diff --git a/experiments/regression_exp/commands b/experiments/regression_exp/commands
index fa53d52..61fc1e0 100644
--- a/experiments/regression_exp/commands
+++ b/experiments/regression_exp/commands
@@ -3,4 +3,11 @@ python experiments/regression_exp/sweep_regression_exp.py --data_source pendulum
 python experiments/regression_exp/sweep_regression_exp.py --data_source pendulum --exp_name may15 --model BNN_FSVGD_SimPrior_gp --num_hparam_samples 30 --yes &&
 python experiments/regression_exp/sweep_regression_exp.py --data_source pendulum --exp_name may15 --model BNN_FSVGD_SimPrior_ssge --num_hparam_samples 30 --yes &&
 python experiments/regression_exp/sweep_regression_exp.py --data_source pendulum --exp_name may15 --model BNN_FSVGD_SimPrior_kde --num_hparam_samples 20 --yes &&
-python experiments/regression_exp/sweep_regression_exp.py --data_source pendulum --exp_name may15 --model BNN_FSVGD_SimPrior_nu-method --num_hparam_samples 30  --gpu --yes --num_cpus 4
\ No newline at end of file
+python experiments/regression_exp/sweep_regression_exp.py --data_source pendulum --exp_name may15 --model BNN_FSVGD_SimPrior_nu-method --num_hparam_samples 30  --gpu --yes --num_cpus 4
+
+
+python experiments/regression_exp/sweep_regression_exp.py --data_source racecar --exp_name jan09 --model BNN_SVGD --num_hparam_samples 10 --pred_diff 1 --yes &&
+python experiments/regression_exp/sweep_regression_exp.py --data_source racecar --exp_name jan09 --model BNN_FSVGD --num_hparam_samples 20 --pred_diff 1 --yes &&
+python experiments/regression_exp/sweep_regression_exp.py --data_source racecar --exp_name jan08 --model BNN_FSVGD_SimPrior_gp --num_hparam_samples 20 --yes
+
+python experiments/regression_exp/sweep_regression_exp.py --data_source racecar --exp_name jan09 --model BNN_FSVGD_SimPrior_kde --pred_diff 1 --num_hparam_samples 20 -gpu --yes --num_cpus 4
\ No newline at end of file
diff --git a/experiments/regression_exp/commands_num_data b/experiments/regression_exp/commands_num_data
new file mode 100644
index 0000000..79974e5
--- /dev/null
+++ b/experiments/regression_exp/commands_num_data
@@ -0,0 +1,34 @@
+# racecar (fixed likelihood)
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data --data_source pendulum --learn_likelihood_std 0 --model BNN_FSVGD_SimPrior_gp --gpu --yes --num_cpus 4 &&
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data --data_source pendulum --learn_likelihood_std 0 --model BNN_FSVGD_SimPrior_nu-method --gpu --yes --num_cpus 4 &&
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data --data_source pendulum --learn_likelihood_std 0 --model BNN_FSVGD_SimPrior_kde --gpu --yes --num_cpus 4 &&
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data --data_source pendulum --learn_likelihood_std 0 --model BNN_FSVGD --yes --num_cpus 4 &&
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data --data_source pendulum --learn_likelihood_std 0 --model BNN_SVGD --yes --num_cpus 4 &&
+python experiments/meta_learning_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data --data_source pendulum --learn_likelihood_std 0 --model PACOH  --yes --num_cpus 4
+python experiments/meta_learning_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data --data_source pendulum --learn_likelihood_std 0 --model NP  --yes --num_cpus 4 --gpu --long
+
+# racecar (learned likelihood)
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data_std --data_source pendulum --learn_likelihood_std 1 --model BNN_FSVGD_SimPrior_gp --gpu --yes --num_cpus 4 &&
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data_std --data_source pendulum --learn_likelihood_std 1 --model BNN_FSVGD_SimPrior_nu-method --gpu --yes --num_cpus 4 &&
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data_std --data_source pendulum --learn_likelihood_std 1 --model BNN_FSVGD_SimPrior_kde --gpu --yes --num_cpus 4 &&
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data_std --data_source pendulum --learn_likelihood_std 1 --model BNN_FSVGD --gpu --yes --num_cpus 4 &&
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data_std --data_source pendulum --learn_likelihood_std 1 --model BNN_SVGD --gpu --yes --num_cpus 4
+
+
+
+# racecar (fixed likelihood)
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data --data_source racecar --learn_likelihood_std 0 --model BNN_FSVGD_SimPrior_gp --gpu --yes --num_cpus 4 &&
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data --data_source racecar --learn_likelihood_std 0 --model BNN_FSVGD_SimPrior_nu-method --gpu --yes --num_cpus 4 &&
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data --data_source racecar --learn_likelihood_std 0 --model BNN_FSVGD_SimPrior_kde --gpu --yes --num_cpus 4 &&
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data --data_source racecar --learn_likelihood_std 0 --model BNN_FSVGD --yes --num_cpus 4 &&
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data --data_source racecar --learn_likelihood_std 0 --model BNN_SVGD --yes --num_cpus 4 &&
+python experiments/meta_learning_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data --data_source racecar --learn_likelihood_std 0 --model PACOH --yes --num_cpus 4
+python experiments/meta_learning_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data --data_source racecar --learn_likelihood_std 0 --model NP  --yes --num_cpus 4 --gpu --long
+
+
+# racecar (learned likelihood)
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data_std --data_source racecar --learn_likelihood_std 1 --model BNN_FSVGD_SimPrior_gp --gpu --yes --num_cpus 4 &&
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data_std --data_source racecar --learn_likelihood_std 1 --model BNN_FSVGD_SimPrior_nu-method --gpu --yes --num_cpus 4 &&
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data_std --data_source racecar --learn_likelihood_std 1 --model BNN_FSVGD_SimPrior_kde --gpu --yes --num_cpus 4 &&
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data_std --data_source racecar --learn_likelihood_std 1 --model BNN_FSVGD --gpu --yes --num_cpus 4 &&
+python experiments/regression_exp/sweep_regression_exp_num_data.py --exp_name jan10_num_data_std --data_source racecar --learn_likelihood_std 1 --model BNN_SVGD --gpu --yes --num_cpus 4
\ No newline at end of file
diff --git a/experiments/regression_exp/plots_num_data.py b/experiments/regression_exp/plots_num_data.py
new file mode 100644
index 0000000..ed6ffc9
--- /dev/null
+++ b/experiments/regression_exp/plots_num_data.py
@@ -0,0 +1,125 @@
+import pandas as pd
+import numpy as np
+import argparse
+
+from typing import Tuple
+from matplotlib import pyplot as plt
+from experiments.util import collect_exp_results, ucb, lcb, median, count
+import math
+
+
+def different_method_plot(df_agg: pd.DataFrame, metric: str = 'nll', display: bool = True,
+                          filter_std_higher_than: float = math.inf) \
+        -> Tuple[pd.DataFrame, plt.Figure]:
+    models = set(df_agg['model'])
+
+    best_rows = []
+    for model in models:
+        df_agg_model = df_agg.loc[df_agg['model'] == model]
+        df_agg_model = df_agg_model[df_agg_model[(metric, 'std')] < filter_std_higher_than]
+        df_agg_model.sort_values(by=(metric, 'mean'), ascending=True, inplace=True)
+        best_rows.append(df_agg_model.iloc[0])
+
+    best_rows_df = pd.DataFrame(best_rows)
+
+    fig, ax = plt.subplots()
+    x_pos = 2 * np.arange(len(models))
+    ax.bar(x_pos, best_rows_df[(metric, 'mean')], color='#ADD8E6')
+    ax.errorbar(x_pos, best_rows_df[(metric, 'mean')], yerr=best_rows_df[(metric, 'std')],
+                linestyle='', capsize=4., color='black')
+    ax.set_xticks(x_pos)
+    ax.set_xticklabels(best_rows_df['model'], rotation=-20)
+    ax.set_ylabel(metric)
+    fig.tight_layout()
+
+    if display:
+        plt.show()
+        print(best_rows_df[[('model', ''), ('nll', 'mean'), ('nll', 'std'),
+                            ('rmse', 'mean'), ('rmse', 'std'), ('dirname', '')]].to_string())
+
+    return best_rows_df, fig
+
+
+def main(args, drop_nan=False):
+    df_full, param_names = collect_exp_results(exp_name=args.exp_name)
+    df_full = df_full[df_full['data_source'] == args.data_source]
+    #df_full = df_full[df_full['model'] == args.data_source]
+
+    df_full = df_full[df_full['num_samples_train'] >= 10]
+
+    for col in ['bandwidth_kde', 'bandwidth_ssge', 'bandwidth_score_estim']:
+        if col in df_full.columns:
+            df_full[col] = df_full[col].fillna(value='auto')
+
+    if drop_nan:
+        result_is_nan = df_full['nll'].isna()
+        df_full_dropped = df_full[result_is_nan]
+        print(f'Dropped results due to NaNs: {df_full_dropped.shape[0]}')
+        df_full = df_full[~result_is_nan]
+    else:
+        # replace nan with bad values
+        for col, noise_std in [('nll', 1000.), ('rmse', 5.)]:
+            series = df_full[col]
+            max_value = series.max()
+            is_nan = series.isna()
+            noise = np.random.normal(0, noise_std, size=is_nan.sum())  # Adjust mean and stddev as needed
+            series[is_nan] = max_value + noise
+            df_full[col] = series
+
+
+    # group over everything except seeds and aggregate over the seeds
+    groupby_names = list(set(param_names) - {'model_seed', 'data_seed'})
+
+    # rmove the likelihood_std column since it's a constant list which is not hashable
+    groupby_names.remove('likelihood_std')
+    # groupby_names.remove('added_gp_outputscale')
+    #df_full['added_gp_outputscale'] = df_full['added_gp_outputscale'].apply(lambda x: x[0])
+
+    # replace all the nans in hyperparameter columns with 'N/A'
+    for column in groupby_names:
+        df_full[column] = df_full[column].fillna('N/A')
+
+    # first take mean over the data seeds
+    df_mean = df_full.groupby(by=groupby_names + ['model_seed'], axis=0).mean()
+    df_mean.reset_index(drop=False, inplace=True)
+
+    # then compute the stats over the model seeds
+    df_agg = df_mean.groupby(by=groupby_names, axis=0).aggregate(['mean', 'std', ucb, lcb, median, count], axis=0)
+    df_agg.reset_index(drop=False, inplace=True)
+    # df_agg.sort_values(by=[('nll', 'mean')], ascending=True, inplace=True)
+
+    # filter all the rows where the count is less than 3
+    df_agg = df_agg[df_agg['rmse']['count'] >= 3]
+
+    available_models = set(df_agg['model'])
+    for metric in ['nll', 'rmse']:
+        fig, ax = plt.subplots()
+        for model in available_models:
+            df_model = df_agg[df_agg['model'] == model].sort_values(by=[('num_samples_train', '')], ascending=True)
+            if args.quantile_cis:
+                ax.plot(df_model[('num_samples_train', '')], df_model[(metric, 'median')], label=model)
+                lower_ci = df_model[(metric, 'lcb')]
+                upper_ci = df_model[(metric, 'ucb')]
+            else:
+                ax.plot(df_model[('num_samples_train', '')], df_model[(metric, 'mean')], label=model)
+                CI_width = 2 * np.sqrt(df_model[(metric, 'count')])
+                lower_ci = df_model[(metric, 'mean')] - CI_width * df_model[(metric, 'std')]
+                upper_ci = df_model[(metric, 'mean')] + CI_width * df_model[(metric, 'std')]
+            ax.fill_between(df_model[('num_samples_train', '')], lower_ci, upper_ci, alpha=0.3)
+        ax.set_title(f'{args.data_source} - {metric}')
+        #ax.set_xscale('log')
+        #ax.set_ylim((-3.8, -2.))
+
+        ax.legend()
+        fig.show()
+    print('Models:', set(df_agg['model']))
+
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Inspect results of a regression experiment.')
+    parser.add_argument('--exp_name', type=str, default='jan10_num_data')
+    parser.add_argument('--quantile_cis', type=int, default=1)
+    parser.add_argument('--data_source', type=str, default='pendulum')
+    args = parser.parse_args()
+    main(args)
\ No newline at end of file
diff --git a/experiments/regression_exp/run_regression_exp.py b/experiments/regression_exp/run_regression_exp.py
index f40dc36..04c3379 100644
--- a/experiments/regression_exp/run_regression_exp.py
+++ b/experiments/regression_exp/run_regression_exp.py
@@ -74,11 +74,11 @@ def regression_experiment(
     # provide data and sim
     x_train, y_train, x_test, y_test, sim = provide_data_and_sim(
         data_source=data_source,
-        data_spec={'num_samples_train': 1000},
+        data_spec={'num_samples_train': 10000},
         data_seed=data_seed)
 
     # only take num_samples_train datapoints
-    assert num_samples_train <= 1000
+    assert num_samples_train <= 10000
     x_train, y_train = x_train[:num_samples_train], y_train[:num_samples_train]
 
     # handle pred diff mode
@@ -241,12 +241,12 @@ def main(args):
 
     # data parameters
     parser.add_argument('--data_source', type=str, default='racecar')
-    parser.add_argument('--num_samples_train', type=int, default=10)
+    parser.add_argument('--num_samples_train', type=int, default=100)
     parser.add_argument('--data_seed', type=int, default=77698)
     parser.add_argument('--pred_diff', type=int, default=1)
 
     # standard BNN parameters
-    parser.add_argument('--model', type=str, default='BNN_FSVGD_SimPrior_gp')
+    parser.add_argument('--model', type=str, default='BNN_SVGD')
     parser.add_argument('--model_seed', type=int, default=892616)
     parser.add_argument('--likelihood_std', type=float, default=None)
     parser.add_argument('--learn_likelihood_std', type=int, default=0)
@@ -273,7 +273,7 @@ def main(args):
     # FSVGD_SimPrior parameters
     parser.add_argument('--bandwidth_score_estim', type=float, default=10.)
     parser.add_argument('--ssge_kernel_type', type=str, default='IMQ')
-    parser.add_argument('--num_f_samples', type=int, default=64)
+    parser.add_argument('--num_f_samples', type=int, default=1024)
     parser.add_argument('--switch_score_estimator_frac', type=float, default=0.6667)
 
     # FSVGD_SimPrior parameters
diff --git a/experiments/regression_exp/sweep_regression_exp.py b/experiments/regression_exp/sweep_regression_exp.py
index 3ebf243..5a62237 100644
--- a/experiments/regression_exp/sweep_regression_exp.py
+++ b/experiments/regression_exp/sweep_regression_exp.py
@@ -17,13 +17,13 @@
         'bandwidth_svgd': {'distribution': 'log_uniform_10', 'min': -1.0, 'max': 0.0},
         'bandwidth_gp_prior': {'distribution': 'log_uniform', 'min': -2., 'max': 0.},
         'num_train_steps': {'values': [5000, 10000, 20000]},
-        'num_measurement_points': {'values': [32, 64, 128]},
+        'num_measurement_points': {'values': [32]},
     },
     'BNN_FSVGD_SimPrior_gp': {
         'bandwidth_svgd': {'distribution': 'log_uniform_10', 'min': -1.0, 'max': 0.0},
         'num_train_steps': {'values': [40000]},
-        'num_measurement_points': {'values': [16, 32]},
-        'num_f_samples': {'values': [512, 1024]},
+        'num_measurement_points': {'values': [32]},
+        'num_f_samples': {'values': [1024]},
     },
     'BNN_FSVGD_SimPrior_ssge': {
         'bandwidth_svgd': {'distribution': 'log_uniform_10', 'min': -1.0, 'max': 1.0},
@@ -38,7 +38,13 @@
         'num_measurement_points': {'values': [32]},
         'num_f_samples': {'values': [512]},
         #'bandwidth_score_estim': {'distribution': 'log_uniform_10', 'min': -0.5, 'max': 0.5},
-        'bandwidth_score_estim': {'distribution': 'uniform', 'min': 0.8, 'max': 2.0},
+        'bandwidth_score_estim': {'distribution': 'uniform', 'min': 1.0, 'max': 2.0},
+    },
+    'BNN_FSVGD_SimPrior_kde': {
+        'bandwidth_svgd': {'distribution': 'log_uniform_10', 'min': -1.0, 'max': 1.0},
+        'num_train_steps': {'values': [60000, 80000, 100000]},
+        'num_measurement_points': {'values': [16, 32]},
+        'num_f_samples': {'values': [1024, 2056]},
     },
     'BNN_FSVGD_SimPrior_gp+nu-method': {
         'bandwidth_svgd': {'distribution': 'log_uniform_10', 'min': -1.0, 'max': 0.0},
@@ -48,17 +54,6 @@
         'switch_score_estimator_frac': {'values': [0.6667]},
         'bandwidth_score_estim': {'distribution': 'log_uniform_10', 'min': 0.0, 'max': 0.5},
     },
-    'BNN_FSVGD_SimPrior_kde': {
-        'bandwidth_svgd': {'distribution': 'log_uniform', 'min': -2., 'max': 2.},
-        'num_train_steps': {'values': [40000]},
-        'num_measurement_points': {'values': [16, 32]},
-        'num_f_samples': {'values': [512, 1024, 2056]},
-    },
-    'BNN_MMD_SimPrior': {
-        'num_train_steps': {'values': [20000, 40000]},
-        'num_measurement_points': {'values': [8, 16, 32, 64]},
-        'num_f_samples': {'values': [64, 128, 256, 512]},
-    },
     'BNN_SVGD_DistillPrior': {
         'bandwidth_svgd': {'distribution': 'log_uniform', 'min': -2., 'max': 2.},
         'num_train_steps': {'values': [20000, 40000]},
@@ -80,7 +75,7 @@ def main(args):
         'num_samples_train': DATASET_CONFIGS[args.data_source]['num_samples_train'],
         'model': {'value': args.model},
         'learn_likelihood_std': {'value': args.learn_likelihood_std},
-        #'likelihood_std': {'value': None},
+        'pred_diff': {'value': args.pred_diff},
         'num_particles': {'value': 20},
         'data_batch_size': {'value': 8},
     }
@@ -118,7 +113,7 @@ def main(args):
     # sweep args
     parser.add_argument('--num_hparam_samples', type=int, default=20)
     parser.add_argument('--num_model_seeds', type=int, default=3, help='number of model seeds per hparam')
-    parser.add_argument('--num_data_seeds', type=int, default=4, help='number of model seeds per hparam')
+    parser.add_argument('--num_data_seeds', type=int, default=3, help='number of model seeds per hparam')
     parser.add_argument('--num_cpus', type=int, default=1, help='number of cpus to use')
     parser.add_argument('--run_mode', type=str, default='euler')
 
@@ -132,8 +127,9 @@ def main(args):
     parser.add_argument('--data_source', type=str, default='racecar')
 
     # # standard BNN parameters
-    parser.add_argument('--model', type=str, default='BNN_SVGD')
+    parser.add_argument('--model', type=str, default='BNN_FSVGD_SimPrior_kde')
     parser.add_argument('--learn_likelihood_std', type=int, default=0)
+    parser.add_argument('--pred_diff', type=int, default=1)
 
     args = parser.parse_args()
     main(args)
diff --git a/experiments/regression_exp/sweep_regression_exp_num_data.py b/experiments/regression_exp/sweep_regression_exp_num_data.py
new file mode 100644
index 0000000..67a95f3
--- /dev/null
+++ b/experiments/regression_exp/sweep_regression_exp_num_data.py
@@ -0,0 +1,136 @@
+from experiments.util import (generate_run_commands, generate_base_command, RESULT_DIR, sample_param_flags, hash_dict)
+from experiments.data_provider import DATASET_CONFIGS
+
+import experiments.regression_exp.run_regression_exp
+import numpy as np
+import datetime
+import itertools
+import argparse
+import os
+
+MODEL_SPECIFIC_CONFIG = {
+    'BNN_SVGD': {
+        'bandwidth_svgd': {'values': [5.]},
+        'num_train_steps': {'values': [20000]},
+    },
+    'BNN_FSVGD': {
+        'bandwidth_svgd': {'values': [0.2]},
+        'bandwidth_gp_prior': {'values': [0.4]},
+        'num_train_steps': {'values': [20000]},
+        'num_measurement_points': {'values': [32]},
+    },
+    'BNN_FSVGD_SimPrior_gp': {
+        'bandwidth_svgd': {'values': [0.2]},
+        'num_train_steps': {'values': [40000]},
+        'num_measurement_points': {'values': [32]},
+        'num_f_samples': {'values': [1024]},
+    },
+    'BNN_FSVGD_SimPrior_ssge': {
+        'bandwidth_svgd': {'distribution': 'log_uniform_10', 'min': -1.0, 'max': 1.0},
+        'num_train_steps': {'values': [10000, 20000, 40000]},
+        'num_measurement_points': {'values': [8, 16, 32]},
+        'num_f_samples': {'values': [512]},
+        'bandwidth_score_estim': {'distribution': 'log_uniform_10', 'min': -0.5, 'max': 1.},
+    },
+    'BNN_FSVGD_SimPrior_nu-method': {
+        'bandwidth_svgd': {'values': [0.2]},
+        'num_train_steps': {'values': [60000]},
+        'num_measurement_points': {'values': [32]},
+        'num_f_samples': {'values': [512]},
+        'bandwidth_score_estim': {'values': [1.2]},
+    },
+    'BNN_FSVGD_SimPrior_kde': {
+        'bandwidth_svgd': {'values': [0.2]},
+        'num_train_steps': {'values': [80000]},
+        'num_measurement_points': {'values': [32]},
+        'num_f_samples': {'values': [2056]},
+    },
+    'BNN_SVGD_DistillPrior': {
+        'bandwidth_svgd': {'distribution': 'log_uniform', 'min': -2., 'max': 2.},
+        'num_train_steps': {'values': [20000, 40000]},
+        'num_measurement_points': {'values': [8, 16, 32]},
+        'num_f_samples': {'values': [64, 128, 256]},
+        'num_distill_steps': {'values': [30000, 60000]},
+    },
+}
+
+
+def main(args):
+    # setup random seeds
+    rds = np.random.RandomState(args.seed)
+    model_seeds = list(rds.randint(0, 10**6, size=(100,)))
+    data_seeds = list(rds.randint(0, 10**6, size=(100,)))
+
+    sweep_config = {
+        'data_source': {'value': args.data_source},
+        'model': {'value': args.model},
+        'learn_likelihood_std': {'value': args.learn_likelihood_std},
+        'pred_diff': {'value': args.pred_diff},
+        'num_particles': {'value': 20},
+        'data_batch_size': {'value': 8},
+    }
+    # update with model specific sweep ranges
+    assert args.model in MODEL_SPECIFIC_CONFIG
+    sweep_config.update(MODEL_SPECIFIC_CONFIG[args.model])
+
+    # determine name of experiment
+    exp_base_path = os.path.join(RESULT_DIR, args.exp_name)
+    exp_path = os.path.join(exp_base_path, f'{args.data_source}_{args.model}')
+
+    if args.data_source == 'racecar':
+        N_SAMPLES_LIST = [50, 100, 200, 400, 800, 1600, 3200]
+    elif args.data_source == 'pendulum':
+        N_SAMPLES_LIST = [10, 20, 40, 80, 160, 320, 640]
+    else:
+        raise NotImplementedError(f'Unknown data source {args.data_source}.')
+
+    command_list = []
+    output_file_list = []
+    for _ in range(args.num_hparam_samples):
+        flags = sample_param_flags(sweep_config)
+        exp_hash = hash_dict(flags)
+
+        for num_samples_train in N_SAMPLES_LIST:
+
+            exp_result_folder = os.path.join(exp_path, f'{exp_hash}_{num_samples_train}')
+            flags['exp_result_folder'] = exp_result_folder
+
+            for model_seed, data_seed in itertools.product(model_seeds[:args.num_model_seeds],
+                                                           data_seeds[:args.num_data_seeds]):
+                cmd = generate_base_command(experiments.regression_exp.run_regression_exp,
+                                            flags=dict(**flags, **{'model_seed': model_seed, 'data_seed': data_seed,
+                                                                   'num_samples_train': num_samples_train}))
+                command_list.append(cmd)
+                output_file_list.append(os.path.join(exp_result_folder, f'{model_seed}_{data_seed}.out'))
+
+    generate_run_commands(command_list, output_file_list, num_cpus=args.num_cpus,
+                          num_gpus=1 if args.gpu else 0, mode=args.run_mode, prompt=not args.yes)
+
+
+if __name__ == '__main__':
+    current_date = datetime.datetime.now().strftime("%b%d").lower()
+    parser = argparse.ArgumentParser(description='Meta-BO run')
+
+    # sweep args
+    parser.add_argument('--num_hparam_samples', type=int, default=1)
+    parser.add_argument('--num_model_seeds', type=int, default=5, help='number of model seeds per hparam')
+    parser.add_argument('--num_data_seeds', type=int, default=5, help='number of model seeds per hparam')
+    parser.add_argument('--num_cpus', type=int, default=1, help='number of cpus to use')
+    parser.add_argument('--run_mode', type=str, default='euler')
+
+    # general args
+    parser.add_argument('--exp_name', type=str, default=f'test_{current_date}')
+    parser.add_argument('--seed', type=int, default=94563)
+    parser.add_argument('--gpu', default=False, action='store_true')
+    parser.add_argument('--yes', default=False, action='store_true')
+
+    # data parameters
+    parser.add_argument('--data_source', type=str, default='racecar')
+
+    # # standard BNN parameters
+    parser.add_argument('--model', type=str, default='BNN_SVGD')
+    parser.add_argument('--learn_likelihood_std', type=int, default=0)
+    parser.add_argument('--pred_diff', type=int, default=1)
+
+    args = parser.parse_args()
+    main(args)