title stringlengths 17 147 | url stringlengths 42 42 | detail_url stringlengths 42 42 | authors stringlengths 8 486 | tags stringclasses 2
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Cascade Speculative Drafting for Even Faster LLM Inference | https://openreview.net/forum?id=lZY9u0ijP7 | https://openreview.net/forum?id=lZY9u0ijP7 | Ziyi Chen,Xiaocong Yang,Jiacheng Lin,Chenkai Sun,Kevin Chang,Jie Huang | NIPS 2024,Poster | Introduced to enhance the efficiency of large language model (LLM) inference, speculative decoding operates by having a smaller model generate a draft. A larger target model then reviews this draft to align with its output, and any acceptance by the target model results in a reduction of the number of the target model ... | https://openreview.net/pdf/7d6cf3bf7fac4f5e70a8ef98098a47f541169c34.pdf |
Quantum Deep Equilibrium Models | https://openreview.net/forum?id=CWhwKb0Q4k | https://openreview.net/forum?id=CWhwKb0Q4k | Philipp Schleich,Marta Skreta,Lasse Bjørn Kristensen,Rodrigo Vargas-Hernandez,Alan Aspuru-Guzik | NIPS 2024,Poster | The feasibility of variational quantum algorithms, the most popular correspondent of neural networks on noisy, near-term quantum hardware, is highly impacted by the circuit depth of the involved parametrized quantum circuits (PQCs). Higher depth increases expressivity, but also results in a detrimental accumulation of ... | https://openreview.net/pdf/ee5a435e0f6ba173f8dfa3a37a2e0d0fbba4d37d.pdf |
GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs | https://openreview.net/forum?id=hW5QWiCctl | https://openreview.net/forum?id=hW5QWiCctl | Zhao Zhang,Ziwei Zhao,Dong Wang,Liwei Wang | NIPS 2024,Poster | Accurately restoring topology is both challenging and crucial in tubular structure extraction tasks, such as blood vessel segmentation and road network extraction. Diverging from traditional approaches based on pixel-level classification, our proposed method, named GraphMorph, focuses on branch-level features of tubula... | https://openreview.net/pdf/1c697327e7b2306dcb3fa000f7f7712661179b29.pdf |
Rapid Plug-in Defenders | https://openreview.net/forum?id=UMPedMhKWm | https://openreview.net/forum?id=UMPedMhKWm | Kai Wu,Yujian Betterest Li,Jian Lou,Xiaoyu Zhang,Handing Wang,Jing Liu | NIPS 2024,Poster | In the realm of daily services, the deployment of deep neural networks underscores the paramount importance of their reliability. However, the vulnerability of these networks to adversarial attacks, primarily evasion-based, poses a concerning threat to their functionality. Common methods for enhancing robustness involv... | https://openreview.net/pdf/cc7da26977f10358e4e756a435a638d2ad7405d3.pdf |
Ordered Momentum for Asynchronous SGD | https://openreview.net/forum?id=U2Mx0hSRwA | https://openreview.net/forum?id=U2Mx0hSRwA | Chang-Wei Shi,Yi-Rui Yang,Wu-Jun Li | NIPS 2024,Poster | Distributed learning is essential for training large-scale deep models.
Asynchronous SGD (ASGD) and its variants are commonly used distributed learning methods, particularly in scenarios where the computing capabilities of workers in the cluster are heterogeneous.
Momentum has been acknowledged for its benefits in both... | https://openreview.net/pdf/70e16903503ce6fa76e9df2a300c8f95295f2509.pdf |
Is the MMI Criterion Necessary for Interpretability? Degenerating Non-causal Features to Plain Noise for Self-Rationalization | https://openreview.net/forum?id=eAqcVZx30k | https://openreview.net/forum?id=eAqcVZx30k | Wei Liu,Zhiying Deng,Zhongyu Niu,Jun Wang,Haozhao Wang,YuanKai Zhang,Ruixuan Li | NIPS 2024,Poster | An important line of research in the field of explainability is to extract a small subset of crucial rationales from the full input. The most widely used criterion for rationale extraction is the maximum mutual information (MMI) criterion. However, in certain datasets, there are spurious features non-causally correlate... | https://openreview.net/pdf/2081235c67059bc2acdc065ef0fcce259f7eb1b5.pdf |
Are More LLM Calls All You Need? Towards the Scaling Properties of Compound AI Systems | https://openreview.net/forum?id=m5106RRLgx | https://openreview.net/forum?id=m5106RRLgx | Lingjiao Chen,Jared Quincy Davis,Boris Hanin,Peter Bailis,Ion Stoica,Matei Zaharia,James Zou | NIPS 2024,Poster | Many recent state-of-the-art results in language tasks were achieved using compound systems that perform multiple Language Model (LM) calls and aggregate their responses. However, there is little understanding of how the number of LM calls -- e.g., when asking the LM to answer each question multiple times and taking a ... | https://openreview.net/pdf/933389eba2d451a433d83e7d55975efe12b0a17b.pdf |
Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficient | https://openreview.net/forum?id=aBmiyi7iA7 | https://openreview.net/forum?id=aBmiyi7iA7 | Vu C. Dinh,Lam Si Tung Ho,Cuong V. Nguyen | NIPS 2024,Poster | We analyze the error rates of the Hamiltonian Monte Carlo algorithm with leapfrog integrator for Bayesian neural network inference. We show that due to the non-differentiability of activation functions in the ReLU family, leapfrog HMC for networks with these activation functions has a large local error rate of $\Omega(... | https://openreview.net/pdf/942c535cbf05e39d929d1238b3c761fba01fa6da.pdf |
A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning | https://openreview.net/forum?id=4OJdZhcwBb | https://openreview.net/forum?id=4OJdZhcwBb | Jacob Adkins,Michael Bowling,Adam White | NIPS 2024,Poster | The performance of modern reinforcement learning algorithms critically relies
on tuning ever increasing numbers of hyperparameters. Often, small changes in
a hyperparameter can lead to drastic changes in performance, and different environments require very different hyperparameter settings to achieve state-of-the-art
p... | https://openreview.net/pdf/8eff860c8489c171cf2a36abbbeddb2bfad51ac8.pdf |
TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation | https://openreview.net/forum?id=ZpVTRQVX5b | https://openreview.net/forum?id=ZpVTRQVX5b | Chenyang Le,Yao Qian,Dongmei Wang,Long Zhou,Shujie LIU,Xiaofei Wang,Midia Yousefi,Yanmin Qian,Jinyu Li,Michael Zeng | NIPS 2024,Poster | There is a rising interest and trend in research towards directly translating speech from one language to another, known as end-to-end speech-to-speech translation. However, most end-to-end models struggle to outperform cascade models, i.e., a pipeline framework by concatenating speech recognition, machine translation ... | https://openreview.net/pdf/b8d1936c6491d6c912b49703bfa1f9d232db22ca.pdf |
Adaptive Labeling for Efficient Out-of-distribution Model Evaluation | https://openreview.net/forum?id=uuQQwrjMzb | https://openreview.net/forum?id=uuQQwrjMzb | Daksh Mittal,Yuanzhe Ma,Shalmali Joshi,Hongseok Namkoong | NIPS 2024,Poster | Datasets often suffer severe selection bias; clinical labels are only available on patients for whom doctors ordered medical exams. To assess model performance outside the support of available data, we present a computational framework for adaptive labeling, providing cost-efficient model evaluations under severe distr... | https://openreview.net/pdf/4eefea64ae0afe13d30727e1893f662df3e3b799.pdf |
NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping | https://openreview.net/forum?id=y6qhVtFG77 | https://openreview.net/forum?id=y6qhVtFG77 | Yamin Li,Ange Lou,Ziyuan Xu,SHENGCHAO ZHANG,Shiyu Wang,Dario J. Englot,Soheil Kolouri,Daniel Moyer,Roza G Bayrak,Catie Chang | NIPS 2024,Poster | Functional magnetic resonance imaging (fMRI) is an indispensable tool in modern neuroscience, providing a non-invasive window into whole-brain dynamics at millimeter-scale spatial resolution. However, fMRI is constrained by issues such as high operation costs and immobility. With the rapid advancements in cross-modalit... | https://openreview.net/pdf/7637d729304c503ef5c555a139062365ae9005dc.pdf |
Bayesian Adaptive Calibration and Optimal Design | https://openreview.net/forum?id=m906PS5G9x | https://openreview.net/forum?id=m906PS5G9x | Rafael Oliveira,Dino Sejdinovic,David Howard,Edwin V. Bonilla | NIPS 2024,Poster | The process of calibrating computer models of natural phenomena is essential for applications in the physical sciences, where plenty of domain knowledge can be embedded into simulations and then calibrated against real observations. Current machine learning approaches, however, mostly rely on rerunning simulations over... | https://openreview.net/pdf/1a3b58749c0c68a59d59059df64282b1a3adc0a4.pdf |
FineStyle: Fine-grained Controllable Style Personalization for Text-to-image Models | https://openreview.net/forum?id=1SmXUGzrH8 | https://openreview.net/forum?id=1SmXUGzrH8 | Gong Zhang,Kihyuk Sohn,Meera Hahn,Humphrey Shi,Irfan Essa | NIPS 2024,Poster | Few-shot fine-tuning of text-to-image (T2I) generation models enables people to create unique images in their own style using natural languages without requiring extensive prompt engineering. However, fine-tuning with only a handful, as little as one, of image-text paired data prevents fine-grained control of style att... | https://openreview.net/pdf/75bf1ad3580ab645399dbf37996275aa30130566.pdf |
Linking In-context Learning in Transformers to Human Episodic Memory | https://openreview.net/forum?id=AYDBFxNon4 | https://openreview.net/forum?id=AYDBFxNon4 | Li Ji-An,Corey Yishan Zhou,Marcus K. Benna,Marcelo G Mattar | NIPS 2024,Poster | Understanding connections between artificial and biological intelligent systems can reveal fundamental principles of general intelligence. While many artificial intelligence models have a neuroscience counterpart, such connections are largely missing in Transformer models and the self-attention mechanism. Here, we exam... | https://openreview.net/pdf/0bd34b9f6d0eaba66fd4ba873e8e84a2bdd91e14.pdf |
AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment | https://openreview.net/forum?id=G0yxFmP87g | https://openreview.net/forum?id=G0yxFmP87g | Yonggan Fu,Zhongzhi Yu,Junwei Li,Jiayi Qian,Yongan Zhang,Xiangchi Yuan,Dachuan Shi,Roman Yakunin,Yingyan Celine Lin | NIPS 2024,Poster | Motivated by the transformative capabilities of large language models (LLMs) across various natural language tasks, there has been a growing demand to deploy these models effectively across diverse real-world applications and platforms. However, the challenge of efficiently deploying LLMs has become increasingly pronou... | https://openreview.net/pdf/6cccf970913f515d37d602734d01d0c947705492.pdf |
N-agent Ad Hoc Teamwork | https://openreview.net/forum?id=q7TxGUWlhD | https://openreview.net/forum?id=q7TxGUWlhD | Caroline Wang,Arrasy Rahman,Ishan Durugkar,Elad Liebman,Peter Stone | NIPS 2024,Poster | Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings. In standard fully cooperative multi-agent reinforcement learning, the learning algorithm controls *all* agents in the scenario, while in ad hoc teamwork, the learning algorithm usually assumes control over only a *s... | https://openreview.net/pdf/b2a493d4f38a4116108b0ba02a974d3b686c5421.pdf |
Provably Faster Algorithms for Bilevel Optimization via Without-Replacement Sampling | https://openreview.net/forum?id=BNnZwbZGpm | https://openreview.net/forum?id=BNnZwbZGpm | Junyi Li,Heng Huang | NIPS 2024,Poster | Bilevel Optimization has experienced significant advancements recently with the introduction of new efficient algorithms. Mirroring the success in single-level optimization, stochastic gradient-based algorithms are widely used in bilevel optimization. However, a common limitation in these algorithms is the presumption ... | https://openreview.net/pdf/02a1d8edd1255179e52012fdd5a11e5b2a4e5acc.pdf |
FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning | https://openreview.net/forum?id=QXkFC7D6p4 | https://openreview.net/forum?id=QXkFC7D6p4 | Evelyn Ma,Chao Pan,S. Rasoul Etesami,Han Zhao,Olgica Milenkovic | NIPS 2024,Poster | The performance of Transfer Learning (TL) significantly depends on effective pretraining, which not only requires extensive amounts of data but also substantial computational resources. As a result, in practice, it is challenging to successfully perform TL at the level of individual model developers. Federated Learning... | https://openreview.net/pdf/5654ab819d08c8951c309ec6e440949b8155196b.pdf |
SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning | https://openreview.net/forum?id=PnlCHQrM69 | https://openreview.net/forum?id=PnlCHQrM69 | Yangruibo Ding,Jinjun Peng,Marcus J. Min,Gail Kaiser,Junfeng Yang,Baishakhi Ray | NIPS 2024,Poster | Code Large Language Models (Code LLMs) have excelled at tasks like code completion but often miss deeper semantics such as execution effects and dynamic states. This paper aims to bridge the gap between Code LLMs' reliance on static text data and the need for semantic understanding for complex tasks like debugging and ... | https://openreview.net/pdf/1e07d8e51eb5f904f2122b7a56ed6151e47c5cc0.pdf |
On $f$-Divergence Principled Domain Adaptation: An Improved Framework | https://openreview.net/forum?id=xSU27DgWEr | https://openreview.net/forum?id=xSU27DgWEr | Ziqiao Wang,Yongyi Mao | NIPS 2024,Poster | Unsupervised domain adaptation (UDA) plays a crucial role in addressing distribution shifts in machine learning. In this work, we improve the theoretical foundations of UDA proposed in Acuna et al. (2021) by refining their $f$-divergence-based discrepancy and additionally introducing a new measure, $f$-domain discrepan... | https://openreview.net/pdf/e6f6280a04e2892629381753602bc9e403e994ea.pdf |
Improved Generation of Adversarial Examples Against Safety-aligned LLMs | https://openreview.net/forum?id=8hBc843g1p | https://openreview.net/forum?id=8hBc843g1p | Qizhang Li,Yiwen Guo,Wangmeng Zuo,Hao Chen | NIPS 2024,Poster | Adversarial prompts (or say, adversarial examples) generated using gradient-based methods exhibit outstanding performance in performing automatic jailbreak attacks against safety-aligned LLMs. Nevertheless, due to the discrete nature of texts, the input gradient of LLMs struggles to precisely reflect the magnitude of l... | https://openreview.net/pdf/993ca7e4d8e5f38ab0bcc37328fa57307b6f0ea9.pdf |
Multi-model Ensemble Conformal Prediction in Dynamic Environments | https://openreview.net/forum?id=J1Y70keorq | https://openreview.net/forum?id=J1Y70keorq | Erfan Hajihashemi,Yanning Shen | NIPS 2024,Poster | Conformal prediction is an uncertainty quantification method that constructs a prediction set for a previously unseen datum, ensuring the true label is included with a predetermined coverage probability. Adaptive conformal prediction has been developed to address data distribution shifts in dynamic environments. Howeve... | https://openreview.net/pdf/e8099609fd67117212f5fdae1d419adf13be51f9.pdf |
Disentangled Representation Learning in Non-Markovian Causal Systems | https://openreview.net/forum?id=uLGyoBn7hm | https://openreview.net/forum?id=uLGyoBn7hm | Adam Li,Yushu Pan,Elias Bareinboim | NIPS 2024,Poster | Considering various data modalities, such as images, videos, and text, humans perform causal reasoning using high-level causal variables, as opposed to operating at the low, pixel level from which the data comes.
In practice, most causal reasoning methods assume that the data is described as granular as the underlying... | https://openreview.net/pdf/8350116f8253990dda7ce413729df73f9a61f109.pdf |
Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment | https://openreview.net/forum?id=57OQXxbTbY | https://openreview.net/forum?id=57OQXxbTbY | Teng Xiao,Yige Yuan,Huaisheng Zhu,Mingxiao Li,Vasant G Honavar | NIPS 2024,Poster | We study the problem of aligning large language models (LLMs) with human preference data. Contrastive preference optimization has shown promising results in aligning LLMs with available preference data by optimizing the implicit reward associated with the policy. However, the contrastive objective focuses mainly on the... | https://openreview.net/pdf/deafc46d7f78e13f7390612cd2ea92bd3459b277.pdf |
Stochastic contextual bandits with graph feedback: from independence number to MAS number | https://openreview.net/forum?id=t8iosEWoyd | https://openreview.net/forum?id=t8iosEWoyd | Yuxiao Wen,Yanjun Han,Zhengyuan Zhou | NIPS 2024,Poster | We consider contextual bandits with graph feedback, a class of interactive learning problems with richer structures than vanilla contextual bandits, where taking an action reveals the rewards for all neighboring actions in the feedback graph under all contexts. Unlike the multi-armed bandits setting where a growing lit... | https://openreview.net/pdf/513ccbfe70b63a8134c688af5c125c0ddad739c2.pdf |
OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step | https://openreview.net/forum?id=vAOgaPvgYr | https://openreview.net/forum?id=vAOgaPvgYr | Owen M Dugan,Donato M. Jiménez Benetó,Charlotte Loh,Zhuo Chen,Rumen Dangovski,Marin Soljacic | NIPS 2024,Poster | Despite significant advancements in text generation and reasoning, Large Language Models (LLMs) still face challenges in accurately performing complex arithmetic operations. Language model systems often enable LLMs to generate code for arithmetic operations to achieve accurate calculations. However, this approach compr... | https://openreview.net/pdf/2f805a9041d7d2e112fd00bc3259fa9079805498.pdf |
Sample Complexity of Interventional Causal Representation Learning | https://openreview.net/forum?id=XL9aaXl0u6 | https://openreview.net/forum?id=XL9aaXl0u6 | Emre Acartürk,Burak Varıcı,Karthikeyan Shanmugam,Ali Tajer | NIPS 2024,Poster | Consider a data-generation process that transforms low-dimensional _latent_ causally-related variables to high-dimensional _observed_ variables. Causal representation learning (CRL) is the process of using the observed data to recover the latent causal variables and the causal structure among them. Despite the multitud... | https://openreview.net/pdf/3cd848f730138b8b2afd1dcc6c71c80ba6f6a6a1.pdf |
On the Complexity of Teaching a Family of Linear Behavior Cloning Learners | https://openreview.net/forum?id=4SAR7IRqmB | https://openreview.net/forum?id=4SAR7IRqmB | Shubham Kumar Bharti,Stephen Wright,Adish Singla,Jerry Zhu | NIPS 2024,Poster | We study optimal teaching for a family of Behavior Cloning learners that learn using a linear hypothesis class. In this setup, a knowledgeable teacher can demonstrate a dataset of state and action tuples and is required to teach an optimal policy to an entire family of BC learners using the smallest possible dataset. W... | https://openreview.net/pdf/efe5b979cd3307122d731db58d556f69dc10e559.pdf |
Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution | https://openreview.net/forum?id=nRRJsDahEg | https://openreview.net/forum?id=nRRJsDahEg | Yizi Zhang,Yanchen Wang,Donato M. Jiménez-Benetó,Zixuan Wang,Mehdi Azabou,Blake Aaron Richards,Renee Tung,Olivier Winter,International Brain Laboratory,Eva L Dyer,Liam Paninski,Cole Lincoln Hurwitz | NIPS 2024,Poster | Neuroscience research has made immense progress over the last decade, but our understanding of the brain remains fragmented and piecemeal: the dream of probing an arbitrary brain region and automatically reading out the information encoded in its neural activity remains out of reach. In this work, we build towards a fi... | https://openreview.net/pdf/e7664eef58345c56b265804ba3f72932b5f88c14.pdf |
Simple and Effective Masked Diffusion Language Models | https://openreview.net/forum?id=L4uaAR4ArM | https://openreview.net/forum?id=L4uaAR4ArM | Subham Sekhar Sahoo,Marianne Arriola,Aaron Gokaslan,Edgar Mariano Marroquin,Alexander M Rush,Yair Schiff,Justin T Chiu,Volodymyr Kuleshov | NIPS 2024,Poster | While diffusion models excel at generating high-quality images, prior work reports a significant performance gap between diffusion and autoregressive (AR) methods in language modeling.
In this work, we show that simple masked discrete diffusion is more performant than previously thought.
We apply an effective training ... | https://openreview.net/pdf/3a7ac707cefd8a4120d5e11741324aa678d7ce77.pdf |
A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings | https://openreview.net/forum?id=hilGwNabqB | https://openreview.net/forum?id=hilGwNabqB | Disha Makhija,Joydeep Ghosh,Nhat Ho | NIPS 2024,Poster | Federated learning (FL), through its privacy-preserving collaborative learning approach, has significantly empowered decentralized devices. However, constraints in either data and/or computational resources among participating clients introduce several challenges in learning, including the inability to train large mod... | https://openreview.net/pdf/eae39129243dc6c4b8c87d448599e80d0b9fce05.pdf |
Kaleido Diffusion: Improving Conditional Diffusion Models with Autoregressive Latent Modeling | https://openreview.net/forum?id=qZSwlcLMCS | https://openreview.net/forum?id=qZSwlcLMCS | Jiatao Gu,Ying Shen,Shuangfei Zhai,Yizhe Zhang,Navdeep Jaitly,Joshua M. Susskind | NIPS 2024,Poster | Diffusion models have emerged as a powerful tool for generating high-quality images from textual descriptions. Despite their successes, these models often exhibit limited diversity in the sampled images, particularly when sampling with a high classifier-free guidance weight. To address this issue, we present Kaleido, a... | https://openreview.net/pdf/6bf6fdec8dd6eed85f84157b0809edad3641d855.pdf |
FairWire: Fair Graph Generation | https://openreview.net/forum?id=V0JvwCQlJe | https://openreview.net/forum?id=V0JvwCQlJe | Oyku Deniz Kose,Yanning Shen | NIPS 2024,Poster | Machine learning over graphs has recently attracted growing attention due to its ability to analyze and learn complex relations within critical interconnected systems. However, the disparate impact that is amplified by the use of biased graph structures in these algorithms has raised significant concerns for their depl... | https://openreview.net/pdf/1f5eea2983c84e12175cd2b978aa11cb3f7ce158.pdf |
Apathetic or Empathetic? Evaluating LLMs' Emotional Alignments with Humans | https://openreview.net/forum?id=pwRVGRWtGg | https://openreview.net/forum?id=pwRVGRWtGg | Jen-tse Huang,Man Ho LAM,Eric John Li,Shujie Ren,Wenxuan Wang,Wenxiang Jiao,Zhaopeng Tu,Michael Lyu | NIPS 2024,Poster | Evaluating Large Language Models’ (LLMs) anthropomorphic capabilities has become increasingly important in contemporary discourse. Utilizing the emotion appraisal theory from psychology, we propose to evaluate the empathy ability of LLMs, i.e., how their feelings change when presented with specific situations. After a ... | https://openreview.net/pdf/4d6e71e0ca7fffae0c70fd69763ea99167e3d197.pdf |
Scaling transformer neural networks for skillful and reliable medium-range weather forecasting | https://openreview.net/forum?id=aBP01akha9 | https://openreview.net/forum?id=aBP01akha9 | Tung Nguyen,Rohan Shah,Hritik Bansal,Troy Arcomano,Romit Maulik,Veerabhadra Kotamarthi,Ian Foster,Sandeep Madireddy,Aditya Grover | NIPS 2024,Poster | Weather forecasting is a fundamental problem for anticipating and mitigating the impacts of climate change. Recently, data-driven approaches for weather forecasting based on deep learning have shown great promise, achieving accuracies that are competitive with operational systems. However, those methods often employ co... | https://openreview.net/pdf/2fdb23d735460d3e9df36b9d966b324b7a000548.pdf |
A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning | https://openreview.net/forum?id=3tj3A26wsV | https://openreview.net/forum?id=3tj3A26wsV | Tom Yan,Zachary Chase Lipton | NIPS 2024,Poster | A key source of complexity in next-generation AI models is the size of model outputs, making it time-consuming to parse and provide reliable feedback on. To ensure such models are aligned, we will need to bolster our understanding of scalable oversight and how to scale up human feedback. To this end, we study the chall... | https://openreview.net/pdf/a143b9d7d28c1a6e8cdea9a18adc4fa9293ed1a7.pdf |
Causal Imitation for Markov Decision Processes: a Partial Identification Approach | https://openreview.net/forum?id=KHX0dKXdqH | https://openreview.net/forum?id=KHX0dKXdqH | Kangrui Ruan,Junzhe Zhang,Xuan Di,Elias Bareinboim | NIPS 2024,Poster | Imitation learning enables an agent to learn from expert demonstrations when the performance measure is unknown and the reward signal is not specified. Standard imitation methods do not generally apply when the learner and the expert's sensory capabilities mismatch and demonstrations are contaminated with unobserved co... | https://openreview.net/pdf/44332f130be85fa6cd9ebf2c17a3b40392bccbae.pdf |
Learning from Uncertain Data: From Possible Worlds to Possible Models | https://openreview.net/forum?id=v9RqRFSLQ2 | https://openreview.net/forum?id=v9RqRFSLQ2 | Jiongli Zhu,Su Feng,Boris Glavic,Babak Salimi | NIPS 2024,Poster | We introduce an efficient method for learning linear models from uncertain data, where uncertainty is represented as a set of possible variations in the data, leading to predictive multiplicity. Our approach leverages abstract interpretation and zonotopes, a type of convex polytope, to compactly represent these dataset... | https://openreview.net/pdf/87fd59808dc5ed78d3e3e6ef14d35b6e060362d8.pdf |
Adaptive Exploration for Data-Efficient General Value Function Evaluations | https://openreview.net/forum?id=HC6iqpPt3L | https://openreview.net/forum?id=HC6iqpPt3L | Arushi Jain,Josiah P. Hanna,Doina Precup | NIPS 2024,Poster | General Value Functions (GVFs) (Sutton et al., 2011) represent predictive knowledge in reinforcement learning. Each GVF computes the expected return for a given policy, based on a unique reward. Existing methods relying on fixed behavior policies or pre-collected data often face data efficiency issues when learning mul... | https://openreview.net/pdf/20c5e327d236868140f6e856c42c6b8592a50482.pdf |
One-Layer Transformer Provably Learns One-Nearest Neighbor In Context | https://openreview.net/forum?id=WDX45LNZXE | https://openreview.net/forum?id=WDX45LNZXE | Zihao Li,Yuan Cao,Cheng Gao,Yihan He,Han Liu,Jason Matthew Klusowski,Jianqing Fan,Mengdi Wang | NIPS 2024,Poster | Transformers have achieved great success in recent years. Interestingly, transformers have shown particularly strong in-context learning capability -- even without fine-tuning, they are still able to solve unseen tasks well purely based on task-specific prompts. In this paper, we study the capability of one-layer trans... | https://openreview.net/pdf/69e3d7430a05e0d5696f5dbe23746ff3a22096e9.pdf |
SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximation | https://openreview.net/forum?id=65UoJ0z7Kp | https://openreview.net/forum?id=65UoJ0z7Kp | Yixia Li,Boya Xiong,Guanhua Chen,Yun Chen | NIPS 2024,Poster | Out-of-distribution (OOD) detection is crucial for the safe deployment of neural networks. Existing CLIP-based approaches perform OOD detection by devising novel scoring functions or sophisticated fine-tuning methods. In this work, we propose SeTAR, a novel, training-free OOD detection method that leverages selective l... | https://openreview.net/pdf/1f941a564d6513eccbbda6e05d521e80daf9ffcc.pdf |
OptEx: Expediting First-Order Optimization with Approximately Parallelized Iterations | https://openreview.net/forum?id=MzNjnbgcPN | https://openreview.net/forum?id=MzNjnbgcPN | Yao Shu,Jiongfeng Fang,Ying Tiffany He,Fei Richard Yu | NIPS 2024,Poster | First-order optimization (FOO) algorithms are pivotal in numerous computational domains, such as reinforcement learning and deep learning. However, their application to complex tasks often entails significant optimization inefficiency due to their need of many sequential iterations for convergence. In response, we intr... | https://openreview.net/pdf/6355a6b19af5c8832921ce57986888808909ddc1.pdf |
FineCLIP: Self-distilled Region-based CLIP for Better Fine-grained Understanding | https://openreview.net/forum?id=nExI4FuKWD | https://openreview.net/forum?id=nExI4FuKWD | Dong Jing,Xiaolong He,Yutian Luo,Nanyi Fei,Guoxing Yang,Wei Wei,Huiwen Zhao,Zhiwu Lu | NIPS 2024,Poster | Contrastive Language-Image Pre-training (CLIP) achieves impressive performance on tasks like image classification and image-text retrieval by learning on large-scale image-text datasets. However, CLIP struggles with dense prediction tasks due to the poor grasp of the fine-grained details. Although existing works pay at... | https://openreview.net/pdf/e77b9bf69974b22ae77ee4209dc907d97148cbdd.pdf |
On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games | https://openreview.net/forum?id=QgMC8ftbNd | https://openreview.net/forum?id=QgMC8ftbNd | Awni Altabaa,Zhuoran Yang | NIPS 2024,Poster | In sequential decision-making problems, the *information structure* describes the causal dependencies between system variables, encompassing the dynamics of the environment and the agents' actions. Classical models of reinforcement learning (e.g., MDPs, POMDPs) assume a restricted and highly regular information structu... | https://openreview.net/pdf/48f357d190f35342349977f6fc217aacfb61f634.pdf |
SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection | https://openreview.net/forum?id=QNieOPt4fg | https://openreview.net/forum?id=QNieOPt4fg | Liangxin Liu,Xuebo Liu,Derek F. Wong,Dongfang Li,Ziyi Wang,Baotian Hu,Min Zhang | NIPS 2024,Poster | Instruction tuning (IT) is crucial to tailoring large language models (LLMs) towards human-centric interactions. Recent advancements have shown that the careful selection of a small, high-quality subset of IT data can significantly enhance the performance of LLMs. Despite this, common approaches often rely on addition... | https://openreview.net/pdf/9ee81561e94050705f358e4b646c204f4ac6cb24.pdf |
Aligning Large Language Models with Representation Editing: A Control Perspective | https://openreview.net/forum?id=yTTomSJsSW | https://openreview.net/forum?id=yTTomSJsSW | Lingkai Kong,Haorui Wang,Wenhao Mu,Yuanqi Du,Yuchen Zhuang,Yifei Zhou,Yue Song,Rongzhi Zhang,Kai Wang,Chao Zhang | NIPS 2024,Poster | Aligning large language models (LLMs) with human objectives is crucial for real-world applications. However, fine-tuning LLMs for alignment often suffers from unstable training and requires substantial computing resources. Test-time alignment techniques, such as prompting and guided decoding, do not modify the underlyi... | https://openreview.net/pdf/5b01199621eef2e71cc22c61871a279fc51beeba.pdf |
Nearly Tight Black-Box Auditing of Differentially Private Machine Learning | https://openreview.net/forum?id=cCDMXXiamP | https://openreview.net/forum?id=cCDMXXiamP | Meenatchi Sundaram Muthu Selva Annamalai,Emiliano De Cristofaro | NIPS 2024,Poster | This paper presents an auditing procedure for the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm in the black-box threat model that is substantially tighter than prior work.
The main intuition is to craft worst-case initial model parameters, as DP-SGD's privacy analysis is agnostic to the choice ... | https://openreview.net/pdf/4b8080bdff94b173112c6cc0c6042066baef4b32.pdf |
Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training | https://openreview.net/forum?id=Gug7wc0BSs | https://openreview.net/forum?id=Gug7wc0BSs | Pihe Hu,Shaolong Li,Zhuoran Li,Ling Pan,Longbo Huang | NIPS 2024,Poster | Deep Multi-agent Reinforcement Learning (MARL) relies on neural networks with numerous parameters in multi-agent scenarios, often incurring substantial computational overhead. Consequently, there is an urgent need to expedite training and enable model compression in MARL. This paper proposes the utilization of dynamic ... | https://openreview.net/pdf/bf8bb00ab8e48a246aea7bd4371261f2f92f54dd.pdf |
Guiding Neural Collapse: Optimising Towards the Nearest Simplex Equiangular Tight Frame | https://openreview.net/forum?id=z4FaPUslma | https://openreview.net/forum?id=z4FaPUslma | Evan Markou,Thalaiyasingam Ajanthan,Stephen Gould | NIPS 2024,Poster | Neural Collapse (NC) is a recently observed phenomenon in neural networks that characterises the solution space of the final classifier layer when trained until zero training loss. Specifically, NC suggests that the final classifier layer converges to a Simplex Equiangular Tight Frame (ETF), which maximally separates t... | https://openreview.net/pdf/ac02c11fa162633bf19fadb27beddf13e3c58e97.pdf |
Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit | https://openreview.net/forum?id=rblaF2euXQ | https://openreview.net/forum?id=rblaF2euXQ | Seok-Jin Kim,Min-hwan Oh | NIPS 2024,Poster | We study the performance guarantees of exploration-free greedy algorithms for the linear contextual bandit problem.
We introduce a novel condition, named the \textit{Local Anti-Concentration} (LAC) condition, which enables a greedy bandit algorithm to achieve provable efficiency.
We show that the LAC condition is sat... | https://openreview.net/pdf/4de6a468dddbfb975396e4c31c95c83e157b2eae.pdf |
MambaSCI: Efficient Mamba-UNet for Quad-Bayer Patterned Video Snapshot Compressive Imaging | https://openreview.net/forum?id=U4WeoyRHPd | https://openreview.net/forum?id=U4WeoyRHPd | Zhenghao Pan,Haijin Zeng,Jiezhang Cao,Yongyong Chen,Kai Zhang,Yong Xu | NIPS 2024,Poster | Color video snapshot compressive imaging (SCI) employs computational imaging techniques to capture multiple sequential video frames in a single Bayer-patterned measurement. With the increasing popularity of quad-Bayer pattern in mainstream smartphone cameras for capturing high-resolution videos, mobile photography has ... | https://openreview.net/pdf/72c3ea7ea0eeba8e9718d04e2061a016d54bfee0.pdf |
KnowGPT: Knowledge Graph based Prompting for Large Language Models | https://openreview.net/forum?id=PacBluO5m7 | https://openreview.net/forum?id=PacBluO5m7 | Qinggang Zhang,Junnan Dong,Hao Chen,Daochen Zha,Zailiang Yu,Xiao Huang | NIPS 2024,Poster | Large Language Models (LLMs) have demonstrated remarkable capabilities in many real-world applications. Nonetheless, LLMs are often criticized for their tendency to produce hallucinations, wherein the models fabricate incorrect statements on tasks beyond their knowledge and perception. To alleviate this issue, graph re... | https://openreview.net/pdf/4ec9739895ff72a71118e7b64bb98e28f109616b.pdf |
Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments | https://openreview.net/forum?id=Y1rOWS2Z4i | https://openreview.net/forum?id=Y1rOWS2Z4i | Siddharth Nayak,Adelmo Morrison Orozco,Marina Ten Have,Jackson Zhang,Vittal Thirumalai,Darren Chen,Aditya Kapoor,Eric Robinson,Karthik Gopalakrishnan,James Harrison,Anuj Mahajan,brian ichter,Hamsa Balakrishnan | NIPS 2024,Poster | The ability of Language Models (LMs) to understand natural language makes them a powerful tool for parsing human instructions into task plans for autonomous robots. Unlike traditional planning methods that rely on domain-specific knowledge and handcrafted rules, LMs generalize from diverse data and adapt to various tas... | https://openreview.net/pdf/c6dfb94fb019cc2a364a5a1bc89c8064812a935d.pdf |
Cost-efficient Knowledge-based Question Answering with Large Language Models | https://openreview.net/forum?id=pje1Y71jad | https://openreview.net/forum?id=pje1Y71jad | Junnan Dong,Qinggang Zhang,Chuang Zhou,Hao Chen,Daochen Zha,Xiao Huang | NIPS 2024,Poster | Knowledge-based question answering (KBQA) is widely used in many scenarios that necessitate domain knowledge. Large language models (LLMs) bring opportunities to KBQA, while their costs are significantly higher and absence of domain-specific knowledge during pre-training. We are motivated to combine LLMs and prior smal... | https://openreview.net/pdf/0c4dc789433d497c2f2c0f0da165be3b5c9f715b.pdf |
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes | https://openreview.net/forum?id=da0ZJatRCN | https://openreview.net/forum?id=da0ZJatRCN | Syrine Belakaria,Benjamin Letham,Jana Doppa,Barbara E Engelhardt,Stefano Ermon,Eytan Bakshy | NIPS 2024,Poster | We consider the problem of active learning for global sensitivity analysis of expensive black-box functions. Our aim is to efficiently learn the importance of different input variables, e.g., in vehicle safety experimentation, we study the impact of the thickness of various components on safety objectives. Since functi... | https://openreview.net/pdf/e7cbe6f405410f66193a63a86f7ddaae0e3eb870.pdf |
Divergences between Language Models and Human Brains | https://openreview.net/forum?id=DpP5F3UfKw | https://openreview.net/forum?id=DpP5F3UfKw | Yuchen Zhou,Emmy Liu,Graham Neubig,Michael J. Tarr,Leila Wehbe | NIPS 2024,Poster | Do machines and humans process language in similar ways? Recent research has hinted at the affirmative, showing that human neural activity can be effectively predicted using the internal representations of language models (LMs). Although such results are thought to reflect shared computational principles between LMs an... | https://openreview.net/pdf/3f5c514423f1a9678561f73def188118d5bcf7d3.pdf |
Covariate Shift Corrected Conditional Randomization Test | https://openreview.net/forum?id=Me5esZTRqW | https://openreview.net/forum?id=Me5esZTRqW | Bowen Xu,Yiwen Huang,Chuan Hong,Shuangning Li,Molei Liu | NIPS 2024,Poster | Conditional independence tests are crucial across various disciplines in determining the independence of an outcome variable $Y$ from a treatment variable $X$, conditioning on a set of confounders $Z$. The Conditional Randomization Test (CRT) offers a powerful framework for such testing by assuming known distributions ... | https://openreview.net/pdf/74507c2335d3c2e774426629dc05a2f7ad13d3bb.pdf |
Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context | https://openreview.net/forum?id=uHcG5Y6fdB | https://openreview.net/forum?id=uHcG5Y6fdB | Kazusato Oko,Yujin Song,Taiji Suzuki,Denny Wu | NIPS 2024,Poster | Transformers can efficiently learn in-context from example demonstrations. Most existing theoretical analyses studied the in-context learning (ICL) ability of transformers for linear function classes, where it is typically shown that the minimizer of the pretraining loss implements one gradient descent step on the leas... | https://openreview.net/pdf/ebe3fdc5e357d327b920801545a353f902eefb86.pdf |
An effective framework for estimating individualized treatment rules | https://openreview.net/forum?id=G7L65B2P0y | https://openreview.net/forum?id=G7L65B2P0y | Joowon Lee,Jared Davis Huling,Guanhua Chen | NIPS 2024,Poster | Estimating individualized treatment rules (ITRs) is fundamental in causal inference, particularly for precision medicine applications. Traditional ITR estimation methods rely on inverse probability weighting (IPW) to address confounding factors and $L_{1}$-penalization for simplicity and interpretability. However, IPW ... | https://openreview.net/pdf/3b195f1ab7b8f324455c2d592ed796416f102aeb.pdf |
DiMSUM: Diffusion Mamba - A Scalable and Unified Spatial-Frequency Method for Image Generation | https://openreview.net/forum?id=KqbLzSIXkm | https://openreview.net/forum?id=KqbLzSIXkm | Hao Phung,Quan Dao,Trung Tuan Dao,Hoang Phan,Dimitris N. Metaxas,Anh Tuan Tran | NIPS 2024,Poster | We introduce a novel state-space architecture for diffusion models, effectively harnessing spatial and frequency information to enhance the inductive bias towards local features in input images for image generation tasks. While state-space networks, including Mamba, a revolutionary advancement in recurrent neural netwo... | https://openreview.net/pdf/0ae8bfdeeec0ac6c1b9be00728313d0eee7040d2.pdf |
Rule Based Rewards for Language Model Safety | https://openreview.net/forum?id=QVtwpT5Dmg | https://openreview.net/forum?id=QVtwpT5Dmg | Tong Mu,Alec Helyar,Johannes Heidecke,Joshua Achiam,Andrea Vallone,Ian D Kivlichan,Molly Lin,Alex Beutel,John Schulman,Lilian Weng | NIPS 2024,Poster | Reinforcement learning based fine-tuning of large language models (LLMs) on human preferences has been shown to enhance both their capabilities and safety behavior.
However, in cases related to safety, without precise instructions to human annotators, the data collected may cause the model to become overly cautious, ... | https://openreview.net/pdf/e963b11386699f5b75503a72861c8a01fb09a180.pdf |
Alias-Free Mamba Neural Operator | https://openreview.net/forum?id=gUEBXGV8JM | https://openreview.net/forum?id=gUEBXGV8JM | Jianwei Zheng,LiweiNo,Ni Xu,Junwei Zhu,XiaoxuLin,Xiaoqin Zhang | NIPS 2024,Poster | Benefiting from the booming deep learning techniques, neural operators (NO) are considered as an ideal alternative to break the traditions of solving Partial Differential Equations (PDE) with expensive cost.
Yet with the remarkable progress, current solutions concern little on the holistic function features--both globa... | https://openreview.net/pdf/a1a1561a826925c5f0083b9694af193271f8b359.pdf |
On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions | https://openreview.net/forum?id=x7usmidzxj | https://openreview.net/forum?id=x7usmidzxj | Yusu Hong,Junhong Lin | NIPS 2024,Poster | In this paper, we study Adam in non-convex smooth scenarios with potential unbounded gradients and affine variance noise. We consider a general noise model which governs affine variance noise, bounded noise, and sub-Gaussian noise. We show that Adam with a specific hyper-parameter setup can find a stationary point with... | https://openreview.net/pdf/53b61c417a7761bfb6f0d648f3d93f54a1153174.pdf |
Ad Auctions for LLMs via Retrieval Augmented Generation | https://openreview.net/forum?id=Ujo8V7iXmR | https://openreview.net/forum?id=Ujo8V7iXmR | MohammadTaghi Hajiaghayi,Sebastien Lahaie,Keivan Rezaei,Suho Shin | NIPS 2024,Poster | In the field of computational advertising, the integration of ads into the outputs of large language models (LLMs) presents an opportunity to support these services without compromising content integrity. This paper introduces novel auction mechanisms for ad allocation and pricing within the textual outputs of LLMs, le... | https://openreview.net/pdf/1a43bddd10e8ed2f5ca0b3de382ea2aca7da548b.pdf |
WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models | https://openreview.net/forum?id=VzOgnDJMgh | https://openreview.net/forum?id=VzOgnDJMgh | Jinghan Jia,Jiancheng Liu,Yihua Zhang,Parikshit Ram,Nathalie Baracaldo,Sijia Liu | NIPS 2024,Poster | The need for effective unlearning mechanisms in large language models (LLMs) is increasingly urgent, driven by the necessity to adhere to data regulations and foster ethical generative AI practices. LLM unlearning is designed to reduce the impact of undesirable data influences and associated model capabilities without ... | https://openreview.net/pdf/f8e6edd72761c9672749d9c628233d2df16aae08.pdf |
Who’s Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptation | https://openreview.net/forum?id=PXGY9Fz8vC | https://openreview.net/forum?id=PXGY9Fz8vC | Trenton Chang,Lindsay Warrenburg,Sae-Hwan Park,Ravi B Parikh,Maggie Makar,Jenna Wiens | NIPS 2024,Poster | In many settings, machine learning models may be used to inform decisions that impact individuals or entities who interact with the model. Such entities, or *agents,* may *game* model decisions by manipulating their inputs to the model to obtain better outcomes and maximize some utility. We consider a multi-agent setti... | https://openreview.net/pdf/7f31354db4587aad2bba879b475c0a1ac5a5c57e.pdf |
Achieving $\tilde{O}(1/\epsilon)$ Sample Complexity for Constrained Markov Decision Process | https://openreview.net/forum?id=psG4LXlDNs | https://openreview.net/forum?id=psG4LXlDNs | Jiashuo Jiang,Yinyu Ye | NIPS 2024,Poster | We consider the reinforcement learning problem for the constrained Markov decision process (CMDP), which plays a central role in satisfying safety or resource constraints in sequential learning and decision-making. In this problem, we are given finite resources and a MDP with unknown transition probabilities. At each s... | https://openreview.net/pdf/d266944c7c83f38bc65d9643812af49872f309c1.pdf |
Scaling Laws in Linear Regression: Compute, Parameters, and Data | https://openreview.net/forum?id=PH7sdEanXP | https://openreview.net/forum?id=PH7sdEanXP | Licong Lin,Jingfeng Wu,Sham M. Kakade,Peter Bartlett,Jason D. Lee | NIPS 2024,Poster | Empirically, large-scale deep learning models often satisfy a neural scaling law: the test error of the trained model improves polynomially as the model size and data size grow. However, conventional wisdom suggests the test error consists of approximation, bias, and variance errors, where the variance error increases ... | https://openreview.net/pdf/0f2b588586b8f9357aca924f3353b0c13b102112.pdf |
Learning Image Priors Through Patch-Based Diffusion Models for Solving Inverse Problems | https://openreview.net/forum?id=HGnxhHz6ss | https://openreview.net/forum?id=HGnxhHz6ss | Jason Hu,Bowen Song,Xiaojian Xu,Liyue Shen,Jeffrey A Fessler | NIPS 2024,Poster | Diffusion models can learn strong image priors from underlying data distribution and use them to solve inverse problems,
but the training process is computationally expensive and requires lots of data.
Such bottlenecks prevent most existing works from being feasible for high-dimensional and high-resolution data such as... | https://openreview.net/pdf/5c1849cec489253b53dd5ced49cd88613b54d884.pdf |
Amortized Fourier Neural Operators | https://openreview.net/forum?id=a6em980M9x | https://openreview.net/forum?id=a6em980M9x | Zipeng Xiao,Siqi Kou,Zhongkai Hao,Bokai Lin,Zhijie Deng | NIPS 2024,Poster | Fourier Neural Operators (FNOs) have shown promise for solving partial differential equations (PDEs).
Typically, FNOs employ separate parameters for different frequency modes to specify tunable kernel integrals in Fourier space, which, yet, results in an undesirably large number of parameters when solving high-dimensio... | https://openreview.net/pdf/ac3e9bb4adc6f5e7eda9fb232b311cc5daf2ded2.pdf |
Retrieval-Augmented Diffusion Models for Time Series Forecasting | https://openreview.net/forum?id=dRJJt0Ji48 | https://openreview.net/forum?id=dRJJt0Ji48 | Jingwei Liu,Ling Yang,Hongyan Li,Shenda Hong | NIPS 2024,Poster | While time series diffusion models have received considerable focus from many recent works, the performance of existing models remains highly unstable. Factors limiting time series diffusion models include insufficient time series datasets and the absence of guidance. To address these limitations, we propose a Retrieva... | https://openreview.net/pdf/e87ce496bc882c66ee7f20b01c0a67af85c06f6f.pdf |
MACM: Utilizing a Multi-Agent System for Condition Mining in Solving Complex Mathematical Problems | https://openreview.net/forum?id=VR2RdSxtzs | https://openreview.net/forum?id=VR2RdSxtzs | Bin Lei,Yi Zhang,Shan Zuo,Ali Payani,Caiwen Ding | NIPS 2024,Poster | Recent advancements in large language models, such as GPT-4, have demonstrated remarkable capabilities in processing standard queries. Despite these advancements, their performance substantially declines in advanced mathematical problems requiring complex, multi-step logical reasoning. To enhance their inferential capa... | https://openreview.net/pdf/361f70e303eefe87ee3e42f46fb2d3d21347df37.pdf |
Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models | https://openreview.net/forum?id=pRQmRaonxf | https://openreview.net/forum?id=pRQmRaonxf | Chengshuai Shi,Kun Yang,Jing Yang,Cong Shen | NIPS 2024,Poster | The in-context learning (ICL) capability of pre-trained models based on the transformer architecture has received growing interest in recent years. While theoretical understanding has been obtained for ICL in reinforcement learning (RL), the previous results are largely confined to the single-agent setting. This work p... | https://openreview.net/pdf/a739b11c92fd5cfc39cc60917a0bafb7c9f5b8cf.pdf |
Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration | https://openreview.net/forum?id=O0nBMRlkc8 | https://openreview.net/forum?id=O0nBMRlkc8 | Junyang Wang,Haiyang Xu,Haitao Jia,Xi Zhang,Ming Yan,Weizhou Shen,Ji Zhang,Fei Huang,Jitao Sang | NIPS 2024,Poster | Mobile device operation tasks are increasingly becoming a popular multi-modal AI application scenario. Current Multi-modal Large Language Models (MLLMs), constrained by their training data, lack the capability to function effectively as operation assistants. Instead, MLLM-based agents, which enhance capabilities throug... | https://openreview.net/pdf/1884d55b0eac95c14035e897dcbb1c8186bcd65e.pdf |
MGF: Mixed Gaussian Flow for Diverse Trajectory Prediction | https://openreview.net/forum?id=muYhNDlxWc | https://openreview.net/forum?id=muYhNDlxWc | Jiahe Chen,Jinkun Cao,Dahua Lin,Kris M. Kitani,Jiangmiao Pang | NIPS 2024,Poster | To predict future trajectories, the normalizing flow with a standard Gaussian prior suffers from weak diversity.
The ineffectiveness comes from the conflict between the fact of asymmetric and multi-modal distribution of likely outcomes and symmetric and single-modal original distribution and supervision losses.
Instea... | https://openreview.net/pdf/a24b2249847a2068a01c6fa992db6a0aad0d0e19.pdf |
Neural Localizer Fields for Continuous 3D Human Pose and Shape Estimation | https://openreview.net/forum?id=RrTjcbcHEH | https://openreview.net/forum?id=RrTjcbcHEH | István Sárándi,Gerard Pons-Moll | NIPS 2024,Poster | With the explosive growth of available training data, single-image 3D human modeling is ahead of a transition to a data-centric paradigm.
A key to successfully exploiting data scale is to design flexible models that can be supervised from various heterogeneous data sources produced by different researchers or vendors.
... | https://openreview.net/pdf/bb9be9482b7a972c9ff1a24f3d75ee22d6195fdd.pdf |
Efficient Prompt Optimization Through the Lens of Best Arm Identification | https://openreview.net/forum?id=FLNnlfBGMo | https://openreview.net/forum?id=FLNnlfBGMo | Chengshuai Shi,Kun Yang,Zihan Chen,Jundong Li,Jing Yang,Cong Shen | NIPS 2024,Poster | The remarkable instruction-following capability of large language models (LLMs) has sparked a growing interest in automatically finding good prompts, i.e., prompt optimization. Most existing works follow the scheme of selecting from a pre-generated pool of candidate prompts. However, these designs mainly focus on the g... | https://openreview.net/pdf/d55bf9078917118b9c52834d084c1245727ed3e9.pdf |
Fast Best-of-N Decoding via Speculative Rejection | https://openreview.net/forum?id=348hfcprUs | https://openreview.net/forum?id=348hfcprUs | Hanshi Sun,Momin Haider,Ruiqi Zhang,Huitao Yang,Jiahao Qiu,Ming Yin,Mengdi Wang,Peter Bartlett,Andrea Zanette | NIPS 2024,Poster | The safe and effective deployment of Large Language Models (LLMs) involves a critical step called alignment, which ensures that the model's responses are in accordance with human preferences. Prevalent alignment techniques, such as DPO, PPO and their variants, align LLMs by changing the pre-trained model weights during... | https://openreview.net/pdf/1185ba27284299162dd748d2582af8def317545b.pdf |
Full-Atom Peptide Design with Geometric Latent Diffusion | https://openreview.net/forum?id=IAQNJUJe8q | https://openreview.net/forum?id=IAQNJUJe8q | Xiangzhe Kong,Yinjun Jia,Wenbing Huang,Yang Liu | NIPS 2024,Poster | Peptide design plays a pivotal role in therapeutics, allowing brand new possibility to leverage target binding sites that are previously undruggable. Most existing methods are either inefficient or only concerned with the target-agnostic design of 1D sequences. In this paper, we propose a generative model for full-atom... | https://openreview.net/pdf/69729ae7bb5ba90164d10c5cefa3f252d78a5c65.pdf |
3D Gaussian Rendering Can Be Sparser: Efficient Rendering via Learned Fragment Pruning | https://openreview.net/forum?id=IVqzbuLfoL | https://openreview.net/forum?id=IVqzbuLfoL | Zhifan Ye,Chenxi Wan,Chaojian Li,Jihoon Hong,Sixu Li,Leshu Li,Yongan Zhang,Yingyan Celine Lin | NIPS 2024,Poster | 3D Gaussian splatting has recently emerged as a promising technique for novel view synthesis from sparse image sets, yet comes at the cost of requiring millions of 3D Gaussian primitives to reconstruct each 3D scene. This largely limits its application to resource-constrained devices and applications.
Despite advances ... | https://openreview.net/pdf/0f4648bcb47f776c689bfd3c5d96e9f9131c9021.pdf |
Dimension-free Private Mean Estimation for Anisotropic Distributions | https://openreview.net/forum?id=kRwQCAIA7z | https://openreview.net/forum?id=kRwQCAIA7z | Yuval Dagan,Michael Jordan,Xuelin Yang,Lydia Zakynthinou,Nikita Zhivotovskiy | NIPS 2024,Poster | We present differentially private algorithms for high-dimensional mean estimation. Previous private estimators on distributions over $\mathbb{R}^d$ suffer from a curse of dimensionality, as they require $\Omega(d^{1/2})$ samples to achieve non-trivial error, even in cases where $O(1)$ samples suffice without privacy. T... | https://openreview.net/pdf/90919b8b60143f0e171dd5310efcb65e20de7354.pdf |
Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents | https://openreview.net/forum?id=Nf4MHF1pi5 | https://openreview.net/forum?id=Nf4MHF1pi5 | Wenkai Yang,Xiaohan Bi,Yankai Lin,Sishuo Chen,Jie Zhou,Xu Sun | NIPS 2024,Poster | Driven by the rapid development of Large Language Models (LLMs), LLM-based agents have been developed to handle various real-world applications, including finance, healthcare, and shopping, etc. It is crucial to ensure the reliability and security of LLM-based agents during applications. However, the safety issues of L... | https://openreview.net/pdf/14dda2e2e067d5c1fc5179293fd0d4072276f210.pdf |
Beyond Accuracy: Tracking more like Human via Visual Search | https://openreview.net/forum?id=LezAEImfoc | https://openreview.net/forum?id=LezAEImfoc | Dailing Zhang,Shiyu Hu,Xiaokun Feng,Xuchen Li,Meiqi Wu,Jing Zhang,Kaiqi Huang | NIPS 2024,Poster | Human visual search ability enables efficient and accurate tracking of an arbitrary moving target, which is a significant research interest in cognitive neuroscience. The recently proposed Central-Peripheral Dichotomy (CPD) theory sheds light on how humans effectively process visual information and track moving targets... | https://openreview.net/pdf/11960c0cf6a34cdc6a956f476a0fb526022a4514.pdf |
Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms | https://openreview.net/forum?id=8W5ADJOKcv | https://openreview.net/forum?id=8W5ADJOKcv | Chengyuan Deng,Jie Gao,Kevin Lu,Feng Luo,Hongbin Sun,Cheng Xin | NIPS 2024,Poster | We introduce \textbf{N}on-\textbf{Euc}lidean-\textbf{MDS} (Neuc-MDS), which extends Multidimensional Scaling (MDS) to generate outputs that can be non-Euclidean and non-metric. The main idea is to generalize the inner product to other symmetric bilinear forms to utilize the negative eigenvalues of dissimiliarity Gram m... | https://openreview.net/pdf/39cf858fefc937ab29191f4ab0dc60436e3a517a.pdf |
Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance? | https://openreview.net/forum?id=4lGPSbGe11 | https://openreview.net/forum?id=4lGPSbGe11 | Garud Iyengar,Henry Lam,Tianyu Wang | NIPS 2024,Poster | Cross-Validation (CV) is the default choice for estimate the out-of-sample performance of machine learning models. Despite its wide usage, their statistical benefits have remained half-understood, especially in challenging nonparametric regimes. In this paper we fill in this gap and show that, in terms of estimating th... | https://openreview.net/pdf/189b1a44f32cdb8dc7985f8314688df2d9804e5f.pdf |
Target-Guided Adversarial Point Cloud Transformer Towards Recognition Against Real-world Corruptions | https://openreview.net/forum?id=FcUyz33OED | https://openreview.net/forum?id=FcUyz33OED | Jie Wang,Tingfa Xu,Lihe Ding,Jianan Li | NIPS 2024,Poster | Achieving robust 3D perception in the face of corrupted data presents an challenging hurdle within 3D vision research. Contemporary transformer-based point cloud recognition models, albeit advanced, tend to overfit to specific patterns, consequently undermining their robustness against corruption. In this work, we intr... | https://openreview.net/pdf/39d342b992430643e7c7bb388857230d156519e2.pdf |
Faster Accelerated First-order Methods for Convex Optimization with Strongly Convex Function Constraints | https://openreview.net/forum?id=pG380vLYRU | https://openreview.net/forum?id=pG380vLYRU | Zhenwei Lin,Qi Deng | NIPS 2024,Poster | In this paper, we introduce faster accelerated primal-dual algorithms for minimizing a convex function subject to strongly convex function constraints.
Prior to our work, the best complexity bound was $\mathcal{O}(1/{\varepsilon})$, regardless of the strong convexity of the constraint function.
It is unclear whether t... | https://openreview.net/pdf/39d2a679b231a9c79f5e0031e24c97e052f8d1b3.pdf |
Exactly Minimax-Optimal Locally Differentially Private Sampling | https://openreview.net/forum?id=Dr7UarlhVE | https://openreview.net/forum?id=Dr7UarlhVE | Hyun-Young Park,Shahab Asoodeh,Si-Hyeon Lee | NIPS 2024,Poster | The sampling problem under local differential privacy has recently been studied with potential applications to generative models, but a fundamental analysis of its privacy-utility trade-off (PUT) remains incomplete. In this work, we define the fundamental PUT of private sampling in the minimax sense, using the $f$-dive... | https://openreview.net/pdf/4db55857d23f0b9e879bd411410654e42341a38f.pdf |
Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation | https://openreview.net/forum?id=x4Kk4FxLs3 | https://openreview.net/forum?id=x4Kk4FxLs3 | Lingxiao Zhao,Xueying Ding,Leman Akoglu | NIPS 2024,Poster | Graph generation has been dominated by autoregressive models due to their simplicity and effectiveness, despite their sensitivity to ordering. Yet diffusion models have garnered increasing attention, as they offer comparable performance while being permutation-invariant. Current graph diffusion models generate graphs i... | https://openreview.net/pdf/c1a62f1c53db519f90d14aed3ca68d4ed80a9146.pdf |
Robust Reinforcement Learning with General Utility | https://openreview.net/forum?id=8Uyfr5TcNR | https://openreview.net/forum?id=8Uyfr5TcNR | Ziyi Chen,Yan Wen,Zhengmian Hu,Heng Huang | NIPS 2024,Poster | Reinforcement Learning (RL) problem with general utility is a powerful decision making framework that covers standard RL with cumulative cost, exploration problems, and demonstration learning. Existing works on RL with general utility do not consider the robustness under environmental perturbation, which is important t... | https://openreview.net/pdf/bd1e309302d23d5c27fb998972d826f00ff8c3cc.pdf |
Online Estimation via Offline Estimation: An Information-Theoretic Framework | https://openreview.net/forum?id=sks7x4I8Bh | https://openreview.net/forum?id=sks7x4I8Bh | Dylan J Foster,Yanjun Han,Jian Qian,Alexander Rakhlin | NIPS 2024,Poster | The classical theory of statistical estimation aims to estimate a parameter of interest under data generated from a fixed design (''offline estimation''), while the contemporary theory of online learning provides algorithms for estimation under adaptively chosen covariates (''online estimation''). Motivated by connecti... | https://openreview.net/pdf/b8cfbc277ea416f34c48378cb8a72149176fc155.pdf |
Diffusion of Thought: Chain-of-Thought Reasoning in Diffusion Language Models | https://openreview.net/forum?id=G0v0TxX01N | https://openreview.net/forum?id=G0v0TxX01N | Jiacheng Ye,Shansan Gong,Liheng Chen,Lin Zheng,Jiahui Gao,Han Shi,Chuan Wu,Xin Jiang,Zhenguo Li,Wei Bi,Lingpeng Kong | NIPS 2024,Poster | Recently, diffusion models have garnered significant interest in the field of text processing due to their many potential advantages compared to conventional autoregressive models.
In this work, we propose Diffusion-of-Thought (DoT), a novel approach that integrates diffusion models with Chain-of-Thought, a well-estab... | https://openreview.net/pdf/c87cdf6b6e90f2c3f736be50639670dba4245f12.pdf |
No-Regret Bandit Exploration based on Soft Tree Ensemble Model | https://openreview.net/forum?id=cKKXBhyijL | https://openreview.net/forum?id=cKKXBhyijL | Shogo Iwazaki,Shinya Suzumura | NIPS 2024,Poster | We propose a novel stochastic bandit algorithm that employs reward estimates using a tree ensemble model. Specifically, our focus is on a soft tree model, a variant of the conventional decision tree that has undergone both practical and theoretical scrutiny in recent years. By deriving several non-trivial properties of... | https://openreview.net/pdf/5f22cbcc4e1f4f15297ff48ae857d328731de108.pdf |
Transfer Learning for Diffusion Models | https://openreview.net/forum?id=6emETARnWi | https://openreview.net/forum?id=6emETARnWi | Yidong Ouyang,Liyan Xie,Hongyuan Zha,Guang Cheng | NIPS 2024,Poster | Diffusion models, a specific type of generative model, have achieved unprecedented performance in recent years and consistently produce high-quality synthetic samples. A critical prerequisite for their notable success lies in the presence of a substantial number of training samples, which can be impractical in real-wor... | https://openreview.net/pdf/b69df4de4e1f7e40fdc5f172023f56acde8ecf7a.pdf |
Clustering in Causal Attention Masking | https://openreview.net/forum?id=OiVxYf9trg | https://openreview.net/forum?id=OiVxYf9trg | Nikita Karagodin,Yury Polyanskiy,Philippe Rigollet | NIPS 2024,Poster | This work presents a modification of the self-attention dynamics proposed in Geshkovski et al to better reflect the practically relevant, causally masked attention used in transformer architectures for generative AI. This modification translates into an interacting particle system that cannot be interpreted as a mean-f... | https://openreview.net/pdf/1361fab0c43791b9c9dcfb0a70e718f4ecb7d356.pdf |
Active Set Ordering | https://openreview.net/forum?id=GkJbXpd3wM | https://openreview.net/forum?id=GkJbXpd3wM | Quoc Phong Nguyen,Sunil Gupta,Svetha Venkatesh,Bryan Kian Hsiang Low,Patrick Jaillet | NIPS 2024,Poster | In this paper, we formalize the active set ordering problem, which involves actively discovering a set of inputs based on their orderings determined by expensive evaluations of a blackbox function. We then propose the mean prediction (MP) algorithm and theoretically analyze it in terms of the regret of predicted pairw... | https://openreview.net/pdf/a84bb38a2dbbe31a1fdccd16481727e5c72a82a0.pdf |
HGDL: Heterogeneous Graph Label Distribution Learning | https://openreview.net/forum?id=OwguhIAh8R | https://openreview.net/forum?id=OwguhIAh8R | Yufei Jin,Heng Lian,Yi He,Xingquan Zhu | NIPS 2024,Poster | Label Distribution Learning (LDL) has been extensively studied in IID data applications such as computer vision, thanks to its more generic setting over single-label and multi-label classification.
This paper advances LDL into graph domains and aims to tackle a novel and fundamental
heterogeneous graph label distribut... | https://openreview.net/pdf/c98fe7ec6e30f7f8475be8f25e8e979518ad86be.pdf |
Compressing Large Language Models using Low Rank and Low Precision Decomposition | https://openreview.net/forum?id=lkx3OpcqSZ | https://openreview.net/forum?id=lkx3OpcqSZ | Rajarshi Saha,Naomi Sagan,Varun Srivastava,Andrea Goldsmith,Mert Pilanci | NIPS 2024,Poster | The prohibitive sizes of Large Language Models (LLMs) today make it difficult to deploy them on memory-constrained edge devices. This work introduces $\rm CALDERA$ -- a new post-training LLM compression algorithm that harnesses the inherent low-rank structure of a weight matrix $\mathbf{W}$ by approximating it via a lo... | https://openreview.net/pdf/2b6005c971c3343b98f66b536c29add85a496414.pdf |
Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning | https://openreview.net/forum?id=aDQlAz09dS | https://openreview.net/forum?id=aDQlAz09dS | Xuechen Zhang,Zijian Huang,Ege Onur Taga,Carlee Joe-Wong,Samet Oymak,Jiasi Chen | NIPS 2024,Poster | Recent successes in natural language processing have led to the proliferation of large language models (LLMs) by multiple providers. Each LLM offering has different inference accuracy, monetary cost, and latency, and their accuracy further depends on the exact wording of the question (i.e., the specific prompt). At the... | https://openreview.net/pdf/6edc8a474ffa7f439968f38dd2ced40f203ae8db.pdf |
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