GenAI4ELab/papercli-papers huggingface dataset:
The Merit of River Network Topology for Neural Flood Forecasting
Authors: Nikolas Kirschstein; Yixuan Sun
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Climate change exacerbates riverine floods, which occur with higher frequency and intensity than ever. The much-needed forecasting systems typically rely on accurate river discharge predictions. To this end, the SOTA data-driven approaches treat forecasting at spatially distributed gauge stations as isolated problems, even within the same river network. However, incorporating the known topology of the river network into the prediction model has the potential to leverage the adjacency relationship between gauges. Thus, we model river discharge for a network of gauging stations with GNNs and compare the forecasting performance achieved by different adjacency definitions. Our results show that the model fails to benefit from the river network topology information, both on the entire network and small subgraphs. The learned edge weights correlate with neither of the static definitions and exhibit no regular pattern. Furthermore, the GNNs struggle to predict sudden, narrow discharge spikes. Our work hints at a more general underlying phenomenon of neural prediction not always benefitting from graphical structure and may inspire a systematic study of the conditions under which this happens.
Position: Video as the New Language for Real-World Decision Making
Authors: Sherry Yang; Jacob C Walker; Jack Parker-Holder; Yilun Du; Jake Bruce; Andre Barreto; Pieter Abbeel; Dale Schuurmans
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Both text and video data are abundant on the internet and support large-scale self-supervised learning through next token or frame prediction. However, they have not been equally leveraged: language models have had significant real-world impact, whereas video generation has remained largely limited to media entertainment. Yet video data captures important information about the physical world that is difficult to express in language. To address this gap, we discuss an under-appreciated opportunity to extend video generation to solve tasks in the real world. We observe how, akin to language, video can serve as a unified interface that can absorb internet knowledge and represent diverse tasks. Moreover, we demonstrate how, like language models, video generation can serve as planners, agents, compute engines, and environment simulators through techniques such as in-context learning, planning and reinforcement learning. We identify major impact opportunities in domains such as robotics, self-driving, and science, supported by recent work that demonstrates how such advanced capabilities in video generation are plausibly within reach. Lastly, we identify key challenges in video generation that mitigate progress. Addressing these challenges will enable video generation models to demonstrate unique value alongside language models in a wider array of AI applications.
DPZero: Private Fine-Tuning of Language Models without Backpropagation
Authors: Liang Zhang; Bingcong Li; Kiran Koshy Thekumparampil; Sewoong Oh; Niao He
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: The widespread practice of fine-tuning large language models (LLMs) on domain-specific data faces two major challenges in memory and privacy. First, as the size of LLMs continues to grow, the memory demands of gradient-based training methods via backpropagation become prohibitively high. Second, given the tendency of LLMs to memorize training data, it is important to protect potentially sensitive information in the fine-tuning data from being regurgitated. Zeroth-order methods, which rely solely on forward passes, substantially reduce memory consumption during training. However, directly combining them with standard differentially private gradient descent suffers more as model size grows. To bridge this gap, we introduce DPZero, a novel private zeroth-order algorithm with nearly dimension-independent rates. The memory efficiency of DPZero is demonstrated in privately fine-tuning RoBERTa and OPT on several downstream tasks. Our code is available at https://github.com/Liang137/DPZero.
Reward Model Learning vs. Direct Policy Optimization: A Comparative Analysis of Learning from Human Preferences
Authors: Andi Nika; Debmalya Mandal; Parameswaran Kamalaruban; Georgios Tzannetos; Goran Radanovic; Adish Singla
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: In this paper, we take a step towards a deeper understanding of learning from human preferences by systematically comparing the paradigm of reinforcement learning from human feedback (RLHF) with the recently proposed paradigm of direct preference optimization (DPO). We focus our attention on the class of loglinear policy parametrization and linear reward functions. In order to compare the two paradigms, we first derive minimax statistical bounds on the suboptimality gap induced by both RLHF and DPO, assuming access to an oracle that exactly solves the optimization problems. We provide a detailed discussion on the relative comparison between the two paradigms, simultaneously taking into account the sample size, policy and reward class dimensions, and the regularization temperature. Moreover, we extend our analysis to the approximate optimization setting and derive exponentially decaying convergence rates for both RLHF and DPO. Next, we analyze the setting where the ground-truth reward is not realizable and find that, while RLHF incurs a constant additional error, DPO retains its asymptotically decaying gap by just tuning the temperature accordingly. Finally, we extend our comparison to the Markov decision process setting, where we generalize our results with exact optimization. To the best of our knowledge, we are the first to provide such a comparative analysis for RLHF and DPO.
Exploiting Negative Samples: A Catalyst for Cohort Discovery in Healthcare Analytics
Authors: Kaiping Zheng; Horng-Ruey Chua; Melanie Herschel; H. V. Jagadish; Beng Chin Ooi; James Wei Luen Yip
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: In healthcare analytics, addressing binary diagnosis or prognosis tasks presents unique challenges due to the inherent asymmetry between positive and negative samples. While positive samples, indicating patients with a disease, are defined based on stringent medical criteria, negative samples are defined in an open-ended manner and remain underexplored in prior research. To bridge this gap, we propose an innovative approach to facilitate cohort discovery within negative samples, leveraging a Shapley-based exploration of interrelationships between these samples, which holds promise for uncovering valuable insights concerning the studied disease, and related comorbidity and complications. We quantify each sample’s contribution using data Shapley values, subsequently constructing the Negative Sample Shapley Field to model the distribution of all negative samples. Next, we transform this field through manifold learning, preserving the essential data structure information while imposing an isotropy constraint in data Shapley values. Within this transformed space, we pinpoint cohorts of medical interest via density-based clustering. We empirically evaluate the effectiveness of our approach on the real-world electronic medical records from National University Hospital in Singapore, yielding clinically valuable insights aligned with existing knowledge, and benefiting medical research and clinical decision-making.
Generalization Bounds for Heavy-Tailed SDEs through the Fractional Fokker-Planck Equation
Authors: Benjamin Dupuis; Umut Simsekli
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Understanding the generalization properties of heavy-tailed stochastic optimization algorithms has attracted increasing attention over the past years. While illuminating interesting aspects of stochastic optimizers by using heavy-tailed stochastic differential equations as proxies, prior works either provided expected generalization bounds, or introduced non-computable information theoretic terms. Addressing these drawbacks, in this work, we prove high-probability generalization bounds for heavy-tailed SDEs which do not contain any nontrivial information theoretic terms. To achieve this goal, we develop new proof techniques based on estimating the entropy flows associated with the so-called fractional Fokker-Planck equation (a partial differential equation that governs the evolution of the distribution of the corresponding heavy-tailed SDE). In addition to obtaining high-probability bounds, we show that our bounds have a better dependence on the dimension of parameters as compared to prior art. Our results further identify a phase transition phenomenon, which suggests that heavy tails can be either beneficial or harmful depending on the problem structure. We support our theory with experiments conducted in a variety of settings.
Asymptotics of Learning with Deep Structured (Random) Features
Authors: Dominik Schröder; Daniil Dmitriev; Hugo Cui; Bruno Loureiro
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: For a large class of feature maps we provide a tight asymptotic characterisation of the test error associated with learning the readout layer, in the high-dimensional limit where the input dimension, hidden layer widths, and number of training samples are proportionally large. This characterization is formulated in terms of the population covariance of the features. Our work is partially motivated by the problem of learning with Gaussian rainbow neural networks, namely deep non-linear fully-connected networks with random but structured weights, whose row-wise covariances are further allowed to depend on the weights of previous layers. For such networks we also derive a closed-form formula for the feature covariance in terms of the weight matrices. We further find that in some cases our results can capture feature maps learned by deep, finite-width neural networks trained under gradient descent.
A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness
Authors: Xiaochuan Gong; Jie Hao; Mingrui Liu
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: This paper studies the problem of stochastic bilevel optimization where the upper-level function is nonconvex with potentially unbounded smoothness and the lower-level function is strongly convex. This problem is motivated by meta-learning applied to sequential data, such as text classification using recurrent neural networks, where the smoothness constant of the upper-level loss function scales linearly with the gradient norm and can be potentially unbounded. Existing algorithm crucially relies on the nested loop design, which requires significant tuning efforts and is not practical. In this paper, we address this issue by proposing a Single Loop bIlevel oPtimizer (SLIP). The proposed algorithm first updates the lower-level variable by a few steps of stochastic gradient descent, and then simultaneously updates the upper-level variable by normalized stochastic gradient descent with momentum and the lower-level variable by stochastic gradient descent. Under standard assumptions, we show that our algorithm finds an $\epsilon$-stationary point within $\widetilde{O}(1/\epsilon^4)$[Here $\widetilde{O}(\cdot)$ compresses logarithmic factors of $1/\epsilon$ and $1/\delta$, where $\delta\in(0,1)$ denotes the failure probability.] oracle calls of stochastic gradient or Hessian-vector product, both in expectation and with high probability. This complexity result is nearly optimal up to logarithmic factors without mean-square smoothness of the stochastic gradient oracle. Our proof relies on (i) a refined characterization and control of the lower-level variable and (ii) establishing a novel connection between bilevel optimization and stochastic optimization under distributional drift. Our experiments on various tasks show that our algorithm significantly outperforms strong baselines in bilevel optimization.
How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model
Authors: Umberto Maria Tomasini; Matthieu Wyart
Venue: ICML 2024 (ICML 2024 Spotlight)

Abstract: Understanding what makes high-dimensional data learnable is a fundamental question in machine learning. On the one hand, it is believed that the success of deep learning lies in its ability to build a hierarchy of representations that become increasingly more abstract with depth, going from simple features like edges to more complex concepts. On the other hand, learning to be insensitive to invariances of the task, such as smooth transformations for image datasets, has been argued to be important for deep networks and it strongly correlates with their performance. In this work, we aim to explain this correlation and unify these two viewpoints. We show that by introducing sparsity to generative hierarchical models of data, the task acquires insensitivity to spatial transformations that are discrete versions of smooth transformations. In particular, we introduce the Sparse Random Hierarchy Model (SRHM), where we observe and rationalize that a hierarchical representation mirroring the hierarchical model is learnt precisely when such insensitivity is learnt, thereby explaining the strong correlation between the latter and performance. Moreover, we quantify how the sample complexity of CNNs learning the SRHM depends on both the sparsity and hierarchical structure of the task.
Environment Design for Inverse Reinforcement Learning
Authors: Thomas Kleine Buening; Victor Villin; Christos Dimitrakakis
Venue: ICML 2024 (ICML 2024 Oral)

Abstract: Learning a reward function from demonstrations suffers from low sample-efficiency. Even with abundant data, current inverse reinforcement learning methods that focus on learning from a single environment can fail to handle slight changes in the environment dynamics. We tackle these challenges through adaptive environment design. In our framework, the learner repeatedly interacts with the expert, with the former selecting environments to identify the reward function as quickly as possible from the expert’s demonstrations in said environments. This results in improvements in both sample-efficiency and robustness, as we show experimentally, for both exact and approximate inference.
Saliency strikes back: How filtering out high frequencies improves white-box explanations
Authors: Sabine Muzellec; Thomas FEL; Victor Boutin; Léo Andéol; Rufin VanRullen; Thomas Serre
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Attribution methods correspond to a class of explainability methods (XAI) that aim to assess how individual inputs contribute to a model's decision-making process. We have identified a significant limitation in one type of attribution methods, known as ``white-box" methods. Although highly efficient, as we will show, these methods rely on a gradient signal that is often contaminated by high-frequency artifacts. To overcome this limitation, we introduce a new approach called "FORGrad". This simple method effectively filters out these high-frequency artifacts using optimal cut-off frequencies tailored to the unique characteristics of each model architecture. Our findings show that FORGrad *consistently enhances* the performance of already existing white-box methods, enabling them to compete effectively with more accurate yet computationally demanding "black-box" methods. We anticipate that our research will foster broader adoption of simpler and more efficient white-box methods for explainability, offering a better balance between faithfulness and computational efficiency.
Vision Transformers as Probabilistic Expansion from Learngene
Authors: Qiufeng Wang; Xu Yang; Haokun Chen; Xin Geng
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Deep learning has advanced through the combination of large datasets and computational power, leading to the development of extensive pre-trained models like Vision Transformers (ViTs). However, these models often assume a one-size-fits-all utility, lacking the ability to initialize models with elastic scales tailored to the resource constraints of specific downstream tasks. To address these issues, we propose Probabilistic Expansion from LearnGene (PEG) for mixture sampling and elastic initialization of Vision Transformers. Specifically, PEG utilizes a probabilistic mixture approach to sample Multi-Head Self-Attention layers and Feed-Forward Networks from a large ancestry model into a more compact part termed as learngene. Theoretically, we demonstrate that these learngene can approximate the parameter distribution of the original ancestry model, thereby preserving its significant knowledge. Next, PEG expands the sampled learngene through non-linear mapping, enabling the initialization of descendant models with elastic scales to suit various resource constraints. Our extensive experiments demonstrate the effectiveness of PEG and outperforming traditional initialization strategies.
On The Statistical Complexity of Offline Decision-Making
Authors: Thanh Nguyen-Tang; Raman Arora
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: We study the statistical complexity of offline decision-making with function approximation, establishing (near) minimax-optimal rates for stochastic contextual bandits and Markov decision processes. The performance limits are captured by the pseudo-dimension of the (value) function class and a new characterization of the behavior policy that *strictly* subsumes all the previous notions of data coverage in the offline decision-making literature. In addition, we seek to understand the benefits of using offline data in online decision-making and show nearly minimax-optimal rates in a wide range of regimes.
Learning Latent Dynamic Robust Representations for World Models
Authors: Ruixiang Sun; Hongyu Zang; Xin Li; Riashat Islam
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Visual Model-Based Reinforcement Learning (MBRL) promises to encapsulate agent's knowledge about the underlying dynamics of the environment, enabling learning a world model as a useful planner. However, top MBRL agents such as Dreamer often struggle with visual pixel-based inputs in the presence of exogenous or irrelevant noise in the observation space, due to failure to capture task-specific features while filtering out irrelevant spatio-temporal details. To tackle this problem, we apply a spatio-temporal masking strategy, a bisimulation principle, combined with latent reconstruction, to capture endogenous task-specific aspects of the environment for world models, effectively eliminating non-essential information. Joint training of representations, dynamics, and policy often leads to instabilities. To further address this issue, we develop a Hybrid Recurrent State-Space Model (HRSSM) structure, enhancing state representation robustness for effective policy learning. Our empirical evaluation demonstrates significant performance improvements over existing methods in a range of visually complex control tasks such as Maniskill with exogenous distractors from the Matterport environment. Our code is avaliable at https://github.com/bit1029public/HRSSM.
TimeX++: Learning Time-Series Explanations with Information Bottleneck
Authors: Zichuan Liu; Tianchun Wang; Jimeng Shi; Xu Zheng; Zhuomin Chen; Lei Song; Wenqian Dong; Jayantha  Obeysekera; Farhad Shirani; Dongsheng Luo
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Explaining deep learning models operating on time series data is crucial in various applications of interest which require interpretable and transparent insights from time series signals. In this work, we investigate this problem from an information theoretic perspective and show that most existing measures of explainability may suffer from trivial solutions and distributional shift issues. To address these issues, we introduce a simple yet practical objective function for time series explainable learning. The design of the objective function builds upon the principle of information bottleneck (IB), and modifies the IB objective function to avoid trivial solutions and distributional shift issues. We further present TimeX++, a novel explanation framework that leverages a parametric network to produce explanation-embedded instances that are both in-distributed and label-preserving. We evaluate TimeX++ on both synthetic and real-world datasets comparing its performance against leading baselines, and validate its practical efficacy through case studies in a real-world environmental application. Quantitative and qualitative evaluations show that TimeX++ outperforms baselines across all datasets, demonstrating a substantial improvement in explanation quality for time series data. The source code is available at https://github.com/zichuan-liu/TimeXplusplus.
Scalable AI Safety via Doubly-Efficient Debate
Authors: Jonah Brown-Cohen; Geoffrey Irving; Georgios Piliouras
Venue: ICML 2024 (ICML 2024 Oral)

Abstract: The emergence of pre-trained AI systems with powerful capabilities across a diverse and ever-increasing set of complex domains has raised a critical challenge for AI safety as tasks can become too complicated for humans to judge directly. Irving et al (2018). proposed a debate method in this direction with the goal of pitting the power of such AI models against each other until the problem of identifying (mis)-alignment is broken down into a manageable subtask. While the promise of this approach is clear, the original framework was based on the assumption that the honest strategy is able to simulate *deterministic* AI systems for an *exponential* number of steps, limiting its applicability. In this paper, we show how to address these challenges by designing a new set of debate protocols where the honest strategy can always succeed using a simulation of a *polynomial* number of steps, whilst being able to verify the alignment of *stochastic* AI systems, even when the dishonest strategy is allowed to use exponentially many simulation steps.
Reinformer: Max-Return Sequence Modeling for Offline RL
Authors: Zifeng Zhuang; Dengyun Peng; Jinxin Liu; Ziqi Zhang; Donglin Wang
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: As a data-driven paradigm, offline reinforcement learning (RL) has been formulated as sequence modeling that conditions on the hindsight information including returns, goal or future trajectory. Although promising, this supervised paradigm overlooks the core objective of RL that maximizes the return. This overlook directly leads to the lack of trajectory stitching capability that affects the sequence model learning from sub-optimal data. In this work, we introduce the concept of max-return sequence modeling which integrates the goal of maximizing returns into existing sequence models. We propose **Rein*for***ced Trans***for*mer** (**Rein*for*mer**), indicating the sequence model is reinforced by the RL objective. **Rein*for*mer** additionally incorporates the objective of maximizing returns in the training phase, aiming to predict the maximum future return within the distribution. During inference, this in-distribution maximum return will guide the selection of optimal actions. Empirically, **Rein*for*mer** is competitive with classical RL methods on the D4RL benchmark and outperforms state-of-the-art sequence model particularly in trajectory stitching ability. Code is public at https://github.com/Dragon-Zhuang/Reinformer.