GenAI4ELab/papercli-papers huggingface dataset:
Generative Pretraining for Black-Box Optimization
Authors: Siddarth Krishnamoorthy; Satvik Mehul Mashkaria; Aditya Grover
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Many problems in science and engineering involve optimizing an expensive black-box function over a high-dimensional space. For such black-box optimization (BBO) problems, we typically assume a small budget for online function evaluations, but also often have access to a fixed, offline dataset for pretraining. Prior approaches seek to utilize the offline data to approximate the function or its inverse but are not sufficiently accurate far from the data distribution. We propose BONET, a generative framework for pretraining a novel black-box optimizer using offline datasets. In BONET, we train an autoregressive model on fixed-length trajectories corresponding to runs of implicit black-box function optimizers. We design a sampling strategy to synthesize trajectories from offline data using a simple heuristic of rolling out monotonic transitions from low-fidelity to high-fidelity samples. Empirically, we instantiate BONET using a causally masked Transformer and evaluate it on Design-Bench, where we rank the best on average, outperforming state-of-the-art baselines.
Empirical analysis of representation learning and exploration in neural kernel bandits
Authors: Michal Lisicki; Arash Afkanpour; Graham W. Taylor
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Neural bandits have been shown to provide an efficient solution to practical sequential decision tasks that have nonlinear reward functions. The main contributor to that success is approximate Bayesian inference, which enables neural network (NN) training with uncertainty estimates. However, Bayesian NNs often suffer from a prohibitive computational overhead or operate on a subset of parameters. Alternatively, certain classes of infinite neural networks were shown to directly correspond to Gausian processes (GP) with neural kernels (NK). NK-GPs provide accurate uncertainty estimates and can be trained faster than most Bayesian NNs. We propose to guide common bandit policies with NK distributions and show that NK bandits achieve state-of-the-art performance on nonlinear structured data. Moreover, we propose a framework for measuring independently the ability of a bandit algorithm to learn representations and explore, and use it to analyze the impact of NK distributions w.r.t. those two aspects. We consider policies based on a GP and a Student's t-process (TP). Furthermore, we study practical considerations, such as training frequency and model partitioning. We believe our work will help better understand the impact of utilizing NKs in applied settings.
Penalizing the High-likelihood: A Novel Sampling Method for Open-ended Neural Text Generation via Inverse Probability Weighting
Authors: Xinran Zhang; Maosong Sun; Jiafeng Liu; Xiaobing Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Traditional stochastic sampling methods for open-ended neural text generation focus on truncating the low-likelihood part of the predicted distribution. They do not directly manipulate the high-likelihood part, which leads to the likelihood trap that induces repetition and boredom. They also do not directly leverage that human does not always favor high-likelihood texts. Inspired by these, we propose a novel sampling method that rescales the high-likelihood part of the distribution with inverse probability weighting. It increases the diversity by rescaling and penalizing the high-likelihood words, and preserves the fluency by using multi-filtering truncation on the low-likelihood words. We use pre-trained language models to compare our algorithm with traditional sampling methods. Results show that our algorithm can significantly increase the diversity and novelty of generated texts without corrupting the fluency.
Bringing robotics taxonomies to continuous domains via GPLVM on hyperbolic manifolds
Authors: Noémie Jaquier; Leonel Rozo; Miguel González-Duque; Viacheslav Borovitskiy; Tamim Asfour
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Robotic taxonomies have appeared as high-level hierarchical abstractions that classify how humans move and interact with their environment. They have proven useful to analyse grasps, manipulation skills, and whole-body support poses. Despite the efforts devoted to design their hierarchy and underlying categories, their use in application fields remains scarce. This may be attributed to the lack of computational models that fill the gap between the discrete hierarchical structure of the taxonomy and the high-dimensional heterogeneous data associated to its categories. To overcome this problem, we propose to model taxonomy data via hyperbolic embeddings that capture the associated hierarchical structure. To do so, we formulate a Gaussian process hyperbolic latent variable model and enforce the taxonomy structure through graph-based priors on the latent space and distance-preserving back constraints. We test our model on the whole-body support pose taxonomy to learn hyperbolic embeddings that comply with the original graph structure. We show that our model properly encodes unseen poses from existing or new taxonomy categories, it can be used to generate trajectories between the embeddings, and it outperforms its Euclidean counterparts.
Learning Object Affordance with Contact and Grasp Generation
Authors: Haoming Li; Xinzhuo Lin; Yang Zhou; Xiang Li; Jiming Chen; Qi Ye
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Understanding object affordance can help in designing better and more robust robotic grasping. Existing work in the computer vision community formulates the object affordance understanding as a grasping pose generation problem, which treats the problem as a black box by learning a mapping between objects and the distributions of possible grasping poses for the objects. On the other hand, in the robotics community, estimating object affordance represented by contact maps is of the most importance as localizing the positions of the possible affordance can help the planning of grasping actions. In this paper, we propose to formulate the object affordance understanding as both contacts and grasp poses generation. we factorize the learning task into two sequential stages, rather than the black-box strategy: (1) we first reason the contact maps by allowing multi-modal contact generation; (2) assuming that grasping poses are fully constrained given contact maps, we learn a one-to-one mapping from the contact maps to the grasping poses. Further, we propose a penetration-aware partial optimization from the intermediate contacts. It combines local and global optimization for the refinement of the partial poses of the generated grasps exhibiting penetration. Extensive validations on two public datasets show our method outperforms state-of-the-art methods regarding grasp generation on various metrics.
Two Birds, One Stone: An Equivalent Transformation for Hyper-relational Knowledge Graph Modeling
Authors: Yu Liu; Shu Yang; Jingtao Ding; quanming yao; Yong Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: By representing knowledge in a primary triple associated with additional attribute value qualifiers, hyper-relational knowledge graph (HKG) that generalizes triple based knowledge graph (KG) has been attracting research attention recently. Compared with KG, HKG is enriched with the semantic difference between the primary triple and additional qualifiers as well as the structural connection between entities in hyper-relational graph structure. However, to model HKG, existing studies mainly focus on either semantic information or structural information therein, fail to capture both simultaneously. To tackle this issue, in this paper, we propose an equivalent transformation for HKG modeling, referred to as TransEQ. Specifically, the equivalent transformation transforms a HKG to a KG, which considers both semantic and structural characteristics. Then a generalized encoder-decoder framework is developed to bridge the modeling research between KG and HKG. In the encoder part, KG-based graph neural networks are leveraged for structural modeling; while in the decoder part, various HKG-based scoring functions are exploited for semantic modeling. Especially, we design the sharing embedding mechanism in the encoder-decoder framework with semantic relatedness captured. We further theoretically prove that TransEQ preserves complete information in the equivalent transformation, and also achieves full expressivity. Finally, extensive experiments on three benchmarks demonstrate the superior performance of TransEQ in terms of both effectiveness and efficiency. On the largest benchmark WikiPeople, TransEQ significantly improves the state-of-the-art models by 15% on MRR.
Enhancing the Transferability of Adversarial Examples via a Few Queries and Fuzzy Domain Eliminating
Authors: Xiangyuan Yang; Jie Lin; hanlin Zhang; Xinyu Yang; Peng Zhao
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Due to the vulnerability of deep neural networks, the black-box attack has drawn great attention from the community. Though transferable priors decrease the query number of the black-box query attacks in recent efforts, the average number of queries is still larger than 100, which is easily affected by the number of queries limit policy. In this work, we propose a novel method called query prior-based method to enhance the attack transferability of the family of fast gradient sign methods by using a few queries. Specifically, for the untargeted attack, we find that the successful attacked adversarial examples prefer to be classified as the wrong categories with higher probability by the victim model. Therefore, the weighted augmented cross-entropy loss is proposed to reduce the gradient angle between the surrogate model and the victim model for enhancing the transferability of the adversarial examples. In addition, the fuzzy domain eliminating technique is proposed to avoid the generated adversarial examples getting stuck in the local optimum. Specifically, we define the fuzzy domain of the input example $x$ in the $\epsilon$-ball of $x$. Then, temperature scaling and fuzzy scaling are utilized to eliminate the fuzzy domain for enhancing the transferability of the generated adversarial examples. Theoretical analysis and extensive experiments demonstrate that our method could significantly improve the transferability of gradient-based adversarial attacks on CIFAR10/100 and ImageNet and outperform the black-box query attack with the same few queries.
BiBench: Benchmarking and Analyzing Network Binarization
Authors: Haotong Qin; Mingyuan Zhang; Yifu Ding; Aoyu Li; Zhongang Cai; Ziwei Liu; Fisher Yu; Xianglong Liu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Neural network binarization emerges as one of the most promising compression approaches with extraordinary computation and memory savings by minimizing the bit-width of weight and activation. However, despite being a generic technique, recent works reveal that applying binarization in a wide range of realistic scenarios involving diverse tasks, architectures, and hardware is not trivial. Moreover, common challenges, such as severe degradation in accuracy and limited efficiency gains, suggest that specific attributes of binarization are not thoroughly studied and adequately understood. To close this gap, we present BiBench, a rigorously designed benchmark with in-depth analysis for network binarization. We first carefully scrutinize the requirements of binarization in the actual production setting. We thus define the evaluation tracks and metrics for a fair and systematic investigation. We then perform a comprehensive evaluation with a rich collection of milestone binarization algorithms. Our benchmark results show binarization still faces severe accuracy challenges but diminishing improvements brought by newer state-of-the-art binarization algorithms, even at the expense of efficiency. Moreover, the actual deployment of certain binarization operations reveals a surprisingly large deviation from their theoretical consumption. Finally, we provide suggestions based on our benchmark results and analysis, devoted to establishing a paradigm for accurate and efficient binarization among existing techniques. We hope BiBench paves the way towards more extensive adoption of network binarization and serves as a foundation for future research.
TT-NF: Tensor Train Neural Fields
Authors: Anton Obukhov; Mikhail Usvyatsov; Christos Sakaridis; Konrad Schindler; Luc Van Gool
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Learning neural fields has been an active topic in deep learning research, focusing, among other issues, on finding more compact and easy-to-fit representations. In this paper, we introduce a novel low-rank representation termed Tensor Train Neural Fields (TT-NF) for learning neural fields on dense regular grids and efficient methods for sampling from them. Our representation is a TT parameterization of the neural field, trained with backpropagation to minimize a non-convex objective. We analyze the effect of low-rank compression on the downstream task quality metrics in two settings. First, we demonstrate the efficiency of our method in a sandbox task of tensor denoising, which admits comparison with SVD-based schemes designed to minimize reconstruction error. Furthermore, we apply the proposed approach to Neural Radiance Fields, where the low-rank structure of the field corresponding to the best quality can be discovered only through learning.
Selective Classifier Ensemble
Authors: Qiang Ding; Yixuan Cao; Ping Luo
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Selective classification allows a machine learning model to abstain on some hard inputs and thus improve the safety of its predictions. In this paper, we study the ensemble of selective classifiers, i.e. selective classifier ensemble, which combines several weak selective classifiers to obtain a more powerful model. We prove that under some assumptions, the ensemble has a lower selective risk than the individual model under a range of coverage. This is nontrivial since the selective risk is a non-convex function of the model prediction. The assumptions and the theoretical result are supported by systematic experiments on both computer vision and natural language processing tasks. A surprising empirical result is that a simple selective classifier ensemble, namely, the ensemble model with maximum probability as confidence, is the state-of-the-art selective classifier. For instance, on CIFAR-10, using the same VGG-16 backbone model, this ensemble reduces the AURC (Area Under Risk-Coverage Curve) by about 24%, relative to the previous state-of-the-art method.
Defending against Reconstruction attacks using Rényi Differential Privacy
Authors: Pierre Stock; Igor Shilov; Ilya Mironov; Alexandre Sablayrolles
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Reconstruction attacks allow an adversary to regenerate data samples of the training set using access to only a trained model. It has been recently shown that simple heuristics can reconstruct data samples from language models, making this threat scenario an important aspect of model release. Differential privacy is a known solution to such attacks, but is often used with a large privacy budget (epsilon > 8) which does not translate to meaningful guarantees. In this paper we show that, for a same mechanism, we can derive privacy guarantees for reconstruction attacks that are better than the traditional ones from the literature. In particular, we show that larger privacy budgets do not provably protect against membership inference, but can still protect extraction of rare secrets. We design a method to efficiently run reconstruction attacks with lazy sampling and empirically show that we can surface at-risk training samples from non-private language models. We show experimentally that our guarantees hold on real-life language models trained with differential privacy for difficult scenarios, including GPT-2 finetuned on Wikitext-103.
Detecting and Mitigating Indirect Stereotypes in Word Embeddings
Authors: Erin George; Deanna Needell
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Societal biases in the usage of words, including harmful stereotypes, are frequently learned by common word embedding methods.  These biases manifest not only between a word and an explicit marker of its stereotype, but also between words that share related stereotypes.  This latter phenomenon, sometimes called ``indirect bias,'' has resisted prior attempts at debiasing.  In this paper, we propose a novel method to mitigate indirect bias in distributional word embeddings by modifying biased relationships between words before embeddings are learned.  This is done by considering how the co-occurrence probability of a given pair of words changes in the presence of words marking an attribute of bias, and using this to average out the effect of a bias attribute.  To evaluate this method, we perform a series of common tests and demonstrate that the semantic quality of the word embeddings is retained while measures of bias in the embeddings are reduced.  In addition, we conduct novel tests for measuring indirect stereotypes by extending the Word Embedding Association Test (WEAT) with new test sets for indirect binary gender stereotypes.  With these tests, we demonstrate that this method can reduce the presence of more subtle stereotypes not properly addressed by previous work.
Elicitation Inference Optimization for Multi-Principal-Agent Alignment
Authors: Andrew Konya; Yeping Lina Qiu; Michael P Varga; Aviv Ovadya
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In multi-principal-agent alignment scenarios spanning governance, markets, diplomacy, and AI, it is infeasible to elicit every principal's view on all perspectives relevant to agent decisions. Elicitation inference optimization (EIO) aims to minimize the $n$ elicitations needed to approximate $N$ principal's views across $K$ perspectives. In this work, we demonstrate an EIO approach where data efficiency ($NK/n$) increases with scale. We introduce STUMP: an elicitation inference model which integrates an LLM with a latent factor model to enable learning transfer across samples, contexts, and languages.  Then, we characterize STUMP's performance on a set of elicitation primitives from which scalable elicitation (sampling) protocols can be constructed. Building from these results, we design and demonstrate two scalable elicitation protocols for STUMP where data efficiency grows boundlessly, scaling like $O(n)$ in the number of elicitations $n$. This makes it possible to obtain complex, high-dimensional preference signals spanning principal populations at any scale.
Neural Probabilistic Logic Programming in Discrete-Continuous Domains
Authors: Lennert De Smet; Pedro Zuidberg Dos Martires; Robin Manhaeve; Giuseppe Marra; Angelika Kimmig; Luc De Raedt
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Neural-symbolic AI (NeSy) methods allow neural networks to exploit symbolic background knowledge. NeSy has been shown to aid learning in the limited data regime and to facilitate inference on out-of-distribution data. Neural probabilistic logic programming (NPLP) is a popular NeSy approach that integrates probabilistic models with neural networks and logic programming. A major limitation of current NPLP systems, such as DeepProbLog, is their restriction to discrete and finite probability distributions, e.g., binary random variables. To overcome this limitation, we introduce DeepSeaProbLog, an NPLP language that supports discrete and continuous random variables on (possibly) infinite and even uncountable domains. Our main contributions are 1) the introduction of DeepSeaProbLog and its semantics, 2) an implementation of DeepSeaProbLog that supports inference and gradient-based learning, and 3) an experimental evaluation of our approach.
COC curve: operating neural networks at high accuracy and low manual effort
Authors: Sara Sangalli; Ertunc Erdil; Ender Konukoglu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In human-AI collaboration systems for critical applications based on neural networks, humans should set an operating point based on a model's confidence to determine when the decision should be delegated to experts.
The underlying assumption is that the network's confident predictions are also correct.
However, modern neural networks are notoriously overconfident in their predictions, thus they achieve lower accuracy even when operated at high confidence. Network calibration methods mitigate this problem by encouraging models to make predictions whose confidence is consistent with the accuracy, i.e., encourage confidence to reflect the number of mistakes the network is expected to make.  However, they do not consider that data need to be manually analysed by experts in critical applications if the confidence of the network is below a certain level. This can be crucial for applications where available expert time is limited and expensive, e.g., medical ones.
In this paper, we propose (1) Confidence Operating Characteristics (COC) curve that assesses a predictive model in terms of accuracy and manual analysis it requires for varying operating points on confidence, and (2) a new loss function for classification that takes into account both aspects and derived from the COC curve.
We perform extensive experiments on multiple computer vision and medical image datasets for classification and compare the proposed approach with the existing network calibration methods. Our results demonstrate that our method improves classification accuracy while delegating less number of decisions to human experts, achieves better out-of-distribution samples detection and on par calibration performance compared to existing methods.
A Unified Causal View of Domain Invariant Representation Learning
Authors: Zihao Wang; Victor Veitch
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Machine learning methods can be unreliable when deployed in domains that differ from the domains on which they were trained. One intuitive approach for addressing this is to learn representations of data that are domain-invariant in the sense that they preserve data structure that is stable across domains, but throw out spuriously-varying parts. There are many approaches aimed at this kind of representation-learning, including methods based on data augmentation, distributional invariances, and risk invariance. Unfortunately, it is often unclear when a given method actually learns domain-invariant structure, and whether learning domain-invariant structure actually yields robust models. The key issue is that, in general, it's unclear how to formalize ``domain-invariant structure''. The purpose of this paper is to study these questions in the context of a particular natural domain shift notion that admits a natural formal notion of domain invariance.
This notion is a formalization of the idea that causal relationships are invariant, but non-causal relationships (e.g., due to confounding) may vary. We find that whether a given method learns domain-invariant structure, and whether this leads to robust prediction, both depend critically on the true underlying causal structure of the data.