learning for Semantic Segmentation under Domain Shift | https://openreview.net/forum?id=hKdJPMQvew | [
"Luca Franco",
"Paolo Mandica",
"Konstantinos Kallidromitis",
"Devin Guillory",
"Yu-Teng Li",
"Trevor Darrell",
"Fabio Galasso"
] | Poster | null | We introduce a hyperbolic neural network approach to pixel-level active learning for semantic segmentation. Analysis of the data statistics leads to a novel interpretation of the hyperbolic radius as an indicator of data scarcity. In HALO (Hyperbolic Active Learning Optimization), for the first time, we propose the use... | [] | null | 9,542 | 2306.11180 | title_snapshot |
DkqiId4AuR | Failures Are Fated, But Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-Scale Vision and Language Models | https://openreview.net/forum?id=DkqiId4AuR | [
"Som Sagar",
"Aditya Taparia",
"Ransalu Senanayake"
] | Spotlight | null | In large deep neural networks that seem to perform surprisingly well on many tasks, we also observe a few failures related to accuracy, social biases, and alignment with human values, among others. Therefore, before deploying these models, it is crucial to characterize this failure landscape for engineers to debug and ... | [] | null | 9,539 | 2406.07145 | title_snapshot |
a9bzTv9SzO | Rolling Diffusion Models | https://openreview.net/forum?id=a9bzTv9SzO | [
"David Ruhe",
"Jonathan Heek",
"Tim Salimans",
"Emiel Hoogeboom"
] | Poster | null | Diffusion models have recently been increasingly applied to temporal data such as video, fluid mechanics simulations, or climate data. These methods generally treat subsequent frames equally regarding the amount of noise in the diffusion process. This paper explores Rolling Diffusion: a new approach that uses a sliding... | [] | null | 9,536 | 2402.09470 | title_snapshot |
a2uFstsHPb | Efficient Pareto Manifold Learning with Low-Rank Structure | https://openreview.net/forum?id=a2uFstsHPb | [
"Weiyu Chen",
"James Kwok"
] | Spotlight | null | Multi-task learning, which optimizes performance across multiple tasks, is inherently a multi-objective optimization problem. Various algorithms are developed to provide discrete trade-off solutions on the Pareto front. Recently, continuous Pareto front approximations using a linear combination of base networks have em... | [] | null | 9,531 | 2407.20734 | title_snapshot |
OS0szhkPmF | Disentangled Graph Self-supervised Learning for Out-of-Distribution Generalization | https://openreview.net/forum?id=OS0szhkPmF | [
"Haoyang Li",
"Xin Wang",
"Zeyang Zhang",
"Haibo Chen",
"Ziwei Zhang",
"Wenwu Zhu"
] | Poster | null | Graph out-of-distribution (OOD) generalization, aiming to generalize graph neural networks (GNNs) under distribution shifts between training and testing environments, has attracted ever-increasing attention recently. However, existing literature heavily relies on sufficient task-dependent graph labels, which are often ... | [] | null | 9,527 | null | null |
VyoY3Wh9Wd | In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization | https://openreview.net/forum?id=VyoY3Wh9Wd | [
"Herilalaina Rakotoarison",
"Steven Adriaensen",
"Neeratyoy Mallik",
"Samir Garibov",
"Eddie Bergman",
"Frank Hutter"
] | Poster | null | With the increasing computational costs associated with deep learning, automated hyperparameter optimization methods, strongly relying on black-box Bayesian optimization (BO), face limitations. Freeze-thaw BO offers a promising grey-box alternative, strategically allocating scarce resources incrementally to different ... | [] | null | 9,512 | 2404.16795 | title_snapshot |
st2BTty53v | Transferable Facial Privacy Protection against Blind Face Restoration via Domain-Consistent Adversarial Obfuscation | https://openreview.net/forum?id=st2BTty53v | [
"Kui Zhang",
"Hang Zhou",
"Jie Zhang",
"Wenbo Zhou",
"Weiming Zhang",
"Nenghai Yu"
] | Poster | null | With the rise of social media and the proliferation of facial recognition surveillance, concerns surrounding privacy have escalated significantly. While numerous studies have concentrated on safeguarding users against unauthorized face recognition, a new and often overlooked issue has emerged due to advances in facial ... | [] | null | 9,508 | null | null |
Bq2THeNXRr | Selecting Large Language Model to Fine-tune via Rectified Scaling Law | https://openreview.net/forum?id=Bq2THeNXRr | [
"Haowei Lin",
"Baizhou Huang",
"Haotian Ye",
"Qinyu Chen",
"Zihao Wang",
"Sujian Li",
"Jianzhu Ma",
"Xiaojun Wan",
"James Zou",
"Yitao Liang"
] | Poster | null | The ever-growing ecosystem of LLMs has posed a challenge in selecting the most appropriate pre-trained model to fine-tune amidst a sea of options. Given constrained resources, fine-tuning all models and making selections afterward is unrealistic. In this work, we formulate this resource-constrained selection task into ... | [] | null | 9,501 | 2402.02314 | title_snapshot |
YNbCbcGyXE | What Can Transformer Learn with Varying Depth? Case Studies on Sequence Learning Tasks | https://openreview.net/forum?id=YNbCbcGyXE | [
"Xingwu Chen",
"Difan Zou"
] | Poster | null | We study the capabilities of the transformer architecture with varying depth. Specifically, we designed a novel set of sequence learning tasks to systematically evaluate and comprehend how the depth of transformer affects its ability to perform memorization, reasoning, generalization, and contextual generalization. We ... | [] | null | 9,499 | 2404.01601 | title_snapshot |
CgO2cuWWLV | Provable Interactive Learning with Hindsight Instruction Feedback | https://openreview.net/forum?id=CgO2cuWWLV | [
"Dipendra Misra",
"Aldo Pacchiano",
"Robert E. Schapire"
] | Poster | null | We study interactive learning in a setting where the agent has to generate a response (e.g., an action or trajectory) given a context and an instruction. In contrast, to typical approaches that train the system using reward or expert supervision on response, we study _learning with hindsight labeling_ where a teacher p... | [] | null | 9,492 | 2404.09123 | title_snapshot |
0ksNeD1SJT | Scaling Exponents Across Parameterizations and Optimizers | https://openreview.net/forum?id=0ksNeD1SJT | [
"Katie E Everett",
"Lechao Xiao",
"Mitchell Wortsman",
"Alexander A Alemi",
"Roman Novak",
"Peter J Liu",
"Izzeddin Gur",
"Jascha Sohl-Dickstein",
"Leslie Pack Kaelbling",
"Jaehoon Lee",
"Jeffrey Pennington"
] | Poster | null | Robust and effective scaling of models from small to large width typically requires the precise adjustment of many algorithmic and architectural details, such as parameterization and optimizer choices. In this work, we propose a new perspective on parameterization by investigating a key assumption in prior work about t... | [] | null | 9,489 | 2407.05872 | title_snapshot |
7tyAO5tUF8 | Synergistic Integration of Coordinate Network and Tensorial Feature for Improving Neural Radiance Fields from Sparse Inputs | https://openreview.net/forum?id=7tyAO5tUF8 | [
"Mingyu Kim",
"Kim Jun-Seong",
"Se-Young Yun",
"Jin-Hwa Kim"
] | Poster | null | The multi-plane representation has been highlighted for its fast training and inference across static and dynamic neural radiance fields. This approach constructs relevant features via projection onto learnable grids and interpolating adjacent vertices. However, it has limitations in capturing low-frequency details and... | [] | null | 9,481 | 2405.07857 | title_snapshot |
RbiBKPtuHp | Improving Transformers with Dynamically Composable Multi-Head Attention | https://openreview.net/forum?id=RbiBKPtuHp | [
"Da Xiao",
"Qingye Meng",
"Shengping Li",
"xingyuan yuan"
] | Oral | null | Multi-Head Attention (MHA) is a key component of Transformer. In MHA, attention heads work independently, causing problems such as low-rank bottleneck of attention score matrices and head redundancy. We propose Dynamically Composable Multi-Head Attention (DCMHA), a parameter and computation efficient attention architec... | [] | null | 9,473 | 2405.08553 | title_snapshot |
t82Y3fmRtk | Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning | https://openreview.net/forum?id=t82Y3fmRtk | [
"Zhiheng Xi",
"Wenxiang Chen",
"Boyang Hong",
"Senjie Jin",
"Rui Zheng",
"Wei He",
"Yiwen Ding",
"Shichun Liu",
"Xin Guo",
"Junzhe Wang",
"Honglin Guo",
"Wei Shen",
"Xiaoran Fan",
"Yuhao Zhou",
"Shihan Dou",
"Xiao Wang",
"Xinbo Zhang",
"peng sun",
"Tao Gui",
"Qi Zhang",
"Xuan... | Poster | null | In this paper, we propose **R**$^3$: Learning **R**easoning through **R**everse Curriculum **R**einforcement Learning (RL), a novel method that employs only outcome supervision to achieve the benefits of process supervision for large language models. The core challenge in applying RL to complex reasoning is to identify... | [] | null | 9,461 | 2402.05808 | title_snapshot |
DWT9uiGjxT | Localizing Task Information for Improved Model Merging and Compression | https://openreview.net/forum?id=DWT9uiGjxT | [
"Ke Wang",
"Nikolaos Dimitriadis",
"Guillermo Ortiz-Jimenez",
"François Fleuret",
"Pascal Frossard"
] | Poster | null | Model merging and task arithmetic have emerged as promising scalable approaches to merge multiple single-task checkpoints to one multi-task model, but their applicability is reduced by significant performance loss. Previous works have linked these drops to interference in the weight space and erasure of important task-... | [] | null | 9,460 | 2405.07813 | title_snapshot |
FSxTEvuFa7 | CarbonNovo: Joint Design of Protein Structure and Sequence Using a Unified Energy-based Model | https://openreview.net/forum?id=FSxTEvuFa7 | [
"Milong Ren",
"Tian Zhu",
"Haicang Zhang"
] | Poster | null | De novo protein design aims to create novel protein structures and sequences unseen in nature. Recent structure-oriented design methods typically employ a two-stage strategy, where structure design and sequence design modules are trained separately, and the backbone structures and sequences are generated sequentially i... | [] | null | 9,454 | null | null |
M4Htd52HMH | Embodied CoT Distillation From LLM To Off-the-shelf Agents | https://openreview.net/forum?id=M4Htd52HMH | [
"Wonje Choi",
"Woo Kyung Kim",
"Minjong Yoo",
"Honguk Woo"
] | Poster | null | We address the challenge of utilizing large language models (LLMs) for complex embodied tasks, in the environment where decision-making systems operate timely on capacity-limited, off-the-shelf devices. We present DeDer, a framework for decomposing and distilling the embodied reasoning capabilities from LLMs to efficie... | [] | null | 9,451 | 2412.11499 | title_snapshot |
sF9epWkNUG | Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations | https://openreview.net/forum?id=sF9epWkNUG | [
"Jan Hagnberger",
"Marimuthu Kalimuthu",
"Daniel Musekamp",
"Mathias Niepert"
] | Poster | null | Transformer models are increasingly used for solving Partial Differential Equations (PDEs). Several adaptations have been proposed, all of which suffer from the typical problems of Transformers, such as quadratic memory and time complexity. Furthermore, all prevalent architectures for PDE solving lack at least one of s... | [] | null | 9,450 | 2406.03919 | title_snapshot |
v7I5FtL2pV | Tabular Insights, Visual Impacts: Transferring Expertise from Tables to Images | https://openreview.net/forum?id=v7I5FtL2pV | [
"Jun-Peng Jiang",
"Han-Jia Ye",
"Leye Wang",
"Yang Yang",
"Yuan Jiang",
"De-Chuan Zhan"
] | Spotlight | null | Transferring knowledge across diverse data modalities is receiving increasing attention in machine learning. This paper tackles the task of leveraging expert-derived, yet expensive, tabular data to enhance image-based predictions when tabular data is unavailable during inference. The primary challenges stem from the in... | [] | null | 9,447 | null | null |
p1kDNFs62o | Nesting Particle Filters for Experimental Design in Dynamical Systems | https://openreview.net/forum?id=p1kDNFs62o | [
"Sahel Iqbal",
"Adrien Corenflos",
"Simo Särkkä",
"Hany Abdulsamad"
] | Poster | null | In this paper, we propose a novel approach to Bayesian experimental design for non-exchangeable data that formulates it as risk-sensitive policy optimization. We develop the Inside-Out SMC$^2$ algorithm, a nested sequential Monte Carlo technique to infer optimal designs, and embed it into a particle Markov chain Monte ... | [] | null | 9,443 | 2402.07868 | title_snapshot |
qGEEso256L | Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks | https://openreview.net/forum?id=qGEEso256L | [
"Duy Minh Ho Nguyen",
"Nina Lukashina",
"Tai Nguyen",
"An Thai Le",
"TrungTin Nguyen",
"Nhat Ho",
"Jan Peters",
"Daniel Sonntag",
"Viktor Zaverkin",
"Mathias Niepert"
] | Poster | null | A molecule’s 2D representation consists of its atoms, their attributes, and the molecule’s covalent bonds. A 3D (geometric) representation of a molecule is called a conformer and consists of its atom types and Cartesian coordinates. Every conformer has a potential energy, and the lower this energy, the more likely it o... | [] | null | 9,441 | 2402.01975 | title_snapshot |
fVg9YrSllr | Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling | https://openreview.net/forum?id=fVg9YrSllr | [
"Denis Blessing",
"Xiaogang Jia",
"Johannes Esslinger",
"Francisco Vargas",
"Gerhard Neumann"
] | Poster | null | Monte Carlo methods, Variational Inference, and their combinations play a pivotal role in sampling from intractable probability distributions. However, current studies lack a unified evaluation framework, relying on disparate performance measures and limited method comparisons across diverse tasks, complicating the ass... | [] | null | 9,437 | 2406.07423 | title_snapshot |
9yADTDHgGu | When is Transfer Learning Possible? | https://openreview.net/forum?id=9yADTDHgGu | [
"My Phan",
"Kianté Brantley",
"Stephanie Milani",
"Soroush Mehri",
"Gokul Swamy",
"Geoffrey J. Gordon"
] | Poster | null | We present a general framework for transfer learning that is flexible enough to capture transfer in supervised, reinforcement, and imitation learning. Our framework enables new insights into the fundamental question of *when* we can successfully transfer learned information across problems. We model the learner as inte... | [] | null | 9,435 | null | null |
Lg8nw3ltvX | Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning | https://openreview.net/forum?id=Lg8nw3ltvX | [
"Jinsoo Yoo",
"Yunpeng Liu",
"Frank Wood",
"Geoff Pleiss"
] | Poster | null | In online continual learning, a neural network incrementally learns from a non-i.i.d. data stream. Nearly all online continual learning methods employ experience replay to simultaneously prevent catastrophic forgetting and underfitting on past data. Our work demonstrates a limitation of this approach: neural networks t... | [] | null | 9,433 | 2402.09542 | title_snapshot |
CHz7WshPcp | Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer | https://openreview.net/forum?id=CHz7WshPcp | [
"Toru Shirakawa",
"Yi Li",
"Yulun Wu",
"Sky Qiu",
"Yuxuan Li",
"Mingduo Zhao",
"Hiroyasu Iso",
"Mark J. van der Laan"
] | Poster | null | We propose Deep Longitudinal Targeted Minimum Loss-based Estimation (Deep LTMLE), a novel approach to estimate the counterfactual mean of outcome under dynamic treatment policies in longitudinal problem settings. Our approach utilizes a transformer architecture with heterogeneous type embedding trained using temporal-d... | [] | null | 9,431 | 2404.04399 | title_snapshot |
vYYIuJDTHq | Partial Optimality in the Linear Ordering Problem | https://openreview.net/forum?id=vYYIuJDTHq | [
"David Stein",
"Bjoern Andres"
] | Poster | null | The linear ordering problem consists in finding a linear order $<$ on a finite set $A$ so as to minimize the sum of costs associated with pairs of elements $a, b$ for which $a < b$. The problem is NP-hard and APX-hard. We introduce algorithms for solving the problem *partially* by deciding efficiently for some pairs $(... | [] | null | 9,430 | null | null |
sDjszMb2Ir | LASER: Linear Compression in Wireless Distributed Optimization | https://openreview.net/forum?id=sDjszMb2Ir | [
"Ashok Vardhan Makkuva",
"Marco Bondaschi",
"Thijs Vogels",
"Martin Jaggi",
"Hyeji Kim",
"Michael Gastpar"
] | Poster | null | Data-parallel SGD is the de facto algorithm for distributed optimization, especially for large scale machine learning. Despite its merits, communication bottleneck is one of its persistent issues. Most compression schemes to alleviate this either assume noiseless communication links, or fail to achieve good performance... | [] | null | 9,429 | 2310.13033 | title_snapshot |
MsnJl6JkZS | Easing Concept Bleeding in Diffusion via Entity Localization and Anchoring | https://openreview.net/forum?id=MsnJl6JkZS | [
"Jiewei Zhang",
"Song Guo",
"Peiran Dong",
"Jie ZHANG",
"Ziming Liu",
"Yue Yu",
"Xiao-Ming Wu"
] | Poster | null | Recent diffusion models have manifested extraordinary capabilities in generating high-quality, diverse, and innovative images guided by textual prompts. Nevertheless, these state-of-the-art models may encounter the challenge of concept bleeding when generating images with multiple entities or attributes in the prompt, ... | [] | null | 9,428 | null | null |
87ZrVHDqmR | Unlocking the Power of Spatial and Temporal Information in Medical Multimodal Pre-training | https://openreview.net/forum?id=87ZrVHDqmR | [
"Jinxia Yang",
"Bing Su",
"Xin Zhao",
"Ji-Rong Wen"
] | Poster | null | Medical vision-language pre-training methods mainly leverage the correspondence between paired medical images and radiological reports. Although multi-view spatial images and temporal sequences of image-report pairs are available in off-the-shelf multi-modal medical datasets, most existing methods have not thoroughly t... | [] | null | 9,426 | 2405.19654 | title_snapshot |
LWRI4uPG2X | eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data | https://openreview.net/forum?id=LWRI4uPG2X | [
"Bo Peng",
"Xinyi Ling",
"Ziru Chen",
"Huan Sun",
"Xia Ning"
] | Poster | null | With tremendous efforts on developing effective e-commerce models, conventional e-commerce models show limited success in generalist e-commerce modeling, and suffer from unsatisfactory performance on new users and new products – a typical out-of-domain generalization challenge. Meanwhile, large language models (LLMs) d... | [] | null | 9,420 | 2402.08831 | title_snapshot |
1xKgDANODx | Retrieval-Augmented Score Distillation for Text-to-3D Generation | https://openreview.net/forum?id=1xKgDANODx | [
"Junyoung Seo",
"Susung Hong",
"Wooseok Jang",
"Inès Hyeonsu Kim",
"Min-Seop Kwak",
"Doyup Lee",
"Seungryong Kim"
] | Poster | null | Text-to-3D generation has achieved significant success by incorporating powerful 2D diffusion models, but insufficient 3D prior knowledge also leads to the inconsistency of 3D geometry. Recently, since large-scale multi-view datasets have been released, fine-tuning the diffusion model on the multi-view datasets becomes... | [] | null | 9,419 | 2402.02972 | title_snapshot |
elF0QoBSFV | NDOT: Neuronal Dynamics-based Online Training for Spiking Neural Networks | https://openreview.net/forum?id=elF0QoBSFV | [
"Haiyan Jiang",
"Giulia De Masi",
"Huan Xiong",
"Bin Gu"
] | Poster | null | Spiking Neural Networks (SNNs) are attracting great attention for their energy-efficient and fast-inference properties in neuromorphic computing. However, the efficient training of deep SNNs poses challenges in gradient calculation due to the non-differentiability of their binary spike-generating activation functions. ... | [] | null | 9,417 | null | null |
F3Ds71Xgo1 | Entropy-Reinforced Planning with Large Language Models for Drug Discovery | https://openreview.net/forum?id=F3Ds71Xgo1 | [
"Xuefeng Liu",
"Chih-chan Tien",
"Peng Ding",
"Songhao Jiang",
"Rick L. Stevens"
] | Poster | null | The objective of drug discovery is to identify chemical compounds that possess specific pharmaceutical properties toward a binding target. Existing large language models (LLMS) can achieve high token matching scores in terms of likelihood for molecule generation. However, relying solely on LLM decoding often results in... | [] | null | 9,415 | 2406.07025 | title_snapshot |
vsOF7qDNhl | What is the Long-Run Distribution of Stochastic Gradient Descent? A Large Deviations Analysis | https://openreview.net/forum?id=vsOF7qDNhl | [
"Waïss Azizian",
"Franck Iutzeler",
"Jerome Malick",
"Panayotis Mertikopoulos"
] | Poster | null | In this paper, we examine the long-run distribution of stochastic gradient descent (SGD) in general, non-convex problems. Specifically, we seek to understand which regions of the problem's state space are more likely to be visited by SGD, and by how much. Using an approach based on the theory of large deviations and ra... | [] | null | 9,413 | 2406.09241 | title_snapshot |
yhpDKSw7yA | Provably Robust DPO: Aligning Language Models with Noisy Feedback | https://openreview.net/forum?id=yhpDKSw7yA | [
"Sayak Ray Chowdhury",
"Anush Kini",
"Nagarajan Natarajan"
] | Poster | null | Learning from preference-based feedback has recently gained traction as a promising approach to align language models with human interests. While these aligned generative models have demonstrated impressive capabilities across various tasks, their dependence on high-quality human preference data poses a bottleneck in p... | [] | null | 9,411 | 2403.00409 | title_snapshot |
e0SKaKEEdr | Positive Concave Deep Equilibrium Models | https://openreview.net/forum?id=e0SKaKEEdr | [
"Mateusz Gabor",
"Tomasz Piotrowski",
"Renato L. G. Cavalcante"
] | Poster | null | Deep equilibrium (DEQ) models are widely recognized as a memory efficient alternative to standard neural networks, achieving state-of-the-art performance in language modeling and computer vision tasks. These models solve a fixed point equation instead of explicitly computing the output, which sets them apart from stand... | [] | null | 9,407 | 2402.04029 | title_snapshot |
LJ34pX1U5g | Collaborative Heterogeneous Causal Inference Beyond Meta-analysis | https://openreview.net/forum?id=LJ34pX1U5g | [
"Tianyu Guo",
"Sai Praneeth Karimireddy",
"Michael Jordan"
] | Poster | null | Collaboration between different data centers is often challenged by heterogeneity across sites. To account for the heterogeneity, the state-of-the-art method is to re-weight the covariate distributions in each site to match the distribution of the target population. Nevertheless, this method still relies on the concept... | [] | null | 9,403 | 2404.15746 | title_snapshot |
Pbey7LqBRl | Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics | https://openreview.net/forum?id=Pbey7LqBRl | [
"Artur Toshev",
"Jonas A. Erbesdobler",
"Nikolaus A. Adams",
"Johannes Brandstetter"
] | Poster | null | Smoothed particle hydrodynamics (SPH) is omnipresent in modern engineering and scientific disciplines. SPH is a class of Lagrangian schemes that discretize fluid dynamics via finite material points that are tracked through the evolving velocity field. Due to the particle-like nature of the simulation, graph neural netw... | [] | null | 9,392 | 2402.06275 | title_snapshot |
CEfr3h68KU | Purifying Quantization-conditioned Backdoors via Layer-wise Activation Correction with Distribution Approximation | https://openreview.net/forum?id=CEfr3h68KU | [
"Boheng Li",
"Yishuo Cai",
"Jisong Cai",
"Yiming Li",
"Han Qiu",
"Run Wang",
"Tianwei Zhang"
] | Poster | null | Model quantization is a compression technique that converts a full-precision model to a more compact low-precision version for better storage. Despite the great success of quantization, recent studies revealed the feasibility of malicious exploiting model quantization via implanting quantization-conditioned backdoors (... | [] | null | 9,389 | null | null |
ZwrfsrCduj | AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors | https://openreview.net/forum?id=ZwrfsrCduj | [
"Yucen Wang",
"Shenghua Wan",
"Le Gan",
"Shuai Feng",
"De-Chuan Zhan"
] | Poster | null | Model-based methods have significantly contributed to distinguishing task-irrelevant distractors for visual control. However, prior research has primarily focused on heterogeneous distractors like noisy background videos, leaving homogeneous distractors that closely resemble controllable agents largely unexplored, whic... | [] | null | 9,386 | 2403.09976 | title_snapshot |
OnkA4zaEU9 | Triadic-OCD: Asynchronous Online Change Detection with Provable Robustness, Optimality, and Convergence | https://openreview.net/forum?id=OnkA4zaEU9 | [
"Yancheng Huang",
"Kai Yang",
"Zelin Zhu",
"Leian Chen"
] | Poster | null | The primary goal of online change detection (OCD) is to promptly identify changes in the data stream. OCD problem find a wide variety of applications in diverse areas, e.g., security detection in smart grids and intrusion detection in communication networks. Prior research usually assumes precise knowledge of the syste... | [] | null | 9,385 | 2405.02372 | title_snapshot |
QhqQJqe0Wq | Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation | https://openreview.net/forum?id=QhqQJqe0Wq | [
"Mingyuan Zhou",
"Huangjie Zheng",
"Zhendong Wang",
"Mingzhang Yin",
"Hai Huang"
] | Poster | null | We introduce Score identity Distillation (SiD), an innovative data-free method that distills the generative capabilities of pretrained diffusion models into a single-step generator. SiD not only facilitates an exponentially fast reduction in Fréchet inception distance (FID) during distillation but also approaches or ev... | [] | null | 9,382 | 2404.04057 | title_snapshot |
geajNKab7g | First-Order Manifold Data Augmentation for Regression Learning | https://openreview.net/forum?id=geajNKab7g | [
"Ilya Kaufman",
"Omri Azencot"
] | Poster | null | Data augmentation (DA) methods tailored to specific domains generate synthetic samples by applying transformations that are appropriate for the characteristics of the underlying data domain, such as rotations on images and time warping on time series data. In contrast, *domain-independent* approaches, e.g. *mixup*, are... | [] | null | 9,378 | 2406.10914 | title_snapshot |
JVhUR8q27o | Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components | https://openreview.net/forum?id=JVhUR8q27o | [
"Zhiliang Chen",
"Chuan-Sheng Foo",
"Bryan Kian Hsiang Low"
] | Poster | null | *Machine learning* (ML) models in the real world typically do not exist in isolation. They are usually part of a complex system (e.g., healthcare systems, self-driving cars) containing multiple ML and *black-box* components. The problem of optimizing such systems, which we refer to as *automated AI* (AutoAI), requires ... | [] | null | 9,377 | null | null |
1YsQI04KaN | Antibody Design Using a Score-based Diffusion Model Guided by Evolutionary, Physical and Geometric Constraints | https://openreview.net/forum?id=1YsQI04KaN | [
"Tian Zhu",
"Milong Ren",
"Haicang Zhang"
] | Poster | null | Antibodies are central proteins in adaptive immune responses, responsible for protecting against viruses and other pathogens. Rational antibody design has proven effective in the diagnosis and treatment of various diseases like cancers and virus infections. While recent diffusion-based generative models show promise in... | [] | null | 9,375 | null | null |
LO4xhXmFal | DE-COP: Detecting Copyrighted Content in Language Models Training Data | https://openreview.net/forum?id=LO4xhXmFal | [
"André Vicente Duarte",
"Xuandong Zhao",
"Arlindo L. Oliveira",
"Lei Li"
] | Poster | null | *How can we detect if copyrighted content was used in the training process of a language model, considering that the training data is typically undisclosed?* We are motivated by the premise that a language model is likely to identify verbatim excerpts from its training text. We propose DE-COP, a method to determine whe... | [] | null | 9,355 | 2402.09910 | title_snapshot |
RPMTNGMq0O | Dealing With Unbounded Gradients in Stochastic Saddle-point Optimization | https://openreview.net/forum?id=RPMTNGMq0O | [
"Gergely Neu",
"Nneka Okolo"
] | Poster | null | We study the performance of stochastic first-order methods for finding saddle points of convex-concave functions. A notorious challenge faced by such methods is that the gradients can grow arbitrarily large during optimization, which may result in instability and divergence. In this paper, we propose a simple and effec... | [] | null | 9,354 | 2402.13903 | title_snapshot |
nvHlHfjJPe | $H$-Consistency Guarantees for Regression | https://openreview.net/forum?id=nvHlHfjJPe | [
"Anqi Mao",
"Mehryar Mohri",
"Yutao Zhong"
] | Poster | null | We present a detailed study of $H$-consistency bounds for regression. We first present new theorems that generalize the tools previously given to establish $H$-consistency bounds. This generalization proves essential for analyzing $H$-consistency bounds specific to regression. Next, we prove a series of novel $H$-consi... | [] | null | 9,352 | 2403.19480 | title_snapshot |
9zlZuAAb08 | Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization | https://openreview.net/forum?id=9zlZuAAb08 | [
"Li Ding",
"Jenny Zhang",
"Jeff Clune",
"Lee Spector",
"Joel Lehman"
] | Poster | null | Reinforcement Learning from Human Feedback (RLHF) has shown potential in qualitative tasks where easily defined performance measures are lacking. However, there are drawbacks when RLHF is commonly used to optimize for average human preferences, especially in generative tasks that demand diverse model responses. Meanwhi... | [] | null | 9,342 | 2310.12103 | title_snapshot |
QBj7Uurdwf | Learning Useful Representations of Recurrent Neural Network Weight Matrices | https://openreview.net/forum?id=QBj7Uurdwf | [
"Vincent Herrmann",
"Francesco Faccio",
"Jürgen Schmidhuber"
] | Oral | null | Recurrent Neural Networks (RNNs) are general-purpose parallel-sequential computers. The program of an RNN is its weight matrix. How to learn useful representations of RNN weights that facilitate RNN analysis as well as downstream tasks? While the _mechanistic approach_ directly looks at some RNN's weights to predict it... | [] | null | 9,340 | 2403.11998 | title_snapshot |
CaxQ5IbHgF | Convergence of Some Convex Message Passing Algorithms to a Fixed Point | https://openreview.net/forum?id=CaxQ5IbHgF | [
"Vaclav Voracek",
"Tomas Werner"
] | Spotlight | null | A popular approach to the MAP inference problem in graphical models is to minimize an upper bound obtained from a dual linear programming or Lagrangian relaxation by (block-)coordinate descent. This is also known as convex/convergent message passing; examples are max-sum diffusion and sequential tree-reweighted message... | [] | null | 9,330 | 2403.07004 | title_snapshot |
rMV86cAOh6 | Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis | https://openreview.net/forum?id=rMV86cAOh6 | [
"Juyeon Ko",
"Inho Kong",
"Dogyun Park",
"Hyunwoo J. Kim"
] | Poster | null | Semantic image synthesis (SIS) is a task to generate realistic images corresponding to semantic maps (labels). However, in real-world applications, SIS often encounters noisy user inputs. To address this, we propose Stochastic Conditional Diffusion Model (SCDM), which is a robust conditional diffusion model that featur... | [] | null | 9,329 | 2402.16506 | title_snapshot |
ohH3sbUue2 | Optimal bounds for $\ell_p$ sensitivity sampling via $\ell_2$ augmentation | https://openreview.net/forum?id=ohH3sbUue2 | [
"Alexander Munteanu",
"Simon Omlor"
] | Poster | null | Data subsampling is one of the most natural methods to approximate a massively large data set by a small representative proxy. In particular, sensitivity sampling received a lot of attention, which samples points proportional to an individual importance measure called sensitivity. This framework reduces in very general... | [] | null | 9,327 | 2406.00328 | title_snapshot |
HrzQZXzrN2 | Predictive Performance Comparison of Decision Policies Under Confounding | https://openreview.net/forum?id=HrzQZXzrN2 | [
"Luke Guerdan",
"Amanda Lee Coston",
"Ken Holstein",
"Steven Wu"
] | Poster | null | Predictive models are often introduced to decision-making tasks under the rationale that they improve performance over an existing decision-making policy. However, it is challenging to compare predictive performance against an existing decision-making policy that is generally under-specified and dependent on unobservab... | [] | null | 9,319 | 2404.00848 | title_snapshot |
SlRcJvf1yd | Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates | https://openreview.net/forum?id=SlRcJvf1yd | [
"Riccardo Grazzi",
"Massimiliano Pontil",
"Saverio Salzo"
] | Poster | null | We study the problem of efficiently computing the derivative of the fixed-point of a parametric nondifferentiable contraction map. This problem has wide applications in machine learning, including hyperparameter optimization, meta-learning and data poisoning attacks. We analyze two popular approaches: iterative differe... | [] | null | 9,317 | 2403.11687 | title_snapshot |
9DMMvMTDur | EvIL: Evolution Strategies for Generalisable Imitation Learning | https://openreview.net/forum?id=9DMMvMTDur | [
"Silvia Sapora",
"Gokul Swamy",
"Chris Lu",
"Yee Whye Teh",
"Jakob Nicolaus Foerster"
] | Poster | null | Often times in imitation learning (IL), the environment we collect expert demonstrations in and the environment we want to deploy our learned policy in aren't exactly the same (e.g. demonstrations collected in simulation but deployment in the real world). Compared to policy-centric approaches to IL like behavioural clo... | [] | null | 9,311 | 2406.11905 | title_snapshot |
uku9r6RROl | DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design | https://openreview.net/forum?id=uku9r6RROl | [
"Samuel Garcin",
"James Doran",
"Shangmin Guo",
"Christopher G. Lucas",
"Stefano V Albrecht"
] | Poster | null | Autonomous agents trained using deep reinforcement learning (RL) often lack the ability to successfully generalise to new environments, even when these environments share characteristics with the ones they have encountered during training. In this work, we investigate how the sampling of individual environment instance... | [] | null | 9,306 | 2402.03479 | title_snapshot |
6Zl9rv6PDx | Causal Action Influence Aware Counterfactual Data Augmentation | https://openreview.net/forum?id=6Zl9rv6PDx | [
"Núria Armengol Urpí",
"Marco Bagatella",
"Marin Vlastelica",
"Georg Martius"
] | Poster | null | Offline data are both valuable and practical resources for teaching robots complex behaviors. Ideally, learning agents should not be constrained by the scarcity of available demonstrations, but rather generalize beyond the training distribution. However, the complexity of real-world scenarios typically requires huge am... | [] | null | 9,304 | 2405.18917 | title_snapshot |
aP0H8A1ywk | How Smooth Is Attention? | https://openreview.net/forum?id=aP0H8A1ywk | [
"Valérie Castin",
"Pierre Ablin",
"Gabriel Peyré"
] | Poster | null | Self-attention and masked self-attention are at the heart of Transformers' outstanding success. Still, our mathematical understanding of attention, in particular of its Lipschitz properties — which are key when it comes to analyzing robustness and expressive power — is incomplete. We provide a detailed study of the Lip... | [] | null | 9,302 | 2312.14820 | title_snapshot |
4Vqr8SRfyX | Case-Based or Rule-Based: How Do Transformers Do the Math? | https://openreview.net/forum?id=4Vqr8SRfyX | [
"Yi Hu",
"Xiaojuan Tang",
"Haotong Yang",
"Muhan Zhang"
] | Poster | null | Despite the impressive performance in a variety of complex tasks, modern large language models (LLMs) still have trouble dealing with some math problems that are simple and intuitive for humans, such as addition. While we can easily learn basic *rules* of addition and apply them to new problems of any length, LLMs stru... | [] | null | 9,299 | 2402.17709 | title_snapshot |
biE1uHyG0l | Fundamental Limits of Distributed Covariance Matrix Estimation Under Communication Constraints | https://openreview.net/forum?id=biE1uHyG0l | [
"Mohammad Reza Rahmani",
"Mohammad Hossein Yassaee",
"Mohammad Ali Maddah-Ali",
"Mohammad Reza Aref"
] | Poster | null | Estimating high-dimensional covariance matrices is crucial in various domains. This work considers a scenario where two collaborating agents access disjoint dimensions of $m$ samples from a high--dimensional random vector, and they can only communicate a limited number of bits to a central server, which wants to accura... | [] | null | 9,298 | 2507.16953 | title_judge |
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