GenAI4ELab/papercli-papers huggingface dataset:
Thin-Shell Object Manipulations With Differentiable Physics Simulations
Authors: Yian Wang; Juntian Zheng; Zhehuan Chen; Zhou Xian; Gu Zhang; Chao Liu; Chuang Gan
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: In this work, we aim to teach robots to manipulate various thin-shell materials. 
Prior works studying thin-shell object manipulation mostly rely on heuristic policies or learn policies from real-world video demonstrations, and only focus on limited material types and tasks (e.g., cloth unfolding). However, these approaches face significant challenges when extended to a wider variety of thin-shell materials and a diverse range of tasks.
On the other hand, while virtual simulations are shown to be effective in diverse robot skill learning and evaluation, prior thin-shell simulation environments only support a subset of thin-shell materials, which also limits their supported range of tasks. 
To fill in this gap, we introduce ThinShellLab - a fully differentiable simulation platform tailored for robotic interactions with diverse thin-shell materials possessing varying material properties, enabling flexible thin-shell manipulation skill learning and evaluation. Building on top of our developed simulation engine, we design a diverse set of manipulation tasks centered around different thin-shell objects. Our experiments suggest that manipulating thin-shell objects presents several unique challenges: 1) thin-shell manipulation relies heavily on frictional forces due to the objects' co-dimensional nature, 2) the materials being manipulated are highly sensitive to minimal variations in interaction actions, and 3) the constant and frequent alteration in contact pairs makes trajectory optimization methods susceptible to local optima, and neither standard reinforcement learning algorithms nor trajectory optimization methods (either gradient-based or gradient-free) are able to solve the tasks alone. To overcome these challenges, we present an optimization scheme that couples sampling-based trajectory optimization and gradient-based optimization, boosting both learning efficiency and converged performance across various proposed tasks. In addition, the differentiable nature of our platform facilitates a smooth sim-to-real transition. By tuning simulation parameters with a minimal set of real-world data, we demonstrate successful deployment of the learned skills to real-robot settings.  ThinShellLab will be publicly available. Video demonstration and more information can be found on the project website https://vis-www.cs.umass.edu/ThinShellLab/.
Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models
Authors: Archiki Prasad; Elias Stengel-Eskin; Mohit Bansal
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: An increasing number of vision-language tasks can be handled with little to no training, i.e., in a zero and few-shot manner, by marrying large language models (LLMs) to vision encoders, resulting in large vision-language models (LVLMs). While this has huge upsides, such as not requiring training data or custom architectures, how an input is presented to an LVLM can have a major impact on zero-shot model performance. In particular, inputs phrased in an underspecified way can result in incorrect answers due to factors like missing visual information, complex implicit reasoning, or linguistic ambiguity. Therefore, adding visually-grounded information to the input as a preemptive clarification should improve model performance by reducing underspecification, e.g., by localizing objects and disambiguating references. Similarly, in the VQA setting, changing the way questions are framed can make them easier for models to answer. To this end, we present **Rep**hrase, **A**ugment and **Re**ason (RepARe), a gradient-free framework that extracts salient details about the image using the underlying LVLM as a captioner and reasoner, in order to propose modifications to the original question. We then use the LVLM’s confidence over a generated answer as an unsupervised scoring function to select the rephrased question most likely to improve zero-shot performance. Focusing on three visual question answering tasks, we show that RepARe can result in a 3.85% (absolute) increase in zero-shot accuracy on VQAv2, 6.41%, and 7.94% points increase on A-OKVQA, and VizWiz respectively. Additionally, we find that using gold answers for oracle question candidate selection achieves a substantial gain in VQA accuracy by up to 14.41%. Through extensive analysis, we demonstrate that outputs from RepARe increase syntactic complexity, and effectively utilize vision-language interaction and the frozen LLM.
A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning
Authors: Haozhe Jiang; Qiwen Cui; Zhihan Xiong; Maryam Fazel; Simon Shaolei Du
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: We investigate learning the equilibria in non-stationary multi-agent systems and address the challenges that differentiate multi-agent learning from single-agent learning. Specifically, we focus on games with bandit feedback, where testing an equilibrium can result in substantial regret even when the gap to be tested is small, and the existence of multiple optimal solutions (equilibria) in stationary games poses extra challenges. To overcome these obstacles, we propose a versatile black-box approach applicable to a broad spectrum of problems, such as general-sum games, potential games, and Markov games, when equipped with appropriate learning and testing oracles for stationary environments. Our algorithms can achieve $\widetilde{O}\left(\Delta^{1/4}T^{3/4}\right)$ regret when the degree of nonstationarity, as measured by total variation $\Delta$, is known, and $\widetilde{O}\left(\Delta^{1/5}T^{4/5}\right)$ regret when $\Delta$ is unknown, where $T$ is the number of rounds. Meanwhile, our algorithm inherits the favorable dependence on number of agents from the oracles. As a side contribution that may be independent of interest, we show how to test for various types of equilibria by a black-box reduction to single-agent learning, which includes Nash equilibria, correlated equilibria, and coarse correlated equilibria.
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models
Authors: Thomas P Zollo; Todd Morrill; Zhun Deng; Jake Snell; Toniann Pitassi; Richard Zemel
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: With the explosion of the zero-shot capabilities of (and thus interest in) pre-trained large language models, there has come accompanying interest in how best to prompt a language model to perform a given task. While it may be tempting to choose a prompt based on empirical results on a validation set, this can lead to a deployment where an unexpectedly high loss occurs. To mitigate this prospect, we propose a lightweight framework, Prompt Risk Control, for selecting a prompt based on rigorous upper bounds on families of informative risk measures. We provide and compare different methods for producing bounds on a diverse set of risk metrics like mean, CVaR, and the Gini coefficient of the loss distribution. In addition, we extend the underlying statistical bounding techniques to accommodate the possibility of distribution shifts in deployment. Extensive experiments on high-impact applications like chatbots, medical question answering, and news summarization highlight why such a framework is necessary to reduce exposure to the worst outcomes.
Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials
Authors: Ivan Grega; Ilyes Batatia; Gabor Csanyi; Sri Karlapati; Vikram Deshpande
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Lattices are architected metamaterials whose properties strongly depend on their geometrical design. The analogy between lattices and graphs enables the use of graph neural networks (GNNs) as a faster surrogate model compared to traditional methods such as finite element modelling. In this work, we generate a big dataset of structure-property relationships for strut-based lattices. The dataset is made available to the community which can fuel the development of methods anchored in physical principles for the fitting of fourth-order tensors. In addition, we present a higher-order GNN model trained on this dataset. The key features of the model are (i) SE(3) equivariance, and (ii) consistency with the thermodynamic law of conservation of energy. We compare the model to non-equivariant models based on a number of error metrics and demonstrate its benefits in terms of predictive performance and reduced training requirements. Finally, we demonstrate an example application of the model to an architected material design task. The methods which we developed are applicable to fourth-order tensors beyond elasticity such as piezo-optical tensor etc.
Adaptive Federated Learning with Auto-Tuned Clients
Authors: Junhyung Lyle Kim; Taha Toghani; Cesar A Uribe; Anastasios Kyrillidis
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Federated learning (FL) is a distributed machine learning framework where the global model of a central server is trained via multiple collaborative steps by participating clients without sharing their data. While being a flexible framework, where the distribution of local data, participation rate, and computing power of each client can greatly vary, such flexibility gives rise to many new challenges, especially in the hyperparameter tuning on the client side. We propose $\Delta$-SGD, a simple step size rule for SGD that enables each client to use its own step size by adapting to the local smoothness of the function each client is optimizing. We provide theoretical and empirical results where the benefit of the client adaptivity is shown in various FL scenarios.
Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks
Authors: Vaidehi Patil; Peter Hase; Mohit Bansal
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: Pretrained language models sometimes possess knowledge that we do not wish them to, including memorized personal information and knowledge that could be used to harm people. They can also output toxic or harmful text. To mitigate these safety and informational issues, we propose an attack-and-defense framework for studying the task of deleting sensitive information directly from model weights. We study direct edits to model weights because (1) this approach should guarantee that particular deleted information is never extracted by future prompt attacks, and (2) it should protect against whitebox attacks, which is necessary for making claims about safety/privacy in a setting where publicly available model weights could be used to elicit sensitive information. Our threat model assumes that an attack succeeds if the answer to a sensitive question is located among a set of B generated candidates, based on scenarios where the information would be insecure if the answer is among B candidates. Experimentally, we show that even state-of-the-art model editing methods such as ROME struggle to truly delete factual information from models like GPT-J, as our whitebox and blackbox attacks can recover “deleted” information from an edited model 38% of the time. These attacks leverage two key observations: (1) that traces of deleted information can be found in intermediate model hidden states, and (2) that applying an editing method for one question may not delete information across rephrased versions of the question. Finally, we provide new defense methods that protect against some extraction attacks, but we do not find a single universally effective defense method. Our results suggest that truly deleting sensitive information is a tractable but difficult problem, since even relatively low attack success rates have potentially severe implications for the deployment of language models in a world where individuals enjoy ownership of their personal data, a right to privacy, and safety from harmful model outputs.
CrossLoco: Human Motion Driven Control of Legged Robots via Guided Unsupervised Reinforcement Learning
Authors: Tianyu Li; Hyunyoung Jung; Matthew Gombolay; Yong Cho; Sehoon Ha
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Human motion driven control (HMDC) is an effective approach for generating natural and compelling robot motions while preserving high-level semantics. However, establishing the correspondence between humans and robots with different body structures is not straightforward due to the mismatches in kinematics and dynamics properties, which causes intrinsic ambiguity to the problem. Many previous algorithms approach this motion retargeting problem with unsupervised learning, which requires the prerequisite skill sets. However, it will be extremely costly to learn all the skills without understanding the given human motions, particularly for high-dimensional robots. In this work, we introduce CrossLoco, a guided unsupervised reinforcement learning framework that simultaneously learns robot skills and their correspondence to human motions. Our key innovation is to introduce a cycle-consistency-based reward term designed to maximize the mutual information between human motions and robot states. We demonstrate that the proposed framework can generate compelling robot motions by translating diverse human motions, such as running, hopping, and dancing. We quantitatively compare our CrossLoco against the manually engineered and unsupervised baseline algorithms along with the ablated versions of our framework and demonstrate that our method translates human motions with better accuracy, diversity, and user preference. We also showcase its utility in other applications, such as synthesizing robot movements from language input and enabling interactive robot control.
Zoology: Measuring and Improving  Recall in Efficient Language Models
Authors: Simran Arora; Sabri Eyuboglu; Aman Timalsina; Isys Johnson; Michael Poli; James Zou; Atri Rudra; Christopher Re
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Attention-free language models that combine gating and convolutions are growing in popularity due to their efficiency and increasingly competitive performance. To better understand these architectures, we pretrain a suite of 17 attention and gated-convolution language models, finding that SoTA gated-convolution architectures still underperform attention by up to 2.1 perplexity points on the Pile. In fine-grained analysis, we find 82% of the gap is explained by each model's ability to recall information that is previously mentioned in-context, e.g. "Hakuna Matata means no worries Hakuna Matata it means no" -> ??. On this task, termed "associative recall", we find that attention outperforms gated-convolutions by a large margin: a 70M parameter attention model outperforms a 1.4 billion parameter gated-convolution model on associative recall. This is surprising because prior work shows gated convolutions can perfectly solve synthetic tests for AR capability.  To close the gap between synthetics and real language, we develop a new formalization of the task called multi-query associative recall (MQAR) that better reflects actual language. We perform an empirical and theoretical study of MQAR that elucidates differences in the parameter-efficiency of attention and gated-convolution recall. Informed by our analysis, we evaluate simple convolution-attention hybrids and show that hybrids with input-dependent sparse attention patterns can close 97.4% of the gap to attention, while maintaining sub-quadratic scaling. Code is at: https://github.com/HazyResearch/zoology.
Learning to Reject Meets Long-tail Learning
Authors: Harikrishna Narasimhan; Aditya Krishna Menon; Wittawat Jitkrittum; Neha Gupta; Sanjiv Kumar
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: Learning to reject (L2R) is a classical problem where one seeks a classifier capable of abstaining on low-confidence samples. Most prior work on L2R has focused on minimizing the standard misclassification error. However, in many real-world applications, the label distribution is highly imbalanced,  necessitating alternate evaluation metrics such as the balanced error or the worst-group error that enforce equitable performance across both the head and tail classes. In this paper, we establish that traditional L2R methods can be grossly sub-optimal for such metrics, and show that this is due to an intricate  dependence in the objective between the label costs and the rejector. We then derive the form of the Bayes-optimal classifier and rejector for the balanced error, propose a novel plug-in approach to mimic this solution, and extend our results to general evaluation metrics. Through experiments on benchmark  image classification tasks, we show that our approach yields better trade-offs in both the balanced and worst-group error compared to L2R baselines.
On the Foundations of Shortcut Learning
Authors: Katherine Hermann; Hossein Mobahi; Thomas FEL; Michael Curtis Mozer
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: Deep-learning models can extract a rich assortment of features from data. Which features a model uses depends not only on *predictivity*---how reliably a feature indicates training-set labels---but also on *availability*---how easily the feature can be extracted from inputs. The literature on shortcut learning has noted examples in which models privilege one feature over another, for example texture over shape and image backgrounds over foreground objects. Here, we test hypotheses about which input properties are more available to a model, and systematically study how predictivity and availability interact to shape models' feature use. We construct a minimal, explicit generative framework for synthesizing classification datasets with two latent features that vary in predictivity and in factors we hypothesize to relate to availability, and we quantify a model's shortcut bias---its over-reliance on the shortcut (more available, less predictive) feature at the expense of the core (less available, more predictive) feature. We find that linear models are relatively unbiased, but introducing a single hidden layer with ReLU or Tanh units yields a bias. Our empirical findings are consistent with a theoretical account based on Neural Tangent Kernels. Finally, we study how models used in practice trade off predictivity and availability in naturalistic datasets, discovering availability manipulations which increase models' degree of shortcut bias. Taken together, these findings suggest that the propensity to learn shortcut features is a fundamental characteristic of deep nonlinear architectures warranting systematic study given its role in shaping how models solve tasks.
Let Models Speak Ciphers: Multiagent Debate through Embeddings
Authors: Chau Pham; Boyi Liu; Yingxiang Yang; Zhengyu Chen; Tianyi Liu; Jianbo Yuan; Bryan A. Plummer; Zhaoran Wang; Hongxia Yang
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Discussion and debate among Large Language Models (LLMs) have gained considerable attention due to their potential to enhance the reasoning ability of LLMs. Although natural language is an obvious choice for communication due to LLM's language understanding capability, the token sampling step needed when generating natural language poses a potential risk of information loss, as it uses only one token to represent the model's belief across the entire vocabulary. In this paper, we introduce a communication regime named CIPHER (Communicative Inter-Model Protocol Through Embedding Representation) to address this issue. Specifically, we remove the token sampling step from LLMs and let them communicate their beliefs across the vocabulary through the expectation of the raw transformer output embeddings. Remarkably, by deviating from natural language, CIPHER offers an advantage of encoding a broader spectrum of information without any modification to the model weights, outperforming the state-of-the-art LLM debate methods using natural language by 0.5-5.0% across five reasoning tasks and multiple open-source LLMs of varying sizes. This showcases the superiority and robustness of embeddings as an alternative "language" for communication among LLMs. We anticipate that CIPHER will inspire further exploration for the design of interactions within LLM agent systems, offering a new direction that could significantly influence future developments in the field.
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion
Authors: Alexandru Meterez; Amir Joudaki; Francesco Orabona; Alexander Immer; Gunnar Ratsch; Hadi Daneshmand
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Normalization layers are one of the key building blocks for deep neural networks. Several theoretical studies have shown that batch normalization improves the signal propagation, by avoiding the representations from becoming collinear across the layers. However, results on mean-field theory of batch normalization also conclude that this benefit comes at the expense of exploding gradients in depth. Motivated by these two aspects of batch normalization, in this study we pose the following question: 
*Can a batch-normalized network keep the optimal signal propagation properties, but avoid exploding gradients?* We answer this question in the affirmative by giving a particular construction of an *MLP with linear activations* and batch-normalization that provably has *bounded gradients* at any depth. Based on Weingarten calculus, we develop a rigorous and non-asymptotic theory for this constructed MLP that gives a precise characterization of forward signal propagation, while proving that gradients remain bounded for linearly independent input samples, which holds in most practical settings. Inspired by our theory, we also design an activation shaping scheme that empirically achieves the same properties for non-linear activations.
On the Joint Interaction of Models, Data, and Features
Authors: Yiding Jiang; Christina Baek; J Zico Kolter
Venue: ICLR 2024 (ICLR 2024 oral)

Abstract: Learning features from data is one of the defining characteristics of deep learning,
but the theoretical understanding of the role features play in deep learning is still in
early development. To address this gap, we introduce a new tool, the interaction
tensor, for empirically analyzing the interaction between data and model through
features. With the interaction tensor, we make several key observations about
how features are distributed in data and how models with different random seeds
learn different features. Based on these observations, we propose a conceptual
framework for feature learning. Under this framework, the expected accuracy for a
single hypothesis and agreement for a pair of hypotheses can both be derived in
closed form. We demonstrate that the proposed framework can explain empirically
observed phenomena, including the recently discovered Generalization Disagreement Equality (GDE) that allows for estimating the generalization error with only
unlabeled data. Further, our theory also provides explicit construction of natural
data distributions that break the GDE. Thus, we believe this work provides valuable
new insight into our understanding of feature learning.