mtybilly/Instruct2DS huggingface dataset:
Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts
Authors: Zhi-Yi Chin, Chieh Ming Jiang, Ching-Chun Huang, Pin-Yu Chen, Wei-Chen Chiu
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Text-to-image diffusion models, e.g. Stable Diffusion (SD), lately have shown remarkable ability in high-quality content generation, and become one of the representatives for the recent wave of transformative AI. Nevertheless, such advance comes with an intensifying concern about the misuse of this generative technology, especially for producing copyrighted or NSFW (i.e. not safe for work) images. Although efforts have been made to filter inappropriate images/prompts or remove undesirable concepts/styles via model fine-tuning, the reliability of these safety mechanisms against diversified problematic prompts remains largely unexplored. In this work, we propose <b>Prompting4Debugging (P4D)</b> as a debugging and red-teaming tool that automatically finds problematic prompts for diffusion models to test the reliability of a deployed safety mechanism. We demonstrate the efficacy of our P4D tool in uncovering new vulnerabilities of SD models with safety mechanisms. Particularly, our result shows that around half of prompts in existing safe prompting benchmarks which were originally considered "safe" can actually be manipulated to bypass many deployed safety mechanisms, including concept removal, negative prompt, and safety guidance. Our findings suggest that, without comprehensive testing, the evaluations on limited safe prompting benchmarks can lead to a false sense of safety for text-to-image models.
Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams
Authors: Brian M Cho, Kyra Gan, Nathan Kallus
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: 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 $α$-level test across any stopping time. Our contributions are two-fold: (1) we propose a novel betting scheme and provide theoretical guarantees on type-I error control, power, and asymptotic growth rate/$e$-power in the setting of a single data stream; (2) we introduce PEAK, a generalization of this betting scheme to multiple streams, that (i) avoids using wasteful union bounds via averaging, (ii) is a test of power one under mild regularity conditions on the sampling scheme of the streams, and (iii) reduces computational overhead when applying the testing-as-betting approaches for pure-exploration bandit problems. We illustrate the practical benefits of PEAK using both synthetic and real-world HeartSteps datasets. Our experiments show that PEAK provides up to an 85% reduction in the number of samples before stopping compared to existing stopping rules for pure-exploration bandit problems, and matches the performance of state-of-the-art sequential tests while improving upon computational complexity.
Parameterized Physics-informed Neural Networks for Parameterized PDEs
Authors: Woojin Cho, Minju Jo, Haksoo Lim, Kookjin Lee, Dongeun Lee, Sanghyun Hong, Noseong Park
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Complex physical systems are often described by partial differential equations (PDEs) that depend on parameters such as the Raynolds number in fluid mechanics. In applications such as design optimization or uncertainty quantification, solutions of those PDEs need to be evaluated at numerous points in the parameter space. While physics-informed neural networks (PINNs) have emerged as a new strong competitor as a surrogate, their usage in this scenario remains underexplored due to the inherent need for repetitive and time-consuming training. In this paper, we address this problem by proposing a novel extension, parameterized physics-informed neural networks (P$^2$INNs). P$^2$INNs enable modeling the solutions of parameterized PDEs via explicitly encoding a latent representation of PDE parameters. With the extensive empirical evaluation, we demonstrate that P$^2$INNs outperform the baselines both in accuracy and parameter efficiency on benchmark 1D and 2D parameterized PDEs and are also effective in overcoming the known “failure modes”.
Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameters
Authors: Brian M Cho, Yaroslav Mukhin, Kyra Gan, Ivana Malenica
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: When estimating target parameters in nonparametric models with nuisance parameters, substituting the unknown nuisances with nonparametric estimators can introduce "plug-in bias." Traditional methods addressing this suboptimal bias-variance trade-off rely on the influence function (IF) of the target parameter. When estimating multiple target parameters, these methods require debiasing the nuisance parameter multiple times using the corresponding IFs, which poses analytical and computational challenges. In this work, we leverage the targeted maximum likelihood estimation (TMLE) framework to propose a novel method named kernel debiased plug-in estimation (KDPE). KDPE refines an initial estimate through regularized likelihood maximization steps, employing a nonparametric model based on reproducing kernel Hilbert spaces. We show that KDPE: (i) simultaneously debiases all pathwise differentiable target parameters that satisfy our regularity conditions, (ii) does not require the IF for implementation, and (iii) remains computationally tractable. We numerically illustrate the use of KDPE and validate our theoretical results.
Hard Tasks First: Multi-Task Reinforcement Learning Through Task Scheduling
Authors: Myungsik Cho, Jongeui Park, Suyoung Lee, Youngchul Sung
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: 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 prioritizes more challenging tasks, thereby enhancing overall learning efficiency. SMT introduces a dynamic task prioritization strategy, underpinned by an effective metric for assessing task difficulty. This metric ensures an efficient and targeted allocation of training resources, significantly improving learning outcomes. Additionally, SMT incorporates a reset mechanism that periodically reinitializes key network parameters to mitigate the simplicity bias, further enhancing the adaptability and robustness of the learning process across diverse tasks. The efficacy of SMT’s scheduling method is validated by significantly improving performance on challenging Meta-World benchmarks.
KV-Runahead: Scalable Causal LLM Inference by Parallel Key-Value Cache Generation
Authors: Minsik Cho, Mohammad Rastegari, Devang Naik
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Large Language Model or LLM inference has two phases, the prompt (or prefill) phase to output the first token and the extension (or decoding) phase to the generate subsequent tokens. In this work, we propose an efficient parallelization scheme, KV-Runahead to accelerate the prompt phase. The key observation is that the extension phase generates tokens faster than the prompt phase because of key-value cache (KV-cache). Hence, KV-Runahead parallelizes the prompt phase by orchestrating multiple processes to populate the KV-cache and minimizes the time-to-first-token (TTFT). Dual-purposing the KV-cache scheme has two main benefits. First, since KV-cache is designed to leverage the causal attention map, we minimize computation and computation automatically. Second, since it already exists for the extension phase, KV-Runahead is easy to implement. We further propose context-level load-balancing to handle uneven KV-cache generation (due to the causal attention) and to optimize TTFT. Compared with an existing parallelization scheme such as tensor or sequential parallelization where keys and values are locally generated and exchanged via all-gather collectives, our experimental results demonstrate that KV-Runahead can offer over 1.4× and 1.6× speedups for Llama 7B and Falcon 7B respectively.
Neurodegenerative Brain Network Classification via Adaptive Diffusion with Temporal Regularization
Authors: Hyuna Cho, Jaeyoon Sim, Guorong Wu, Won Hwa Kim
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Analysis of neurodegenerative diseases on brain connectomes is important in facilitating early diagnosis and predicting its onset. However, investigation of the progressive and irreversible dynamics of these diseases remains underexplored in cross-sectional studies as its diagnostic groups are considered independent. Also, as in many real-world graphs, brain networks exhibit intricate structures with both homophily and heterophily. To address these challenges, we propose Adaptive Graph diffusion network with Temporal regularization (AGT). AGT introduces node-wise convolution to adaptively capture low (i.e., homophily) and high-frequency (i.e., heterophily) characteristics within an optimally tailored range for each node. Moreover, AGT captures sequential variations within progressive diagnostic groups with a novel temporal regularization, considering the relative feature distance between the groups in the latent space. As a result, our proposed model yields interpretable results at both node-level and group-level. The superiority of our method is validated on two neurodegenerative disease benchmarks for graph classification: Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Parkinson’s Progression Markers Initiative (PPMI) datasets.
Tilt and Average : Geometric Adjustment of the Last Layer for Recalibration
Authors: Gyusang Cho, Chan-Hyun Youn
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: After the revelation that neural networks tend to produce overconfident predictions, the problem of calibration, which aims to align confidence with accuracy to enhance the reliability of predictions, has gained significant importance. Several solutions based on calibration maps have been proposed to address the problem of recalibrating a trained classifier using additional datasets. In this paper, we offer an algorithm that transforms the weights of the last layer of the classifier, distinct from the calibration-map-based approach. We concentrate on the geometry of the final linear layer, specifically its angular aspect, and adjust the weights of the corresponding layer. We name the method Tilt and Average, and validate the calibration effect empirically and theoretically. Through this, we demonstrate that our approach, in addition to the existing calibration-map-based techniques, can yield improved calibration performance.
Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transport
Authors: Jaemoo Choi, Jaewoong Choi, Myungjoo Kang
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: 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 method for this is the JKO scheme. In this regard, previous WGF models employ the JKO scheme and parametrized transport map for each JKO step. However, this approach results in quadratic training complexity $O(K^2)$ with the number of JKO step $K$. This severely limits the scalability of WGF models. In this paper, we introduce a scalable WGF-based generative model, called Semi-dual JKO (S-JKO). Our model is based on the semi-dual form of the JKO step, derived from the equivalence between the JKO step and the Unbalanced Optimal Transport. Our approach reduces the training complexity to $O(K)$. We demonstrate that our model significantly outperforms existing WGF-based generative models, achieving FID scores of 2.62 on CIFAR-10 and 6.42 on CelebA-HQ-256, which are comparable to state-of-the-art image generative models.
Listwise Reward Estimation for Offline Preference-based Reinforcement Learning
Authors: Heewoong Choi, Sangwon Jung, Hongjoon Ahn, Taesup Moon
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: In Reinforcement Learning (RL), designing precise reward functions remains to be a challenge, particularly when aligning with human intent. Preference-based RL (PbRL) was introduced to address this problem by learning reward models from human feedback. However, existing PbRL methods have limitations as they often overlook the <em>second-order</em> preference that indicates the relative strength of preference. In this paper, we propose Listwise Reward Estimation (LiRE), a novel approach for offline PbRL that leverages second-order preference information by constructing a Ranked List of Trajectories (RLT), which can be efficiently built by using the same ternary feedback type as traditional methods. To validate the effectiveness of LiRE, we propose a new offline PbRL dataset that objectively reflects the effect of the estimated rewards. Our extensive experiments on the dataset demonstrate the superiority of LiRE, <em>i.e.,</em> outperforming state-of-the-art baselines even with modest feedback budgets and enjoying robustness with respect to the number of feedbacks and feedback noise. Our code is available at https://github.com/chwoong/LiRE
BWS: Best Window Selection Based on Sample Scores for Data Pruning across Broad Ranges
Authors: Hoyong Choi, Nohyun Ki, Hye Won Chung
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Data subset selection aims to find a smaller yet informative subset of a large dataset that can approximate the full-dataset training, addressing challenges associated with training neural networks on large-scale datasets. However, existing methods tend to specialize in either high or low selection ratio regimes, lacking a universal approach that consistently achieves competitive performance across a broad range of selection ratios. We introduce a universal and efficient data subset selection method, Best Window Selection (BWS), by proposing a method to choose the best window subset from samples ordered based on their difficulty scores. This approach offers flexibility by allowing the choice of window intervals that span from easy to difficult samples. Furthermore, we provide an efficient mechanism for selecting the best window subset by evaluating its quality using kernel ridge regression. Our experimental results demonstrate the superior performance of BWS compared to other baselines across a broad range of selection ratios over datasets, including CIFAR-10/100 and ImageNet, and the scenarios involving training from random initialization or fine-tuning of pre-trained models.
Embodied CoT Distillation From LLM To Off-the-shelf Agents
Authors: Wonje Choi, Woo Kyung Kim, Minjong Yoo, Honguk Woo
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: We address the challenge of utilizing large language models (LLMs) for complex embodied tasks, in the environment where decision-making systems operate timely on capacity-limited, off-the-shelf devices. We present DeDer, a framework for decomposing and distilling the embodied reasoning capabilities from LLMs to efficient, small language model (sLM)-based policies. In DeDer, the decision-making process of LLM-based strategies is restructured into a hierarchy with a reasoning-policy and planning-policy. The reasoning-policy is distilled from the data that is generated through the embodied in-context learning and self-verification of an LLM, so it can produce effective rationales. The planning-policy, guided by the rationales, can render optimized plans efficiently. In turn, DeDer allows for adopting sLMs for both policies, deployed on off-the-shelf devices. Furthermore, to enhance the quality of intermediate rationales, specific to embodied tasks, we devise the embodied knowledge graph, and to generate multiple rationales timely through a single inference, we also use the contrastively prompted attention model. Our experiments with the ALFRED benchmark demonstrate that DeDer surpasses leading language planning and distillation approaches, indicating the applicability and efficiency of sLM-based embodied policies derived through DeDer.
PICLe: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context Learning
Authors: Hyeong Kyu Choi, Yixuan Li
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Large Language Models (LLMs) are trained on massive text corpora, which are encoded with diverse personality traits. This triggers an interesting goal of eliciting a desired personality trait from the LLM, and probing its behavioral preferences. Accordingly, we formalize the persona elicitation task, aiming to customize LLM behaviors to align with a target persona. We present Persona In-Context Learning (PICLe), a novel persona elicitation framework grounded in Bayesian inference. At the core, PICLe introduces a new ICL example selection criterion based on likelihood ratio, which is designed to optimally guide the model in eliciting a specific target persona. We demonstrate the effectiveness of PICLe through extensive comparisons against baseline methods across three contemporary LLMs. Code is available at https://github.com/deeplearning-wisc/picle.
PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
Authors: Jeongwhan Choi, Sumin Park, Hyowon Wi, Sung-Bae Cho, Noseong Park
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Recent research in the field of graph neural network (GNN) has identified a critical issue known as "over-squashing," resulting from the bottleneck phenomenon in graph structures, which impedes the propagation of long-range information. Prior works have proposed a variety of graph rewiring concepts that aim at optimizing the spatial or spectral properties of graphs to promote the signal propagation. However, such approaches inevitably deteriorate the original graph topology, which may lead to a distortion of information flow. To address this, we introduce an ex<b>pand</b>ed width-<b>a</b>ware (<b>PANDA</b>) message passing, a new message passing paradigm where nodes with high centrality, a potential source of over-squashing, are selectively expanded in width to encapsulate the growing influx of signals from distant nodes. Experimental results show that our method outperforms existing rewiring methods, suggesting that selectively expanding the hidden state of nodes can be a compelling alternative to graph rewiring for addressing the over-squashing.
Online bipartite matching with imperfect advice
Authors: Davin Choo, Themistoklis Gouleakis, Chun Kai Ling, Arnab Bhattacharyya
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: We study the problem of online unweighted bipartite matching with $n$ offline vertices and $n$ online vertices where one wishes to be competitive against the optimal offline algorithm. While the classic RANKING algorithm of (Karp et al., 1990) provably attains competitive ratio of $1-1/e > 1/2$, we show that no learning-augmented method can be both 1-consistent and strictly better than 1/2-robust under the adversarial arrival model. Meanwhile, under the random arrival model, we show how one can utilize methods from distribution testing to design an algorithm that takes in external advice about the online vertices and provably achieves competitive ratio interpolating between any ratio attainable by advice-free methods and the optimal ratio of 1, depending on the advice quality.
A connection between Tempering and Entropic Mirror Descent
Authors: Nicolas Chopin, Francesca Crucinio, Anna Korba
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: This paper explores the connections between tempering (for Sequential Monte Carlo; SMC) and entropic mirror descent to sample from a target probability distribution whose unnormalized density is known. We establish that tempering SMC corresponds to entropic mirror descent applied to the reverse Kullback-Leibler (KL) divergence and obtain convergence rates for the tempering iterates. Our result motivates the tempering iterates from an optimization point of view, showing that tempering can be seen as a descent scheme of the KL divergence with respect to the Fisher-Rao geometry, in contrast to Langevin dynamics that perform descent of the KL with respect to the Wasserstein-2 geometry. We exploit the connection between tempering and mirror descent iterates to justify common practices in SMC and derive adaptive tempering rules that improve over other alternative benchmarks in the literature.
Learning Linear Block Error Correction Codes
Authors: Yoni Choukroun, Lior Wolf
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Error correction codes are a crucial part of the physical communication layer, ensuring the reliable transfer of data over noisy channels. The design of optimal linear block codes capable of being efficiently decoded is of major concern, especially for short block lengths. While neural decoders have recently demonstrated their advantage over classical decoding techniques, the neural design of the codes remains a challenge. In this work, we propose for the first time a unified encoder-decoder training of binary linear block codes. To this end, we adapt the coding setting to support efficient and differentiable training of the code for end-to-end optimization over the order two Galois field. We also propose a novel Transformer model in which the self-attention masking is performed in a differentiable fashion for the efficient backpropagation of the code gradient. Our results show that (i) the proposed decoder outperforms existing neural decoding on conventional codes, (ii) the suggested framework generates codes that outperform the analogous conventional codes, and (iii) the codes we developed not only excel with our decoder but also show enhanced performance with traditional decoding techniques.
A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts
Authors: Mohammed Nowaz Rabbani Chowdhury, Meng Wang, Kaoutar El Maghraoui, Naigang Wang, Pin-Yu Chen, Christopher Carothers
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: The sparsely gated mixture of experts (MoE) architecture sends different inputs to different subnetworks (experts), through trainable routers. MoE reduces the training computation significantly for large models, but its deployment can be still memory/computation expensive for some downstream tasks. Model pruning is a popular approach to reduce inference computation, but its application in MoE architecture is largely unexplored. To the best of our knowledge, this paper provides the first provably efficient technique for pruning experts in fine-tuned MoE models. We theoretically prove that prioritizing the pruning of the experts with a smaller change of the router’s $l_2$ norm from the pre-trained model guarantees the preservation of test accuracy, while significantly reducing the model size and the computational requirements. Although our theoretical analysis is centered on binary classification tasks on simplified MoE architecture, our expert pruning method is verified on large vision MoE models such as V-MoE and $\textE^3$-MoE fine-tuned on benchmark datasets such as CIFAR-10, CIFAR-100, and ImageNet.
SPABA: A Single-Loop and Probabilistic Stochastic Bilevel Algorithm Achieving Optimal Sample Complexity
Authors: Tianshu Chu, Dachuan Xu, Wei Yao, Jin Zhang
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: While stochastic bilevel optimization methods have been extensively studied for addressing large-scale nested optimization problems in machine learning, it remains an open question whether the optimal complexity bounds for solving bilevel optimization are the same as those in single-level optimization. Our main result resolves this question: SPABA, an adaptation of the PAGE method for nonconvex optimization in (Li et al., 2021) to the bilevel setting, can achieve optimal sample complexity in both the finite-sum and expectation settings. We show the optimality of SPABA by proving that there is no gap in complexity analysis between stochastic bilevel and single-level optimization when implementing PAGE. Notably, as indicated by the results of (Dagréou et al., 2022), there might exist a gap in complexity analysis when implementing other stochastic gradient estimators, like SGD and SAGA. In addition to SPABA, we propose several other single-loop stochastic bilevel algorithms, that either match or improve the state-of-the-art sample complexity results, leveraging our convergence rate and complexity analysis. Numerical experiments demonstrate the superior practical performance of the proposed methods.
How Private are DP-SGD Implementations?
Authors: Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: We demonstrate a substantial gap between the privacy guarantees of the Adaptive Batch Linear Queries (ABLQ) mechanism under different types of batch sampling: (i) Shuffling, and (ii) Poisson subsampling; the typical analysis of Differentially Private Stochastic Gradient Descent (DP-SGD) follows by interpreting it as a post-processing of ABLQ. While shuffling-based DP-SGD is more commonly used in practical implementations, it has not been amenable to easy privacy analysis, either analytically or even numerically. On the other hand, Poisson subsampling-based DP-SGD is challenging to scalably implement, but has a well-understood privacy analysis, with multiple open-source numerically tight privacy accountants available. This has led to a common practice of using shuffling-based DP-SGD in practice, but using the privacy analysis for the corresponding Poisson subsampling version. Our result shows that there can be a substantial gap between the privacy analysis when using the two types of batch sampling, and thus advises caution in reporting privacy parameters for DP-SGD.
Sampling-based Multi-dimensional Recalibration
Authors: Youngseog Chung, Ian Char, Jeff Schneider
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Calibration of probabilistic forecasts in the regression setting has been widely studied in the single dimensional case, where the output variables are assumed to be univariate. In many problem settings, however, the output variables are multi-dimensional, and in the presence of dependence across the output dimensions, measuring calibration and performing recalibration for each dimension separately can be both misleading and detrimental. In this work, we focus on representing predictive uncertainties via samples, and propose a recalibration method which accounts for the joint distribution across output dimensions to produce calibrated samples. Based on the concept of highest density regions (HDR), we define the notion of HDR calibration, and show that our recalibration method produces samples which are HDR calibrated. We demonstrate the performance of our method and the quality of the recalibrated samples on a suite of benchmark datasets in multi-dimensional regression, a real-world dataset in modeling plasma dynamics during nuclear fusion reactions, and on a decision-making application in forecasting demand.
Prompt-tuning Latent Diffusion Models for Inverse Problems
Authors: Hyungjin Chung, Jong Chul Ye, Peyman Milanfar, Mauricio Delbracio
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: We propose a new method for solving imaging inverse problems using text-to-image latent diffusion models as general priors. Existing methods using latent diffusion models for inverse problems typically rely on simple null text prompts, which can lead to suboptimal performance. To improve upon this, we introduce a method for prompt tuning, which jointly optimizes the text embedding on-the-fly while running the reverse diffusion. This allows us to generate images that are more faithful to the diffusion prior. Specifically, our approach involves a unified optimization framework that simultaneously considers the prompt, latent, and pixel values through alternating minimization. This significantly diminishes image artifacts - a major problem when using latent diffusion models instead of pixel-based diffusion ones. Our method, called P2L, outperforms both pixel- and latent-diffusion model-based inverse problem solvers on a variety of tasks, such as super-resolution, deblurring, and inpainting. Furthermore, P2L demonstrates remarkable scalability to higher resolutions without artifacts.
MusicRL: Aligning Music Generation to Human Preferences
Authors: Geoffrey Cideron, Sertan Girgin, Mauro Verzetti, Damien Vincent, Matej Kastelic, Zalán Borsos, Brian Mcwilliams, Victor Ungureanu, Olivier Bachem, Olivier Pietquin, Matthieu Geist, Leonard Hussenot, Neil Zeghidour, Andrea Agostinelli
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: We propose MusicRL, the first music generation system finetuned from human feedback. Appreciation of text-to-music models is particularly subjective since the concept of musicality as well as the specific intention behind a caption are user-dependent (e.g. a caption such as “upbeat workout music” can map to a retro guitar solo or a technopop beat). Not only this makes supervised training of such models challenging, but it also calls for integrating continuous human feedback in their post-deployment finetuning. MusicRL is a pretrained autoregressive MusicLM model of discrete audio tokens finetuned with reinforcement learning to maximize sequence-level rewards. We design reward functions related specifically to text-adherence and audio quality with the help from selected raters, and use those to finetune MusicLM into MusicRL-R. We deploy MusicLM to users and collect a substantial dataset comprising 300,000 pairwise preferences. Using Reinforcement Learning from Human Feedback (RLHF), we train MusicRL-U, the first text-to-music model that incorporates human feedback at scale. Human evaluations show that both MusicRL-R and MusicRL-U are preferred to the baseline. Ultimately, MusicRL-RU combines the two approaches and results in the best model according to human raters. Ablation studies shed light on the musical attributes influencing human preferences, indicating that text adherence and quality only account for a part of it. This underscores the prevalence of subjectivity in musical appreciation and calls for further involvement of human listeners in the finetuning of music generation models. Samples can be found at google-research.github.io/seanet/musiclm/rlhf/.
Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
Authors: Andrea Cini, Danilo Mandic, Cesare Alippi
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Relationships among time series can be exploited as inductive biases in learning effective forecasting models. In hierarchical time series, relationships among subsets of sequences induce hard constraints (hierarchical inductive biases) on the predicted values. In this paper, we propose a graph-based methodology to unify relational and hierarchical inductive biases in the context of deep learning for time series forecasting. In particular, we model both types of relationships as dependencies in a pyramidal graph structure, with each pyramidal layer corresponding to a level of the hierarchy. By exploiting modern - trainable - graph pooling operators we show that the hierarchical structure, if not available as a prior, can be learned directly from data, thus obtaining cluster assignments aligned with the forecasting objective. A differentiable reconciliation stage is incorporated into the processing architecture, allowing hierarchical constraints to act both as an architectural bias as well as a regularization element for predictions. Simulation results on representative datasets show that the proposed method compares favorably against the state of the art.
Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens
Authors: Ross M Clarke, José Miguel Hernández-Lobato
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Research into optimisation for deep learning is characterised by a tension between the computational efficiency of first-order, gradient-based methods (such as SGD and Adam) and the theoretical efficiency of second-order, curvature-based methods (such as quasi-Newton methods and K-FAC). Noting that second-order methods often only function effectively with the addition of stabilising heuristics (such as Levenberg-Marquardt damping), we ask how much these (as opposed to the second-order curvature model) contribute to second-order algorithms’ performance. We thus study <em>AdamQLR</em>: an optimiser combining damping and learning rate selection techniques from K-FAC (Martens & Grosse, 2015) with the update directions proposed by Adam, inspired by considering Adam through a second-order lens. We evaluate AdamQLR on a range of regression and classification tasks at various scales and hyperparameter tuning methodologies, concluding K-FAC’s adaptive heuristics are of variable standalone general effectiveness, and finding an <em>untuned</em> AdamQLR setting can achieve comparable performance vs runtime to <em>tuned</em> benchmarks.
$\mathttVITS$ : Variational Inference Thompson Sampling for contextual bandits
Authors: Pierre Clavier, Tom Huix, Alain Oliviero Durmus
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: In this paper, we introduce and analyze a variant of the Thompson sampling (TS) algorithm for contextual bandits. At each round, traditional TS requires samples from the current posterior distribution, which is usually intractable. To circumvent this issue, approximate inference techniques can be used and provide samples with distribution close to the posteriors. However, current approximate techniques yield to either poor estimation (Laplace approximation) or can be computationally expensive (MCMC methods, Ensemble sampling...). In this paper, we propose a new algorithm, Varational Inference TS $\mathttVITS$, based on Gaussian Variational Inference. This scheme provides powerful posterior approximations which are easy to sample from, and is computationally efficient, making it an ideal choice for TS. In addition, we show that $\mathttVITS$ achieves a sub-linear regret bound of the same order in the dimension and number of round as traditional TS for linear contextual bandit. Finally, we demonstrate experimentally the effectiveness of $\mathttVITS$ on both synthetic and real world datasets
CogBench: a large language model walks into a psychology lab
Authors: Julian Coda-Forno, Marcel Binz, Jane X Wang, Eric Schulz
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Large language models (LLMs) have significantly advanced the field of artificial intelligence. Yet, evaluating them comprehensively remains challenging. We argue that this is partly due to the predominant focus on performance metrics in most benchmarks. This paper introduces <em>CogBench</em>, a benchmark that includes ten behavioral metrics derived from seven cognitive psychology experiments. This novel approach offers a toolkit for phenotyping LLMs’ behavior. We apply <em>CogBench</em> to 40 LLMs, yielding a rich and diverse dataset. We analyze this data using statistical multilevel modeling techniques, accounting for the nested dependencies among fine-tuned versions of specific LLMs. Our study highlights the crucial role of model size and reinforcement learning from human feedback (RLHF) in improving performance and aligning with human behavior. Interestingly, we find that open-source models are less risk-prone than proprietary models and that fine-tuning on code does not necessarily enhance LLMs’ behavior. Finally, we explore the effects of prompt-engineering techniques. We discover that chain-of-thought prompting improves probabilistic reasoning, while take-a-step-back prompting fosters model-based behaviors.
Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal Slices
Authors: Nathaniel Cohen, Vladimir Kulikov, Matan Kleiner, Inbar Huberman-Spiegelglas, Tomer Michaeli
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Text-to-image (T2I) diffusion models achieve state-of-the-art results in image synthesis and editing. However, leveraging such pre-trained models for video editing is considered a major challenge. Many existing works attempt to enforce temporal consistency in the edited video through explicit correspondence mechanisms, either in pixel space or between deep features. These methods, however, struggle with strong nonrigid motion. In this paper, we introduce a fundamentally different approach, which is based on the observation that spatiotemporal slices of natural videos exhibit similar characteristics to natural images. Thus, the same T2I diffusion model that is normally used only as a prior on video frames, can also serve as a strong prior for enhancing temporal consistency by applying it on spatiotemporal slices. Based on this observation, we present Slicedit, a method for text-based video editing that utilizes a pre-trained T2I diffusion model to process both spatial and spatiotemporal slices. Our method generates videos that retain the structure and motion of the original video while adhering to the target text. Through extensive experiments, we demonstrate Slicedit’s ability to edit a wide range of real-world videos, confirming its clear advantages compared to existing baselines.
Improving Token-Based World Models with Parallel Observation Prediction
Authors: Lior Cohen, Kaixin Wang, Bingyi Kang, Shie Mannor
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Motivated by the success of Transformers when applied to sequences of discrete symbols, token-based world models (TBWMs) were recently proposed as sample-efficient methods. In TBWMs, the world model consumes agent experience as a language-like sequence of tokens, where each observation constitutes a sub-sequence. However, during imagination, the sequential token-by-token generation of next observations results in a severe bottleneck, leading to long training times, poor GPU utilization, and limited representations. To resolve this bottleneck, we devise a novel Parallel Observation Prediction (POP) mechanism. POP augments a Retentive Network (RetNet) with a novel forward mode tailored to our reinforcement learning setting. We incorporate POP in a novel TBWM agent named REM (Retentive Environment Model), showcasing a 15.4x faster imagination compared to prior TBWMs. REM attains superhuman performance on 12 out of 26 games of the Atari 100K benchmark, while training in less than 12 hours. Our code is available at https://github.com/leor-c/REM
Perturb-and-Project: Differentially Private Similarities and Marginals
Authors: Vincent Cohen-Addad, Tommaso D'Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: We revisit the objective perturbations framework for differential privacy where noise is added to the input $Aın \mathcalS$ and the result is then projected back to the space of admissible datasets $\mathcalS$. Through this framework, we first design novel efficient algorithms to privately release pair-wise cosine similarities. Second, we derive a novel algorithm to compute $k$-way marginal queries over $n$ features. Prior work could achieve comparable guarantees only for $k$ even. Furthermore, we extend our results to $t$-sparse datasets, where our efficient algorithms yields novel, stronger guarantees whenever $tłe n^5/6/łog n.$ Finally, we provide a theoretical perspective on why <em>fast</em> input perturbation algorithms works well in practice. The key technical ingredients behind our results are tight sum-of-squares certificates upper bounding the Gaussian complexity of sets of solutions.
Multi-View Stochastic Block Models
Authors: Vincent Cohen-Addad, Tommaso D'Orsi, Silvio Lattanzi, Rajai Nasser
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Graph clustering is a central topic in unsupervised learning with a multitude of practical applications. In recent years, multi-view graph clustering has gained a lot of attention for its applicability to real-world instances where one often has access to multiple data sources. In this paper we formalize a new family of models, called <em>multi-view stochastic block models</em> that capture this setting. For this model, we first study efficient algorithms that naively work on the union of multiple graphs. Then, we introduce a new efficient algorithm that provably outperforms previous approaches by analyzing the structure of each graph separately. Finally, we complement our results with an information-theoretic lower bound studying the limits of what can be done in this model.
A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering
Authors: Vincent Cohen-Addad, Tommaso D'Orsi, Aida Mousavifar
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: We consider the semi-random graph model of [Makarychev, Makarychev and Vijayaraghavan, STOC’12], where, given a random bipartite graph with $α$ edges and an unknown bipartition $(A, B)$ of the vertex set, an adversary can add arbitrary edges inside each community and remove arbitrary edges from the cut $(A, B)$ (i.e. all adversarial changes are <em>monotone</em> with respect to the bipartition). For this model, a polynomial time algorithm [MMV’12] is known to approximate the Balanced Cut problem up to value $O(α)$ as long as the cut $(A, B)$ has size $Ømega(α)$. However, it consists of slow subroutines requiring optimal solutions for logarithmically many semidefinite programs. We study the fine-grained complexity of the problem and present the first near-linear time algorithm that achieves similar performances to that of [MMV’12]. Our algorithm runs in time $O(|V(G)|^1+o(1) + |E(G)|^1+o(1))$ and finds a balanced cut of value $O(α).$ Our approach appears easily extendible to related problem, such as Sparsest Cut, and also yields an near-linear time $O(1)$-approximation to Dagupta’s objective function for hierarchical clustering [Dasgupta, STOC’16] for the semi-random hierarchical stochastic block model inputs of [Cohen-Addad, Kanade, Mallmann-Trenn, Mathieu, JACM’19].
Dynamic Correlation Clustering in Sublinear Update Time
Authors: Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori, Nikos Parotsidis
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: We study the classic problem of correlation clustering in dynamic vertex streams. In this setting, vertices are either added or randomly deleted over time, and each vertex pair is connected by a positive or negative edge. The objective is to continuously find a partition which minimizes the sum of positive edges crossing clusters and negative edges within clusters. We present an algorithm that maintains an $O(1)$-approximation with $O(\textpolylog n)$ amortized update time. Prior to our work Behnezhad et al. in SODA 2023 achieved a $5$-approximation with $O(1)$ expected update time in edge streams which translates in vertex streams to an $O(D)$-update time where $D$ is the maximum possible degree. Finally we complement our theoretical analysis with experiments on real world data.
A2Q+: Improving Accumulator-Aware Weight Quantization
Authors: Ian Colbert, Alessandro Pappalardo, Jakoba Petri-Koenig, Yaman Umuroglu
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Quantization techniques commonly reduce the inference costs of neural networks by restricting the precision of weights and activations. Recent studies show that also reducing the precision of the accumulator can further improve hardware efficiency at the risk of numerical overflow, which introduces arithmetic errors that can degrade model accuracy. To avoid numerical overflow while maintaining accuracy, recent work proposed accumulator-aware quantization (A2Q)—a quantization-aware training method that constrains model weights during training to safely use a target accumulator bit width during inference. Although this shows promise, we demonstrate that A2Q relies on an overly restrictive constraint and a sub-optimal weight initialization strategy that each introduce superfluous quantization error. To address these shortcomings, we introduce: (1) an improved bound that alleviates accumulator constraints without compromising overflow avoidance; and (2) a new strategy for initializing quantized weights from pre-trained floating-point checkpoints. We combine these contributions with weight normalization to introduce A2Q+. We identify and characterize the various tradeoffs that arise as a consequence of accumulator constraints and support our analysis with experiments that show A2Q+ significantly improves these trade-offs when compared to prior methods.