GenAI4ELab/papercli-papers huggingface dataset:
ContraGen: Effective Contrastive Learning For Causal Language Model
Authors: Nihal Jain; Dejiao Zhang; Wasi Uddin Ahmad; Zijian Wang; Feng Nan; Xiaopeng Li; Ming Tan; Ramesh Nallapati; Baishakhi Ray; Parminder Bhatia; Xiaofei Ma; Bing Xiang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Despite exciting progress in large-scale language generation, the expressiveness of its representations is severely limited by the \textit{anisotropy} issue where the hidden representations are distributed into a narrow cone in the vector space. To address this issue, we present ContraGen, a novel contrastive learning framework to improve the representation with better uniformity and discrimination at both sequence-level and token-level. We assess ContraGen on a wide range of downstream tasks in natural and programming languages. We show that ContraGen can effectively enhance both uniformity and discrimination of the representations and lead to the desired improvement on various language understanding tasks where discriminative representations are crucial for attaining good performance. Specifically, we attain $45.9\%$ relative improvement on the Semantic Textual Similarity tasks and $33.5\%$ on Code-to-Code Search tasks. Furthermore, by improving the expressiveness of the representations, ContraGen also boosts the source code generation capability with $9\%$ relative improvement on execution accuracy on HumanEval benchmark.
Guided Safe Shooting: model based reinforcement learning with safety constraints
Authors: Giuseppe Paolo; Jonas Gonzalez-Billandon; Albert Thomas; Balázs Kégl
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In the last decade, reinforcement learning successfully solved complex control
tasks and decision-making problems, like the Go board game. Yet, there are few
success stories when it comes to deploying those algorithms to real-world scenarios.
One of the reasons is the lack of guarantees when dealing with and avoiding unsafe
states, a fundamental requirement in critical control engineering systems. In this
paper, we introduce Guided Safe Shooting (GuSS), a model-based RL approach
that can learn to control systems with minimal violations of the safety constraints.
The model is learned on the data collected during the operation of the system in an
iterated batch fashion, and is then used to plan for the best action to perform at each
time step. We propose three different safe planners, one based on a simple random
shooting strategy and two based on MAP-Elites, a more advanced divergent-search
algorithm. Experiments show that these planners help the learning agent avoid
unsafe situations while maximally exploring the state space, a necessary aspect
when learning an accurate model of the system. Furthermore, compared to model-
free approaches, learning a model allows GuSS reducing the number of interactions
with the real-system while still reaching high rewards, a fundamental requirement
when handling engineering systems.
Constant-Factor Approximation Algorithms for Socially Fair $k$-Clustering
Authors: Mehrdad Ghadiri; Mohit Singh; Santosh Vempala
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We study approximation algorithms for the socially fair $(\ell_p, k)$-clustering problem with $m$ groups which include the socially fair $k$-median ($p=1$) and $k$-means ($p=2$). We present (1) a polynomial-time $(5+2\sqrt{6})^p$-approximation with at most $k+m$ centers (2) a $(5+2\sqrt{6}+\epsilon)^p$-approximation with $k$ centers in time $(nk)^{{2^{O(p)} m^2}/\epsilon}$, and (3) a $(15+6\sqrt{6})^p$ approximation with $k$ centers in time $k^{m}\cdot\text{poly}(n)$. The former is obtained by a refinement of the iterative rounding method via a sequence of linear programs. The latter two are obtained by converting a solution with up to $k+m$ centers to one with $k$ centers by sparsification methods for (2) and via an exhaustive search for (3). We also compare the performance of our algorithms with existing approximation algorithms on benchmark datasets, and find that our algorithms outperform existing methods.
Make-A-Video: Text-to-Video Generation without Text-Video Data
Authors: Uriel Singer; Adam Polyak; Thomas Hayes; Xi Yin; Jie An; Songyang Zhang; Qiyuan Hu; Harry Yang; Oron Ashual; Oran Gafni; Devi Parikh; Sonal Gupta; Yaniv Taigman
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We propose Make-A-Video -- an approach for directly translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V). Our intuition is simple: learn what the world looks like and how it is described from paired text-image data, and learn how the world moves from unsupervised video footage. Make-A-Video has three advantages: (1) it accelerates training of the T2V model (it does not need to learn visual and multimodal representations from scratch), (2) it does not require paired text-video data, and (3) the generated videos inherit the vastness (diversity in aesthetic, fantastical depictions, etc.) of today's image generation models. 
We design a simple yet effective way to build on T2I models with novel and effective spatial-temporal modules. First, we decompose the full temporal U-Net and attention tensors and approximate them in space and time. Second, we design a spatial temporal pipeline to generate high resolution and frame rate videos with a video decoder, interpolation model and two super resolution models that can enable various applications besides T2V. In all aspects, spatial and temporal resolution, faithfulness to text, and quality, Make-A-Video sets the new state-of-the-art in text-to-video generation, as determined by both qualitative and quantitative measures.
Neural Bregman Divergences for Distance Learning
Authors: Fred Lu; Edward Raff; Francis Ferraro
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Many metric learning tasks, such as triplet learning, nearest neighbor retrieval, and visualization, are treated primarily as embedding tasks where the ultimate metric is some variant of the Euclidean distance (e.g., cosine or Mahalanobis), and the algorithm must learn to embed points into the pre-chosen space. The study of non-Euclidean geometries is often not explored, which we believe is due to a lack of tools for learning non-Euclidean measures of distance. Recent work has shown that Bregman divergences can be learned from data, opening a promising approach to learning asymmetric distances. We propose a new approach to learning arbitrary Bergman divergences in a differentiable manner via input convex neural networks and show that it overcomes significant limitations of previous works. We also demonstrate that our method more faithfully learns divergences over a set of both new and previously studied tasks, including asymmetric regression, ranking, and clustering. Our tests further extend to known asymmetric, but non-Bregman tasks, where our method still performs competitively despite misspecification, showing the general utility of our approach for asymmetric learning.
Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning
Authors: Beren Millidge; Yuhang Song; Tommaso Salvatori; Thomas Lukasiewicz; Rafal Bogacz
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: How the brain performs credit assignment is a fundamental unsolved problem in neuroscience. Many `biologically plausible' algorithms have been proposed, which compute gradients that approximate those computed by backpropagation (BP), and which operate in ways that more closely satisfy the constraints imposed by neural circuitry. Many such algorithms utilize the framework of energy-based models (EBMs), in which all free variables in the model are optimized to minimize a global energy function. However, in the literature, these algorithms exist in isolation and no unified theory exists linking them together. Here, we provide a comprehensive theory of the conditions under which EBMs can approximate BP, which lets us unify many of the BP approximation results in the literature (namely,  predictive coding, equilibrium propagation, and contrastive Hebbian learning) and demonstrate that their approximation to BP arises from a simple and general mathematical property of EBMs at free-phase equilibrium. This property can then be exploited in different ways with different energy functions, and these specific choices yield a family of BP-approximating algorithms, which both includes the known results in the literature and can be used to derive new ones.
Model Transferability with Responsive Decision Subjects 
Authors: Yang Liu; Yatong Chen; Zeyu Tang; Kun Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: This paper studies model transferability when human decision subjects respond to a deployed machine learning model. In our setting, an agent or a user corresponds to a sample $(X,Y)$ drawn from a distribution $\mathcal{D}$ and will face a model $h$ and its classification result $h(X)$. Agents can modify $X$ to adapt to $h$, which will incur a distribution shift on $(X,Y)$. Therefore, when training $h$, the learner will need to consider the subsequently ``induced" distribution when the output model is deployed. Our formulation is motivated by applications where the deployed machine learning models interact with human agents, and will ultimately face \emph{responsive} and interactive data distributions. We formalize the discussions of the transferability of a model by studying how the model trained on the available source distribution (data) would translate to the performance on the induced domain. We provide both upper bounds for the performance gap due to the induced domain shift, as well as lower bound for the trade-offs that a classifier has to suffer on either the source training distribution or the induced target distribution. We provide further instantiated analysis for two popular domain adaptation settings with covariate shift and target shift.
Unsupervised visualization of image datasets using contrastive learning
Authors: Niklas Böhm; Philipp Berens; Dmitry Kobak
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Visualization methods based on the nearest neighbor graph, such as t-SNE or UMAP, are widely used for visualizing high-dimensional data. Yet, these approaches only produce meaningful results if the nearest neighbors themselves are meaningful. For images represented in pixel space this is not the case, as distances in pixel space are often not capturing our sense of similarity and therefore neighbors are not semantically close. This problem can be circumvented by self-supervised approaches based on contrastive learning, such as SimCLR, relying on data augmentation to generate implicit neighbors, but these methods do not produce two-dimensional embeddings suitable for visualization. Here, we present a new method, called t-SimCNE, for unsupervised visualization of image data. T-SimCNE combines ideas from contrastive learning and neighbor embeddings, and trains a parametric mapping from the high-dimensional pixel space into two dimensions. We show that the resulting 2D embeddings achieve classification accuracy comparable to the state-of-the-art high-dimensional SimCLR representations, thus faithfully capturing semantic relationships. Using t-SimCNE, we obtain informative visualizations of the CIFAR-10 and CIFAR-100 datasets, showing rich cluster structure and highlighting artifacts and outliers.
DiffusER: Diffusion via Edit-based Reconstruction
Authors: Machel Reid; Vincent Josua Hellendoorn; Graham Neubig
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In text generation, models that generate text from scratch one token at a time are currently the dominant paradigm. Despite being performant, these models lack the ability to revise existing text, which limits their usability in many practical scenarios. We look to address this, with DiffusER (Diffusion via Edit-based Reconstruction), a new edit-based generative model for text based on denoising diffusion models -- a class of models that use a Markov chain of denoising steps to incrementally generate data. DiffusER is not only a strong generative model in general, rivalling autoregressive models on several tasks spanning machine translation, summarization, and style transfer; it can also perform other varieties of generation that standard autoregressive models are not well-suited for. For instance, we demonstrate that DiffusER makes it possible for a user to condition generation on a prototype, or an incomplete sequence, and continue revising based on previous edit steps.
InfoOT: Information Maximizing Optimal Transport
Authors: Ching-Yao Chuang; Stefanie Jegelka; David Alvarez-Melis
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Optimal transport aligns samples across distributions by minimizing the transportation cost between them, e.g., the geometric distances. Yet, it ignores coherence structure in the data such as clusters, does not handle outliers well, and cannot integrate new data points. To address these drawbacks, we propose InfoOT, an information-theoretic extension of optimal transport that maximizes the mutual information between domains while minimizing geometric distances. The resulting objective can still be formulated as a (generalized) optimal transport problem, and can be efficiently solved by projected gradient descent. This formulation yields a new projection method that is robust to outliers and generalizes to unseen samples. Empirically, InfoOT improves the quality of alignments across benchmarks in domain adaptation, cross-domain retrieval, and single-cell alignment.
System identification of neural systems: If we got it right, would we know?
Authors: Yena Han; Tomaso Poggio; Brian Cheung
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Various artificial neural networks developed by engineers are now proposed as models of parts of the brain, such as the ventral stream in the primate visual cortex. The network activations are compared to recordings of biological neurons, and good performance in reproducing neural responses is considered to support the model's validity. This system identification approach, however, is only part of the traditional ways to develop and test models in the natural sciences. A key question is how much the ability to predict neural responses tells us. In particular, do these functional tests about neuron activation allow us to distinguish between different model architectures? We benchmark existing techniques to correctly identify a model by replacing brain recordings with known ground truth models. We evaluate the most commonly used identification approaches, such as a linear encoding model and centered kernel alignment. Even in the setting where the correct model is among the candidates, system identification performance is quite variable; it also depends significantly on factors independent of the ground truth architecture, such as stimuli images. In addition, we show the limitations of using functional similarity scores in identifying higher-level architectural motifs.
Learning Portable Skills by Identifying Generalizing Features with an Attention-Based Ensemble
Authors: Anita Taosheng De Mello Koch; Zhiyuan Zhou; Akhil Bagaria; Haotian Fu; Cameron Allen; George Konidaris
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The ability to rapidly generalize is crucial for reinforcement learning to be practical in real-world tasks. However, generalization is complicated by the fact that, in many settings, some state features reliably support generalization while others do not. We consider the problem of learning generalizable policies and skills (in the form of options) by identifying feature sets that generalize across instances. We propose an attention-ensemble approach, where a collection of minimally overlapping feature masks is learned, each of which individually maximizes performance on the source instance. Subsequent tasks are instantiated using the ensemble, and transfer performance is used to update the estimated probability that each feature set will generalize in the future. We show that our approach leads to fast policy generalization for eight tasks in the Procgen benchmark. We then show its use in learning portable options in Montezuma's Revenge, where it is able to generalize skills learned in the first screen to the remainder of the game.
Hyperparameter Optimization through Neural Network Partitioning
Authors: Bruno Kacper Mlodozeniec; Matthias Reisser; Christos Louizos
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Well-tuned hyperparameters are crucial for obtaining good generalization behavior in neural networks. They can enforce appropriate inductive biases, regularize the model and improve performance --- especially in the presence of limited data. In this work, we propose a simple and efficient way for optimizing hyperparameters inspired by the marginal likelihood, an optimization objective that requires no validation data. Our method partitions the training data and a neural network model into $K$ data shards and parameter partitions, respectively. Each partition is associated with and optimized only on specific data shards. Combining these partitions into subnetworks allows us to define the "out-of-training-sample" loss of a subnetwork, i.e., the loss on data shards unseen by the subnetwork, as the objective for hyperparameter optimization. We demonstrate that we can apply this objective to optimize a variety of different hyperparameters in a single training run while being significantly computationally cheaper than alternative methods aiming to optimize the marginal likelihood for neural networks. Lastly, we also focus on optimizing hyperparameters in federated learning, where retraining and cross-validation are particularly challenging.
Post-hoc Concept Bottleneck Models
Authors: Mert Yuksekgonul; Maggie Wang; James Zou
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Concept Bottleneck Models (CBMs) map the inputs onto a set of interpretable concepts (``the bottleneck'') and use the concepts to make predictions. A concept bottleneck enhances interpretability since it can be investigated to understand what concepts the model "sees" in an input and which of these concepts are deemed important. However, CBMs are restrictive in practice as they require dense concept annotations in the training data to learn the bottleneck. Moreover, CBMs often do not match the accuracy of an unrestricted neural network, reducing the incentive to deploy them in practice. In this work, we address these limitations of CBMs by introducing Post-hoc Concept Bottleneck models (PCBMs). We show that we can turn any neural network into a PCBM without sacrificing model performance while still retaining the interpretability benefits. When concept annotations are not available on the training data, we show that PCBM can transfer concepts from other datasets or from natural language descriptions of concepts via multimodal models. A key benefit of PCBM is that it enables users to quickly debug and update the model to reduce spurious correlations and improve generalization to new distributions. PCBM allows for global model edits, which can be more efficient than previous works on local interventions that fix a specific prediction. Through a model-editing user study, we show that editing PCBMs via concept-level feedback can provide significant performance gains without using data from the target domain or model retraining.
LA-BALD: An Information-Theoretic Image Labeling Task Sampler
Authors: Yuan-Hong Liao; Sanja Fidler
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Large-scale visual recognition datasets with high-quality labels enable many computer vision applications, but also come with enormous annotation costs, especially since multiple annotators are typically queried per image to obtain a more reliable label. Recent work in label aggregation consolidates human annotations by combining them with the predictions of an online-learned predictive model. In this work, we devise an image labeling task sampler that actively selects image-worker pairs to efficiently reduce the noise in the human annotations and improve the predictive model at the same time. We propose an information-theoretic task sampler, Label Aggregation BALD (LA-BALD), to maximize the information contributing to the labeled dataset via human annotations and the model. The simulated experiments on ImageNet100-sandbox show that LA-BALD reduces the number of annotations by 19% and 12% on average compared to the two types of baselines. Our analysis shows that LA-BALD provides both more accurate annotations and a better online-learned predictive model, leading to better labeling efficiency over the baselines.
Robust Neural ODEs via Contractivity-promoting Regularization
Authors: Liang Xu; Muhammad Zakwan; Giancarlo Ferrari-Trecate
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Neural networks can be fragile to input noise and adversarial attacks. In this work, we consider Neural Ordinary Differential Equations (NODEs) – a family of continuous-depth neural networks represented by dynamical systems - and propose to use contraction theory to improve their robustness. A dynamical system is contractive if two trajectories starting from different initial conditions converge to each other exponentially fast. Contractive NODEs can enjoy increased robustness as slight perturbations of the features do not cause a significant change in the output. Contractivity can be induced during training by using a regularization term involving the Jacobian of the system dynamics. To reduce the computational burden, we show that it can also be promoted using carefully selected weight regularization terms for a class of NODEs with slope-restricted activation functions, including convolutional networks commonly used in image classification. The performance of the proposed regularizers is illustrated through benchmark image classification tasks on MNIST and FashionMNIST datasets, where images are corrupted by different kinds of noise and attacks.