GenAI4ELab/papercli-papers huggingface dataset:
Stability and Multigroup Fairness in Ranking with Uncertain Predictions
Authors: Siddartha Devic; Aleksandra Korolova; David Kempe; Vatsal Sharan
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Rankings are ubiquitous across many applications, from search engines to hiring committees. In practice, many rankings are derived from the output of predictors. However, when predictors trained for classification tasks have intrinsic uncertainty, it is not obvious how this uncertainty should be represented in the derived rankings. Our work considers ranking functions: maps from individual predictions for a classification task to distributions over rankings. We focus on two aspects of ranking functions: stability to perturbations in predictions and fairness towards both individuals and subgroups. Not only is stability an important requirement for its own sake, but --- as we show --- it composes harmoniously with individual fairness in the sense of Dwork et al. (2012). While deterministic ranking functions cannot be stable aside from trivial scenarios, we show that the recently proposed uncertainty aware (UA) ranking functions of Singh et al. (2021) are stable. Our main result is that UA rankings also achieve group fairness through successful composition with multiaccurate or multicalibrated predictors. Our work demonstrates that UA rankings naturally interpolate between group and individual level fairness guarantees, while simultaneously satisfying stability guarantees important whenever machine-learned predictions are used.
An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series
Authors: Qiang Huang; Chuizheng Meng; Defu Cao; Biwei Huang; Yi Chang; Yan Liu
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings.
Codebook Features: Sparse and Discrete Interpretability for Neural Networks
Authors: Alex Tamkin; Mohammad Taufeeque; Noah Goodman
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Understanding neural networks is challenging in part because of the dense, continuous nature of their hidden states. We explore whether we can train neural networks to have hidden states that are sparse, discrete, and more interpretable by quantizing their continuous features into what we call codebook features. Codebook features are produced by finetuning neural networks with vector quantization bottlenecks at each layer, producing a network whose hidden features are the sum of a small number of discrete vector codes chosen from a larger codebook. Surprisingly, we find that neural networks can operate under this extreme bottleneck with only modest degradation in performance. In addition, we can control a model's behavior by finding codes that activate on a desired behavior, then activating those same codes during generation. We first validate codebook features on a finite state machine dataset with far more hidden states than neurons. In this setting, our approach overcomes the superposition problem by assigning states to distinct codes, and we find that we can make the neural network behave as if it is in a different state by activating the code for that state. We then train Transformer language models with up to 410M parameters on two natural language datasets. We identify codes in these models representing diverse, disentangled concepts (ranging from negative emotions to months of the year) and find that we can guide the model to generate different topics and pronoun genders by activating these codes during inference. Overall, codebook features appear to be a promising unit of analysis and control for neural networks and interpretability. Our codebase and models are open-sourced at [this URL](https://github.com/taufeeque9/codebook-features).
Revisit the Essence of Distilling Knowledge through Calibration
Authors: Wen-Shu Fan; Su Lu; Xin-Chun Li; De-Chuan Zhan; Le Gan
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Knowledge Distillation (KD) has evolved into a practical technology for transferring knowledge from a well-performing model (teacher) to a weak model (student). A counter-intuitive phenomenon known as capacity mismatch has been identified, wherein KD performance may not be good when a better teacher instructs the student. Various preliminary methods have been proposed to alleviate capacity mismatch, but a unifying explanation for its cause remains lacking. In this paper, we propose *a unifying analytical framework to pinpoint the core of capacity mismatch based on calibration*. Through extensive analytical experiments, we observe a positive correlation between the calibration of the teacher model and the KD performance with original KD methods. As this correlation arises due to the sensitivity of metrics (e.g., KL divergence) to calibration, we recommend employing measurements insensitive to calibration such as ranking-based loss. Our experiments demonstrate that ranking-based loss can effectively replace KL divergence, aiding large models with poor calibration to teach better.
Fine-grained Classes and How to Find Them
Authors: Matej Grcic; Artyom Gadetsky; Maria Brbic
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: In many practical applications, coarse-grained labels are readily available compared to fine-grained labels that reflect subtle differences between classes. However, existing methods cannot leverage coarse labels to infer fine-grained labels in an unsupervised manner. To bridge this gap, we propose FALCON, a method that discovers fine-grained classes from coarsely labeled data without any supervision at the fine-grained level. FALCON simultaneously infers unknown fine-grained classes and underlying relationships between coarse and fine-grained classes. Moreover, FALCON is a modular method that can effectively learn from multiple datasets labeled with different strategies. We evaluate FALCON on eight image classification tasks and a single-cell classification task. FALCON outperforms baselines by a large margin, achieving 22% improvement over the best baseline on the tieredImageNet dataset with over 600 fine-grained classes.
GliDe with a CaPE: A Low-Hassle Method to Accelerate Speculative Decoding
Authors: Cunxiao Du; Jing Jiang; Xu Yuanchen; Jiawei Wu; Sicheng Yu; Yongqi Li; Shenggui Li; Kai Xu; Liqiang Nie; Zhaopeng Tu; Yang You
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Speculative decoding is a relatively new decoding framework that leverages small and efficient draft models to reduce the latency of LLMs. In this study, we introduce GliDe and CaPE, two low-hassle modifications to vanilla speculative decoding to further improve the decoding speed of a frozen LLM. Specifically, GliDe is a modified draft model architecture that reuses the cached keys and values from the target LLM, while CaPE is a proposal expansion method that uses the draft model's confidence scores to help select additional candidate tokens for verification. Extensive experiments on different benchmarks demonstrate that our proposed GliDe draft model significantly reduces the expected decoding latency. Additional evaluation using walltime reveals that GliDe can accelerate Vicuna models up to 2.17x and further extend the improvement to 2.61x with CaPE. We will release our code, data, and the trained draft models.
Offline Transition Modeling via Contrastive Energy Learning
Authors: Ruifeng Chen; Chengxing Jia; Zefang Huang; Tian-Shuo Liu; Xu-Hui Liu; Yang Yu
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Learning a high-quality transition model is of great importance for sequential decision-making tasks, especially in offline settings. Nevertheless, the complex behaviors of transition dynamics in real-world environments pose challenges for the standard forward models because of their inductive bias towards smooth regressors, conflicting with the inherent nature of transitions such as discontinuity or large curvature. In this work, we propose to model the transition probability implicitly through a scalar-value energy function, which enables not only flexible distribution prediction but also capturing complex transition behaviors. The Energy-based Transition Models (ETM) are shown to accurately fit the discontinuous transition functions and better generalize to out-of-distribution transition data. Furthermore, we demonstrate that energy-based transition models improve the evaluation accuracy and significantly outperform other off-policy evaluation methods in DOPE benchmark. Finally, we show that energy-based transition models also benefit reinforcement learning and outperform prior offline RL algorithms in D4RL Gym-Mujoco tasks.
Model-based Reinforcement Learning for Confounded POMDPs
Authors: Mao Hong; Zhengling Qi; Yanxun Xu
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: We propose a model-based offline reinforcement learning (RL) algorithm for confounded partially observable Markov decision processes (POMDPs) under general function approximations and show it is provably efficient under some technical conditions such as the partial coverage imposed on the offline data distribution. Specifically, we first establish a novel model-based identification result for learning the effect of any action on the reward and future transitions in the confounded POMDP. Using this identification result, we then design a nonparametric two-stage estimation procedure to construct an estimator for off-policy evaluation (OPE), which permits general function approximations. Finally, we learn the optimal policy by performing a conservative policy optimization within the confidence regions based on the proposed estimation procedure for OPE. Under some mild conditions, we establish a finite-sample upper bound on the suboptimality of the learned policy in finding the optimal one, which depends on the sample size and the length of horizons polynomially.
Enhancing Class-Imbalanced Learning with Pre-Trained Guidance through Class-Conditional Knowledge Distillation
Authors: Lan Li; Xin-Chun Li; Han-Jia Ye; De-Chuan Zhan
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: In class-imbalanced learning, the scarcity of information about minority classes presents challenges in obtaining generalizable features for these classes. Leveraging large-scale pre-trained models with powerful generalization capabilities as teacher models can help fill this information gap. Traditional knowledge distillation transfers the label distribution $p(\boldsymbol{y}|\boldsymbol{x})$ predicted by the teacher model to the student model. However, this method falls short on imbalanced data as it fails to capture the class-conditional probability distribution $p(\boldsymbol{x}|\boldsymbol{y})$ from the teacher model, which is crucial for enhancing generalization. To overcome this, we propose Class-Conditional Knowledge Distillation (CCKD), a novel approach that enables learning of the teacher model’s class-conditional probability distribution during the distillation process. Additionally, we introduce Augmented CCKD (ACCKD), which involves distillation on a constructed class-balanced dataset (formed through data mixing) and feature imitation on the entire dataset to further facilitate the learning of features. Experimental results on various imbalanced datasets demonstrate an average accuracy improvement of 7.4% using our method.
BiE: Bi-Exponent Block Floating-Point for Large Language Models Quantization
Authors: Lancheng Zou; Wenqian Zhao; Shuo Yin; Chen Bai; Qi Sun; Bei Yu
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Nowadays, Large Language Models (LLMs) mostly possess billions of parameters, bringing significant challenges to hardware platforms. Although quantization is an efficient approach to reduce computation and memory overhead for inference optimization, we stress the challenge that mainstream low-bit quantization approaches still suffer from either various data distribution outliers or a lack of hardware efficiency. We also find that low-bit data format has further potential expressiveness to cover the atypical language data distribution. In this paper, we propose a novel numerical representation, Bi-Exponent Block Floating Point (BiE), and a new quantization flow. BiE quantization shows accuracy superiority and hardware friendliness on various models and benchmarks.
Position: Automatic Environment Shaping is the Next Frontier in RL
Authors: Younghyo Park; Gabriel B. Margolis; Pulkit Agrawal
Venue: ICML 2024 (ICML 2024 Oral)

Abstract: Many roboticists dream of presenting a robot with a task in the evening and returning the next morning to find the robot capable of solving the task. What is preventing us from achieving this? Sim-to-real reinforcement learning (RL) has achieved impressive performance on challenging robotics tasks, but requires substantial human effort to set up the task in a way that is amenable to RL. It's our position that algorithmic improvements in policy optimization and other ideas should be guided towards resolving the primary bottleneck of shaping the training environment, i.e., designing observations, actions, rewards and simulation dynamics. Most practitioners don't tune the RL algorithm, but other environment parameters to obtain a desirable controller. We posit that scaling RL to diverse robotic tasks will only be achieved if the community focuses on automating environment shaping procedures.
Online Matching with Stochastic Rewards: Provable Better Bound via Adversarial Reinforcement Learning
Authors: Qiankun Zhang; Aocheng Shen; Boyu Zhang; Hanrui Jiang; Bingqian Du
Venue: ICML 2024 (ICML 2024 Oral)

Abstract: For a specific online optimization problem, for example, online bipartite matching (OBM), research efforts could be made in two directions before it is finally closed, i.e., the optimal competitive online algorithm is found. One is to continuously design algorithms with better performance. To this end, reinforcement learning (RL) has demonstrated great success in literature. However, little is known on the other direction: whether RL helps explore how hard an online problem is. In this paper, we study a generalized model of OBM, named online matching with stochastic rewards (OMSR, FOCS 2012), for which the optimal competitive ratio is still unknown. We adopt an adversarial RL approach that trains two RL agents adversarially and iteratively: the algorithm agent learns for algorithms with larger competitive ratios, while the adversarial agent learns to produce a family of hard instances. Through such a framework, agents converge at the end with a robust algorithm, which empirically outperforms the state of the art (STOC 2020). Much more significantly, it allows to track how the hard instances are generated. We succeed in distilling two structural properties from the learned graph patterns, which remarkably reduce the action space, and further enable theoretical improvement on the best-known hardness result of OMSR, from $0.621$ (FOCS 2012) to $0.597$. To the best of our knowledge, this gives the first evidence that RL can help enhance the theoretical understanding of an online problem.
On the Unexpected Effectiveness of Reinforcement Learning for Sequential Recommendation
Authors: Álvaro Labarca Silva; Denis Parra; Rodrigo Toro Icarte
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: In recent years, Reinforcement Learning (RL) has shown great promise in session-based recommendation. Sequential models that use RL have reached state-of-the-art performance for the Next-item Prediction (NIP) task. This result is intriguing, as the NIP task only evaluates how well the system can correctly recommend the next item to the user, while the goal of RL is to find a policy that optimizes rewards in the long term -- sometimes at the expense of suboptimal short-term performance. Then, how can RL improve the system's performance on short-term metrics? This article investigates this question by exploring proxy learning objectives, which we identify as goals RL models might be following, and thus could explain the performance boost. We found that RL -- when used as an auxiliary loss -- promotes the learning of embeddings that capture information about the user's previously interacted items. Subsequently, we replaced the RL objective with a straightforward auxiliary loss designed to predict the number of items the user interacted with. This substitution results in performance gains comparable to RL. These findings pave the way to improve performance and understanding of RL methods for recommender systems.
Quantum Positional Encodings for Graph Neural Networks
Authors: Slimane Thabet; Mehdi Djellabi; Igor Olegovich Sokolov; Sachin Kasture; Louis-Paul Henry; Loic Henriet
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: In this work, we propose novel families of positional encodings tailored to graph neural networks obtained with quantum computers. These encodings leverage the long-range correlations inherent in quantum systems that arise from mapping the topology of a graph onto interactions between qubits in a quantum computer. Our inspiration stems from the recent advancements in quantum processing units, which offer computational capabilities beyond the reach of classical hardware. We prove that some of these quantum features are theoretically more expressive for certain graphs than the commonly used relative random walk probabilities. Empirically, we show that the performance of state-of-the-art models can be improved on standard benchmarks and large-scale datasets by computing tractable versions of quantum features. Our findings highlight the potential of leveraging quantum computing capabilities to enhance the performance of transformers in handling graph data.
Diffuse, Sample, Project: Plug-And-Play Controllable Graph Generation
Authors: Kartik Sharma; Srijan Kumar; Rakshit Trivedi
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Diffusion models lend transformative capabilities to the graph generation task, yet controlling the properties of the generated graphs remains challenging. Recent approaches augment support for controlling soft, differentiable properties but they fail to handle user-specified hard constraints that are non-differentiable. This often results in vague control, unsuitable for applications like drug discovery that demand satisfaction of precise constraints, e.g., the maximum number of bonds. To address this, we formalize the problem of controlled graph generation and introduce PRODIGY (PROjected DIffusion for controlled Graph Generation), an innovative plug-and-play approach enabling the generation of graphs with precise control, from any pre-trained diffusion model. PRODIGY employs a novel operator to project the samples at each diffusion step onto the specified constrained space. For a large class of practical constraints and a variety of graphs, our extensive experiments demonstrate that PRODIGY empowers state-of-the-art continuous and discrete diffusion models to produce graphs meeting specific, hard constraints. Our approach achieves up to 100% constraint satisfaction for non-attributed and molecular graphs, under a variety of constraints, marking a significant step forward in precise, interpretable graph generation. Code is provided on the project webpage: [https://prodigy-diffusion.github.io/](https://prodigy-diffusion.github.io/).
Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities
Authors: Golnoosh Farnadi; Mohammad Havaei; Negar Rostamzadeh
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: The rise of foundation models holds immense promise for advancing AI, but this progress may amplify existing risks and inequalities, leaving marginalized communities behind. In this position paper, we discuss that disparities towards marginalized communities – performance, representation, privacy, robustness, interpretability and safety – are not isolated concerns but rather interconnected elements of a cascading disparity phenomenon. We contrast foundation models with traditional models and highlight the potential for exacerbated disparity against marginalized communities. Moreover, we emphasize the unique threat of cascading impacts in foundation models, where interconnected disparities can trigger long-lasting negative consequences, specifically to the people on the margin. We define marginalized communities within the machine learning context and explore the multifaceted nature of disparities. We analyze the sources of these disparities, tracing them from data creation, training and deployment procedures to highlight the complex technical and socio-technical landscape. To mitigate the pressing crisis, we conclude with a set of calls to action to mitigate disparity at its source.
Jetfire: Efficient and Accurate Transformer Pretraining with INT8 Data Flow and Per-Block Quantization
Authors: Haocheng Xi; Yuxiang Chen; Kang Zhao; KAI JUN TEH; Jianfei Chen; Jun Zhu
Venue: ICML 2024 (ICML 2024 Spotlight)

Abstract: Pretraining transformers are generally time-consuming. Fully quantized training (FQT) is a promising approach to speed up pretraining. However, most FQT methods adopt a quantize-compute-dequantize procedure, which often leads to suboptimal speedup and significant performance degradation when used in transformers due to the high memory access overheads and low-precision computations. In this work, we propose Jetfire, an efficient and accurate INT8 training method specific to transformers. Our method features an INT8 data flow to optimize memory access and a per-block quantization method to maintain the accuracy of pretrained transformers. Extensive experiments demonstrate that our INT8 FQT method achieves comparable accuracy to the FP16 training baseline and outperforms the existing INT8 training works for transformers. Moreover, for a standard transformer block, our method offers an end-to-end training speedup of 1.42x and a 1.49x memory reduction compared to the FP16 baseline.