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On Discrete Prompt Optimization for Diffusion Models | https://openreview.net/forum?id=Fw4fBE2rqW | [
"Ruochen Wang",
"Ting Liu",
"Cho-Jui Hsieh",
"Boqing Gong"
] | Poster | null | This paper introduces the first gradient-based framework for prompt optimization in text-to-image diffusion models. We formulate prompt engineering as a discrete optimization problem over the language space. Two major challenges arise in efficiently finding a solution to this problem: (1) Enormous Domain Space: Setting... | [] | null | 10,207 | 2407.01606 | title_snapshot | [
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Multi-View Clustering by Inter-cluster Connectivity Guided Reward | https://openreview.net/forum?id=uEx2bSAJu8 | [
"Hao Dai",
"Yang Liu",
"Peng Su",
"Hecheng Cai",
"Shudong Huang",
"Jiancheng Lv"
] | Poster | null | Multi-view clustering has been widely explored for its effectiveness in harmonizing heterogeneity along with consistency in different views of data. Despite the significant progress made by recent works, the performance of most existing methods is heavily reliant on strong priori information regarding the true cluster ... | [] | null | 10,190 | null | null | [
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Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning | https://openreview.net/forum?id=5vZzmCeTYu | [
"Michal Nauman",
"Michał Bortkiewicz",
"Piotr Miłoś",
"Tomasz Trzcinski",
"Mateusz Ostaszewski",
"Marek Cygan"
] | Poster | null | Recent advancements in off-policy Reinforcement Learning (RL) have significantly improved sample efficiency, primarily due to the incorporation of various forms of regularization that enable more gradient update steps than traditional agents. However, many of these techniques have been tested in limited settings, often... | [] | null | 10,168 | 2403.00514 | title_snapshot | [
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Copyright Traps for Large Language Models | https://openreview.net/forum?id=LDq1JPdc55 | [
"Matthieu Meeus",
"Igor Shilov",
"Manuel Faysse",
"Yves-Alexandre de Montjoye"
] | Poster | null | Questions of fair use of copyright-protected content to train Large Language Models (LLMs) are being actively debated. Document-level inference has been proposed as a new task: inferring from black-box access to the trained model whether a piece of content has been seen during training. SOTA methods however rely on nat... | [] | null | 10,167 | 2402.09363 | title_snapshot | [
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Implicit meta-learning may lead language models to trust more reliable sources | https://openreview.net/forum?id=Fzp1DRzCIN | [
"Dmitrii Krasheninnikov",
"Egor Krasheninnikov",
"Bruno Kacper Mlodozeniec",
"Tegan Maharaj",
"David Krueger"
] | Poster | null | We demonstrate that large language models (LLMs) may learn indicators of document usefulness and modulate their updates accordingly. We introduce random strings ("tags") as indicators of usefulness in a synthetic fine-tuning dataset. Fine-tuning on this dataset leads to **implicit meta-learning (IML)**: in further fine... | [] | null | 10,166 | 2310.15047 | title_snapshot | [
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Dr. Strategy: Model-Based Generalist Agents with Strategic Dreaming | https://openreview.net/forum?id=HsseRq2FAx | [
"Hany Hamed",
"Subin Kim",
"Dongyeong Kim",
"Jaesik Yoon",
"Sungjin Ahn"
] | Poster | null | Model-based reinforcement learning (MBRL) has been a primary approach to ameliorating the sample efficiency issue as well as to make a generalist agent. However, there has not been much effort toward enhancing the strategy of dreaming itself. Therefore, it is a question *whether and how an agent can ``*dream better*''*... | [] | null | 10,161 | 2402.18866 | title_snapshot | [
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When Will Gradient Regularization Be Harmful? | https://openreview.net/forum?id=60vC1FY0dZ | [
"Yang Zhao",
"Hao Zhang",
"Xiuyuan Hu"
] | Poster | null | Gradient regularization (GR), which aims to penalize the gradient norm atop the loss function, has shown promising results in training modern over-parameterized deep neural networks. However, can we trust this powerful technique? This paper reveals that GR can cause performance degeneration in adaptive optimization sce... | [] | null | 10,159 | 2406.09723 | title_snapshot | [
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Efficient Algorithms for Empirical Group Distributionally Robust Optimization and Beyond | https://openreview.net/forum?id=pOJbk4Nzmi | [
"Dingzhi Yu",
"Yunuo Cai",
"Wei Jiang",
"Lijun Zhang"
] | Poster | null | In this paper, we investigate the empirical counterpart of Group Distributionally Robust Optimization (GDRO), which aims to minimize the maximal empirical risk across $m$ distinct groups. We formulate empirical GDRO as a *two-level* finite-sum convex-concave minimax optimization problem and develop an algorithm called ... | [] | null | 10,129 | 2403.03562 | title_snapshot | [
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Evaluation of Trajectory Distribution Predictions with Energy Score | https://openreview.net/forum?id=FCmWhJQ14I | [
"Novin Shahroudi",
"Mihkel Lepson",
"Meelis Kull"
] | Poster | null | Predicting the future trajectory of surrounding objects is inherently uncertain and vital in the safe and reliable planning of autonomous systems such as in self-driving cars. Although trajectory prediction models have become increasingly sophisticated in dealing with the complexities of spatiotemporal data, the evalua... | [] | null | 10,124 | null | null | [
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Causal Discovery with Fewer Conditional Independence Tests | https://openreview.net/forum?id=HpT19AKddu | [
"Kirankumar Shiragur",
"Jiaqi Zhang",
"Caroline Uhler"
] | Poster | null | Many questions in science center around the fundamental problem of understanding causal relationships. However, most constraint-based causal discovery algorithms, including the well-celebrated PC algorithm, often incur an _exponential_ number of conditional independence (CI) tests, posing limitations in various applica... | [] | null | 10,108 | 2406.01823 | title_snapshot | [
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Referee Can Play: An Alternative Approach to Conditional Generation via Model Inversion | https://openreview.net/forum?id=hZ0fWhgVch | [
"Xuantong LIU",
"Tianyang Hu",
"Wenjia Wang",
"Kenji Kawaguchi",
"Yuan Yao"
] | Poster | null | As a dominant force in text-to-image generation tasks, Diffusion Probabilistic Models (DPMs) face a critical challenge in controllability, struggling to adhere strictly to complex, multi-faceted instructions. In this work, we aim to address this alignment challenge for conditional generation tasks. First, we provide an... | [] | null | 10,107 | 2402.16305 | title_snapshot | [
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HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal | https://openreview.net/forum?id=f3TUipYU3U | [
"Mantas Mazeika",
"Long Phan",
"Xuwang Yin",
"Andy Zou",
"Zifan Wang",
"Norman Mu",
"Elham Sakhaee",
"Nathaniel Li",
"Steven Basart",
"Bo Li",
"David Forsyth",
"Dan Hendrycks"
] | Poster | null | Automated red teaming holds substantial promise for uncovering and mitigating the risks associated with the malicious use of large language models (LLMs), yet the field lacks a standardized evaluation framework to rigorously assess new methods. To address this issue, we introduce HarmBench, a standardized evaluation fr... | [] | null | 10,106 | 2402.04249 | title_snapshot | [
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Adversarial Attacks on Combinatorial Multi-Armed Bandits | https://openreview.net/forum?id=0tPBk24xNj | [
"Rishab Balasubramanian",
"Jiawei Li",
"Prasad Tadepalli",
"Huazheng Wang",
"Qingyun Wu",
"Haoyu Zhao"
] | Poster | null | We study reward poisoning attacks on Combinatorial Multi-armed Bandits (CMAB). We first provide a sufficient and necessary condition for the attackability of CMAB, a notion to capture the vulnerability and robustness of CMAB. The attackability condition depends on the intrinsic properties of the corresponding CMAB inst... | [] | null | 10,092 | 2310.05308 | title_snapshot | [
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Exact Conversion of In-Context Learning to Model Weights in Linearized-Attention Transformers | https://openreview.net/forum?id=LVF4P1NNwO | [
"Brian K Chen",
"Tianyang Hu",
"Hui Jin",
"Hwee Kuan Lee",
"Kenji Kawaguchi"
] | Poster | null | In-Context Learning (ICL) has been a powerful emergent property of large language models that has attracted increasing attention in recent years. In contrast to regular gradient-based learning, ICL is highly interpretable and does not require parameter updates. In this paper, we show that, for linearized transformer ne... | [] | null | 10,091 | 2406.02847 | title_snapshot | [
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Reshape and Adapt for Output Quantization (RAOQ): Quantization-aware Training for In-memory Computing Systems | https://openreview.net/forum?id=fM9xTkpAdu | [
"Bonan Zhang",
"Chia-Yu Chen",
"Naveen Verma"
] | Poster | null | In-memory computing (IMC) has emerged as a promising solution to address both computation and data-movement challenges, by performing computation on data in-place directly in the memory array. IMC typically relies on analog operation, which makes analog-to-digital converters (ADCs) necessary, for converting results bac... | [] | null | 10,086 | null | null | [
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Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method | https://openreview.net/forum?id=Gp0xZDmrA2 | [
"Qinghua Tao",
"Francesco Tonin",
"Alex Lambert",
"Yingyi Chen",
"Panagiotis Patrinos",
"Johan Suykens"
] | Poster | null | In contrast with Mercer kernel-based approaches as used e.g. in Kernel Principal Component Analysis (KPCA), it was previously shown that Singular Value Decomposition (SVD) inherently relates to asymmetric kernels and Asymmetric Kernel Singular Value Decomposition (KSVD) has been proposed. However, the existing formulat... | [] | null | 10,082 | 2406.08748 | title_snapshot | [
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Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo | https://openreview.net/forum?id=frA0NNBS1n | [
"Stephen Zhao",
"Rob Brekelmans",
"Alireza Makhzani",
"Roger Baker Grosse"
] | Oral | null | Numerous capability and safety techniques of Large Language Models (LLMs), including RLHF, automated red-teaming, prompt engineering, and infilling, can be cast as sampling from an unnormalized target distribution defined by a given reward or potential function over the full sequence. In this work, we leverage the rich... | [] | null | 10,076 | 2404.17546 | title_snapshot | [
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Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent | https://openreview.net/forum?id=OF7e0w1uon | [
"Yingru Li",
"Jiawei Xu",
"Lei Han",
"Zhi-Quan Luo"
] | Poster | null | We propose HyperAgent, a reinforcement learning (RL) algorithm based on the hypermodel framework for exploration in RL. HyperAgent allows for the efficient incremental approximation of posteriors associated with an optimal action-value function ($Q^\star$) without the need for conjugacy and follows the greedy policies ... | [] | null | 10,071 | 2402.10228 | title_snapshot | [
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Simplicity Bias via Global Convergence of Sharpness Minimization | https://openreview.net/forum?id=VUTyzH63Xa | [
"Khashayar Gatmiry",
"Zhiyuan Li",
"Sashank J. Reddi",
"Stefanie Jegelka"
] | Poster | null | The remarkable generalization ability of neural networks is usually attributed to the implicit bias of SGD, which often yields models with lower complexity using simpler (e.g. linear) and low-rank features. Recent works have provided empirical and theoretical evidence for the bias of particular variants of SGD (such as... | [] | null | 10,069 | 2410.16401 | title_snapshot | [
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STEER: Assessing the Economic Rationality of Large Language Models | https://openreview.net/forum?id=nU1mtFDtMX | [
"Narun Krishnamurthi Raman",
"Taylor Lundy",
"Samuel Joseph Amouyal",
"Yoav Levine",
"Kevin Leyton-Brown",
"Moshe Tennenholtz"
] | Poster | null | There is increasing interest in using LLMs as decision-making "agents". Doing so includes many degrees of freedom: which model should be used; how should it be prompted; should it be asked to introspect, conduct chain-of-thought reasoning, etc? Settling these questions---and more broadly, determining whether an LLM age... | [] | null | 10,056 | 2402.09552 | title_snapshot | [
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Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws | https://openreview.net/forum?id=0bmXrtTDUu | [
"Nikhil Sardana",
"Jacob Portes",
"Sasha Doubov",
"Jonathan Frankle"
] | Poster | null | Large language model (LLM) scaling laws are empirical formulas that estimate changes in model quality as a result of increasing parameter count and training data. However, these formulas, including the popular Deepmind Chinchilla scaling laws, neglect to include the cost of inference. We modify the Chinchilla scaling l... | [] | null | 10,055 | 2401.00448 | title_snapshot | [
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One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts | https://openreview.net/forum?id=edHLN40DWu | [
"Ruochen Wang",
"Sohyun An",
"Minhao Cheng",
"Tianyi Zhou",
"Sung Ju Hwang",
"Cho-Jui Hsieh"
] | Poster | null | Large Language Models (LLMs) exhibit strong generalization capabilities to novel tasks when prompted with language instructions and in-context demos. Since this ability sensitively depends on the quality of prompts, various methods have been explored to automate the instruction design. While these methods demonstrated ... | [] | null | 10,053 | 2407.00256 | title_snapshot | [
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Double-Step Alternating Extragradient with Increasing Timescale Separation for Finding Local Minimax Points: Provable Improvements | https://openreview.net/forum?id=nUVForc3VP | [
"Kyuwon Kim",
"Donghwan Kim"
] | Poster | null | In nonconvex-nonconcave minimax optimization, two-timescale gradient methods have shown their potential to find local minimax (optimal) points, provided that the timescale separation between the min and the max player is sufficiently large. However, existing two-timescale variants of gradient descent ascent and extragr... | [] | null | 10,043 | null | null | [
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Unveiling and Harnessing Hidden Attention Sinks: Enhancing Large Language Models without Training through Attention Calibration | https://openreview.net/forum?id=DLTjFFiuUJ | [
"Zhongzhi Yu",
"Zheng Wang",
"Yonggan Fu",
"Huihong Shi",
"Khalid Shaikh",
"Yingyan Celine Lin"
] | Poster | null | Attention is a fundamental component behind the remarkable achievements of large language models (LLMs). However, our current understanding of the attention mechanism, especially regarding how attention distributions are established, remains limited. Inspired by recent studies that explore the presence of attention sin... | [] | null | 10,042 | 2406.15765 | title_snapshot | [
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TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision | https://openreview.net/forum?id=v1I4zRAjMb | [
"Zhuo Chen",
"Jacob McCarran",
"Esteban Vizcaino",
"Marin Soljacic",
"Di Luo"
] | Poster | null | Partial differential equations (PDEs) are instrumental for modeling dynamical systems in science and engineering. The advent of neural networks has initiated a significant shift in tackling these complexities though challenges in accuracy persist, especially for initial value problems. In this paper, we introduce the *... | [] | null | 10,038 | 2404.10771 | title_snapshot | [
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Learning Adaptive and View-Invariant Vision Transformer for Real-Time UAV Tracking | https://openreview.net/forum?id=eaNLvrP8n1 | [
"Yongxin Li",
"Mengyuan Liu",
"You Wu",
"Xucheng Wang",
"Xiangyang Yang",
"Shuiwang Li"
] | Poster | null | Harnessing transformer-based models, visual tracking has made substantial strides. However, the sluggish performance of current trackers limits their practicality on devices with constrained computational capabilities, especially for real-time unmanned aerial vehicle (UAV) tracking. Addressing this challenge, we introd... | [] | null | 10,036 | 2412.20002 | title_judge | [
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Conditional Common Entropy for Instrumental Variable Testing and Partial Identification | https://openreview.net/forum?id=Wnni3cu39x | [
"Ziwei Jiang",
"Murat Kocaoglu"
] | Poster | null | Instrumental variables (IVs) are widely used for estimating causal effects. There are two main challenges when using instrumental variables. First of all, using IV without additional assumptions such as linearity, the causal effect may still not be identifiable. Second, when selecting an IV, the validity of the selecte... | [] | null | 10,033 | null | null | [
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Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption | https://openreview.net/forum?id=Z0S6fUdW68 | [
"Chenlu Ye",
"Jiafan He",
"Quanquan Gu",
"Tong Zhang"
] | Poster | null | This study tackles the challenges of adversarial corruption in model-based reinforcement learning (RL), where the transition dynamics can be corrupted by an adversary. Existing studies on corruption-robust RL mostly focus on the setting of model-free RL, where robust least-square regression is often employed for value ... | [] | null | 10,022 | 2402.08991 | title_snapshot | [
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Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models | https://openreview.net/forum?id=fdroxYsgzQ | [
"Amrith Setlur",
"Saurabh Garg",
"Virginia Smith",
"Sergey Levine"
] | Poster | null | Machine learning models fail catastrophically under distribution shift, but a surprisingly effective way to empirically improve robustness to some types of shift (*e.g.*, Imagenet-A/C) is to use stronger open-vocabulary classifiers derived from foundation models. In this work, we first note that for shifts governed by ... | [] | null | 10,017 | null | null | [
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Precise Accuracy / Robustness Tradeoffs in Regression: Case of General Norms | https://openreview.net/forum?id=btYeH65fI3 | [
"Elvis Dohmatob",
"Meyer Scetbon"
] | Poster | null | In this paper, we investigate the impact of test-time adversarial attacks on linear regression models and determine the optimal level of robustness that any model can reach while maintaining a given level of standard predictive performance (accuracy). Through quantitative estimates, we uncover fundamental tradeoffs bet... | [] | null | 10,007 | 2308.00556 | title_judge | [
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Hard Tasks First: Multi-Task Reinforcement Learning Through Task Scheduling | https://openreview.net/forum?id=haUOhXo70o | [
"Myungsik Cho",
"Jongeui Park",
"Suyoung Lee",
"Youngchul Sung"
] | Poster | null | Multi-task reinforcement learning (RL) faces the significant challenge of varying task difficulties, often leading to negative transfer when simpler tasks overshadow the learning of more complex ones. To overcome this challenge, we propose a novel algorithm, Scheduled Multi-Task Training (SMT), that strategically prior... | [] | null | 9,999 | null | null | [
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Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training | https://openreview.net/forum?id=Ht20wtgaty | [
"Tehila Dahan",
"Kfir Yehuda Levy"
] | Poster | null | In this paper, we investigate the challenging framework of Byzantine-robust training in distributed machine learning (ML) systems, focusing on enhancing both efficiency and practicality. As distributed ML systems become integral for complex ML tasks, ensuring resilience against Byzantine failures—where workers may cont... | [] | null | 9,997 | 2405.14759 | title_snapshot | [
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Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations | https://openreview.net/forum?id=xye7iNsgXn | [
"Jiaqi Zhai",
"Lucy Liao",
"Xing Liu",
"Yueming Wang",
"Rui Li",
"Xuan Cao",
"Leon Gao",
"Zhaojie Gong",
"Fangda Gu",
"Jiayuan He",
"Yinghai Lu",
"Yu Shi"
] | Poster | null | Large-scale recommendation systems are characterized by their reliance on high cardinality, heterogeneous features and the need to handle tens of billions of user actions on a daily basis. Despite being trained on huge volume of data with thousands of features, most Deep Learning Recommendation Models (DLRMs) in indust... | [] | null | 9,994 | 2402.17152 | title_snapshot | [
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Trustworthy Alignment of Retrieval-Augmented Large Language Models via Reinforcement Learning | https://openreview.net/forum?id=XwnABAdH5y | [
"Zongmeng Zhang",
"Yufeng Shi",
"Jinhua Zhu",
"Wengang Zhou",
"Xiang Qi",
"peng zhang",
"Houqiang Li"
] | Poster | null | Trustworthiness is an essential prerequisite for the real-world application of large language models. In this paper, we focus on the trustworthiness of language models with respect to retrieval augmentation. Despite being supported with external evidence, retrieval-augmented generation still suffers from hallucinations... | [] | null | 9,991 | 2410.16843 | title_snapshot | [
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Instruction Tuning for Secure Code Generation | https://openreview.net/forum?id=MgTzMaYHvG | [
"Jingxuan He",
"Mark Vero",
"Gabriela Krasnopolska",
"Martin Vechev"
] | Poster | null | Modern language models (LMs) have gained widespread acceptance in everyday and professional contexts, particularly in programming. An essential procedure enabling this adoption is instruction tuning, which substantially enhances LMs' practical utility by training them to follow user instructions and human preferences. ... | [] | null | 9,980 | 2402.09497 | title_snapshot | [
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Don't be so Negative! Score-based Generative Modeling with Oracle-assisted Guidance | https://openreview.net/forum?id=H8pMSJwRD5 | [
"Saeid Naderiparizi",
"Xiaoxuan Liang",
"Setareh Cohan",
"Berend Zwartsenberg",
"Frank Wood"
] | Poster | null | Score-based diffusion models are a powerful class of generative models, widely utilized across diverse domains. Despite significant advancements in large-scale tasks such as text-to-image generation, their application to constrained domains has received considerably less attention. This work addresses model learning in... | [] | null | 9,973 | 2307.16463 | title_snapshot | [
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LangCell: Language-Cell Pre-training for Cell Identity Understanding | https://openreview.net/forum?id=GcZjpKA37R | [
"Suyuan Zhao",
"Jiahuan Zhang",
"Yushuai Wu",
"YIZHEN LUO",
"Zaiqing Nie"
] | Poster | null | Cell identity encompasses various semantic aspects of a cell, including cell type, pathway information, disease information, and more, which are essential for biologists to gain insights into its biological characteristics. Understanding cell identity from the transcriptomic data, such as annotating cell types, has bec... | [] | null | 9,961 | 2405.06708 | title_snapshot | [
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Causal Representation Learning from Multiple Distributions: A General Setting | https://openreview.net/forum?id=Pte6iiXvpf | [
"Kun Zhang",
"Shaoan Xie",
"Ignavier Ng",
"Yujia Zheng"
] | Poster | null | In many problems, the measured variables (e.g., image pixels) are just mathematical functions of the latent causal variables (e.g., the underlying concepts or objects). For the purpose of making predictions in changing environments or making proper changes to the system, it is helpful to recover the latent causal varia... | [] | null | 9,954 | 2402.05052 | title_snapshot | [
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Diffusion Models Demand Contrastive Guidance for Adversarial Purification to Advance | https://openreview.net/forum?id=2NUGeV64y2 | [
"Mingyuan Bai",
"Wei Huang",
"Tenghui Li",
"Andong Wang",
"Junbin Gao",
"Cesar F Caiafa",
"Qibin Zhao"
] | Poster | null | In adversarial defense, adversarial purification can be viewed as a special generation task with the purpose to remove adversarial attacks and diffusion models excel in adversarial purification for their strong generative power. With different predetermined generation requirements, various types of guidance have been p... | [] | null | 9,947 | null | null | [
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PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect | https://openreview.net/forum?id=o5SVr80Rgg | [
"Lokesh Nagalapatti",
"Pranava Singhal",
"Avishek Ghosh",
"Sunita Sarawagi"
] | Poster | null | Given a dataset of individuals each described by a covariate vector, a treatment, and an observed outcome on the treatment, the goal of the individual treatment effect (ITE) estimation task is to predict outcome changes resulting from a change in treatment. A fundamental challenge is that in the observational data, a c... | [] | null | 9,945 | 2406.03864 | title_snapshot | [
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Position: Open-Endedness is Essential for Artificial Superhuman Intelligence | https://openreview.net/forum?id=Bc4vZ2CX7E | [
"Edward Hughes",
"Michael D Dennis",
"Jack Parker-Holder",
"Feryal Behbahani",
"Aditi Mavalankar",
"Yuge Shi",
"Tom Schaul",
"Tim Rocktäschel"
] | Oral | null | In recent years there has been a tremendous surge in the general capabilities of AI systems, mainly fuelled by training foundation models on internet-scale data. Nevertheless, the creation of open-ended, ever self-improving AI remains elusive. **In this position paper, we argue that the ingredients are now in place to ... | [] | null | 9,943 | 2406.04268 | title_judge | [
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Simple linear attention language models balance the recall-throughput tradeoff | https://openreview.net/forum?id=e93ffDcpH3 | [
"Simran Arora",
"Sabri Eyuboglu",
"Michael Zhang",
"Aman Timalsina",
"Silas Alberti",
"James Zou",
"Atri Rudra",
"Christopher Re"
] | Spotlight | null | Recent work has shown that attention-based language models excel at "recall", the ability to ground generations in tokens previously seen in context. However, the efficiency of attention-based models is bottle-necked during inference by the KV-cache's aggressive memory consumption. In this work, we explore whether we c... | [] | null | 9,942 | 2402.18668 | title_snapshot | [
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Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments | https://openreview.net/forum?id=s5PLISyNyP | [
"Jonas Schweisthal",
"Dennis Frauen",
"Mihaela van der Schaar",
"Stefan Feuerriegel"
] | Poster | null | Estimating the conditional average treatment effect (CATE) from observational data is relevant for many applications such as personalized medicine. Here, we focus on the widespread setting where the observational data come from multiple environments, such as different hospitals, physicians, or countries. Furthermore, w... | [] | null | 9,939 | 2406.02464 | title_snapshot | [
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Principled Gradient-Based MCMC for Conditional Sampling of Text | https://openreview.net/forum?id=AwLLSlJAeJ | [
"Li Du",
"Afra Amini",
"Lucas Torroba Hennigen",
"Xinyan Velocity Yu",
"Holden Lee",
"Jason Eisner",
"Ryan Cotterell"
] | Poster | null | We consider the problem of sampling text from an energy-based model. This arises, for example, when sampling text from a neural language model subject to soft constraints. Although the target distribution is discrete, the internal computations of the energy function (given by the language model) are differentiable, so ... | [] | null | 9,938 | null | null | [
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Maestro: Uncovering Low-Rank Structures via Trainable Decomposition | https://openreview.net/forum?id=7bjyambg4x | [
"Samuel Horváth",
"Stefanos Laskaridis",
"Shashank Rajput",
"Hongyi Wang"
] | Poster | null | Deep Neural Networks (DNNs) have been a large driver for AI breakthroughs in recent years, ranging from self-driving cars to intelligent assistants. However, these models have been getting increasingly large as they become more accurate and safe. This means that their training becomes increasingly costly and time-consu... | [] | null | 9,930 | 2308.14929 | title_snapshot | [
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Infinite-Horizon Distributionally Robust Regret-Optimal Control | https://openreview.net/forum?id=h3SGdpI4Ta | [
"Taylan Kargin",
"Joudi Hajar",
"Vikrant Malik",
"Babak Hassibi"
] | Poster | null | We study the infinite-horizon distributionally robust (DR) control of linear systems with quadratic costs, where disturbances have unknown, possibly time-correlated distribution within a Wasserstein-2 ambiguity set. We aim to minimize the worst-case expected regret—the excess cost of a causal policy compared to a non-c... | [] | null | 9,927 | 2406.07248 | title_snapshot | [
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Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them) | https://openreview.net/forum?id=F3936hVwQa | [
"Drew Prinster",
"Samuel Don Stanton",
"Anqi Liu",
"Suchi Saria"
] | Poster | null | As artificial intelligence (AI) / machine learning (ML) gain widespread adoption, practitioners are increasingly seeking means to quantify and control the risk these systems incur. This challenge is especially salient when such systems have autonomy to collect their own data, such as in black-box optimization and activ... | [] | null | 9,926 | 2405.06627 | title_snapshot | [
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StrWAEs to Invariant Representations | https://openreview.net/forum?id=kLZZWvqlEm | [
"Hyunjong Lee",
"Yedarm Seong",
"Sungdong Lee",
"Joong-Ho Won"
] | Poster | null | Autoencoders have become an indispensable tool for generative modeling and representation learning in high dimensions. Imposing structural constraints such as conditional independence in order to capture invariance of latent variables to nuisance information has been attempted through adding *ad hoc* penalties to the l... | [] | null | 9,920 | null | null | [
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Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics | https://openreview.net/forum?id=BrZPj9rEpN | [
"Xinyu Zhang",
"Wenjie Qiu",
"Yi-Chen Li",
"Lei Yuan",
"Chengxing Jia",
"Zongzhang Zhang",
"Yang Yu"
] | Poster | null | Developing policies that can adapt to non-stationary environments is essential for real-world reinforcement learning applications. Nevertheless, learning such adaptable policies in offline settings, with only a limited set of pre-collected trajectories, presents significant challenges. A key difficulty arises because t... | [] | null | 9,919 | 2402.11317 | title_snapshot | [
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Linguistic Calibration of Long-Form Generations | https://openreview.net/forum?id=rJVjQSQ8ye | [
"Neil Band",
"Xuechen Li",
"Tengyu Ma",
"Tatsunori Hashimoto"
] | Poster | null | Language models (LMs) may lead their users to make suboptimal downstream decisions when they confidently hallucinate. This issue can be mitigated by having the LM verbally convey the probability that its claims are correct, but existing models cannot produce long-form text with calibrated confidence statements. Through... | [] | null | 9,911 | 2404.00474 | title_snapshot | [
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R2E: Turning any Github Repository into a Programming Agent Environment | https://openreview.net/forum?id=kXHgEYFyf3 | [
"Naman Jain",
"Manish Shetty",
"Tianjun Zhang",
"King Han",
"Koushik Sen",
"Ion Stoica"
] | Poster | null | While Large Language Models’ (LLMs) coding capabilities have advanced rapidly, corresponding evaluation benchmarks on real-world programming setups are yet to catch up. Building a scalable and interactive testbed for evaluating general-purpose AI coding agents for real-world code has been challenging, particularly due ... | [] | null | 9,908 | null | null | [
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Learning to Explore for Stochastic Gradient MCMC | https://openreview.net/forum?id=aECamk9izk | [
"SeungHyun Kim",
"Seohyeon Jung",
"SeongHyeon Kim",
"Juho Lee"
] | Poster | null | Bayesian Neural Networks(BNNs) with high-dimensional parameters pose a challenge for posterior inference due to the multi-modality of the posterior distributions. Stochastic Gradient Markov Chain Monte Carlo(SGMCMC) with cyclical learning rate scheduling is a promising solution, but it requires a large number of sampli... | [] | null | 9,897 | 2408.09140 | title_snapshot | [
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Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning | https://openreview.net/forum?id=disVlUOH4b | [
"Yizhe Huang",
"Anji Liu",
"Fanqi Kong",
"Yaodong Yang",
"Song-Chun Zhu",
"Xue Feng"
] | Poster | null | Despite the recent successes of multi-agent reinforcement learning (MARL) algorithms, efficiently adapting to co-players in mixed-motive environments remains a significant challenge. One feasible approach is to hierarchically model co-players' behavior based on inferring their characteristics. However, these methods of... | [] | null | 9,889 | 2406.08002 | title_snapshot | [
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How to Explore with Belief: State Entropy Maximization in POMDPs | https://openreview.net/forum?id=LbcNAIgNnB | [
"Riccardo Zamboni",
"Duilio Cirino",
"Marcello Restelli",
"Mirco Mutti"
] | Poster | null | Recent works have studied *state entropy maximization* in reinforcement learning, in which the agent's objective is to learn a policy inducing high entropy over states visitation (Hazan et al., 2019). They typically assume full observability of the state of the system, so that the entropy of the observations is maximiz... | [] | null | 9,887 | 2406.02295 | title_snapshot | [
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Submodular framework for structured-sparse optimal transport | https://openreview.net/forum?id=bfQCO9Vqhk | [
"Piyushi Manupriya",
"Pratik Jawanpuria",
"Karthik S. Gurumoorthy",
"SakethaNath Jagarlapudi",
"Bamdev Mishra"
] | Poster | null | Unbalanced optimal transport (UOT) has recently gained much attention due to its flexible framework for handling un-normalized measures and its robustness properties. In this work, we explore learning (structured) sparse transport plans in the UOT setting, i.e., transport plans have an upper bound on the number of non-... | [] | null | 9,879 | 2406.04914 | title_snapshot | [
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Graph-Triggered Rising Bandits | https://openreview.net/forum?id=bPsohGR6gD | [
"Gianmarco Genalti",
"Marco Mussi",
"Nicola Gatti",
"Marcello Restelli",
"Matteo Castiglioni",
"Alberto Maria Metelli"
] | Poster | null | In this paper, we propose a novel generalization of rested and restless bandits where the evolution of the arms' expected rewards is governed by a graph defined over the arms. An edge connecting a pair of arms $(i,j)$ represents the fact that a pull of arm $i$ *triggers* the evolution of arm $j$, and vice versa. Intere... | [] | null | 9,878 | null | null | [
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Breadth-First Exploration on Adaptive Grid for Reinforcement Learning | https://openreview.net/forum?id=59MYoLghyk | [
"Youngsik Yoon",
"Gangbok Lee",
"Sungsoo Ahn",
"Jungseul Ok"
] | Poster | null | Graph-based planners have gained significant attention for goal-conditioned reinforcement learning (RL), where they construct a graph consisting of confident transitions between *subgoals* as edges and run shortest path algorithms to exploit the confident edges. Meanwhile, identifying and avoiding unattainable transiti... | [] | null | 9,877 | null | null | [
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SILVER: Single-loop variance reduction and application to federated learning | https://openreview.net/forum?id=pOgMluzEIH | [
"Kazusato Oko",
"Shunta Akiyama",
"Denny Wu",
"Tomoya Murata",
"Taiji Suzuki"
] | Poster | null | Most variance reduction methods require multiple times of full gradient computation, which is time-consuming and hence a bottleneck in application to distributed optimization. We present a single-loop variance-reduced gradient estimator named SILVER (SIngle-Loop VariancE-Reduction) for the finite-sum non-convex optimiz... | [] | null | 9,875 | null | null | [
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Generative Conditional Distributions by Neural (Entropic) Optimal Transport | https://openreview.net/forum?id=FoRqdsN4IA | [
"Bao Nguyen",
"Binh Nguyen",
"Hieu Trung Nguyen",
"Viet Anh Nguyen"
] | Poster | null | Learning conditional distributions is challenging because the desired outcome is not a single distribution but multiple distributions that correspond to multiple instances of the covariates. We introduce a novel neural entropic optimal transport method designed to effectively learn generative models of conditional dist... | [] | null | 9,874 | 2406.02317 | title_snapshot | [
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Cell2Sentence: Teaching Large Language Models the Language of Biology | https://openreview.net/forum?id=EWt5wsEdvc | [
"Daniel Levine",
"Syed A Rizvi",
"Sacha Lévy",
"Nazreen Pallikkavaliyaveetil",
"David Zhang",
"Xingyu Chen",
"Sina Ghadermarzi",
"Ruiming Wu",
"Zihe Zheng",
"Ivan Vrkic",
"Anna Zhong",
"Daphne Raskin",
"Insu Han",
"Antonio Henrique de Oliveira Fonseca",
"Josue Ortega Caro",
"Amin Karba... | Poster | null | We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into "cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enabl... | [] | null | 9,873 | null | null | [
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Watermark Stealing in Large Language Models | https://openreview.net/forum?id=Wp054bnPq9 | [
"Nikola Jovanović",
"Robin Staab",
"Martin Vechev"
] | Poster | null | LLM watermarking has attracted attention as a promising way to detect AI-generated content, with some works suggesting that current schemes may already be fit for deployment. In this work we dispute this claim, identifying *watermark stealing* (WS) as a fundamental vulnerability of these schemes. We show that querying ... | [] | null | 9,867 | 2402.19361 | title_snapshot | [
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A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models | https://openreview.net/forum?id=OnOaj3g9fi | [
"Taehong Moon",
"Moonseok Choi",
"EungGu Yun",
"Jongmin Yoon",
"Gayoung Lee",
"Jaewoong Cho",
"Juho Lee"
] | Poster | null | Diffusion models have shown remarkable performance in generation problems over various domains including images, videos, text, and audio. A practical bottleneck of diffusion models is their sampling speed, due to the repeated evaluation of score estimation networks during the inference. In this work, we propose a novel... | [] | null | 9,866 | 2408.05927 | title_snapshot | [
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Stop Regressing: Training Value Functions via Classification for Scalable Deep RL | https://openreview.net/forum?id=dVpFKfqF3R | [
"Jesse Farebrother",
"Jordi Orbay",
"Quan Vuong",
"Adrien Ali Taiga",
"Yevgen Chebotar",
"Ted Xiao",
"Alex Irpan",
"Sergey Levine",
"Pablo Samuel Castro",
"Aleksandra Faust",
"Aviral Kumar",
"Rishabh Agarwal"
] | Oral | null | Value functions are an essential component in deep reinforcement learning (RL), that are typically trained via mean squared error regression to match bootstrapped target values. However, scaling value-based RL methods to large networks has proven challenging. This difficulty is in stark contrast to supervised learning:... | [] | null | 9,864 | 2403.03950 | title_snapshot | [
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Weisfeiler-Leman at the margin: When more expressivity matters | https://openreview.net/forum?id=HTNgNt8CTJ | [
"Billy Joe Franks",
"Christopher Morris",
"Ameya Velingker",
"Floris Geerts"
] | Poster | null | The Weisfeiler--Leman algorithm (1-WL) is a well-studied heuristic for the graph isomorphism problem. Recently, the algorithm has played a prominent role in understanding the expressive power of message-passing graph neural networks (MPNNs) and being effective as a graph kernel. Despite its success, the 1-WL faces chal... | [] | null | 9,855 | 2402.07568 | title_snapshot | [
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Allocation Requires Prediction Only if Inequality Is Low | https://openreview.net/forum?id=WUicA0hOF9 | [
"Ali Shirali",
"Rediet Abebe",
"Moritz Hardt"
] | Spotlight | null | Algorithmic predictions are emerging as a promising solution concept for efficiently allocating societal resources. Fueling their use is an underlying assumption that such systems are necessary to identify individuals for interventions. We propose a principled framework for assessing this assumption: Using a simple mat... | [] | null | 9,854 | 2406.13882 | title_snapshot | [
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Decomposing and Editing Predictions by Modeling Model Computation | https://openreview.net/forum?id=rTBR0eqE4G | [
"Harshay Shah",
"Andrew Ilyas",
"Aleksander Madry"
] | Poster | null | *How does the internal computation of a machine learning model transform inputs into predictions?* To tackle this question, we introduce a framework called *component modeling* for decomposing a model prediction in terms of its components---architectural "building blocks" such as convolution filters or attention heads.... | [] | null | 9,846 | 2404.11534 | title_snapshot | [
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Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transport | https://openreview.net/forum?id=dMhF96PfQi | [
"Jaemoo Choi",
"Jaewoong Choi",
"Myungjoo Kang"
] | Poster | null | Wasserstein gradient flow (WGF) describes the gradient dynamics of probability density within the Wasserstein space. WGF provides a promising approach for conducting optimization over the probability distributions. Numerically approximating the continuous WGF requires the time discretization method. The most well-known... | [] | null | 9,845 | 2402.05443 | title_snapshot | [
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Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation | https://openreview.net/forum?id=pXaEYzrFae | [
"Luca Beurer-Kellner",
"Marc Fischer",
"Martin Vechev"
] | Poster | null | To ensure that text generated by large language models (LLMs) is in an expected format, constrained decoding methods propose to enforce strict formal language constraints during generation. However, as we show in this work, not only do such methods often incur performance overhead during generation, but many of them al... | [] | null | 9,839 | 2403.06988 | title_snapshot | [
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Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams | https://openreview.net/forum?id=hcASxFvmZ5 | [
"Brian M Cho",
"Kyra Gan",
"Nathan Kallus"
] | Poster | null | We propose a novel nonparametric sequential test for composite hypotheses for means of multiple data streams. Our proposed method, peeking with expectation-based averaged capital (PEAK), builds upon the testing-by-betting framework and provides a non-asymptotic $\alpha$-level test across any stopping time. Our contribu... | [] | null | 9,838 | 2402.06122 | title_snapshot | [
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DUPLEX: Dual GAT for Complex Embedding of Directed Graphs | https://openreview.net/forum?id=M3uv4qDKOL | [
"Zhaoru Ke",
"Hang Yu",
"Jianguo Li",
"Haipeng Zhang"
] | Poster | null | Current directed graph embedding methods build upon undirected techniques but often inadequately capture directed edge information, leading to challenges such as: (1) Suboptimal representations for nodes with low in/out-degrees, due to the insufficient neighbor interactions; (2) Limited inductive ability for representi... | [] | null | 9,834 | 2406.05391 | title_snapshot | [
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Exploiting Human-AI Dependence for Learning to Defer | https://openreview.net/forum?id=aiz79FxjaI | [
"Zixi Wei",
"Yuzhou Cao",
"Lei Feng"
] | Poster | null | The learning to defer (L2D) framework allows models to defer their decisions to human experts. For L2D, the Bayes optimality is the basic requirement of theoretical guarantees for the design of consistent surrogate loss functions, which requires the minimizer (i.e., learned classifier) by the surrogate loss to be the B... | [] | null | 9,827 | null | null | [
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Iterative Search Attribution for Deep Neural Networks | https://openreview.net/forum?id=5ToHnqYxjB | [
"Zhiyu Zhu",
"Huaming Chen",
"Xinyi Wang",
"Jiayu Zhang",
"Zhibo Jin",
"Jason Xue",
"Jun Shen"
] | Poster | null | Deep neural networks (DNNs) have achieved state-of-the-art performance across various applications. However, ensuring the reliability and trustworthiness of DNNs requires enhanced interpretability of model inputs and outputs. As an effective means of Explainable Artificial Intelligence (XAI) research, the interpretabil... | [] | null | 9,818 | null | null | [
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Latent Logic Tree Extraction for Event Sequence Explanation from LLMs | https://openreview.net/forum?id=pwfcwEqdUz | [
"Zitao Song",
"Chao Yang",
"Chaojie Wang",
"Bo An",
"Shuang Li"
] | Poster | null | Modern high-stakes systems, such as healthcare or robotics, often generate vast streaming event sequences. Our goal is to design an efficient, plug-and-play tool to elicit logic tree-based explanations from Large Language Models (LLMs) to provide customized insights into each observed event sequence. Built on the tempo... | [] | null | 9,816 | 2406.01124 | title_snapshot | [
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Understanding Finetuning for Factual Knowledge Extraction | https://openreview.net/forum?id=cPsn9AcOYh | [
"Gaurav Rohit Ghosal",
"Tatsunori Hashimoto",
"Aditi Raghunathan"
] | Poster | null | In this work, we study the impact of QA fine-tuning data on downstream factuality. We show that fine-tuning on lesser-known facts that are poorly stored during pretraining yields significantly worse factuality than fine-tuning on well-known facts, even when all facts are seen during pretraining. We prove this phenomeno... | [] | null | 9,813 | 2406.14785 | title_snapshot | [
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Pruner-Zero: Evolving Symbolic Pruning Metric From Scratch for Large Language Models | https://openreview.net/forum?id=1tRLxQzdep | [
"Peijie Dong",
"Lujun Li",
"Zhenheng Tang",
"Xiang Liu",
"Xinglin Pan",
"Qiang Wang",
"Xiaowen Chu"
] | Poster | null | Despite the remarkable capabilities, Large Language Models (LLMs) face deployment challenges due to their extensive size. Pruning methods drop a subset of weights to accelerate, but many of them require retraining, which is prohibitively expensive and computationally demanding. Recently, post-training pruning approache... | [] | null | 9,811 | 2406.02924 | title_snapshot | [
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tinyBenchmarks: evaluating LLMs with fewer examples | https://openreview.net/forum?id=qAml3FpfhG | [
"Felipe Maia Polo",
"Lucas Weber",
"Leshem Choshen",
"Yuekai Sun",
"Gongjun Xu",
"Mikhail Yurochkin"
] | Poster | null | The versatility of large language models (LLMs) led to the creation of diverse benchmarks that thoroughly test a variety of language models’ abilities. These benchmarks consist of tens of thousands of examples making evaluation of LLMs very expensive. In this paper, we investigate strategies to reduce the number of eva... | [] | null | 9,806 | 2402.14992 | title_snapshot | [
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The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling | https://openreview.net/forum?id=2pYTCy4GUV | [
"Jiajun Ma",
"Shuchen Xue",
"Tianyang Hu",
"Wenjia Wang",
"Zhaoqiang Liu",
"Zhenguo Li",
"Zhi-Ming Ma",
"Kenji Kawaguchi"
] | Poster | null | With the incorporation of the UNet architecture, diffusion probabilistic models have become a dominant force in image generation tasks. One key design in UNet is the skip connections between the encoder and decoder blocks. Although skip connections have been shown to improve training stability and model performance, we... | [] | null | 9,801 | 2402.15170 | title_snapshot | [
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Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression | https://openreview.net/forum?id=Yn8xnK90mS | [
"Zhankun Luo",
"Abolfazl Hashemi"
] | Poster | null | We study the trajectory of iterations and the convergence rates of the Expectation-Maximization (EM) algorithm for two-component Mixed Linear Regression (2MLR). The fundamental goal of MLR is to learn the regression models from unlabeled observations. The EM algorithm finds extensive applications in solving the mixture... | [] | null | 9,800 | 2405.18237 | title_snapshot | [
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RoboMP$^2$: A Robotic Multimodal Perception-Planning Framework with Multimodal Large Language Models | https://openreview.net/forum?id=eJFQROkaj0 | [
"Qi Lv",
"Hao Li",
"Xiang Deng",
"Rui Shao",
"Michael Y Wang",
"Liqiang Nie"
] | Poster | null | Multimodal Large Language Models (MLLMs) have shown impressive reasoning abilities and general intelligence in various domains. It inspires researchers to train end-to-end MLLMs or utilize large models to generate policies with human-selected prompts for embodied agents. However, these methods exhibit limited generaliz... | [] | null | 9,799 | 2404.04929 | title_snapshot | [
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Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data | https://openreview.net/forum?id=PlVjIGaFdH | [
"Giannis Daras",
"Alex Dimakis",
"Constantinos Costis Daskalakis"
] | Poster | null | Ambient diffusion is a recently proposed framework for training diffusion models using corrupted data. Both Ambient Diffusion and alternative SURE-based approaches for learning diffusion models from corrupted data resort to approximations which deteriorate performance. We present the first framework for training diffus... | [] | null | 9,798 | 2404.10177 | title_snapshot | [
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Token-level Direct Preference Optimization | https://openreview.net/forum?id=1RZKuvqYCR | [
"Yongcheng Zeng",
"Guoqing Liu",
"Weiyu Ma",
"Ning Yang",
"Haifeng Zhang",
"Jun Wang"
] | Poster | null | Fine-tuning pre-trained Large Language Models (LLMs) is essential to align them with human values and intentions. This process often utilizes methods like pairwise comparisons and KL divergence against a reference LLM, focusing on the evaluation of full answers generated by the models. However, the generation of these ... | [] | null | 9,794 | 2404.11999 | title_snapshot | [
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Mean Field Langevin Actor-Critic: Faster Convergence and Global Optimality beyond Lazy Learning | https://openreview.net/forum?id=FOJE1kRcHG | [
"Kakei Yamamoto",
"Kazusato Oko",
"Zhuoran Yang",
"Taiji Suzuki"
] | Poster | null | This work explores the feature learning capabilities of deep reinforcement learning algorithms in the pursuit of optimal policy determination. We particularly examine an over-parameterized neural actor-critic framework within the mean-field regime, where both actor and critic components undergo updates via policy gradi... | [] | null | 9,792 | null | null | [
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Enabling Uncertainty Estimation in Iterative Neural Networks | https://openreview.net/forum?id=N6A6t6xlKm | [
"Nikita Durasov",
"Doruk Oner",
"Jonathan Donier",
"Hieu Le",
"Pascal Fua"
] | Poster | null | Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of their successive outputs is highly correlated with the accuracy of ... | [] | null | 9,790 | 2403.16732 | title_snapshot | [
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Clifford-Steerable Convolutional Neural Networks | https://openreview.net/forum?id=XTglHJjzQI | [
"Maksim Zhdanov",
"David Ruhe",
"Maurice Weiler",
"Ana Lucic",
"Johannes Brandstetter",
"Patrick Forré"
] | Poster | null | We present Clifford-Steerable Convolutional Neural Networks (CS-CNNs), a novel class of ${\operatorname{E}}(p, q)$-equivariant CNNs. CS-CNNs process multivector fields on pseudo-Euclidean spaces $\mathbb{R}^{p,q}$. They specialize, for instance, to ${\operatorname{E}}(3)$-equivariance on $\mathbb{R}^3$ and Poincaré-equ... | [] | null | 9,787 | 2402.14730 | title_snapshot | [
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Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows | https://openreview.net/forum?id=hG6gddAKnJ | [
"Sibylle Marcotte",
"Rémi Gribonval",
"Gabriel Peyré"
] | Poster | null | Conservation laws are well-established in the context of Euclidean gradient flow dynamics, notably for linear or ReLU neural network training. Yet, their existence and principles for non-Euclidean geometries and momentum-based dynamics remain largely unknown. In this paper, we characterize "all" conservation laws in th... | [] | null | 9,780 | 2405.12888 | title_snapshot | [
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Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization | https://openreview.net/forum?id=CbbTF6tDhW | [
"Nayeong Kim",
"Juwon Kang",
"Sungsoo Ahn",
"Jungseul Ok",
"Suha Kwak"
] | Poster | null | We study the problem of training an unbiased and accurate model given a dataset with multiple biases. This problem is challenging since the multiple biases cause multiple undesirable shortcuts during training, and even worse, mitigating one may exacerbate the other. We propose a novel training method to tackle this cha... | [] | null | 9,772 | 2409.03303 | title_snapshot | [
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Self-Supervised Coarsening of Unstructured Grid with Automatic Differentiation | https://openreview.net/forum?id=kMBvZ40Iu9 | [
"Sergei Shumilin",
"Alexander Ryabov",
"Nikolay Yavich",
"Evgeny Burnaev",
"Vladimir Vanovskiy"
] | Poster | null | Due to the high computational load of modern numerical simulation, there is a demand for approaches that would reduce the size of discrete problems while keeping the accuracy reasonable. In this work, we present an original algorithm to coarsen an unstructured grid based on the concepts of differentiable physics. We ac... | [] | null | 9,769 | 2507.18297 | title_snapshot | [
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FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction | https://openreview.net/forum?id=vye4OgLaTy | [
"Zhonghang Li",
"Lianghao Xia",
"Yong Xu",
"Chao Huang"
] | Poster | null | The objective of traffic prediction is to accurately forecast and analyze the dynamics of transportation patterns, considering both space and time. However, the presence of distribution shift poses a significant challenge in this field, as existing models struggle to generalize well when faced with test data that signi... | [] | null | 9,765 | 2405.17898 | title_snapshot | [
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PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling | https://openreview.net/forum?id=l6Hef6FVd0 | [
"Utsav Singh",
"Wesley A Suttle",
"Brian M. Sadler",
"Vinay P. Namboodiri",
"Amrit Bedi"
] | Poster | null | In this work, we introduce PIPER: Primitive-Informed Preference-based Hierarchical reinforcement learning via Hindsight Relabeling, a novel approach that leverages preference-based learning to learn a reward model, and subsequently uses this reward model to relabel higher-level replay buffers. Since this reward is unaf... | [] | null | 9,762 | 2404.13423 | title_snapshot | [
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Robust Yet Efficient Conformal Prediction Sets | https://openreview.net/forum?id=MrNq6rbcUi | [
"Soroush H. Zargarbashi",
"Mohammad Sadegh Akhondzadeh",
"Aleksandar Bojchevski"
] | Poster | null | Conformal prediction (CP) can convert any model's output into prediction sets guaranteed to include the true label with any user-specified probability. However, same as the model itself, CP is vulnerable to adversarial test examples (evasion) and perturbed calibration data (poisoning). We derive provably robust sets by... | [] | null | 9,761 | 2407.09165 | title_snapshot | [
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Finite Smoothing Algorithm for High-Dimensional Support Vector Machines and Quantile Regression | https://openreview.net/forum?id=RvwMTDYTOb | [
"Qian Tang",
"Yikai Zhang",
"Boxiang Wang"
] | Poster | null | This paper introduces a finite smoothing algorithm (FSA), a novel approach to tackle computational challenges in applying support vector machines (SVM) and quantile regression to high-dimensional data. The critical issue with these methods is the non-smooth nature of their loss functions, which traditionally limits the... | [] | null | 9,755 | null | null | [
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Scale-Free Image Keypoints Using Differentiable Persistent Homology | https://openreview.net/forum?id=fNJbcxhxRj | [
"Giovanni Barbarani",
"Francesco Vaccarino",
"Gabriele Trivigno",
"Marco Guerra",
"Gabriele Berton",
"Carlo Masone"
] | Poster | null | In computer vision, keypoint detection is a fundamental task, with applications spanning from robotics to image retrieval; however, existing learning-based methods suffer from scale dependency, and lack flexibility. This paper introduces a novel approach that leverages Morse theory and persistent homology, powerful too... | [] | null | 9,753 | 2406.01315 | title_snapshot | [
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Smooth Tchebycheff Scalarization for Multi-Objective Optimization | https://openreview.net/forum?id=m4dO5L6eCp | [
"Xi Lin",
"Xiaoyuan Zhang",
"Zhiyuan Yang",
"Fei Liu",
"Zhenkun Wang",
"Qingfu Zhang"
] | Poster | null | Multi-objective optimization problems can be found in many real-world applications, where the objectives often conflict each other and cannot be optimized by a single solution. In the past few decades, numerous methods have been proposed to find Pareto solutions that represent optimal trade-offs among the objectives fo... | [] | null | 9,745 | 2402.19078 | title_snapshot | [
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DNCs Require More Planning Steps | https://openreview.net/forum?id=tu5fCCuua2 | [
"Yara Shamshoum",
"Nitzan Hodos",
"Yuval Sieradzki",
"Assaf Schuster"
] | Poster | null | Many recent works use machine learning models to solve various complex algorithmic problems. However, these models attempt to reach a solution without considering the problem's required computational complexity, which can be detrimental to their ability to solve it correctly. In this work we investigate the effect of c... | [] | null | 9,744 | 2406.02187 | title_snapshot | [
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Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity | https://openreview.net/forum?id=dFEeI51O5j | [
"Hyunki Seong",
"Hyunchul Shim"
] | Poster | null | We introduce MoNet, a novel functionally modular network for self-supervised and interpretable end-to-end learning. By leveraging its functional modularity with a latent-guided contrastive loss function, MoNet efficiently learns task-specific decision-making processes in latent space without requiring task-level superv... | [] | null | 9,741 | 2403.18947 | title_snapshot | [
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Offline Multi-Objective Optimization | https://openreview.net/forum?id=3AuoStfUIH | [
"Ke Xue",
"Rongxi Tan",
"Xiaobin Huang",
"Chao Qian"
] | Poster | null | Offline optimization aims to maximize a black-box objective function with a static dataset and has wide applications. In addition to the objective function being black-box and expensive to evaluate, numerous complex real-world problems entail optimizing multiple conflicting objectives, i.e., multi-objective optimizatio... | [] | null | 9,738 | 2406.03722 | title_snapshot | [
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Model-Free Robust $\phi$-Divergence Reinforcement Learning Using Both Offline and Online Data | https://openreview.net/forum?id=Yug1IEkvcb | [
"Kishan Panaganti",
"Adam Wierman",
"Eric Mazumdar"
] | Poster | null | The robust $\phi$-regularized Markov Decision Process (RRMDP) framework focuses on designing control policies that are robust against parameter uncertainties due to mismatches between the simulator (nominal) model and real-world settings. This work makes *two* important contributions. First, we propose a *model-free* a... | [] | null | 9,725 | 2405.05468 | title_snapshot | [
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Graph Distillation with Eigenbasis Matching | https://openreview.net/forum?id=DYN66IJCI9 | [
"Yang Liu",
"Deyu Bo",
"Chuan Shi"
] | Poster | null | The increasing amount of graph data places requirements on the efficient training of graph neural networks (GNNs). The emerging graph distillation (GD) tackles this challenge by distilling a small synthetic graph to replace the real large graph, ensuring GNNs trained on real and synthetic graphs exhibit comparable perf... | [] | null | 9,721 | 2310.09202 | title_snapshot | [
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Random matrix theory improved Fréchet mean of symmetric positive definite matrices | https://openreview.net/forum?id=uQiFsBil3p | [
"Florent Bouchard",
"Ammar Mian",
"Malik Tiomoko",
"Guillaume Ginolhac",
"Frederic Pascal"
] | Poster | null | In this study, we consider the realm of covariance matrices in machine learning, particularly focusing on computing Fréchet means on the manifold of symmetric positive definite matrices, commonly referred to as Karcher or geometric means. Such means are leveraged in numerous machine learning tasks. Relying on advanced ... | [] | null | 9,715 | 2405.06558 | title_snapshot | [
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MS$^3$D: A RG Flow-Based Regularization for GAN Training with Limited Data | https://openreview.net/forum?id=TuALw8xVum | [
"Jian Wang",
"Xin Lan",
"Yuxin Tian",
"Jiancheng Lv"
] | Poster | null | Generative adversarial networks (GANs) have made impressive advances in image generation, but they often require large-scale training data to avoid degradation caused by discriminator overfitting. To tackle this issue, we investigate the challenge of training GANs with limited data, and propose a novel regularization m... | [] | null | 9,703 | 2408.11135 | title_snapshot | [
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-0.06795992702245712,
... |
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